Wireless sensor networks (WSNs) are networks of sensor nodes that interact wirelessly to gather information about the surrounding environment. Nodes are often low-powered and dispersed in an ad hoc, decentralized manner. Although WSNs have gained in popularity, they still have several serious shortcomings, like limited battery life and bandwidth. In this paper, the cluster head (CH) selection, the Compressive Sensing (CS) theory, the Connection-based Decentralized Clustering (CDC), the relay node selection, and the Multi Objective Genetic Algorithm (MOGA)are all taken into account The initial stage provided a theoretical revision to the concepts of network construction, compressive sensing, and MOGA, which impacted the improvement of network lifetime. In the second stage developed a novel model such as Energy Aware Talented Clustering with Compressive Sensing (TCCS) for the sensor network. This approach considers increasing longevity but also raises the network's overall quality of service (QoS). In the analysis, the TCCS model is applied to both the centralized and distributed networks and compared with the existing methods. When compared to the previous methods, the simulation results show that the proposed work performs better in terms of the calculation of maximum packet delivery ratio of 93.93 percent, minimum energy consumption of 8.04J, maximum energy efficiency of 91.04 percent, maximum network throughput of 465.51kbps, minimum packet loss of 282 packets, and minimum delay of 63.82 msec.
ENERGY EFFICIENT HIERARCHICAL CLUSTER HEAD ELECTION USING EXPONENTIAL DECAY F...ijwmn
In the recent years, wireless sensor network (WSN) have witnessed increased interest in information gathering in applications such as combat field reconnaissance, security surveillance, environmental monitoring, patient health monitoring and so on. Thus, there is a need for scalable and energy-efficient routing, data gathering and aggregation protocols in these WSN environments. Various hierarchical
clustering Protocols have been proposed by authors for WSN to improve system stability, lifetime, and energy efficiency. Clustering involves grouping nodes into disjoint and non-overlapping clusters. In this paper we motivate the need for clustering. Secondly, we present general classification of published clustering schemes. Thirdly, we review some existing clustering algorithms proposed for WSNs; highlighting their objectives, features, and so on. Finally, we develop an Average Energy (AvE) prediction algorithm using exponential decay function y=Ae-ax+B. We then combine this function with the
probabilistic distributed LEACH of algorithm to determine suitable CHs. The combined algorithm was implemented on MATLAB simulator and tested for homogenous network. The result gathered from the simulation shows that the extended algorithm in homogenous network mode is able to achieve 39%
stability, 11% Average energy Dissipation per round and 40% Lifespan better than LEACH-Homo. This paper proposes a new direction in improving energy efficiency of WSN routing protocol, which is desirable in some critical WSN applications. .
CLUSTER HEAD SELECTION ALGORITHMS FOR ENHANCED ENERGY EFFICIENCY IN WIRELESS ...IJCSES Journal
The extension of the sensor node's life span is an essential requirement in a Wireless Sensor Network.
Cluster head selection algorithms undertake the task of cluster head election and rotation among nodes,
and this has significant effects on the network's energy consumption. The objective of this paper is to
analyze existing cluster head selection algorithms and the parameters they implement to enhance energy
efficiency. To achieve this objective, systematic literature review methodology was used. Relevant papers
were extracted from major academic databases Elsevier, Springer, Wiley, IEEE, ACM Digital Library,
Citeseer Library, and preprints posted on arXiv. The results show that there are many existing Cluster
Head Selection Algorithms and they are categorized as deterministic, adaptive and hybrid. These
algorithms use different parameters to elect Cluster Heads. In future the researchers should derive more
parameters that can be used to elect cluster heads to improve on energy consumption
CLUSTER HEAD SELECTION ALGORITHMS FOR ENHANCED ENERGY EFFICIENCY IN WIRELESS ...IJCSES Journal
The extension of the sensor node's life span is an essential requirement in a Wireless Sensor Network.
Cluster head selection algorithms undertake the task of cluster head election and rotation among nodes,
and this has significant effects on the network's energy consumption. The objective of this paper is to
analyze existing cluster head selection algorithms and the parameters they implement to enhance energy
efficiency. To achieve this objective, systematic literature review methodology was used. Relevant papers
were extracted from major academic databases Elsevier, Springer, Wiley, IEEE, ACM Digital Library,
Citeseer Library, and preprints posted on arXiv. The results show that there are many existing Cluster
Head Selection Algorithms and they are categorized as deterministic, adaptive and hybrid. These
algorithms use different parameters to elect Cluster Heads. In future the researchers should derive more
parameters that can be used to elect cluster heads to improve on energy consumption.
In recent years, applications of wireless sensor networks have evolved in many areas such as target
tracking, environmental monitoring, military and medical applications. Wireless sensor network
continuously collect and send data through sensor nodes from a specific region to a base station. But, data
redundancy due to neighbouring sensors consumes energy, compromising the network lifetime. In order to
improve the network lifetime, a novel cluster based local route search method, called, Greedy Cluster-
based Routing (GCR) technique in wireless sensor network. The proposed GCR method uses arbitrary
timer in order to participate cluster head selection process with maximum neighbour nodes and minimum
distance between the source and base station. GCR constructs dynamic routing improving the rate of
network lifetime through Mass Proportion value. Also, GCR uses a greedy route finding strategy for
balancing energy consumption. Experimental results show that GCR achieves significant energy savings
and prolong network lifetime
ENERGY EFFICIENT AGGREGATION WITH DIVERGENT SINK PLACEMENT FOR WIRELESS SENSO...ijasuc
In WSN the data aggregation is a means for condensing the energy requirement by reducing number of
transmission by combining the data and sending the final required result to the base station. The lifetime
of the WSN can be improved by employing the aggregation techniques. During the process of aggregation
the numbers of transmission are reduced by combining the similar data from the nearby areas. By using
the clustering technique and aggregating the correlated data greatly minimize the energy consumed in
collecting and disseminating the data. In this work, we evaluate the performance of a novel energy
efficient cluster based aggregation protocol (EECAP) for WSN. The main focus in this proposed work is
to study the performance of our proposed aggregation protocol with divergent sink placements such as
when sink is at the centre of the sensing field, corner of the sensing field or at a location selected
randomly in the sensor field. We present experimental results by calculating the lifetime of network in
terms of number of sensing rounds using various parameters such as – average remaining energy of
nodes, number of dead nodes after the specified number of sensing rounds. Finally the performance of
various aggregation algorithms such as LEACH, SEP and our proposed aggregation protocol (EECAP)
are compared with divergent sink placements. The simulation results demonstrates that EECAP exhibits
good performance in terms of lifetime and the energy consumption of the wireless sensor networks and
which can be as equally compared with existing clustering protocols.
GREEDY CLUSTER BASED ROUTING FOR WIRELESS SENSOR NETWORKSijcsit
In recent years, applications of wireless sensor networks have evolved in many areas such as target tracking, environmental monitoring, military and medical applications. Wireless sensor network continuously collect and send data through sensor nodes from a specific region to a base station. But, data redundancy due to neighbouring sensors consumes energy, compromising the network lifetime. In order to improve the network lifetime, a novel cluster based local route search method, called, Greedy Clusterbased Routing (GCR) technique in wireless sensor network. The proposed GCR method uses arbitrary timer in order to participate cluster head selection process with maximum neighbour nodes and minimum distance between the source and base station. GCR constructs dynamic routing improving the rate of network lifetime through Mass Proportion value. Also, GCR uses a greedy route finding strategy for
balancing energy consumption. Experimental results show that GCR achieves significant energy savings and prolong network lifetime.
In recent years, applications of wireless sensor networks have evolved in many areas such as target tracking, environmental monitoring, military and medical applications. Wireless sensor network continuously collect and send data through sensor nodes from a specific region to a base station. But, data redundancy due to neighbouring sensors consumes energy, compromising the network lifetime. In order to improve the network lifetime, a novel cluster based local route search method, called, Greedy Clusterbased Routing (GCR) technique in wireless sensor network. The proposed GCR method uses arbitrary timer in order to participate cluster head selection process with maximum neighbour nodes and minimum distance between the source and base station. GCR constructs dynamic routing improving the rate of network lifetime through Mass Proportion value. Also, GCR uses a greedy route finding strategy for balancing energy consumption. Experimental results show that GCR achieves significant energy savings and prolong network lifetime.
Maximizing Lifetime of Homogeneous Wireless Sensor Network through Energy Eff...CSCJournals
The objective of this paper is to develop a mechanism to increase the lifetime of homogeneous wireless sensor networks (WSNs) through minimizing long range communication, efficient data delivery and energy balancing. Energy efficiency is a very important issue for sensor nodes which affects the lifetime of sensor networks. To achieve energy balancing and maximizing network lifetime we divided the whole network into different clusters. In cluster based architecture, the role of aggregator node is very crucial because of extra processing and long range communication. Once the aggregator node becomes non functional, it affects the whole cluster. We introduced a candidate cluster head node on the basis of node density. We proposed a modified cluster based WSN architecture by introducing a server node (SN) that is rich in terms of resources. This server node (SN) takes the responsibility of transmitting data to the base station over longer distances from the cluster head. We proposed cluster head selection algorithm based on residual energy, distance, reliability and degree of mobility. The proposed method can save overall energy consumption and extend the lifetime of the sensor network and also addresses robustness against even/uneven node deployment.
ENERGY EFFICIENT HIERARCHICAL CLUSTER HEAD ELECTION USING EXPONENTIAL DECAY F...ijwmn
In the recent years, wireless sensor network (WSN) have witnessed increased interest in information gathering in applications such as combat field reconnaissance, security surveillance, environmental monitoring, patient health monitoring and so on. Thus, there is a need for scalable and energy-efficient routing, data gathering and aggregation protocols in these WSN environments. Various hierarchical
clustering Protocols have been proposed by authors for WSN to improve system stability, lifetime, and energy efficiency. Clustering involves grouping nodes into disjoint and non-overlapping clusters. In this paper we motivate the need for clustering. Secondly, we present general classification of published clustering schemes. Thirdly, we review some existing clustering algorithms proposed for WSNs; highlighting their objectives, features, and so on. Finally, we develop an Average Energy (AvE) prediction algorithm using exponential decay function y=Ae-ax+B. We then combine this function with the
probabilistic distributed LEACH of algorithm to determine suitable CHs. The combined algorithm was implemented on MATLAB simulator and tested for homogenous network. The result gathered from the simulation shows that the extended algorithm in homogenous network mode is able to achieve 39%
stability, 11% Average energy Dissipation per round and 40% Lifespan better than LEACH-Homo. This paper proposes a new direction in improving energy efficiency of WSN routing protocol, which is desirable in some critical WSN applications. .
CLUSTER HEAD SELECTION ALGORITHMS FOR ENHANCED ENERGY EFFICIENCY IN WIRELESS ...IJCSES Journal
The extension of the sensor node's life span is an essential requirement in a Wireless Sensor Network.
Cluster head selection algorithms undertake the task of cluster head election and rotation among nodes,
and this has significant effects on the network's energy consumption. The objective of this paper is to
analyze existing cluster head selection algorithms and the parameters they implement to enhance energy
efficiency. To achieve this objective, systematic literature review methodology was used. Relevant papers
were extracted from major academic databases Elsevier, Springer, Wiley, IEEE, ACM Digital Library,
Citeseer Library, and preprints posted on arXiv. The results show that there are many existing Cluster
Head Selection Algorithms and they are categorized as deterministic, adaptive and hybrid. These
algorithms use different parameters to elect Cluster Heads. In future the researchers should derive more
parameters that can be used to elect cluster heads to improve on energy consumption
CLUSTER HEAD SELECTION ALGORITHMS FOR ENHANCED ENERGY EFFICIENCY IN WIRELESS ...IJCSES Journal
The extension of the sensor node's life span is an essential requirement in a Wireless Sensor Network.
Cluster head selection algorithms undertake the task of cluster head election and rotation among nodes,
and this has significant effects on the network's energy consumption. The objective of this paper is to
analyze existing cluster head selection algorithms and the parameters they implement to enhance energy
efficiency. To achieve this objective, systematic literature review methodology was used. Relevant papers
were extracted from major academic databases Elsevier, Springer, Wiley, IEEE, ACM Digital Library,
Citeseer Library, and preprints posted on arXiv. The results show that there are many existing Cluster
Head Selection Algorithms and they are categorized as deterministic, adaptive and hybrid. These
algorithms use different parameters to elect Cluster Heads. In future the researchers should derive more
parameters that can be used to elect cluster heads to improve on energy consumption.
In recent years, applications of wireless sensor networks have evolved in many areas such as target
tracking, environmental monitoring, military and medical applications. Wireless sensor network
continuously collect and send data through sensor nodes from a specific region to a base station. But, data
redundancy due to neighbouring sensors consumes energy, compromising the network lifetime. In order to
improve the network lifetime, a novel cluster based local route search method, called, Greedy Cluster-
based Routing (GCR) technique in wireless sensor network. The proposed GCR method uses arbitrary
timer in order to participate cluster head selection process with maximum neighbour nodes and minimum
distance between the source and base station. GCR constructs dynamic routing improving the rate of
network lifetime through Mass Proportion value. Also, GCR uses a greedy route finding strategy for
balancing energy consumption. Experimental results show that GCR achieves significant energy savings
and prolong network lifetime
ENERGY EFFICIENT AGGREGATION WITH DIVERGENT SINK PLACEMENT FOR WIRELESS SENSO...ijasuc
In WSN the data aggregation is a means for condensing the energy requirement by reducing number of
transmission by combining the data and sending the final required result to the base station. The lifetime
of the WSN can be improved by employing the aggregation techniques. During the process of aggregation
the numbers of transmission are reduced by combining the similar data from the nearby areas. By using
the clustering technique and aggregating the correlated data greatly minimize the energy consumed in
collecting and disseminating the data. In this work, we evaluate the performance of a novel energy
efficient cluster based aggregation protocol (EECAP) for WSN. The main focus in this proposed work is
to study the performance of our proposed aggregation protocol with divergent sink placements such as
when sink is at the centre of the sensing field, corner of the sensing field or at a location selected
randomly in the sensor field. We present experimental results by calculating the lifetime of network in
terms of number of sensing rounds using various parameters such as – average remaining energy of
nodes, number of dead nodes after the specified number of sensing rounds. Finally the performance of
various aggregation algorithms such as LEACH, SEP and our proposed aggregation protocol (EECAP)
are compared with divergent sink placements. The simulation results demonstrates that EECAP exhibits
good performance in terms of lifetime and the energy consumption of the wireless sensor networks and
which can be as equally compared with existing clustering protocols.
GREEDY CLUSTER BASED ROUTING FOR WIRELESS SENSOR NETWORKSijcsit
In recent years, applications of wireless sensor networks have evolved in many areas such as target tracking, environmental monitoring, military and medical applications. Wireless sensor network continuously collect and send data through sensor nodes from a specific region to a base station. But, data redundancy due to neighbouring sensors consumes energy, compromising the network lifetime. In order to improve the network lifetime, a novel cluster based local route search method, called, Greedy Clusterbased Routing (GCR) technique in wireless sensor network. The proposed GCR method uses arbitrary timer in order to participate cluster head selection process with maximum neighbour nodes and minimum distance between the source and base station. GCR constructs dynamic routing improving the rate of network lifetime through Mass Proportion value. Also, GCR uses a greedy route finding strategy for
balancing energy consumption. Experimental results show that GCR achieves significant energy savings and prolong network lifetime.
In recent years, applications of wireless sensor networks have evolved in many areas such as target tracking, environmental monitoring, military and medical applications. Wireless sensor network continuously collect and send data through sensor nodes from a specific region to a base station. But, data redundancy due to neighbouring sensors consumes energy, compromising the network lifetime. In order to improve the network lifetime, a novel cluster based local route search method, called, Greedy Clusterbased Routing (GCR) technique in wireless sensor network. The proposed GCR method uses arbitrary timer in order to participate cluster head selection process with maximum neighbour nodes and minimum distance between the source and base station. GCR constructs dynamic routing improving the rate of network lifetime through Mass Proportion value. Also, GCR uses a greedy route finding strategy for balancing energy consumption. Experimental results show that GCR achieves significant energy savings and prolong network lifetime.
Maximizing Lifetime of Homogeneous Wireless Sensor Network through Energy Eff...CSCJournals
The objective of this paper is to develop a mechanism to increase the lifetime of homogeneous wireless sensor networks (WSNs) through minimizing long range communication, efficient data delivery and energy balancing. Energy efficiency is a very important issue for sensor nodes which affects the lifetime of sensor networks. To achieve energy balancing and maximizing network lifetime we divided the whole network into different clusters. In cluster based architecture, the role of aggregator node is very crucial because of extra processing and long range communication. Once the aggregator node becomes non functional, it affects the whole cluster. We introduced a candidate cluster head node on the basis of node density. We proposed a modified cluster based WSN architecture by introducing a server node (SN) that is rich in terms of resources. This server node (SN) takes the responsibility of transmitting data to the base station over longer distances from the cluster head. We proposed cluster head selection algorithm based on residual energy, distance, reliability and degree of mobility. The proposed method can save overall energy consumption and extend the lifetime of the sensor network and also addresses robustness against even/uneven node deployment.
A CLUSTER-BASED ROUTING PROTOCOL AND FAULT DETECTION FOR WIRELESS SENSOR NETWORKIJCNCJournal
In Wireless Sensors Networks (WSN) based application, a large number of sensor devices must be
deployed. Energy efficiency and network lifetime are the two most challenging issues in WSN. As a
consequence, the main goal is to reduce the overall energy consumption using clustering protocols which
have to ensure reliability and connectivity in large-scale WSN. This work presents a new clustering and
routing algorithm based on the properties of the sensor networks. The main goal of this work is to extend
the network lifetime via charge equilibration in the WSN. According to many errors with sensing devices
and to have greater data accuracy, we use a quorum mechanism. The proposed algorithms are evaluated
widely and the results are compared with related works. The experimental results show that the proposed
algorithm provides an effective improvement in terms of energy consumption, data accuracy and network
lifetime
A Cluster-Based Routing Protocol and Fault Detection for Wireless Sensor NetworkIJCNCJournal
In Wireless Sensors Networks (WSN) based application, a large number of sensor devices must be deployed. Energy efficiency and network lifetime are the two most challenging issues in WSN. As a consequence, the main goal is to reduce the overall energy consumption using clustering protocols which have to ensure reliability and connectivity in large-scale WSN. This work presents a new clustering and routing algorithm based on the properties of the sensor networks. The main goal of this work is to extend the network lifetime via charge equilibration in the WSN. According to many errors with sensing devices and to have greater data accuracy, we use a quorum mechanism. The proposed algorithms are evaluated widely and the results are compared with related works. The experimental results show that the proposed algorithm provides an effective improvement in terms of energy consumption, data accuracy and network lifetime.
Fuzzy-Clustering Based Data Gathering in Wireless Sensor Network ijsc
Wireless Sensor Networks (WSN) is spatially distributed, collection of sensor nodes for the purpose of monitoring physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a base station. The critical challenge is to minimize the energy consumption in data gathering and forwarding from sensor nodes to the sink. Cluster based data aggregation is one of the most popular communication protocols in this field. Clustering is an important procedure for extending the network lifetime in wireless sensor networks. Cluster Heads (CH) aggregate data from relevant cluster nodes and send it to the base station. A main challenge in WSNs is to select suitable CHs. Another communication protocol is based on a tree construction. In this protocol, energy consumption is low because there are short paths between the sensors. In this paper, Dynamic Fuzzy Clustering data aggregation is introduced. This approach is based on clustering and minimum spanning tree. The proposed method initially uses fuzzy decision making approach for the selection of CHs. Afterward a minimum spanning tree is constructed based on CHs. CHs are selected efficiently and accurately. The combining clustering and tree structure is reclaiming the advantages of the previous structures. Our method is compared to the well-known data aggregation methods, in terms of energy consumption and the amount of energy residuary in each sensor network lifetime. Our method decreases energy consumption of each node. When the best CHs selected and the minimum spanning tree is formed by the best CHs, the remaining energy of the nodes will be preserved. Node lifetime has an important role in WSN. Using our proposed data aggregation algorithm, survival of the network is improved.
FUZZY-CLUSTERING BASED DATA GATHERING IN WIRELESS SENSOR NETWORK ijsc
Wireless Sensor Networks (WSN) is spatially distributed, collection of sensor nodes for the purpose of
monitoring physical or environmental conditions, such as temperature, sound, pressure, etc. and to
cooperatively pass their data through the network to a base station. The critical challenge is to minimize
the energy consumption in data gathering and forwarding from sensor nodes to the sink. Cluster based
data aggregation is one of the most popular communication protocols in this field. Clustering is an
important procedure for extending the network lifetime in wireless sensor networks. Cluster Heads (CH)
aggregate data from relevant cluster nodes and send it to the base station. A main challenge in WSNs is to
select suitable CHs. Another communication protocol is based on a tree construction. In this protocol,
energy consumption is low because there are short paths between the sensors. In this paper, Dynamic
Fuzzy Clustering data aggregation is introduced. This approach is based on clustering and minimum
spanning tree. The proposed method initially uses fuzzy decision making approach for the selection of CHs.
Afterward a minimum spanning tree is constructed based on CHs. CHs are selected efficiently and
accurately. The combining clustering and tree structure is reclaiming the advantages of the previous
structures. Our method is compared to the well-known data aggregation methods, in terms of energy
consumption and the amount of energy residuary in each sensor network lifetime. Our method decreases
energy consumption of each node. When the best CHs selected and the minimum spanning tree is formed by
the best CHs, the remaining energy of the nodes will be preserved. Node lifetime has an important role in
WSN. Using our proposed data aggregation algorithm, survival of the network is improved
Application of Weighted Centroid Approach in Base Station Localization for Mi...IJMER
A Wireless Sensor Networks (WSNs) consisting of sensor with strategic locations, and a base-stations (BSs) whose locations are relatively flexible. A sensor cluster consists of many small sensor nodes (SNs) that capture, encode, and transmit relevant information from a designated area. This article is focused on the topology of positioning process for BSs in WSNs. Heterogeneous SNs are battery-powered and energy-constrained, their node lifetime directly affects the network lifetime of WSNs. We have proposed an algorithmic approach to locate BSs optimally such that we can maximize the topological network lifetime of WSNs deterministically, even when the initial energy provisioning for SNs is no longer always proportional to their average bit-stream rate. The obtained optimal BS locations are under different length of area field and number of nodes according to the mission criticality of WSNs. By studying energy consumption due to space loss and amplification losses in WSNs, we establish the upper and lower bounds of maximal topological parameters of area and number of nodes, which enable a quick assessment of energy provisioning feasibility and topology necessity. Numerical results and surface plot are given to demonstrate the efficiency and optimality of the proposed topology of BSs positioning approaches designed for maximizing network lifetime of WSNs.
Hierarchical Coordination for Data Gathering (HCDG) in Wireless Sensor NetworksCSCJournals
A wireless sensor network (WSN) consists of large number of sensor nodes where each node operates by a finite battery for sensing, computing, and performing wireless communication tasks. Energy aware routing and MAC protocols were proposed to prolong the lifetime of WSNs. MAC protocols reduce energy consumption by putting the nodes into sleep mode for a relatively longer period of time; thereby minimizing collisions and idle listening time. On the other hand, efficient energy aware routing is achieved by finding the best path from the sensor nodes to the Base Sta-tion (BS) where energy consumption is minimal. In almost all solutions there is always a tradeoff between power consumption and delay reduction. This paper presents an improved hierarchical coordination for data gathering (HCDG) routing schema for WSNs based on multi-level chains formation with data aggregation. Also, this paper provides an analytical model for energy consumption in WSN to compare the performance of our proposed HCDG schema with the near optimal energy reduction methodology, PEGASIS. Our results demonstrate that the proposed routing schema provides relatively lower energy consumption with minimum delay for large scale WSNs.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and
packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which
ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer
from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of
sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads
in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption
and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer
and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers
reduces the energy consumption and increases the throughput of the wireless sensor networks.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and
packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which
ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer
from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of
sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads
in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption
and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer
and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers
reduces the energy consumption and increases the throughput of the wireless sensor networks.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers reduces the energy consumption and increases the throughput of the wireless sensor networks.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers reduces the energy consumption and increases the throughput of the wireless sensor networks.
A Review Paper on Power Consumption Improvements in WSNIJERA Editor
Wireless Sensor network (WSN) is a network of low-cost, low-power, multifunctional, small
size sensor nodes which are densely deployed inside a physical environment to collect, process and transmit the
information to sink node. As Sensor nodes are generally battery-powered, it is necessary to balance between
power consumption and energy storage capacity to sustain sensor node's operational life. Therefore one of the
important challenge in WSN is to improve power consumption efficiently to prolong network lifetime by
minimizing the amount of data transmissions throughout the network and maximizing node's low power
residence time. In this paper, two energy optimization techniques, Cluster-Based energy efficient routing
(CBER) scheme and extension to IEEE 802.15.4 standard by dynamic rate adaption and control for energy
reduction (DRACER) protocol for wireless sensor networks has been reviewed. CBER technique increases
network lifetime by reducing Hot Spot problem and end-to-end energy consumption using multi-hop wireless
routing whereas DRACER protocol reduces network latency and average power consumption by minimizing
network overhead using automatic data rate selection process. So, both of these techniques, if utilized in
combination, it is possible to achieve very high energy efficiency in WSN
A NODE DEPLOYMENT MODEL WITH VARIABLE TRANSMISSION DISTANCE FOR WIRELESS SENS...ijwmn
The deployment of network nodes is essential to ensure the wireless sensor network's regular operation and affects the multiple network performance metrics, such as connectivity, coverage, lifetime, and cost. This paper focuses on the problem of minimizing network costs while meeting network requirements, and proposes a corona-based deployment method by using the variable transmission distance sensor. Based on the analysis of node energy consumption and network cost, an optimization model to minimize Cost Per Unit Area is given. The transmission distances and initial energy of the sensors are obtained by solving the model. The optimization model is improved to ensure the energy consumption balance of nodes in the same corona. Based on these parameters, the process of network node deployment is given. Deploying the
network through this method will greatly reduce network costs.
ENERGY OPTIMISATION SCHEMES FOR WIRELESS SENSOR NETWORKcscpconf
A sensor network is composed of a large number of sensor nodes, which are densely
deployed either inside the phenomenon or very close to it. Sensor nodes have
sensing, processing and transmitting capability . They however have limited energy
and measures need to be taken to make op- timum usage of their energy and save
them from task of only receiving and transmitting data without processing. Various
techniques for energy utilization optimisation have been proposed Ma jor players are
however clustering and relay node placement. In the research related to relay node
placement, it has been proposed to deploy some relay nodes such that the sensors
can transmit the sensed data to a nearby relay node, which in turn delivers the data
to the base stations. In general, the relay node placement problems aim to meet
certain connectivity and/or survivabil- ity requirements of the network by deploying a
minimum number of relay nodes. The other approach is grouping sensor nodes into
clusters with each cluster having a cluster head (CH). The CH nodes aggregate the
data and transmit them to the base station (BS). These two approaches has been
widely adopted by the research community to satisfy the scala- bility objective and generally achieve high energy efficiency and prolong network lifetime in large-scale WSN environments and hence are discussed here along with single hop and multi hop characteristic of sensor node
Cluster Head Selection Techniques for Energy Efficient Wireless Sensor Networ...ijsrd.com
Wireless sensor networks are widely considered as one of the most important technologies. The Wireless Sensor Network (WSN) is a wireless network consisting of ten to thousand small nodes with sensing, computing and wireless communication capabilities. They have been applied to numerous fields such as healthcare, monitoring system, military, and so forth. Recent advances in wireless sensor networks have led to many new protocols specifically designed for sensor networks where energy awareness is an essential consideration. Energy efficiency is thus a primary issue in maintaining the network. Innovative techniques that improve energy efficiency to prolong the network lifetime are highly required. Clustering is an effective topology control approach in wireless sensor networks. This paper elaborates several techniques like LEACH, HEED, LEACH-B, PEACH, EEUC of cluster head selection for energy efficient in wireless sensor networks.
E FFICIENT E NERGY U TILIZATION P ATH A LGORITHM I N W IRELESS S ENSOR...IJCI JOURNAL
With limited amount of energy, nodes are powered by
batteries in wireless networks. Increasing the lif
e
span of the network and reducing the usage of energ
y are two severe problems in Wireless Sensor
Networks. A small number of energy utilization path
algorithms like minimum spanning tree reduces tota
l
energy consumption of a Wireless Sensor Network, ho
wever very heavy load of sending data packets on
many key nodes is used with the intention that the
nodes quickly consumes battery energy, by raising t
he
life span of the network reduced. Our proposal work
aimed on presenting an Energy Conserved Fast and
Secure Data Aggregation Scheme for WSN in time and
security logic occurrence data collection
application. To begin with, initially the goal is m
ade on energy preservation of sensed data gathering
from
event identified sensor nodes to destination. Inven
tion is finished on Energy Efficient Utilization Pa
th
Algorithm (EEUPA), to extend the lifespan by proces
sing the collecting series with path mediators
depending on gene characteristics sequencing of nod
e energy drain rate, energy consumption rate, and
message overhead together with extended network lif
e span. Additionally, a mathematical programming
technique is designed to improve the lifespan of th
e network. Simulation experiments carried out among
different relating conditions of wireless sensor ne
twork by different path algorithms to analyze the
efficiency and effectiveness of planned Efficient E
nergy Utilization Path Algorithm in wireless sensor
network (EEUPA)
Energy Efficient Data Aggregation in Wireless Sensor Networks: A Surveyijsrd.com
The use of Wireless Sensor Networks (WSNs) is anticipated to bring lot of changes in data gathering, processing and dissemination for different environments and applications. However, a WSN is a power constrained system, since nodes run on limited power batteries which shorten its lifespan. Prolonging the network lifetime depends on efficient management of sensing node energy resource. Energy consumption is therefore one of the most crucial design issues in WSN. Hierarchical routing protocols are best known in regard to energy efficiency. By using a clustering technique hierarchical routing protocols greatly minimize energy consumed in collecting and disseminating data. To prolong the lifetime of the sensor nodes, designing efficient routing protocols is critical. In this paper, we have discussed various energy efficient data aggregation protocols for sensor networks.
Designing an Energy Efficient Clustering in Heterogeneous Wireless Sensor Net...IJCNCJournal
Designing an energy-efficient scheme in a Heterogeneous Wireless Sensor Network (HWSN) is a critical issue that degrades the network performance. Recharging and providing security to the sensor devices is very difficult in an unattended environment once the energy is drained off. A Clustering scheme is an important and suitable approach to increase energy efficiency and transmitting secured data which in turn enhances the performance in the network. The proposed algorithm Energy Efficient Clustering (EEC) works for optimum energy utilization in sensor nodes. The algorithm is proposed by combining the rotation-based clustering and energy-saving mechanism for avoiding the node failure and prolonging the network lifetime. This shows MAC layer scheduling is based on optimum energy utilization depending on the residual energy. In the proposed work, a densely populated network is partitioned into clusters and all the cluster heads are formed at a time and selected on rotation based on considering the highest energy of the sensor nodes. Other cluster members are accommodated in a cluster based on Basic Cost Maximum flow (BCMF) to allow the cluster head for transmitting the secured data. Carrier Sense Multiple Access (CSMA), a contention window based protocol is used at the MAC layer for collision detection and to provide channel access prioritization to HWSN of different traffic classes with reduction in End to End delay, energy consumption, and improved throughput and Packet delivery ratio(PDR) and allowing the cluster head for transmission without depleting the energy. Simulation parameters of the proposed system such as Throughput, Energy, and Packet Delivery Ratio are obtained and compared with the existing system.
DESIGNING AN ENERGY EFFICIENT CLUSTERING IN HETEROGENEOUS WIRELESS SENSOR NET...IJCNCJournal
Designing an energy-efficient scheme in a Heterogeneous Wireless Sensor Network (HWSN) is a critical
issue that degrades the network performance. Recharging and providing security to the sensor devices is
very difficult in an unattended environment once the energy is drained off. A Clustering scheme is an
important and suitable approach to increase energy efficiency and transmitting secured data which in turn
enhances the performance in the network. The proposed algorithm Energy Efficient Clustering (EEC)
works for optimum energy utilization in sensor nodes. The algorithm is proposed by combining the
rotation-based clustering and energy-saving mechanism for avoiding the node failure and prolonging the
network lifetime. This shows MAC layer scheduling is based on optimum energy utilization depending on
the residual energy. In the proposed work, a densely populated network is partitioned into clusters and all
the cluster heads are formed at a time and selected on rotation based on considering the highest energy of
the sensor nodes. Other cluster members are accommodated in a cluster based on Basic Cost Maximum
flow (BCMF) to allow the cluster head for transmitting the secured data. Carrier Sense Multiple Access
(CSMA), a contention window based protocol is used at the MAC layer for collision detection and to
provide channel access prioritization to HWSN of different traffic classes with reduction in End to End
delay, energy consumption, and improved throughput and Packet delivery ratio(PDR) and allowing the
cluster head for transmission without depleting the energy. Simulation parameters of the proposed system
such as Throughput, Energy, and Packet Delivery Ratio are obtained and compared with the existing
system.
Energy efficient clustering in heterogeneousIJCNCJournal
Cluster head election is a key technique used to reduce energy consumption and enhancing the throughput
of wireless sensor networks. In this paper, a new energy efficient clustering (E2C) protocol for
heterogeneous wireless sensor networks is proposed. Cluster head is elected based on the predicted
residual energy of sensors, optimal probability of a sensor to become a cluster head, and its degree of
connectivity as the parameters. The probability threshold to compete for the role of cluster head is derived.
The probability threshold has been extended for multi-levels energy heterogeneity in the network. The
proposed E2C protocol is simulated in MATLAB. Results obtained in the simulationshowthat performance
of the proposed E2Cprotocol is betterthan stable election protocol (SEP), and distributed energy efficient
clustering (DEEC) protocol in terms of energy consumption, throughput, and network lifetime.
Cluster Based Routing using Energy and Distance Aware Multi-Objective Golden ...IJCNCJournal
In recent years, WSNs have attracted significant attention due to the improvements in various sectors such as communication, electronics, and information technologies. When the clustering algorithm incorporates both Euclidean distance and energy, it automatically decreases the energy consumption. So, the major goal of this research is to reduce energy consumption for prolong the lifetime of the network. In order to achieve an energy-efficient process, Energy and Distance Aware Multi-Objective Golden Eagle Optimization (ED-MOGEO) is proposed in this research. In addition, this proposed solution reduces retransmissions and delays to improve the performance metrics. And so, this research carried out two major fitness functions (Euclidean distance and energy) for creating an energy-efficient WSN. Furthermore, energy consideration is used to reduce the nodes unavailability which results in packet loss during the transmission. For generating the routing path between the source and the Base Station (BS), the ED-MOGEO algorithm is used. From the simulation results, it shows that Proposed ED-MOGEO achieves better performances in terms of residual energy (14.36 J), end-to-end delay (12.9 ms), packet delivery ratio (0.994), normalized routing overhead (0.11), and throughput (1.131 Mbps) when compared to existing Cluster-Based Data Aggregation (CBDA) and Elephant Herding Optimization (EHO)-Greedy methods.
Vehicle Ad Hoc Networks (VANETs) have become a viable technology to improve traffic flow and safety on the roads. Due to its effectiveness and scalability, the Wingsuit Search-based Optimised Link State Routing Protocol (WS-OLSR) is frequently used for data distribution in VANETs. However, the selection of MultiPoint Relays (MPRs) plays a pivotal role in WS-OLSR's performance. This paper presents an improved MPR selection algorithm tailored to WS-OLSR, designed to enhance the overall routing efficiency and reduce overhead. The analysis found that the current OLSR protocol has problems such as redundancy of HELLO and TC message packets or failure to update routing information in time, so a WS-OLSR routing protocol based on improved-MPR selection algorithm was proposed. Firstly, factors such as node mobility and link changes are comprehensively considered to reflect network topology changes, and the broadcast cycle of node HELLO messages is controlled through topology changes. Secondly, a new MPR selection algorithm is proposed, considering link stability issues and nodes. Finally, evaluate its effectiveness in terms of packet delivery ratio, end-to-end delay, and control message overhead. Simulation results demonstrate the superior performance of our improved MR selection algorithm when compared to traditional approaches.
A Novel Medium Access Control Strategy for Heterogeneous Traffic in Wireless ...IJCNCJournal
So far, Wireless Body Area Networks (WBANs) have played a pivotal role in driving the development of intelligent healthcare systems with broad applicability across various domains. Each WBAN consists of one or more types of sensors that can be embedded in clothing, attached directly to the body, or even implanted beneath an individual's skin. These sensors typically serve asingle application. However, the traffic generated by each sensor may have distinct requirements. This diversity necessitates a dual approach: tailored treatment based on the specific needs of each traffic typeand the fulfillment of application requirements, such asreliability and timeliness. Never the less, the presence of energy constraints and the unreliable nature of wireless communications make QoS provisioning under such networks a non-trivial task. In this context, the current paper introduces a novel Medium AccessControl (MAC) strategy for the regular traffic applications of WBANs, designed to significantly enhance efficiency when compared to the established MAC protocols IEEE 802.15.4 and IEEE 802.15.6, with a particular focus on improving reliability, timeliness, and energy efficiency.
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A CLUSTER-BASED ROUTING PROTOCOL AND FAULT DETECTION FOR WIRELESS SENSOR NETWORKIJCNCJournal
In Wireless Sensors Networks (WSN) based application, a large number of sensor devices must be
deployed. Energy efficiency and network lifetime are the two most challenging issues in WSN. As a
consequence, the main goal is to reduce the overall energy consumption using clustering protocols which
have to ensure reliability and connectivity in large-scale WSN. This work presents a new clustering and
routing algorithm based on the properties of the sensor networks. The main goal of this work is to extend
the network lifetime via charge equilibration in the WSN. According to many errors with sensing devices
and to have greater data accuracy, we use a quorum mechanism. The proposed algorithms are evaluated
widely and the results are compared with related works. The experimental results show that the proposed
algorithm provides an effective improvement in terms of energy consumption, data accuracy and network
lifetime
A Cluster-Based Routing Protocol and Fault Detection for Wireless Sensor NetworkIJCNCJournal
In Wireless Sensors Networks (WSN) based application, a large number of sensor devices must be deployed. Energy efficiency and network lifetime are the two most challenging issues in WSN. As a consequence, the main goal is to reduce the overall energy consumption using clustering protocols which have to ensure reliability and connectivity in large-scale WSN. This work presents a new clustering and routing algorithm based on the properties of the sensor networks. The main goal of this work is to extend the network lifetime via charge equilibration in the WSN. According to many errors with sensing devices and to have greater data accuracy, we use a quorum mechanism. The proposed algorithms are evaluated widely and the results are compared with related works. The experimental results show that the proposed algorithm provides an effective improvement in terms of energy consumption, data accuracy and network lifetime.
Fuzzy-Clustering Based Data Gathering in Wireless Sensor Network ijsc
Wireless Sensor Networks (WSN) is spatially distributed, collection of sensor nodes for the purpose of monitoring physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a base station. The critical challenge is to minimize the energy consumption in data gathering and forwarding from sensor nodes to the sink. Cluster based data aggregation is one of the most popular communication protocols in this field. Clustering is an important procedure for extending the network lifetime in wireless sensor networks. Cluster Heads (CH) aggregate data from relevant cluster nodes and send it to the base station. A main challenge in WSNs is to select suitable CHs. Another communication protocol is based on a tree construction. In this protocol, energy consumption is low because there are short paths between the sensors. In this paper, Dynamic Fuzzy Clustering data aggregation is introduced. This approach is based on clustering and minimum spanning tree. The proposed method initially uses fuzzy decision making approach for the selection of CHs. Afterward a minimum spanning tree is constructed based on CHs. CHs are selected efficiently and accurately. The combining clustering and tree structure is reclaiming the advantages of the previous structures. Our method is compared to the well-known data aggregation methods, in terms of energy consumption and the amount of energy residuary in each sensor network lifetime. Our method decreases energy consumption of each node. When the best CHs selected and the minimum spanning tree is formed by the best CHs, the remaining energy of the nodes will be preserved. Node lifetime has an important role in WSN. Using our proposed data aggregation algorithm, survival of the network is improved.
FUZZY-CLUSTERING BASED DATA GATHERING IN WIRELESS SENSOR NETWORK ijsc
Wireless Sensor Networks (WSN) is spatially distributed, collection of sensor nodes for the purpose of
monitoring physical or environmental conditions, such as temperature, sound, pressure, etc. and to
cooperatively pass their data through the network to a base station. The critical challenge is to minimize
the energy consumption in data gathering and forwarding from sensor nodes to the sink. Cluster based
data aggregation is one of the most popular communication protocols in this field. Clustering is an
important procedure for extending the network lifetime in wireless sensor networks. Cluster Heads (CH)
aggregate data from relevant cluster nodes and send it to the base station. A main challenge in WSNs is to
select suitable CHs. Another communication protocol is based on a tree construction. In this protocol,
energy consumption is low because there are short paths between the sensors. In this paper, Dynamic
Fuzzy Clustering data aggregation is introduced. This approach is based on clustering and minimum
spanning tree. The proposed method initially uses fuzzy decision making approach for the selection of CHs.
Afterward a minimum spanning tree is constructed based on CHs. CHs are selected efficiently and
accurately. The combining clustering and tree structure is reclaiming the advantages of the previous
structures. Our method is compared to the well-known data aggregation methods, in terms of energy
consumption and the amount of energy residuary in each sensor network lifetime. Our method decreases
energy consumption of each node. When the best CHs selected and the minimum spanning tree is formed by
the best CHs, the remaining energy of the nodes will be preserved. Node lifetime has an important role in
WSN. Using our proposed data aggregation algorithm, survival of the network is improved
Application of Weighted Centroid Approach in Base Station Localization for Mi...IJMER
A Wireless Sensor Networks (WSNs) consisting of sensor with strategic locations, and a base-stations (BSs) whose locations are relatively flexible. A sensor cluster consists of many small sensor nodes (SNs) that capture, encode, and transmit relevant information from a designated area. This article is focused on the topology of positioning process for BSs in WSNs. Heterogeneous SNs are battery-powered and energy-constrained, their node lifetime directly affects the network lifetime of WSNs. We have proposed an algorithmic approach to locate BSs optimally such that we can maximize the topological network lifetime of WSNs deterministically, even when the initial energy provisioning for SNs is no longer always proportional to their average bit-stream rate. The obtained optimal BS locations are under different length of area field and number of nodes according to the mission criticality of WSNs. By studying energy consumption due to space loss and amplification losses in WSNs, we establish the upper and lower bounds of maximal topological parameters of area and number of nodes, which enable a quick assessment of energy provisioning feasibility and topology necessity. Numerical results and surface plot are given to demonstrate the efficiency and optimality of the proposed topology of BSs positioning approaches designed for maximizing network lifetime of WSNs.
Hierarchical Coordination for Data Gathering (HCDG) in Wireless Sensor NetworksCSCJournals
A wireless sensor network (WSN) consists of large number of sensor nodes where each node operates by a finite battery for sensing, computing, and performing wireless communication tasks. Energy aware routing and MAC protocols were proposed to prolong the lifetime of WSNs. MAC protocols reduce energy consumption by putting the nodes into sleep mode for a relatively longer period of time; thereby minimizing collisions and idle listening time. On the other hand, efficient energy aware routing is achieved by finding the best path from the sensor nodes to the Base Sta-tion (BS) where energy consumption is minimal. In almost all solutions there is always a tradeoff between power consumption and delay reduction. This paper presents an improved hierarchical coordination for data gathering (HCDG) routing schema for WSNs based on multi-level chains formation with data aggregation. Also, this paper provides an analytical model for energy consumption in WSN to compare the performance of our proposed HCDG schema with the near optimal energy reduction methodology, PEGASIS. Our results demonstrate that the proposed routing schema provides relatively lower energy consumption with minimum delay for large scale WSNs.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and
packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which
ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer
from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of
sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads
in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption
and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer
and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers
reduces the energy consumption and increases the throughput of the wireless sensor networks.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and
packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which
ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer
from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of
sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads
in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption
and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer
and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers
reduces the energy consumption and increases the throughput of the wireless sensor networks.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers reduces the energy consumption and increases the throughput of the wireless sensor networks.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers reduces the energy consumption and increases the throughput of the wireless sensor networks.
A Review Paper on Power Consumption Improvements in WSNIJERA Editor
Wireless Sensor network (WSN) is a network of low-cost, low-power, multifunctional, small
size sensor nodes which are densely deployed inside a physical environment to collect, process and transmit the
information to sink node. As Sensor nodes are generally battery-powered, it is necessary to balance between
power consumption and energy storage capacity to sustain sensor node's operational life. Therefore one of the
important challenge in WSN is to improve power consumption efficiently to prolong network lifetime by
minimizing the amount of data transmissions throughout the network and maximizing node's low power
residence time. In this paper, two energy optimization techniques, Cluster-Based energy efficient routing
(CBER) scheme and extension to IEEE 802.15.4 standard by dynamic rate adaption and control for energy
reduction (DRACER) protocol for wireless sensor networks has been reviewed. CBER technique increases
network lifetime by reducing Hot Spot problem and end-to-end energy consumption using multi-hop wireless
routing whereas DRACER protocol reduces network latency and average power consumption by minimizing
network overhead using automatic data rate selection process. So, both of these techniques, if utilized in
combination, it is possible to achieve very high energy efficiency in WSN
A NODE DEPLOYMENT MODEL WITH VARIABLE TRANSMISSION DISTANCE FOR WIRELESS SENS...ijwmn
The deployment of network nodes is essential to ensure the wireless sensor network's regular operation and affects the multiple network performance metrics, such as connectivity, coverage, lifetime, and cost. This paper focuses on the problem of minimizing network costs while meeting network requirements, and proposes a corona-based deployment method by using the variable transmission distance sensor. Based on the analysis of node energy consumption and network cost, an optimization model to minimize Cost Per Unit Area is given. The transmission distances and initial energy of the sensors are obtained by solving the model. The optimization model is improved to ensure the energy consumption balance of nodes in the same corona. Based on these parameters, the process of network node deployment is given. Deploying the
network through this method will greatly reduce network costs.
ENERGY OPTIMISATION SCHEMES FOR WIRELESS SENSOR NETWORKcscpconf
A sensor network is composed of a large number of sensor nodes, which are densely
deployed either inside the phenomenon or very close to it. Sensor nodes have
sensing, processing and transmitting capability . They however have limited energy
and measures need to be taken to make op- timum usage of their energy and save
them from task of only receiving and transmitting data without processing. Various
techniques for energy utilization optimisation have been proposed Ma jor players are
however clustering and relay node placement. In the research related to relay node
placement, it has been proposed to deploy some relay nodes such that the sensors
can transmit the sensed data to a nearby relay node, which in turn delivers the data
to the base stations. In general, the relay node placement problems aim to meet
certain connectivity and/or survivabil- ity requirements of the network by deploying a
minimum number of relay nodes. The other approach is grouping sensor nodes into
clusters with each cluster having a cluster head (CH). The CH nodes aggregate the
data and transmit them to the base station (BS). These two approaches has been
widely adopted by the research community to satisfy the scala- bility objective and generally achieve high energy efficiency and prolong network lifetime in large-scale WSN environments and hence are discussed here along with single hop and multi hop characteristic of sensor node
Cluster Head Selection Techniques for Energy Efficient Wireless Sensor Networ...ijsrd.com
Wireless sensor networks are widely considered as one of the most important technologies. The Wireless Sensor Network (WSN) is a wireless network consisting of ten to thousand small nodes with sensing, computing and wireless communication capabilities. They have been applied to numerous fields such as healthcare, monitoring system, military, and so forth. Recent advances in wireless sensor networks have led to many new protocols specifically designed for sensor networks where energy awareness is an essential consideration. Energy efficiency is thus a primary issue in maintaining the network. Innovative techniques that improve energy efficiency to prolong the network lifetime are highly required. Clustering is an effective topology control approach in wireless sensor networks. This paper elaborates several techniques like LEACH, HEED, LEACH-B, PEACH, EEUC of cluster head selection for energy efficient in wireless sensor networks.
E FFICIENT E NERGY U TILIZATION P ATH A LGORITHM I N W IRELESS S ENSOR...IJCI JOURNAL
With limited amount of energy, nodes are powered by
batteries in wireless networks. Increasing the lif
e
span of the network and reducing the usage of energ
y are two severe problems in Wireless Sensor
Networks. A small number of energy utilization path
algorithms like minimum spanning tree reduces tota
l
energy consumption of a Wireless Sensor Network, ho
wever very heavy load of sending data packets on
many key nodes is used with the intention that the
nodes quickly consumes battery energy, by raising t
he
life span of the network reduced. Our proposal work
aimed on presenting an Energy Conserved Fast and
Secure Data Aggregation Scheme for WSN in time and
security logic occurrence data collection
application. To begin with, initially the goal is m
ade on energy preservation of sensed data gathering
from
event identified sensor nodes to destination. Inven
tion is finished on Energy Efficient Utilization Pa
th
Algorithm (EEUPA), to extend the lifespan by proces
sing the collecting series with path mediators
depending on gene characteristics sequencing of nod
e energy drain rate, energy consumption rate, and
message overhead together with extended network lif
e span. Additionally, a mathematical programming
technique is designed to improve the lifespan of th
e network. Simulation experiments carried out among
different relating conditions of wireless sensor ne
twork by different path algorithms to analyze the
efficiency and effectiveness of planned Efficient E
nergy Utilization Path Algorithm in wireless sensor
network (EEUPA)
Energy Efficient Data Aggregation in Wireless Sensor Networks: A Surveyijsrd.com
The use of Wireless Sensor Networks (WSNs) is anticipated to bring lot of changes in data gathering, processing and dissemination for different environments and applications. However, a WSN is a power constrained system, since nodes run on limited power batteries which shorten its lifespan. Prolonging the network lifetime depends on efficient management of sensing node energy resource. Energy consumption is therefore one of the most crucial design issues in WSN. Hierarchical routing protocols are best known in regard to energy efficiency. By using a clustering technique hierarchical routing protocols greatly minimize energy consumed in collecting and disseminating data. To prolong the lifetime of the sensor nodes, designing efficient routing protocols is critical. In this paper, we have discussed various energy efficient data aggregation protocols for sensor networks.
Designing an Energy Efficient Clustering in Heterogeneous Wireless Sensor Net...IJCNCJournal
Designing an energy-efficient scheme in a Heterogeneous Wireless Sensor Network (HWSN) is a critical issue that degrades the network performance. Recharging and providing security to the sensor devices is very difficult in an unattended environment once the energy is drained off. A Clustering scheme is an important and suitable approach to increase energy efficiency and transmitting secured data which in turn enhances the performance in the network. The proposed algorithm Energy Efficient Clustering (EEC) works for optimum energy utilization in sensor nodes. The algorithm is proposed by combining the rotation-based clustering and energy-saving mechanism for avoiding the node failure and prolonging the network lifetime. This shows MAC layer scheduling is based on optimum energy utilization depending on the residual energy. In the proposed work, a densely populated network is partitioned into clusters and all the cluster heads are formed at a time and selected on rotation based on considering the highest energy of the sensor nodes. Other cluster members are accommodated in a cluster based on Basic Cost Maximum flow (BCMF) to allow the cluster head for transmitting the secured data. Carrier Sense Multiple Access (CSMA), a contention window based protocol is used at the MAC layer for collision detection and to provide channel access prioritization to HWSN of different traffic classes with reduction in End to End delay, energy consumption, and improved throughput and Packet delivery ratio(PDR) and allowing the cluster head for transmission without depleting the energy. Simulation parameters of the proposed system such as Throughput, Energy, and Packet Delivery Ratio are obtained and compared with the existing system.
DESIGNING AN ENERGY EFFICIENT CLUSTERING IN HETEROGENEOUS WIRELESS SENSOR NET...IJCNCJournal
Designing an energy-efficient scheme in a Heterogeneous Wireless Sensor Network (HWSN) is a critical
issue that degrades the network performance. Recharging and providing security to the sensor devices is
very difficult in an unattended environment once the energy is drained off. A Clustering scheme is an
important and suitable approach to increase energy efficiency and transmitting secured data which in turn
enhances the performance in the network. The proposed algorithm Energy Efficient Clustering (EEC)
works for optimum energy utilization in sensor nodes. The algorithm is proposed by combining the
rotation-based clustering and energy-saving mechanism for avoiding the node failure and prolonging the
network lifetime. This shows MAC layer scheduling is based on optimum energy utilization depending on
the residual energy. In the proposed work, a densely populated network is partitioned into clusters and all
the cluster heads are formed at a time and selected on rotation based on considering the highest energy of
the sensor nodes. Other cluster members are accommodated in a cluster based on Basic Cost Maximum
flow (BCMF) to allow the cluster head for transmitting the secured data. Carrier Sense Multiple Access
(CSMA), a contention window based protocol is used at the MAC layer for collision detection and to
provide channel access prioritization to HWSN of different traffic classes with reduction in End to End
delay, energy consumption, and improved throughput and Packet delivery ratio(PDR) and allowing the
cluster head for transmission without depleting the energy. Simulation parameters of the proposed system
such as Throughput, Energy, and Packet Delivery Ratio are obtained and compared with the existing
system.
Energy efficient clustering in heterogeneousIJCNCJournal
Cluster head election is a key technique used to reduce energy consumption and enhancing the throughput
of wireless sensor networks. In this paper, a new energy efficient clustering (E2C) protocol for
heterogeneous wireless sensor networks is proposed. Cluster head is elected based on the predicted
residual energy of sensors, optimal probability of a sensor to become a cluster head, and its degree of
connectivity as the parameters. The probability threshold to compete for the role of cluster head is derived.
The probability threshold has been extended for multi-levels energy heterogeneity in the network. The
proposed E2C protocol is simulated in MATLAB. Results obtained in the simulationshowthat performance
of the proposed E2Cprotocol is betterthan stable election protocol (SEP), and distributed energy efficient
clustering (DEEC) protocol in terms of energy consumption, throughput, and network lifetime.
Cluster Based Routing using Energy and Distance Aware Multi-Objective Golden ...IJCNCJournal
In recent years, WSNs have attracted significant attention due to the improvements in various sectors such as communication, electronics, and information technologies. When the clustering algorithm incorporates both Euclidean distance and energy, it automatically decreases the energy consumption. So, the major goal of this research is to reduce energy consumption for prolong the lifetime of the network. In order to achieve an energy-efficient process, Energy and Distance Aware Multi-Objective Golden Eagle Optimization (ED-MOGEO) is proposed in this research. In addition, this proposed solution reduces retransmissions and delays to improve the performance metrics. And so, this research carried out two major fitness functions (Euclidean distance and energy) for creating an energy-efficient WSN. Furthermore, energy consideration is used to reduce the nodes unavailability which results in packet loss during the transmission. For generating the routing path between the source and the Base Station (BS), the ED-MOGEO algorithm is used. From the simulation results, it shows that Proposed ED-MOGEO achieves better performances in terms of residual energy (14.36 J), end-to-end delay (12.9 ms), packet delivery ratio (0.994), normalized routing overhead (0.11), and throughput (1.131 Mbps) when compared to existing Cluster-Based Data Aggregation (CBDA) and Elephant Herding Optimization (EHO)-Greedy methods.
Similar to Energy Aware Talented Clustering with Compressive Sensing (TCCS) for Wireless Sensor Networks (20)
Vehicle Ad Hoc Networks (VANETs) have become a viable technology to improve traffic flow and safety on the roads. Due to its effectiveness and scalability, the Wingsuit Search-based Optimised Link State Routing Protocol (WS-OLSR) is frequently used for data distribution in VANETs. However, the selection of MultiPoint Relays (MPRs) plays a pivotal role in WS-OLSR's performance. This paper presents an improved MPR selection algorithm tailored to WS-OLSR, designed to enhance the overall routing efficiency and reduce overhead. The analysis found that the current OLSR protocol has problems such as redundancy of HELLO and TC message packets or failure to update routing information in time, so a WS-OLSR routing protocol based on improved-MPR selection algorithm was proposed. Firstly, factors such as node mobility and link changes are comprehensively considered to reflect network topology changes, and the broadcast cycle of node HELLO messages is controlled through topology changes. Secondly, a new MPR selection algorithm is proposed, considering link stability issues and nodes. Finally, evaluate its effectiveness in terms of packet delivery ratio, end-to-end delay, and control message overhead. Simulation results demonstrate the superior performance of our improved MR selection algorithm when compared to traditional approaches.
A Novel Medium Access Control Strategy for Heterogeneous Traffic in Wireless ...IJCNCJournal
So far, Wireless Body Area Networks (WBANs) have played a pivotal role in driving the development of intelligent healthcare systems with broad applicability across various domains. Each WBAN consists of one or more types of sensors that can be embedded in clothing, attached directly to the body, or even implanted beneath an individual's skin. These sensors typically serve asingle application. However, the traffic generated by each sensor may have distinct requirements. This diversity necessitates a dual approach: tailored treatment based on the specific needs of each traffic typeand the fulfillment of application requirements, such asreliability and timeliness. Never the less, the presence of energy constraints and the unreliable nature of wireless communications make QoS provisioning under such networks a non-trivial task. In this context, the current paper introduces a novel Medium AccessControl (MAC) strategy for the regular traffic applications of WBANs, designed to significantly enhance efficiency when compared to the established MAC protocols IEEE 802.15.4 and IEEE 802.15.6, with a particular focus on improving reliability, timeliness, and energy efficiency.
May_2024 Top 10 Read Articles in Computer Networks & Communications.pdfIJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
A Topology Control Algorithm Taking into Account Energy and Quality of Transm...IJCNCJournal
The efficient use of energy in wireless sensor networks is critical for extending node lifetime. The network topology is one of the factors that have a significant impact on the energy usage at the nodes and the quality of transmission (QoT) in the network. We propose a topology control algorithm for software-defined wireless sensor networks (SDWSNs) in this paper. Our method is to formulate topology control algorithm as a nonlinear programming (NP) problem with the objective to optimizing two metrics, maximum communication range, and desired degree. This NP problem is solved at the SDWSN controller by employing the genetic algorithm (GA) to determine the best topology. The simulation results show that the proposed algorithm outperforms the MaxPower algorithm in terms of average node degree and energy expansion ratio.
Multi-Server user Authentication Scheme for Privacy Preservation with Fuzzy C...IJCNCJournal
The integration of artificial intelligence technology with a scalable Internet of Things (IoT) platform facilitates diverse smart communication services, allowing remote users to access services from anywhere at any time. The multi-server environment within IoT introduces a flexible security service model, enabling users to interact with any server through a single registration. To ensure secure and privacy preservation services for resources, an authentication scheme is essential. Zhao et al. recently introduced a user authentication scheme for the multi-server environment, utilizing passwords and smart cards, claiming resilience against well-known attacks. This paper conducts cryptanalysis on Zhao et al.'s scheme, focusing on denial of service and privacy attacks, revealing a lack of user-friendliness. Subsequently, we propose a new multi-server user authentication scheme for privacy preservation with fuzzy commitment over the IoT environment, addressing the shortcomings of Zhao et al.'s scheme. Formal security verification of the proposed scheme is conducted using the ProVerif simulation tool. Through both formal and informal security analyses, we demonstrate that the proposed scheme is resilient against various known attacks and those identified in Zhao et al.'s scheme.
Advanced Privacy Scheme to Improve Road Safety in Smart Transportation SystemsIJCNCJournal
In -Vehicle Ad-Hoc Network (VANET), vehicles continuously transmit and receive spatiotemporal data with neighboring vehicles, thereby establishing a comprehensive 360-degree traffic awareness system. Vehicular Network safety applications facilitate the transmission of messages between vehicles that are near each other, at regular intervals, enhancing drivers' contextual understanding of the driving environment and significantly improving traffic safety. Privacy schemes in VANETs are vital to safeguard vehicles’ identities and their associated owners or drivers. Privacy schemes prevent unauthorized parties from linking the vehicle's communications to a specific real-world identity by employing techniques such as pseudonyms, randomization, or cryptographic protocols. Nevertheless, these communications frequently contain important vehicle information that malevolent groups could use to Monitor the vehicle over a long period. The acquisition of this shared data has the potential to facilitate the reconstruction of vehicle trajectories, thereby posing a potential risk to the privacy of the driver. Addressing the critical challenge of developing effective and scalable privacy-preserving protocols for communication in vehicle networks is of the highest priority. These protocols aim to reduce the transmission of confidential data while ensuring the required level of communication. This paper aims to propose an Advanced Privacy Vehicle Scheme (APV) that periodically changes pseudonyms to protect vehicle identities and improve privacy. The APV scheme utilizes a concept called the silent period, which involves changing the pseudonym of a vehicle periodically based on the tracking of neighboring vehicles. The pseudonym is a temporary identifier that vehicles use to communicate with each other in a VANET. By changing the pseudonym regularly, the APV scheme makes it difficult for unauthorized entities to link a vehicle's communications to its real-world identity. The proposed APV is compared to the SLOW, RSP, CAPS, and CPN techniques. The data indicates that the efficiency of APV is a better improvement in privacy metrics. It is evident that the AVP offers enhanced safety for vehicles during transportation in the smart city.
April 2024 - Top 10 Read Articles in Computer Networks & CommunicationsIJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
DEF: Deep Ensemble Neural Network Classifier for Android Malware DetectionIJCNCJournal
Malware is one of the threats to security of computer networks and information systems. Since malware instances are available sufficiently, there is increased interest among researchers on usage of Artificial Intelligence (AI). Of late AI-enabled methods such as machine learning (ML) and deep learning paved way for solving many real-world problems. As it is a learning-based approach, accumulated training samples help in improving thequality of training and thus leveraging malware detection accuracy. Existing deep learning methods are focusing on learning-based malware detection systems. However, there is need for improving the state of the art through ensemble approach. Towards this end, in this paper we proposed a framework known as Deep Ensemble Framework (DEF) for automatic malware detection. The framework obtains features from training samples. From given malware instance a grayscale image is generated. There is another process to extract the opcode sequences. Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) techniques are used to obtain grayscale image and opcode sequence respectively. Afterwards, a stacking ensemble is employed in order to achieve efficient malware detection and classification. Malware samples collected fromthe Internet sources and Microsoft are used for theempirical study. An algorithm known as Ensemble Learning for Automatic Malware Detection (EL-AML) is proposed to realize our framework. Another algorithm named Pre-Process is proposed to assist the EL-AML algorithm for obtaining intermediate features required by CNN and LSTM.Empirical study reveals that our framework outperforms many existing methods in terms of speed-up and accuracy.
High Performance NMF Based Intrusion Detection System for Big Data IOT TrafficIJCNCJournal
With the emergence of smart devices and the Internet of Things (IoT), millions of users connected to the network produce massive network traffic datasets. These vast datasets of network traffic, Big Data are challenging to store, deal with and analyse using a single computer. In this paper we developed parallel implementation using a High Performance Computer (HPC) for the Non-Negative Matrix Factorization technique as an engine for an Intrusion Detection System (HPC-NMF-IDS). The large IoT traffic datasets of order of millions samples are distributed evenly on all the computing cores for both storage and speedup purpose. The distribution of computing tasks involved in the Matrix Factorization takes into account the reduction of the communication cost between the computing cores. The experiments we conducted on the proposed HPC-IDS-NMF give better results than the traditional ML-based intrusion detection systems. We could train the HPC model with datasets of one million samples in only 31 seconds instead of the 40 minutes using one processor), that is a speed up of 87 times. Moreover, we have got an excellent detection accuracy rate of 98% for KDD dataset.
A Novel Medium Access Control Strategy for Heterogeneous Traffic in Wireless ...IJCNCJournal
So far, Wireless Body Area Networks (WBANs) have played a pivotal role in driving the development of intelligent healthcare systems with broad applicability across various domains. Each WBAN consists of one or more types of sensors that can be embedded in clothing, attached directly to the body, or even implanted beneath an individual's skin. These sensors typically serve asingle application. However, the traffic generated by each sensor may have distinct requirements. This diversity necessitates a dual approach: tailored treatment based on the specific needs of each traffic typeand the fulfillment of application requirements, such asreliability and timeliness. Never the less, the presence of energy constraints and the unreliable nature of wireless communications make QoS provisioning under such networks a non-trivial task. In this context, the current paper introduces a novel Medium AccessControl (MAC) strategy for the regular traffic applications of WBANs, designed to significantly enhance efficiency when compared to the established MAC protocols IEEE 802.15.4 and IEEE 802.15.6, with a particular focus on improving reliability, timeliness, and energy efficiency.
A Topology Control Algorithm Taking into Account Energy and Quality of Transm...IJCNCJournal
The efficient use of energy in wireless sensor networks is critical for extending node lifetime. The network topology is one of the factors that have a significant impact on the energy usage at the nodes and the quality of transmission (QoT) in the network. We propose a topology control algorithm for software-defined wireless sensor networks (SDWSNs) in this paper. Our method is to formulate topology control algorithm as a nonlinear programming (NP) problem with the objective to optimizing two metrics, maximum communication range, and desired degree. This NP problem is solved at the SDWSN controller by employing the genetic algorithm (GA) to determine the best topology. The simulation results show that the proposed algorithm outperforms the MaxPower algorithm in terms of average node degree and energy expansion ratio.
Multi-Server user Authentication Scheme for Privacy Preservation with Fuzzy C...IJCNCJournal
The integration of artificial intelligence technology with a scalable Internet of Things (IoT) platform facilitates diverse smart communication services, allowing remote users to access services from anywhere at any time. The multi-server environment within IoT introduces a flexible security service model, enabling users to interact with any server through a single registration. To ensure secure and privacy preservation services for resources, an authentication scheme is essential. Zhao et al. recently introduced a user authentication scheme for the multi-server environment, utilizing passwords and smart cards, claiming resilience against well-known attacks. This paper conducts cryptanalysis on Zhao et al.'s scheme, focusing on denial of service and privacy attacks, revealing a lack of user-friendliness. Subsequently, we propose a new multi-server user authentication scheme for privacy preservation with fuzzy commitment over the IoT environment, addressing the shortcomings of Zhao et al.'s scheme. Formal security verification of the proposed scheme is conducted using the ProVerif simulation tool. Through both formal and informal security analyses, we demonstrate that the proposed scheme is resilient against various known attacks and those identified in Zhao et al.'s scheme.
Advanced Privacy Scheme to Improve Road Safety in Smart Transportation SystemsIJCNCJournal
In -Vehicle Ad-Hoc Network (VANET), vehicles continuously transmit and receive spatiotemporal data with neighboring vehicles, thereby establishing a comprehensive 360-degree traffic awareness system. Vehicular Network safety applications facilitate the transmission of messages between vehicles that are near each other, at regular intervals, enhancing drivers' contextual understanding of the driving environment and significantly improving traffic safety. Privacy schemes in VANETs are vital to safeguard vehicles’ identities and their associated owners or drivers. Privacy schemes prevent unauthorized parties from linking the vehicle's communications to a specific real-world identity by employing techniques such as pseudonyms, randomization, or cryptographic protocols. Nevertheless, these communications frequently contain important vehicle information that malevolent groups could use to Monitor the vehicle over a long period. The acquisition of this shared data has the potential to facilitate the reconstruction of vehicle trajectories, thereby posing a potential risk to the privacy of the driver. Addressing the critical challenge of developing effective and scalable privacy-preserving protocols for communication in vehicle networks is of the highest priority. These protocols aim to reduce the transmission of confidential data while ensuring the required level of communication. This paper aims to propose an Advanced Privacy Vehicle Scheme (APV) that periodically changes pseudonyms to protect vehicle identities and improve privacy. The APV scheme utilizes a concept called the silent period, which involves changing the pseudonym of a vehicle periodically based on the tracking of neighboring vehicles. The pseudonym is a temporary identifier that vehicles use to communicate with each other in a VANET. By changing the pseudonym regularly, the APV scheme makes it difficult for unauthorized entities to link a vehicle's communications to its real-world identity. The proposed APV is compared to the SLOW, RSP, CAPS, and CPN techniques. The data indicates that the efficiency of APV is a better improvement in privacy metrics. It is evident that the AVP offers enhanced safety for vehicles during transportation in the smart city.
DEF: Deep Ensemble Neural Network Classifier for Android Malware DetectionIJCNCJournal
Malware is one of the threats to security of computer networks and information systems. Since malware instances are available sufficiently, there is increased interest among researchers on usage of Artificial Intelligence (AI). Of late AI-enabled methods such as machine learning (ML) and deep learning paved way for solving many real-world problems. As it is a learning-based approach, accumulated training samples help in improving thequality of training and thus leveraging malware detection accuracy. Existing deep learning methods are focusing on learning-based malware detection systems. However, there is need for improving the state of the art through ensemble approach. Towards this end, in this paper we proposed a framework known as Deep Ensemble Framework (DEF) for automatic malware detection. The framework obtains features from training samples. From given malware instance a grayscale image is generated. There is another process to extract the opcode sequences. Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) techniques are used to obtain grayscale image and opcode sequence respectively. Afterwards, a stacking ensemble is employed in order to achieve efficient malware detection and classification. Malware samples collected fromthe Internet sources and Microsoft are used for theempirical study. An algorithm known as Ensemble Learning for Automatic Malware Detection (EL-AML) is proposed to realize our framework. Another algorithm named Pre-Process is proposed to assist the EL-AML algorithm for obtaining intermediate features required by CNN and LSTM.Empirical study reveals that our framework outperforms many existing methods in terms of speed-up and accuracy.
High Performance NMF based Intrusion Detection System for Big Data IoT TrafficIJCNCJournal
With the emergence of smart devices and the Internet of Things (IoT), millions of users connected to the network produce massive network traffic datasets. These vast datasets of network traffic, Big Data are challenging to store, deal with and analyse using a single computer. In this paper we developed parallel implementation using a High Performance Computer (HPC) for the Non-Negative Matrix Factorization technique as an engine for an Intrusion Detection System (HPC-NMF-IDS). The large IoT traffic datasets of order of millions samples are distributed evenly on all the computing cores for both storage and speedup purpose. The distribution of computing tasks involved in the Matrix Factorization takes into account the reduction of the communication cost between the computing cores. The experiments we conducted on the proposed HPC-IDS-NMF give better results than the traditional ML-based intrusion detection systems. We could train the HPC model with datasets of one million samples in only 31 seconds instead of the 40 minutes using one processor), that is a speed up of 87 times. Moreover, we have got an excellent detection accuracy rate of 98% for KDD dataset.
IoT Guardian: A Novel Feature Discovery and Cooperative Game Theory Empowered...IJCNCJournal
Cyber intrusion attacks increasingly target the Internet of Things (IoT) ecosystem, exploiting vulnerable devices and networks. Malicious activities must be identified early to minimize damage and mitigate threats. Using actual benign and attack traffic from the CICIoT2023 dataset, this WORK aims to evaluate and benchmark machine-learning techniques for IoT intrusion detection. There are four main phases to the system. First, the CICIoT2023 dataset is refined to remove irrelevant features and clean up missing and duplicate data. The second phase employs statistical models and artificial intelligence to discover novel features. The most significant features are then selected in the third phase based on cooperative game theory. Using the original CICIoT2023 dataset and a dataset containing only novel features, we train and evaluate a variety of machine learning classifiers. On the original dataset, Random Forest achieved the highest accuracy of 99%. Still, with novel features, Random Forest's performance dropped only slightly (96%) while other models achieved significantly lower accuracy. As a whole, the work contributes substantial contributions to tailored feature engineering, feature selection, and rigorous benchmarking of IoT intrusion detection techniques. IoT networks and devices face continuously evolving threats, making it necessary to develop robust intrusion detection systems.
Enhancing Traffic Routing Inside a Network through IoT Technology & Network C...IJCNCJournal
IoT networking uses real items as stationary or mobile nodes. Mobile nodes complicate networking. Internet of Things (IoT) networks have a lot of control overhead messages because devices are mobile. These signals are generated by the constant flow of control data as such device identity, geographical positioning, node mobility, device configuration, and others. Network clustering is a popular overhead communication management method. Many cluster-based routing methods have been developed to address system restrictions. Node clustering based on the Internet of Things (IoT) protocol, may be used to cluster all network nodes according to predefined criteria. Each cluster will have a Smart Designated Node. SDN cluster management is efficient. Many intelligent nodes remain in the network. The network design spreads these signals. This paper presents an intelligent and responsive routing approach for clustered nodes in IoT networks. An existing method builds a new sub-area clustered topology. The Nodes Clustering Based on the Internet of Things (NCIoT) method improves message transmission between any two nodes. This will facilitate the secure and reliable interchange of healthcare data between professionals and patients. NCIoT is a system that organizes nodes in the Internet of Things (IoT) by grouping them together based on their proximity. It also picks SDN routes for these nodes. This approach involves selecting one option from a range of choices and preparing for likely outcomes problem addressing limitations on activities is a primary focus during the review process. Predictive inquiry employs the process of analyzing data to forecast and anticipate future events. This document provides an explanation of compact units. The Predictive Inquiry Small Packets (PISP) improved its backup system and partnered with SDN to establish a routing information table for each intelligent node, resulting in higher routing performance. Both principal and secondary roads are available for use. The simulation findings indicate that NCIoT algorithms outperform CBR protocols. Enhancements lead to a substantial 78% boost in network performance. In addition, the end-to-end latency dropped by 12.5%. The PISP methodology produces 5.9% more inquiry packets compared to alternative approaches. The algorithms are constructed and evaluated against academic ones.
IoT Guardian: A Novel Feature Discovery and Cooperative Game Theory Empowered...IJCNCJournal
Cyber intrusion attacks increasingly target the Internet of Things (IoT) ecosystem, exploiting vulnerable devices and networks. Malicious activities must be identified early to minimize damage and mitigate threats. Using actual benign and attack traffic from the CICIoT2023 dataset, this WORK aims to evaluate and benchmark machine-learning techniques for IoT intrusion detection. There are four main phases to the system. First, the CICIoT2023 dataset is refined to remove irrelevant features and clean up missing and duplicate data. The second phase employs statistical models and artificial intelligence to discover novel features. The most significant features are then selected in the third phase based on cooperative game theory. Using the original CICIoT2023 dataset and a dataset containing only novel features, we train and evaluate a variety of machine learning classifiers. On the original dataset, Random Forest achieved the highest accuracy of 99%. Still, with novel features, Random Forest's performance dropped only slightly (96%) while other models achieved significantly lower accuracy. As a whole, the work contributes substantial contributions to tailored feature engineering, feature selection, and rigorous benchmarking of IoT intrusion detection techniques. IoT networks and devices face continuously evolving threats, making it necessary to develop robust intrusion detection systems.
** Connect, Collaborate, And Innovate: IJCNC - Where Networking Futures Take ...IJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
Enhancing Traffic Routing Inside a Network through IoT Technology & Network C...IJCNCJournal
IoT networking uses real items as stationary or mobile nodes. Mobile nodes complicate networking. Internet of Things (IoT) networks have a lot of control overhead messages because devices are mobile. These signals are generated by the constant flow of control data as such device identity, geographical positioning, node mobility, device configuration, and others. Network clustering is a popular overhead communication management method. Many cluster-based routing methods have been developed to address system restrictions. Node clustering based on the Internet of Things (IoT) protocol, may be used to cluster all network nodes according to predefined criteria. Each cluster will have a Smart Designated Node. SDN cluster management is efficient. Many intelligent nodes remain in the network. The network design spreads these signals. This paper presents an intelligent and responsive routing approach for clustered nodes in IoT networks. An existing method builds a new sub-area clustered topology. The Nodes Clustering Based on the Internet of Things (NCIoT) method improves message transmission between any two nodes. This will facilitate the secure and reliable interchange of healthcare data between professionals and patients. NCIoT is a system that organizes nodes in the Internet of Things (IoT) by grouping them together based on their proximity. It also picks SDN routes for these nodes. This approach involves selecting one option from a range of choices and preparing for likely outcomes problem addressing limitations on activities is a primary focus during the review process. Predictive inquiry employs the process of analyzing data to forecast and anticipate future events. This document provides an explanation of compact units. The Predictive Inquiry Small Packets (PISP) improved its backup system and partnered with SDN to establish a routing information table for each intelligent node, resulting in higher routing performance. Both principal and secondary roads are available for use. The simulation findings indicate that NCIoT algorithms outperform CBR protocols. Enhancements lead to a substantial 78% boost in network performance. In addition, the end-to-end latency dropped by 12.5%. The PISP methodology produces 5.9% more inquiry packets compared to alternative approaches. The algorithms are constructed and evaluated against academic ones.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Energy Aware Talented Clustering with Compressive Sensing (TCCS) for Wireless Sensor Networks
1. International Journal of Computer Networks & Communications (IJCNC) Vol.14, No.4, July 2022
DOI: 10.5121/ijcnc.2022.14404 57
ENERGY AWARE TALENTED CLUSTERING
WITH COMPRESSIVE SENSING (TCCS) FOR
WIRELESS SENSOR NETWORKS
Bejjam.Komuraiah1
and Dr. M.S. Anuradha2
1
Research Scholar, Department of ECE,
AU College of Engineering (A), Visakhapatnam, AP, India 530003
2
Professor, Department of ECE,
AU college of Engineering (A), Visakhapatnam, AP, India 530003.
ABSTRACT
Wireless sensor networks (WSNs) are networks of sensor nodes that interact wirelessly to gather
information about the surrounding environment. Nodes are often low-powered and dispersed in an ad hoc,
decentralized manner. Although WSNs have gained in popularity, they still have several serious
shortcomings, like limited battery life and bandwidth. In this paper, the cluster head (CH) selection, the
Compressive Sensing (CS) theory, the Connection-based Decentralized Clustering (CDC), the relay node
selection, and the Multi Objective Genetic Algorithm (MOGA)are all taken into account The initial stage
provided a theoretical revision to the concepts of network construction, compressive sensing, and MOGA,
which impacted the improvement of network lifetime. In the second stage developed a novel model such as
Energy Aware Talented Clustering with Compressive Sensing (TCCS) for the sensor network. This
approach considers increasing longevity but also raises the network's overall quality of service (QoS). In
the analysis, the TCCS model is applied to both the centralized and distributed networks and compared
with the existing methods. When compared to the previous methods, the simulation results show that the
proposed work performs better in terms of the calculation of maximum packet delivery ratio of 93.93
percent, minimum energy consumption of 8.04J, maximum energy efficiency of 91.04 percent, maximum
network throughput of 465.51kbps, minimum packet loss of 282 packets, and minimum delay of 63.82 msec.
KEYWORDS
Wireless sensor networks, clustering, clusterhead, compressive theory, decentralized clusting, relay node.
1. INTRODUCTION
Wireless sensor network exhibits significant advantages in terms of minimal cost, tiny sensor
nodes and small-scale factor. It can be employed in the dangerous and cumbersome area for
region monitoring and controlling, with the automated mundane tasks for deployment. The
conventional sensor units are expensive and fails to perform effective computational and
communication capabilities for the present sensor nodes [1]. The present sensor node involved in
data sensing, processing, storing and forwarding those are powered with the battery. The vast
range of application are implemented with the wireless sensor network with minimal cost
solutions for monitoring environment for different applications such as military, target detection,
application tracking and civilian, precision farming, health care application. Also, WSN has been
applied in residential application for the management of energy, safety and efficiency of the outer
space explorations [2].
2. International Journal of Computer Networks & Communications (IJCNC) Vol.14, No.4, July 2022
58
Over the period of time, Wireless sensor network are deployed over the region with motes which
provides the short-range radio communication and minimal coverage area [3]. The WSN
comprises of large number of nodes those involved in provision of multi-hop communication in
the network and collaboration for each node to provide appropriate coverage and connectivity.
Apart from the traditional concern ad-hoc environment involved in collaboration and
communication with the plagued power and energy management for the battery-operated nodes
[4]. Sensor nodes are non-rechargeable those are equipped with batteries therefore energy
efficiency is considered as important concern for increasing the lifetime of the network. In WSN
environment, energy consumption and modeling are major concern for the design and
implementation of the network energy optimization. The sensor node energy consumption
depends on the three factors such as sensing, signal processing and communication [5]. The
communication module comprises of the signal transmission and reception which consumes more
energy. Hence, majority of the research concentrated on the reduction of energy consumption to
minimization communication cost of the network.
The key factor involved in management of the drained energy is effective management of the
network coordinates between sensor nodes through clustering. To minimize the data transmission
time and energy consumption in the network the small sensor nodes are grouped together into
small groups known as clusters [6-10]. Also, the grouping of sensor nodes iscalled as clustering.
Within every cluster lead node is elected act as a cluster head (CH). The CH in the sensor node
exhibits higher capabilities rather than the other sensor in the network. Within the respective
cluster the cluster head is selected for the sensor node where CHs are pre-assigned by the user.
The clustering exhibits the advantages involved in transmission of the aggregated data in sink or
base station. The process of clustering provides scalability for large nodes and reduce energy
consumption. In the centralized clustering process the cluster head is fixed. Through this with
failure of the cluster head the complete cluster is collapsed. Hence, it defines centralized
clustering does not have adequate reliability. However, distributed clustering involved in
provision of the data reliability when cluster head fails. In this case, the complete network fails
when all nodes are failed. In WSN environment distributed clustering commonly engaged in
provision of efficient gathering of data and reliability [25-27].
The theory of Compressive Sensing (CS) offers an efficient paradigm for WSN data gathering.
The signal may be captured at a sample rate lower than the Nyquist sampling rate using CS. With
the decrease in sample frequency, the CS technique may be used in the network to increase the
network's lifespan. There are minimum linear measurement features required to reconstruct the
sparse signal using the CS-based techniques. The l1-norm convex optimization problem may be
used to locate the signal source. CS theory is concerned with determining sample frequencies
based on the features of the signal rather than the bandwidth of the signal. One of the most
difficult issues in WSN design and deployment is reducing the network's WSN energy usage. The
components of the network's energy consumption must be evaluated in order to solve the issues
associated with WSN's energy consumption. Additionally, the development of a viable model for
analyzing WSN energy usage has been aided by many suggested models, all of which are linked
to the CS method.
Contributions:
It is suggested to do energy-aware talent grouping using Compressive Sensing (CS)
approaches. The developed model is regarded as a useful tool for the analysis of energy
usage using the CS-based data collection approach to improve network efficiency.
The proposed method considers the Cluster Head selection, Compressive Sensing (CS)
theory, Connection-based Decentralized Clustering (CDC), Relay node selection, and
Multi-Objective Genetic Algorithm (MOGA) for energy-efficient path selection.
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Cluster selection (CH) uses Euclidean distance to decrease network energy use.
Connection-based Decentralized Clustering considers data transport hops and message
weight..
Through relay node selection the consumption of energy is reduced. For energy
efficiency and reconstruction error, Multi Objective Genetic Algorithm (MOGA) is
utilized.
The suggested strategy enhances the network's overall Quality of Service (QoS), in
addition to considering lifetime enhancement.
The present paper is organized as follows: In section 2 presented a related works for energy
aware clustering along with compressive sensing technique. The network model considered for
analysis is presented in section 3. The proposed CH selection and clustering in the WSN model is
elaborated in Section 4. The proposed model involved in formulation of the relay node selection
and multi-objective genetic algorithm. The performance of the proposed model is presented along
with comparison of TCCS in section 5. The overall conclusion for the proposed model is
presented in Section 6.
2. LITERATURE REVIEW
The wireless sensor node's lifetime is inversely proportional to its battery consumption. By
calculating the energy consumption of the network's nodes, applications and routing protocols
may make better judgments to increase the sensor network's lifespan. This section provides the
existing algorithmutilized for reduced energy consumption and reliable routing scheme for the
WSN. KalpnaGuleria et al.,[11] proposed an scheme for minimization of the energy consumption
through Enhanced Energy Proficient Clustering (EEPC) for complete sensor for the application
of field tracking. The constructed nodes are designed based on the mobile and fixed nodes.
Initially, the nodes those are fixed involved in information broadcast and select mobile nodes
form the cluster head with the fixed nodes. The nodes those are mobile elects the cluster head
(CH) with the associated energy and placement level of nodes. With Mobile sensor nodes (SNs)
data has been transmitted to the CH with the introduction of concept relay nodes, with fixed
nodes. The developed EEPC algorithm selects the relay node based on the calculation of the
fitness value based on location and velocity. The developed scheme involved in minimization of
energy depletion to improve network lifetime. However, the proposed methods fail to evaluate
the transmission delay for data transmission.
V. Seedha Devi et al., [12] proposed a Cluster-based data aggregation reduces latency and packet
loss in WSN. Construction of Aggregation Tree and Slot Scheduling are aspects of the proposed
strategy. Every cluster head compresses node data at first. Through the built aggregate tree sink
node employs MST (MST). Based on allotted timeslots and priority, phase 2 aggregated data may
minimize packet loss and latency in the network. The presented approach reduces WSN
retransmission and waiting times, improving network performance. The simulations showed that
the suggested technique reduces delay and overhead while increasing packet delivery rate and
residual energy. However, the planned plan doesn't assess energy use.
Pakdaman Tirani, Shima & Avokh, Avid [13] constructed a Energy-aware CS-based Data
aggregation and "Energy-balanced High level Data aggregation Tree" The technique improves
network longevity and energy balance by considering diverse nodes. The suggested technique is
compatible with mobile sinks and improves network longevity. The developed EHDT technique
featured network load-balancing with weighted routing tree for data transmission and cluster
head creation with sink via effective energy distribution between nodes. Numerical examination
of the suggested method showed that it boosted network transmissions, energy consumption, and
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60
longevity. The designed approach has simple sink mobility. The suggested strategy doesn't
optimize sink node mobility.
Senouci, Mustapha & Mellouk, Abdelhamid [14] evaluated a uncertainty aware cluster-based
deployment approach for the sensor environment. The developed scheme comprises of the
different factors based on consideration of the different characteristics for real-world applications
in sensor measurement, sensor spatial measurements, harsh deployment model, reliability and
unreliable connectivity. The developed results expressed that proposed scheme exhibits
significant performance for the real-world wireless sensor network to achieve desire network
performance.
Osama MohdAlia [15] developed an energy-efficient network model for dynamic reallocation of
the mobile BS within cluster-based infrastructure network with the harmony search algorithm. At
first, the developed model allocated information between nodes for framed optimal cluster
number with formulation of the appropriate cluster. Through optimal CHs formulation in the
sensor cluster order are evenly distributed among sensor with formation of CH. The infrastructure
of dynamic environment is evaluated based on the consideration of the number of alive nodes and
load balancing in the sensor node. Subsequently, with the optimal placement of the BS CH is
determined to minimize the communication distance in the network. Finally, the data
transmission and sensing placed based on placement of each sensor node with CH and aggregate
the data sensed in BS. The simulation results expressed that proposed scheme exhibits improved
lifetime of network, data delivery and energy consumption for static and random BS in the
network models.
Anis Jari and Avid Avokh [16] constructed a plan for clustering, multilink placement, and load
balancing. The suggested technique includes Multi-sink Placement and Anycast Routing (MPAR)
and EMPAR. The proposed MPAR and EMPAR architecture has clustered sensors. Each sensor
node communicates data to the cluster head (CH) through load-balancing tree. In upper level, the
approach uses a modified particle swarm optimization technique to find the ideal sink node
placement. The ant colony algorithm optimises the routing tree based on a high-level analysis.
Every tree for anycast employs compressive sensing (CS) for data forwarding and aggregation.
Simulations show that the suggested system improves efficiency, network longevity, energy
consumption, and variation.
Lv, Cuicui& Wang, Qiang& Li, Jia. [17] constructed a framework to minimize the energy
consumption in WSN. The constructed framework uses the covariance matric for sparsify the
generated sensor data. The developed scheme uses the numerical sparsity to evaluate the
performance of data. The constructed matrix in the network comprises of the sparse binary
measurement with computation of numerical sparsity. For every measurement, the part of sensor
nodes is involved in gathering of sensor data and transmit data for sink node to recover data. The
experimental analysis demonstrated that the constructed sparsify exhibits real temperature data
approximation. Compared with other types of constructed sparsifyingexhibits numerical
temperature data those are smaller and sequential temperature performance recovery of the data.
The proposed framework model exhibits minimal total energy consumption of the proposed
scheme is minimal than the other compressive and data gathering algorithm. The proposed
scheme exhibits limitation of the higher time complexity for the reduced energy consumption.
Zhang, Ce at al.,[18] constructed a algorithm for data gathering to minimize energy consumption
and packet loss. The proposed model involved in formulation of the cluster head with Sparest
random measurement matrix (SRMM) through the received data to minimize the lost node
measurement and reduce the measurements. To employ between cluster spatial correlation is
performed for the constructed sink with the block diagonal matrix (BDM) through reconstruction
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of the SRMMs for the entire network data. Additionally, with the formulation of the optimal
cluster number the developed model reduces the power consumption. The proposed SR-BDM
involved in estimation of the emulated data for the sensor data GreenOrbs respectively. The
simulation results demonstrated that the proposed model exhibits higher precision through
reliable links and packet link of 60% with minimal energy consumption.
G. Yang, M. A et al.,[19] developed a model for energy efficient communication for green
communication with the hierarchical approach based on clustering to monitor health status of
patients. The developed model organizes the devices in the cluster with devices in equal sizes.
Within every cluster the designated cluster collect data from the member devices to broadcast
information with centralized base station. The constructed model evaluates the energy
consumption of the devices at different states such as idle, sleep, awake and active to perform
data transmission between two different states. Through analytical modeling energy consumption
of each device at different states are measured. The simulation results demonstrated that the
analytical approach improves the lifetime of the network with reduced energy consumption at
different states.
S. M. M. H. Daneshvar et al.,[20] constructed a new effective clustering algorithm for election of
cluster head with grey wolf optimizer (GWO). The GWO is class of intelligence algorithm based
on the consideration of the grey wolf’s behavior with comparative examination of results. The
proposed model utilizes the similar clustering with proposed protocol with the formulated
consecutive rounds. The designed protocol concentrated on the energy saving those are utilized
for the clustering reformation. The dual-hop routing strategy is used in the provided model to
choose the CH depending on the base station's distance. The analysis of the results expressed that
the proposed scheme exhibits reduced and balanced energy consumption through single-hop
communication. The performance of the designed protocol expressed that the proposed scheme
increases the lifetime of the network with the similar protocols.
M. A. Mazaideh and J. Levendovszky [21] constructed a model for WSN to perform efficient
data transmission data nodes with the CS-based approach. The developed model uses the energy
efficient optimization model with the multiple objective genetic algorithms (MOGA) to optimize
the transmission range measurements with the matrix sensing scheme. The developed model
aimed to strike the balance between accuracy and energy efficiency. Through the optimized
values the constructed paths are evaluated based on muti hop manner. The numerical analysis and
experiments stated that expressed output model exhibits MOGA to elect the appropriate
combination based on the measurement number and fitting in the transmission range. The
analysis stated that the developed model exhibits the effective balance between the accuracy and
energy efficiency. The experimental results demonstrated that existence of the measurement
matrices exhibits minimal lower coherency and improved accuracy of CS. Ghaderi, M.R et
al.,[22] presented a complete model for analysis of the CS-based energy consumption in the
WSN. The designed model expressed that source energy consumption is based on the CS those
can be divided in to two categories such as communication and computation energy those are
modeled based on the energy components. The distinct number of the data aggregation schemes
are developed and presented based on CS-based in WSN are investigated and discussed
comparatively. The developed model expressed that the proposed optimization scheme perform
effectively in the designing of the CS-based operation in WSN.
Q. Wang, D et al.,[23] proposed a compressive sensing-based (CS-based) for the clustering
strategy. The relationship between the two WSN adjacent layer are exhibited as lemmas, cluster
optimal size, cluster head (CH) optimal distribution and corresponding analysis are evaluated.
Additionally, the problem of the hot spot is designed with the minimization of the energy
consumption resulted in the rotation of the CH role. Finally, the backup CH (BCH) exhibits the
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mechanism to compute the roles and functionality of the CH and BCH. Subsequently, the
performance of the network is developed with the energy-efficient compressive sensing-based
clustering routing (EECSR). With extensive simulation experiments energy performance are
evaluated in the different aspects. Through extensive simulation analysis the evaluation and
experiments are examined for energy performance. The comparative analysis is performed with
the CS-based and clustering algorithm to evaluate the impact of the EECSR to increase the
energy efficiency and improves the lifetime of the WSN.
In this paper the proposed scheme concentrated on the alleviating the challenges associated with
the issues involved in energy consumption. Primarily, the developed theoretical model involved
in evaluation of the concepts related to network construction, MOGA and compressive sensing
those impacts on the network lifetime. Secondly, the proposed Energy Aware Talented Clustering
scheme with the Compressive Sensing (TCCS) in WSN is evaluated. The proposed TCCS
comprises of the optimal selection relay nodes to minimize the CH node energy depletion. The
proposed algorithm effectively minimizes the data transmission complexity and improved
network lifetime.
3. PROPOSED METHOD
Battery-powered sensor nodes have a short life expectancy and are thus very energy-reliant.
Research into reducing the power consumption and the overall size of wireless sensor networks
(WSNs) is critical if these networks are to be widely deployed.
Figure 1. Architecture of the proposed method
The proposed method is sub divided into four main sections namely Network basic models, Multi
objective Generic Algorithm (MOGA), Clustering Module with decentralized Structure and
Relay Node Selection. Multi-objective GA consist of energy efficiency calculation and
reconstruction error parameters. Finally, MOGA based Compressive Sensing with Performance
analysis. The network basic model consists of network model, energy consumption model and
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compressive sensing. With Genetic Algorithm Multi-Objective concept is combined in MOGA.
Clustering involves the construction of clusters, choosing cluster leaders, and using a
decentralized method with relay node selection. The suggested technique architecture is shown in
figure 1.
The Euclidean distance is calculated in this case to pick the Cluster Head, which significantly
lowers the network's energy usage. The connection based decentralized clustering model give
attention to the number of hops used for data transfer and the weight of the message. through
relay node selection the consumption of energy is reduced. Finally, MOGA with compressive
sensing consists of energy efficiency and reconstruction error. Measurements of metrics
including the packet delivery ratio, energy efficiency, energy consumption, end-to-end latency,
network throughput, and packet loss are used in the calculations for performance analysis. The
suggested work's flow diagram is shown in Figure 2.
Figure 2. Workflow of the proposed TCCS methodology
The detailed explanation of the proposed work is given below.
3.1. Network Model
Consider the battery-powered nodes as N in the WSN those are deployed randomly over the
deployment area size 𝑆 = 𝑎 × 𝑎. The network topology for cluster is employed in the network
with partition of the h clusters. The orthonormal basis comprises of the FFT adopted with sparse
representation and reconstruction algorithm model with the orthogonal matching pursuit (OMP)
method. Consider the basis sparse representation as 𝚿 = (𝜙𝑖,𝑗)𝑁×𝑁
and measurement matric is
denoted as 𝚽 = (𝝋𝑖,𝑗)𝑀×𝑁
. The node in the network is considered as dead when energy level are
completely exhausted.
3.2. Energy Expenditure Model
In the proposed model, the energy consumption in radio with first order is defined as in equation
(1) and (2)
ETx(L, d) = Eelec × L + 𝜖amp × L × d2
, (1)
ERx(L) = Eelec × L, (2)
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In above equation (1) and (2) transmitting energy consumption for the L-message for the distance
d is represented as ETx(L, d). The energy consumption for the L-bit message is denoted as ERx(L).
The transceiver required energy for transmission of electrical energy is denoted as Eelecand
transmission amplifier for the energy consumption is denoted as 𝜖amp.
3.3.Compressive Sensing Background
In the compressive sensing the reading of N sensor is represented as 𝑿 = (𝑥1, ⋯ , 𝑥𝑁)T
and the k -
sparse is denoted as 𝚿 ∈ ℝ𝑁×𝑁
: represented as in equation (3)
𝑿 = 𝚿𝜽, (3)
where 𝜽 ∈ ℝ𝑁
denoted the sparse basis of the vector coefficient in the sparse basis 𝚿. The k-
sparse X is denoted as the compressive in the vector is denoted as 𝜽with nonzero components as
𝑘(𝑘 ⩽ 𝑁) and the smallest components are defined as (N- k) those can be ignored.
Assume the 𝜽 ∈ ℝ𝑀𝑥𝑁
measurement matrix those are uncorrelated with the basis 𝚿. Then,the CS
measurement variable with X can be denoted as in equation (4)
𝒀 = 𝚽𝑿 = 𝚽𝚿𝜽 = 𝚯𝜽, (4)
Where, sensing matrix is denoted as M << N, and Θ = ΦΨ. The reconstructed original signal X
can have probability of overwhelming for the measurements M for the l1-norm minimization
denoted as in equation (5)
𝑿
̂ = arg 𝑚𝑖𝑛 ∥ 𝑿 ∥1 subject to : 𝒀 = 𝚽𝑿 (5)
Where, the reconstructed sparse signal X is represented as 𝑿
̂
The two factors considered for reconstruction of X from Y that need to be considered: 1) X is
compressive at Ψ, and 2) 𝚽need to evaluate the RIP denoted as 𝑀 ⩾ 𝑐𝑘lg(𝑁/𝑘). In other words,
the condition for k-sparse X is expressed as conditions in equation (6)
(1 − 𝜀) ∥ 𝜽 ∥2
2
⩽∥ 𝚽𝜽 ∥2
2
⩽ (1 + 𝜀) ∥ 𝜽 ∥2
2
(6)
Where 𝑐, 𝜀 ∈ (0,1) while 𝚽 sto withstand the RIP parameter 𝜀.
3.4. Multi Objective Genetic Algorithm
To perform optimization of the different parameters multiple objective genetic algorithms
(MOGAs). The metaheuristics algorithm uses the MOGA those are applied in the WSN to
achieve optimal contradictory objectives. The set of restriction are adjusted based on the
simultaneous objectives. Those objectives are adjusted based on the simultaneous restriction’s
subjects. With multi-objective optimization for optimal solution specific definition are presented
instead of yielding single solution through conventional genetic algorithm (GA). The set of
MOGA non-dominated solutions are accepted to derive the best solution with subjective those
need to be formulated.
MOGA involved in formulation of vector fitness function denoted as F(x) = [f1(x), f2(x), · · ·,
fn(x)]T to derive the decision variable to compute the inequality and equality constraints, through
those constraints the viable domain are defined to derive acceptable solutions.
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The pareto-Optimality in the MOGA involved in computation of the vector fitness function to
derive the Parento-Optimal solution. The vector in the multi-objective optimization with Pareto-
Optimal solution vector is defined as X = [x1, x2, · · ·, xn] T
those engaged in provision of the
feasible solutions to minimize the least one objective to increases the characteristics of other
objectives. The vector solution of Pareto-optimal set or Pareto-front 𝐗
̂ = [𝑥
̂1,𝑥
̂2,⋯ , 𝑥
̂𝑛]𝑇
those
are not dominated for any other solution space where Xˆ are dominated for the value X if and
only if as presented in equation (7) and (8)
∀𝑖 ∈ 𝑖 = 1,2, ⋯ , 𝑛, 𝑓𝑖(𝑥) ≤ 𝑓𝑖(𝑥
̂) (7)
And
∃𝑖 ∈ 𝑖 = 1,2, ⋯ , 𝑛, 𝑓𝑖(𝑥) < 𝑓𝑖(𝑥
̂) (8)
4. ENERGY AWARE TALENTED CLUSTERING APPROACH USING
COMPRESSIVE SENSING (TCCS)
In energy saving scheme the transmission count is reduced but it is not sufficient enough.
According to first-order radio model for the energy consumption for transmission of the L-bit
messages based on the computation of the distance leis between the nodes. The figure 3 presented
that the comparative examination of the aggregation trees with the 7 nodes in which the near link
is computed based on the link Euclidean length. In figure Figs. 3-a and 3-b presented the total
transmitted packets involved in transmission. In example of both case node 0 involved in
transmission of packets 5 to the sink node. However, the computation of the links between Fig. 3-
b is less than the Fig. 3-a for computation of the energy consumption. The main objective of the
network to maximize the lifetime of the network, in this paper both transmission and remaining
network energy are considered. At the end, the proposed scheme involved in resolving cluster-
based routing problem, CS theory, sink placement and load balancing [24-26].The data
aggregation is constructed based on the trees with round by round.
Figure 3. Process for aggregating data using TCCS
A cycle that involves calculating the amount of time needed to transport the aggregated data to
the sink and gather data using sensor nodes. Each round in the developed model comprises of the
three phases: At first phase, the sensor node effectively forms the clusters. In second phase,
remaining energy is determined based on the CHs energy and position based on the previous
round. In third phase, every node transmits the data with the corresponding CH through
formulation of the shortest path tree. With the CS scheme the data collected is compressed.
Further, the projection of every CH generates the forwarded information towards sink by sink
with the routing tree in high-level scenario. Based on the following constraints different phases
are proposed for TCCS those are explained as follows:
4.1. Clustering Model
As stated above, the sensor nodes are partitioned into C clusters with the implementation of the
significant strategy modifications. The proposed scheme uses the random set of the CHs election.
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The nodes those are near to the CH are connected and formulate the cluster. In each cluster, the
nodes those are near to the CH are elected [27-29]. In every cluster, a CH selects in such a
manner to summarize the hop counts in the cluster nodes to minimize the CH. The similar
process is continued till no changes are observed in the CHs. At first round, the random CH is
stated compatibility with the derived energy consumption model based on the Euclidean metric
distance with hop count. The better CHs involved in minimization of the energy consumption.
The objective to perform effective network load-balancing with consideration of the CH. With
the typical node Euclidean distance calculation typical node are minimal than the CH distance,
the sink cluster member are computed for the node values. This involved in minimization of the
transmission number and improves the load balancing for the sink node.
Thus, the steps involved in processing are presented as follows:
1) The distance of Euclidean between CH and other nodes are computed. Every sensor nodes
become the cluster member based on the Euclidean distance estimation for the minimized CH.
2) In each cluster, the selected CH is based on the summation of the Euclidean distance between
nodes and CH in minimized manner.
3) The above process continues until the no changes are observed in CHs.
4.2. CH Selection
The constructed traffic model of the sensor node computes the data measured based on round by
round. The CHs involved in utilization of the more energy than the other sensors, these impacts
on the lifetime of the network. Thus, the proposed model involved in computation of the different
node energy distribution. This leads to uniform loss of energy with increased First Node Dies
(FND). In first round, the CHs determined the strategy that explained in phase 1. In next round,
based on the first metrices, the hop between 1-hop or 2-hop distance are involved in computation
of the present CH, the candidate node computes the more energy required for the CH as
explained in equation (9)
CH1(𝑗) = arg 𝑚𝑎𝑥
𝑖∈cos didate nodes (𝑗)
Er(𝑖) (9)
Where, in above equation (9)CH1(𝑗)denoted the jth cluster CH based on the considered first
metrics and Er(𝑖) illustrate dthe remaining energy in the ith node those need to be randomly
broken. Based on this, the factor considered are remaining energy and distance are presented in
equation (10)
CH2(𝑗) = arg 𝑚𝑎𝑥
𝑖∈𝐶 tuster nodes(j)
(
Er(𝑖)
𝑑𝑚𝑖𝑛(𝑖,𝐶𝐻𝐹𝑅(𝑗))
) (10)
Where CH2(𝑗), 𝐶𝐻𝐹𝑅(𝑗),and 𝑑𝑚𝑖𝑛(𝑖, 𝐶𝐻𝐹𝑅(𝑗))demonstrate the jth cluster head in the jth
cluster of the second metric, jth cluster of CH is computed in first round and minimal hop count
is computed for nodes I and 𝐶𝐻𝐹𝑅(𝑗), respectively.
Based on the second metric, the nodes those are closer to center are having more chance to select
as CH. The reduced energy consumption and transmission number is based on the remaining
node energy, different node energy consumption.
Energy, distance, and delay are factors that are taken into consideration while choosing a cluster
head.
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The energy consumption between the two nodes is given in below equation (11)
𝑓energy
=
𝑓energy (𝑞)
𝑓energy (𝑝)
(11)
𝑓energy
(𝑞) = ∑𝑗=1
𝑀
𝑢𝐸𝑁(𝑗)
𝑢EN(𝑗) = ∑
𝐿
𝑖=1
𝑖∈𝑗
(1 − EN(𝐷𝑖) ∗ EN(𝐶𝐻𝑗)) ; 1 ⩽ 𝑗 < 𝑀
𝑓energy
(𝑝) = 𝑀 ∗ Max𝑖=1
𝐿
(𝐸𝑁(𝐷𝑖)) ∗ Max𝑗=1
𝑀
(𝐸𝑁(𝐶𝐻𝑗))
where 𝐸𝑁(𝐷𝑖) and 𝐸𝑁(CH𝑗)implies the energy of 𝑖th
the normal node as well as the energy of
𝑗th
the CH, respectively. 𝑓energy (𝑞), refers to the energy between the CH and the normal node
and between the CH and the BS of the network.
Distance is indicated in mathematical Eq. (12). The value of 𝑓dist
(𝑞)should fall within the
category of[0,1].
𝑓dist
=
𝑓dist (𝑞)
𝑓𝑑𝑖𝑠𝑡(𝑝)
𝑓𝑑𝑖𝑠𝑡
(𝑞) = ∑
𝐿
𝑖=1 ∑
𝑀
𝑗=1 ∥
∥𝐷𝑖 − 𝐶𝐻𝑗∥
∥ + ∥
∥𝐶𝐻𝑗 − 𝐵𝑠∥
∥
(12)
where 𝑓dist
(𝑞) shows the distance between the CH and the network's BS, as well as the distance
between the CH and the normal node.
In Eq. (13), the nodes' data transmission delay is described, and the delay value is required to fall
within [0, 1]. As the number of nodes in a cluster decreases, the latency also becomes
significantly decreased.
𝑓delay =
Max(CH𝑗)
𝐿=1
(13)
4.3. Link based Decentralized Clustering (LDC) Approach
In this section, the algorithm for the LDC scheme is developed and pseudocode is presented for
analysis. The developed algorithm is stated with the initiating messages based on the originator’s
node using clustering. The initiated nodes are denoted as O = {O1,O2, . . ., OQ}. The started node
involved in message circulation process with transmission of message to all nearby nodes. Every
message cluster of the tuple comprises of the five fields those are presented as follows:
Originator ID (OID): The originator node field are provided with the unique identity.
Message ID (MID): The constructed field classifies all messages form the all messages
through the originators.
Message Weight (MWeight): The message carried weights for data transmission
Source ID (SourceID): The message visited by the recent node are indicated in this field.
Time to Live (TTL): The maximal hop count those need to be recirculated based on the
number of hops.
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The constructed fields SourceID, the MID, and the OID self-explanatory in the tuple. The
weighted function is estimated based on the probability to reach the destination node from the
source node. The initialized originator node Ol message weights are denoted as 𝑠𝑔. 𝑀𝑊𝑒𝑖𝑔ℎ𝑡 =
1
𝐷𝑒𝑔𝑟𝑒𝑒 (𝑂𝑙)
.
The field of TTL comprises of the small value integer those only constraints the originator node
those uses similar TTL value. Every Vi node compute the value set denoted as
TotalWeight(Vi,Ol). The defined values stated that the all-message weights are generated from
the Oland reaches the destination node as Vi. Upon reception of the message Msg the recipient
updates the Vi as TotalWeight function for the originator message. Every Vi evaluate the TTl
message those are higher than 0. If Viperform data forwarding of messages between nodes. The
recirculation is evaluated based on the recipient MWeight update in the TTL. The weight
messages are classified based on the TTL degree as Vi those are decremented by the value 1. The
message circulation halts are denoted as Vi in TTL as 0 or MWeight those are significantly
minimal. Every node in the network receives multiple messages from the different data nodes.
The computation performs the function of TotalWeight in the originators with the received
messages. The nodes those receives the last message need to wait for few times to ensure high
messages for processing. Then the node involved in the formulation of the originator for the
value of maximal TotalWeight. To construct a particular cluster the messages are originator with
the informing decision based on the node-ID. In case if all nodes lie below TotalWeight values
than the node remains the outlier. The node those are joined in the cluster are decided and remain
in the discover outlier at any point instances. It involved in accumulated weights form the
different originator messages. In those cases, the nodes already in cluster are within the group the
originator cluster make decision about the cluster. It involved in transmission of new messages in
the originator to notify the decision about the cluster.
Algorithm 1: Algorithm Executed by Message Originator 𝑶𝒍
Create a New Message 𝑀𝑠𝑔
𝑀𝑠𝑔. 𝑂𝐼𝐷 ← 𝑂𝑙, 𝑀𝑠𝑔. 𝑀𝑊𝑒𝑖𝑔ℎ𝑡 ←
1
Degree (𝑂𝑙)
𝑀𝑠𝑔. 𝑆𝑜𝑢𝑟𝑐𝑒𝐼𝐷 ← 𝑂𝑙, 𝑀𝑠𝑔. 𝑇𝑇𝐿 ← InitialTT 𝐿
𝑀𝑠𝑔. 𝑀𝐼𝐷 ← Current System Time { A unique value }
for Each node 𝑉𝑖 ∈ 𝑁𝑏𝑟(𝑂𝑙) do
Send 𝑀𝑠𝑔 to 𝑉𝑖
end for
Algorithm 2: Algorithm Executed by Node 𝑽𝒊 on Receiving 𝑴𝒔𝒈
{Check whether I have received messages from 𝑀𝑠𝑔. 𝑂𝐼𝐷}
if I have seen messages from 𝑀𝑠𝑔. 𝑂𝐼𝐷 before then
{Check if the 𝐿𝑎𝑠𝑡𝑀𝑠𝑔𝐼𝑑(𝑂𝑙) == 𝑀𝑠𝑔 ⋅ 𝑀𝐼𝐷}
if LastMsgID(𝑂𝑙) == 𝑀𝑠𝑔 ⋅ 𝑀𝐼𝐷 then
TotalWeight(𝑉𝑖,𝑂𝑙) ← TotalWeight (𝑉𝑖, 𝑂𝑙) + 𝑀𝑠𝑔 ⋅ 𝑀 Weight
else
TotalWeight(𝑉𝑖,𝑂𝑙) ← 𝑀𝑠𝑔. 𝑀𝑊 eight
LastMsgID (𝑂𝑖) ← Msg.MID
end if
else
{This is the first message from 𝑂𝑙}
TotalWeight(𝑉𝑖,𝑂𝑙) ← TotalWeight (𝑉𝑖, 𝑂𝑙) + 𝑀𝑠𝑔 𝑀 Weight
LastMsgID (𝑂𝑙) ← 𝑀𝑠𝑔 ⋅ 𝑀𝐼𝐷
end if
ifTotalWeight(𝑉𝑖,𝑂𝑙) > MaxWeight then
13. International Journal of Computer Networks & Communications (IJCNC) Vol.14, No.4, July 2022
69
MaxWeight ← TotalWeight (𝑉
𝑗,𝑂𝑙)
MaxWeightID ← Msg.OID
end if
if 𝑀𝑠𝑔. 𝑇𝑇𝐿 > 0 and
𝑀𝑠𝑔.𝑀𝑊𝑒𝑖𝑔ℎ𝑡
Degree (𝑉𝑖)
> 𝑀𝑖𝑛𝑊𝑒𝑖𝑔ℎ𝑡 then
Create a New Message New Msg
NewMsg.OID← Msg.OID, NewMsg.SourceI 𝐷 ← 𝑉𝑖
NewMsg.MWeight ←
Msg.MWeight
Degree(V 𝑖)
NewMsg.TTL ← ( Msg.TTL −1), NewMsg.MID ← 𝑀𝑠𝑔. 𝑀𝐼𝐷
for Each node 𝑉
𝑗 ∈ 𝑁𝑏𝑟(𝑉𝑖) do
Send 𝑀𝑠𝑔 to 𝑉
𝑗
end for
end if
Wait for WaitTime in anticipation of other messages
if MaxWeight>WeightThresholdthen
Join the cluster led by MaxWeightID
else
Remain an outlier
end if
4.4. Relay Node Selection
With the selection of CHs, to prevent higher energy depletion CHs are lies far away from the BS,
the developed protocol elects the relay nodes for every node in such as wat the relay is utilized by
the CH. In case if multiple CH uses the relay, then the assigned relay has different timeslots to
handle communication between nodes with the decreased network throughput. Therefore, in the
proposed model relay node uses the only one CH. This leads to lack of relay in CH and directly
transmits the data to the BS. The relay node selection is based on the node distance from the BS
with assignment of appropriate relay node and it proceeds the same procedure for every CHs. To
select the appropriate node relay for CH two characteristics are considered i) reduction of the
toral energy consumption and ii) energy consumption balance between CH and the relay. In
figure 4 presented the hypothetical relay between CH and BS. The distance between BS and CH
are partitioned as equal distance r0 with estimation of relay and BS distance.
Figure 4. Location of CH, Relay node and the Base Station
The proposed model comprises of the dual hop routing and the relation between one-to-one hop
lies between CHs to the relays. The fixed values of the r0with balance and reduction of energy
consumption. In other words, the line segments between point lies are perfect for relay for CH
and BS. To ensure reduced energy consumption data packets need to be delivered between BS
and the CH in which each relay shares equal amount of energy. The proposed model calculates
the r0=1.8169 to select the relay and CH. The relay node between perfect point is lies between
CH and the BS are calculated with the fixed distance ratio r0=1.8169. The point of the CH is the
closest those are not already selected relay. If relay does not have any relay closer to the
threshold value Tr for the perfect time, the CH those does not have relay transmits the BS directly.
14. International Journal of Computer Networks & Communications (IJCNC) Vol.14, No.4, July 2022
70
The developed procedure exhibits different advantages those are presented below:
1) It completely eliminates the hot-spot problem for relay consumes at equal energy amount
of CH where it serves and select the different relays in the rounds.
2) It reduces the amount of energy consumed for packet delivered to BS.
3) It involved in assignment of relays to the different CHs relay as much as possible.
4.5. Multi Objective Genetic Algorithm for Compressive Sensing:
The proposed multi-objective optimization model uses the genetic algorithm to reduce energy
consumption with the error reconstruction. This section presented about the computation of the
reconstruction error and energy efficiency.
4.5.1. Energy Efficiency:
In a WSN network, the transmission distance, represented as d, is used to compute the route loss
exponent α, which is directly proportional to the dα. Statistically, the node transmission range is
defined as R, the communication mean square distance are computed as E[r2
] = R2
/2 [24]. In the
every path in M the average path length is defined as nc, the energy consumption of the node path
is defined as in equation (14)
𝐸𝑐ℎ = 𝑀 ∗ 𝑛𝑐 ∗ (𝑅2
/2)𝛼
(14)
The average distance between path leaders M and BS is represented as dav for the cluster head
total energy consumption for the transmit unit of compressed data denoted in equation (15)
𝐸𝐿𝐵𝑆 = ∑
𝑀
𝑖=1 𝑑𝑎𝑣
𝛼
(15)
The densing field edge square for the D and BS of the centre involved in computation of the
average distance lies between CH and BS as in equation (16)
𝑑𝑎𝑣 = ∫
𝐷
0 ∫
𝐷
0
[(𝑥 −
𝐷
2
)
2
+ (𝑦 −
𝐷
2
)
2
]𝑓(𝑥, 𝑦)𝑑𝑥𝑑𝑦 (16)
where f(x, y) denoted the pdf function joint probability function (pdf), those value is equal to
1/D2 . The total consumed energy between path in the node and CH is presented in equation (17)
𝐸 = 𝑀 (𝑛𝑐 (
𝑅2
2
)
𝛼/2
+ (
𝐷2
6
)
𝛼/2
) (17)
Through the equation (14) it is observed that the increase sin M increases the total energy
consumed.
4.5.2. Reconstruction Error:
The paradigm for the CS reconstruction error is based on the error measured for the original
densed data (eθ), the error in sparse is represented as ex and observed error is denoted as (ey). The
evaluation is based on the consideration of the equation (18) – (20)
𝑒𝜃 =
1
𝑁
∥ 𝜃 − 𝜃
̂ ∥2
2
(18)
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71
𝑒𝑥 =
1
𝑁
∥ 𝑥 − 𝑥
̂ ∥2
2
, (19)
𝑒𝑦 =
1
𝑀
∥ 𝑦 − 𝑦
̂ ∥2
2
, (20)
The sensed vector is represented as θ, x, and 4 and the reconstructed vector is represented as
𝜃
̂, 𝑥
̂, and 𝑦
̂. In this paper the (ey) is reduced with the 𝑦
̂ = Φ𝑥
̂, using 𝑥
̂ = Ψ𝜃
̂,if Ω = ΦΨ ,then (20)
becomes:
𝑒𝑦 =
1
𝑀
∥ 𝑦 − Ω𝑥
̂ ∥2
2
(21)
Through equation (21) illustrated that increase in M minimizes the 𝑒𝑦. Table 1 shows the
different variable indications used in the different equations.
Table 1. Different variables indications
Variable Indication
L Messages
d Distance
ETx(L, d). Transmitting energy consumption
ERx(L). Receiving energy consumption
Eelec Electrical energy
𝜖amp Amplifier for the energy consumption
N Sensor
Ψ Sparse basis
R Transmission range
nc Average path length
ex Error in sparse
ey Observed error
eθ densed data
5. SIMULATION RESULTS
The performance ofthe proposed model is evaluated in NS-2 simulation software. End-to-end
delay, packet loss, packet delivery ratio, throughput, energy consumption, and energy efficiency
are the parameters taken in to consideration for the analysis. The performance of the proposed
Energy Aware Talented Clustering with Compressive Sensing (TCCS) algorithm model is
evaluated comparatively evaluated with the existing model such as Compressive sensing-based
energy consumption model (CDAS)[22], Energy-Efficient Compressive Sensing-based clustering
Routing (EEPC) protocol [23]. Table 2 shows the simulation settings
Table 2. Simulation settings
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72
Energy Consumption
The performance of the proposed TCCS-Distributed comprises of the clustering-based
optimization model for improving the overall performance of the WSN environment. In WSN
due to higher node mobility energy consumption is higher to reduce energy consumption
proposed TCCS-Distributed model uses the optimization model. In table 3 the performance of the
proposed TCCS-Distributed model is compared with the CDAS, EEPC and TCCS-Centralized.
Table 3. Energy consumption calculation(J)
Nodes CDAS EEPC TCCS-Centralized TCCS-Distributed
0 0 0 0 0
20 5.127 3.21 2.01 1.08
40 9.178 7.65 4.20 2.16
60 15.46 10.78 5.19 4.11
80 21.45 14.85 8.16 6.25
100 26.97 17.93 11.85 8.04
The energy consumption for varying time for the WSN environment is presented in figure 4.
Energy consumption defines the amount of energy consumed by the network for different time
instances in the network. Figure 5 shows the energy consumption calculations of the proposed
method. The network's energy consumption is simulated and analysed for nodes 0, 20, 40, 60, 80,
and 100. The energy consumption of the convention CDAS, EEPC and TCCS-Centralized is
significantly higher than the proposed TCCS-Distributed. Initially, for time energy consumption
of all nodes are equal to zero.At different nodes 0,20,40,60,80 and 100energy consumption of the
proposed TCCS-Distributed us measured as 2.47, 4.78, 6.78, 9.12 and 11.11 (J) respectively.
Figure 5. Energy Consumption Calculation
However, the energy consumption of conventional CDAS, EEPC and TCCS-Centralized exhibits
higher energy consumption in the WSN network. The analysis of the simulation results expressed
that proposed TCCS-Distributed exhibits ~ 6% reduced energy consumption compared with the
conventional CDAS, EEPC and TCCS-Centralized.
17. International Journal of Computer Networks & Communications (IJCNC) Vol.14, No.4, July 2022
73
Energy Efficiency
Energy efficiency defines the amount of energy remains in the network upon the transmission of
data in the network. As WSN environment comprises of the higher node mobility and energy
consumption level to improve energy efficiency of the WSN network proposed TCCS-
Distributed model uses the optimization model. In table 4 the performance of the proposed
TCCS-Distributed model is compared with the CDAS, EEPC and TCCS-Centralized.
Table 4. Energy efficiency calculation (J)
Nodes CDAS EEPC TCCS-Centralized TCCS-Distributed
0 0 0 0 0
20 9.24 13.54 15.11 17.22
40 23.46 33.48 39.64 42.19
60 41.89 49.13 53.89 56.11
80 59.17 64.88 71.45 75.23
100 74.86 82.98 88.67 91.04
Energy efficiency defines the amount of energy remains in the network upon the transmission of
data in the network. As WSN environment comprises of the higher node mobility and energy
consumption level to improve energy efficiency of the WSN network proposed TCCS-
Distributed model uses the optimization model. In figure 5 the performance of the proposed
TCCS-Distributed model is compared with the CDAS, EEPC and TCCS-Centralized. Figure 6
shows energy efficiency calculation.
.
Figure 6. Energy Efficiency Calculation
The simulation analysis of the energy efficiency of the network is evaluated for varying nodes
0,20,40,60,80 and 100. The energy efficiency of the convention CDAS, EEPC and TCCS-
Centralized is significantly minimal than the proposed TCCS-Distributed. Initially, for time 0ms
energy efficiency of all nodes are equal to zero. For the different nodes 0,20,40,60,80 and 100
energy efficiency of the proposed TCCS-Distributed us measured as 19.78, 32.14, 56.47, 78.14
and 89.12(J) respectively. However, the energy efficiency of conventional CDAS, EEPC and
TCCS-Centralized exhibits minimal energy efficiency in the WSN network. The analysis of the
simulation results expressed that proposed TCCS-Distributed exhibits ~ 2% improved energy
efficiency compared with the conventional CDAS, EEPC and TCCS-Centralized.
18. International Journal of Computer Networks & Communications (IJCNC) Vol.14, No.4, July 2022
74
Packet Delivery Ratio
The PDR defines the number of packets received to the total packets transmitted in the network.
The analysis stated that the PDR values in the network are evaluated based on the evaluation of
the proposed and conventional methods in the network as illustrated in the table 5.
Table 5. Packet delivery ratio calculation
Nodes CDAS EEPC TCCS-Centralized TCCS-Distributed
0 0 0 0 0
20 17.32 24.55 28.91 32.63
40 32.67 35.91 38.01 42.03
60 41.04 48.26 52.31 50.17
80 63.93 67.94 69.03 71.93
100 80.56 89.02 92.34 93.93
The PDR value of the proposed TCCS-Distributed evaluated for the existing CDAS, EEPC and
TCCS-Centralized. The PDR analysis of the node parameters in the network are presented in
figure 7.
Figure 7. Packet Delivery Ratio Calculation
The simulation analysis of the packet delivery ratio network is evaluated for varying nodes
0,20,40,60,80 and 100. The packet delivery ratio of the convention CDAS, EEPC and TCCS-
Centralized is minimal than the proposed TCCS-Distributed. Initially, for time 0ms packet
delivery ratio of all nodes is equal to zero. At different nodes 0,20,40,60,80 and 100 packet
delivery ratios of the proposed TCCS-Distributed is measured as 22.14, 37.43, 53.86, 76.47 and
94.26 respectively. However, the packet delivery ratio of conventional CDAS, EEPC and TCCS-
Centralized exhibits minimal value in the WSN network. The analysis of the simulation results
expressed that proposed TCCS-Distributed exhibits ~ 3% higher packet delivery ratio compared
with the conventional CDAS, EEPC and TCCS-Centralized.
Packet Loss
The number of packets dropped or missed in the WSN network is presented as shown in table 6.
The comparative analysis of the packet drops for the proposed TCCS-Distributed and the existing
CDAS, EEPC and TCCS-Centralized are presented.
19. International Journal of Computer Networks & Communications (IJCNC) Vol.14, No.4, July 2022
75
Table 6. Packet loss calculation
Nodes CDAS EEPC TCCS-Centralized TCCS-
Distributed
0 0 0 0 0
20 89 76 59 31
40 143 137 147 98
60 221 226 204 149
80 349 318 269 223
100 450 396 320 282
The comparative analysis of the proposed TCCS-Distributed with the conventional technique for
varying time instances are presented in figure 8. The analysis of the packet drop exhibits the
proposed TCCS-Distributed is minimal.
Figure 8. Packet Loss Calculation
The simulation analysis of the packet drop network is evaluated for varying nodes0,20,40,60,80
and 100. The packet drop of the convention CDAS, EEPC and TCCS-Centralized is higher than
the proposed TCCS-Distributed. Initially, for time 0ms packet drop of all nodes are equal to zero.
At varying nodes 0,20,40,60,80 and 100 packet drop of the proposed TCCS-Distributed is
measured as 9, 17, 22, 36 and 46 respectively. However, the packet drop of conventional CDAS,
EEPC and TCCS-Centralized exhibits higher packet drop value in the WSN network. The
analysis of the simulation results expressed that proposed TCCS-Distributed exhibits ~ 5%
minimal packet drop ratio compared with the conventional CDAS, EEPC and TCCS-Centralized.
End to End Delay
The time by the network for transmission of data from source node destination node is defined as
the end-to-end delay. The efficient communication network should have minimal end-to-end
delay in the WSN. In table 7 the measured End-to-End delay for the proposed TCCS-Distributed
is presented comparatively with conventional CDAS, EEPC and TCCS-Centralized.
20. International Journal of Computer Networks & Communications (IJCNC) Vol.14, No.4, July 2022
76
Table 7. End to end delay calculation (ms)
Nodes CDAS EEPC TCCS-Centralized TCCS-Distributed
0 0 0 0 0
20 42.18 31.82 15.48 9.22
40 85.19 61.22 43.71 29.34
60 133.97 96.41 79.12 63.85
80 185.17 146.23 134.96 113.47
100 245.94 193.45 176.68 146.72
The end-to-end delay for varying time in WSN environment is presented in figure 9. As the end-
to-end delay involved in computation of the data transmission time between source to destination
node in the WSN environment.
Figure 9. End to End Delay Calculation
The simulation analysis of the end-to-end delay network is evaluated for time varying nodes
0,20,40,60,80 and 100. The energy efficiency of the convention CDAS, EEPC and TCCS-
Centralized is higher than the proposed TCCS-Distributed. Initially, for time 0ms end-to-end
delay of all nodes are equal to zero. The different node 0,20,40,60,80 and 100 end-to-end delay of
the proposed TCCS-Distributed us measured as 33.26, 61.24, 92.48, 135.48 and 168.25(ms)
respectively. However, the end-to-end delay of conventional AOMDV and EHO-AOMDV
exhibits higher value in the WSN network. The analysis of the simulation results expressed that
proposed TCCS-Distributed exhibits ~ 3% minimal end-to-end delay compared with the
conventional CDAS, EEPC and TCCS-Centralized.
Throughput
The computation of the network defines the ratio between number of successful data received to
the total number of packets in the network. The throughput measurement of the network is shown
in table 8.
Table 8. Throughput calculation (kbps)
Nodes CDAS EEPC TCCS-Centralized TCCS-Distributed
0 0 0 0 0
20 65.22 87.34 98.53 127.39
40 127.49 154.22 187.25 206.33
60 178.39 221.93 267.22 281.24
80 229.57 265.86 298.45 379.23
100 276.89 321.98 357.13 465.51
21. International Journal of Computer Networks & Communications (IJCNC) Vol.14, No.4, July 2022
77
The computation of throughput is comparatively analyzed with the CDAS, EEPC and TCCS-
Centralized. The performance of the proposed TCCS-Distributed for the estimation of the
throughput for successful transmission and reception of data packets in the network is presented
in figure 10.
Figure 10. Proposed method throughput Calculation
The network's throughput is simulated and analyzed for nodes 0, 20, 40, 60, 80, and 100. The
throughput of the convention CDAS, EEPC and TCCS-Centralized is minimal than the proposed
TCCS-Distributed. Initially, for time 0ms packet drop of all nodes are equal to zero. At different
nodes 0,20,40,60,80 and 100packet drop of the proposed TCCS-Distributed is measured as 54.23,
89.47, 134.16, 169.14 and 205.59kbps respectively. However, the throughput of conventional
CDAS, EEPC and TCCS-Centralized exhibits minimal throughput value in the WSN network.
The analysis of the simulation results expressed that proposed TCCS-Distributed exhibits ~ 5%
improved throughput compared with the conventional CDAS, EEPC and TCCS-Centralized.
6. CONCLUSION
In this work, the impact of CS theory and decentralized clustering results in the betterment of
network lifetime and QoS. To achieve this at the initial stage the network construction in done
with formulated manner. Proposed the Energy Aware Talented Clustering with Compressive
Sensing (TCCS). This approach combines principles such as Compressive Sensing (CS),
Connection-based Decentralized Clustering (CDC), Relay node selection, and Multi Objective
Genetic Algorithm (MOGA) (MOGA). CH selection here is done by computing the Euclidean
distance, which considerably reduces the network's energy usage. The Connection-based
Decentralized Clustering model focuses on the number of hops and the message weight employed
in data transport. Selecting the right relay node might help to save energy usage. Finally, Multi
Objective Genetic Algorithm (MOGA) is used for Energy efficiency and reconstruction error.
When compared to the previous methods, the simulation results show that the proposed work
performs better in terms of the calculation of maximum packet delivery ratio of 93.93 percent,
minimum energy consumption of 8.04J, maximum energy efficiency of 91.04 percent, maximum
network throughput of 465.51kbps, minimum packet loss of 282 packets, and minimum delay of
63.82 msec. In future, we will expand this work by creating multi-level trust model for TCCS
method.
CONFLICTS OF INTEREST
The authors declare no conflict of interest
22. International Journal of Computer Networks & Communications (IJCNC) Vol.14, No.4, July 2022
78
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