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 data communication approach in wireless sensor networksijassn
Wireless sensor network has a vast variety of applications. The adoption of energy efficient cluster-based
configuration has many untapped desirable benefits for the WSNs. The limitation of energy in a sensor
node creates challenges for routing in WSNs. The research work presents the organized and detailed
description of energy conservation method for WSNs. In the proposed method reclustering and multihop
data transmission processes are utilized for data reporting to base station by sensor node. The accurate use
of energy in WSNs is the main challenge for exploiting the network to the full extent. The main aim of the
proposed method is that by evenly distributing the energy all over the sensor nodes and by reducing the
total energy dissipation, the lifetime of the network is enhanced, so that the node will remain alive for
longer times inside the cluster. The result shows that the proposed clustering approach has higher stable
region and network life time than Topology-Controlled Adaptive Clustering (TCAC) and Low-Energy
Adaptive Clustering Hierarchy (LEACH) for WSNs.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Every cluster comprise of a leader which is known as cluster head. The cluster head will be chosen by the sensor nodes in the individual cluster or be pre-assigned by the user. The main advantages of clustering are the transmission of aggregated data to the base station, offers scalability for huge number of nodes and trims down energy consumption. Fundamentally, clustering could be classified into centralized clustering, distributed clustering and hybrid clustering. In centralized clustering, the cluster head is fixed. The rest of the nodes in the cluster act as member nodes. In distributed clustering, the cluster head is not fixed. The cluster head keeps on shifting form node to node within the cluster on the basis of some parameters. Hybrid clustering is the combination of both centralized clustering and distributed clustering mechanisms. This paper gives a brief overview on clustering process in wireless sensor networks. A research on the well evaluated distributed clustering algorithm Low Energy Adaptive Clustering Hierarchy (LEACH) and its followers are portrayed artistically. To overcome the drawbacks of these existing algorithms a hybrid distributed clustering model has been proposed for attaining energy efficiency to a larger scale.
Scheduling different types of packets, such as
real-time and non-real-time data packets, at sensor nodes with
resource constraints in Wireless Sensor Networks (WSN) is of
vital importance to reduce sensors’ energy consumptions and endto-end
data transmission delays. Most of the existing packetscheduling
mechanisms of WSN use First Come First Served
(FCFS), non pre-emptive priority and pre-emptive priority
scheduling algorithms. These algorithms incur a high processing
overhead and long end-to-end data transmission delay due to the
FCFS concept, starvation of high priority real-time data packets
due to the transmission of a large data packet in non pre-emptive
priority scheduling, starvation of non-real-time data packets due
to the probable continuous arrival of real-time data in preemptive
priority scheduling, and improper allocation of data
packets to queues in multilevel queue scheduling algorithms.
Moreover, these algorithms are not dynamic to the changing
requirements of WSN applications since their scheduling policies
are predetermined.
In the Advanced Multilevel Priority packet scheduling
scheme, each node except those at the last level has three levels of
priority queues. According to the priority of the packet and
availability of the queue, node will schedule the packet for
transmission. Due to separated queue availability, packet
transmission delay is reduced. Due to reduction in packet
transmission delay, node can goes into sleep mode as soon as
possible. And Expired packets are deleted at the particular node
at itself before reaching the base station, so that processing
burden on the node is reduced. Thus, energy of the node is saved.
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.
This paper considers a heterogeneous network of energy constrained sensors deployed over a region. Each
Normal sensor node in a network is systematically gathering and transmitting sensed data to the clusterhead,
and then cluster head sending data to a base station (via intermediate cluster- heads). This paper
focuses on reducing the energy consumption and hence improving lifetime of wireless sensor Networks.
Clustering sensor node is an effective topology for the energy constrained networks. So energy saving
algorithm has been developed in which clusters are formed considering a subset of high energy nodes as a
cluster-head and another subset of powerful nodes is ask to go to sleep. When Cluster heads deplete their
energy another subset of nodes becomes active and acts as a cluster head. Proposed approach is
implemented in MATLAB, Simulation results shows that it can prolong the network lifetime than LEACH
protocol, and achieves better performance than the existing clustering algorithms such as LEACH.
Energy efficient data communication approach in wireless sensor networksijassn
Wireless sensor network has a vast variety of applications. The adoption of energy efficient cluster-based
configuration has many untapped desirable benefits for the WSNs. The limitation of energy in a sensor
node creates challenges for routing in WSNs. The research work presents the organized and detailed
description of energy conservation method for WSNs. In the proposed method reclustering and multihop
data transmission processes are utilized for data reporting to base station by sensor node. The accurate use
of energy in WSNs is the main challenge for exploiting the network to the full extent. The main aim of the
proposed method is that by evenly distributing the energy all over the sensor nodes and by reducing the
total energy dissipation, the lifetime of the network is enhanced, so that the node will remain alive for
longer times inside the cluster. The result shows that the proposed clustering approach has higher stable
region and network life time than Topology-Controlled Adaptive Clustering (TCAC) and Low-Energy
Adaptive Clustering Hierarchy (LEACH) for WSNs.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Every cluster comprise of a leader which is known as cluster head. The cluster head will be chosen by the sensor nodes in the individual cluster or be pre-assigned by the user. The main advantages of clustering are the transmission of aggregated data to the base station, offers scalability for huge number of nodes and trims down energy consumption. Fundamentally, clustering could be classified into centralized clustering, distributed clustering and hybrid clustering. In centralized clustering, the cluster head is fixed. The rest of the nodes in the cluster act as member nodes. In distributed clustering, the cluster head is not fixed. The cluster head keeps on shifting form node to node within the cluster on the basis of some parameters. Hybrid clustering is the combination of both centralized clustering and distributed clustering mechanisms. This paper gives a brief overview on clustering process in wireless sensor networks. A research on the well evaluated distributed clustering algorithm Low Energy Adaptive Clustering Hierarchy (LEACH) and its followers are portrayed artistically. To overcome the drawbacks of these existing algorithms a hybrid distributed clustering model has been proposed for attaining energy efficiency to a larger scale.
Scheduling different types of packets, such as
real-time and non-real-time data packets, at sensor nodes with
resource constraints in Wireless Sensor Networks (WSN) is of
vital importance to reduce sensors’ energy consumptions and endto-end
data transmission delays. Most of the existing packetscheduling
mechanisms of WSN use First Come First Served
(FCFS), non pre-emptive priority and pre-emptive priority
scheduling algorithms. These algorithms incur a high processing
overhead and long end-to-end data transmission delay due to the
FCFS concept, starvation of high priority real-time data packets
due to the transmission of a large data packet in non pre-emptive
priority scheduling, starvation of non-real-time data packets due
to the probable continuous arrival of real-time data in preemptive
priority scheduling, and improper allocation of data
packets to queues in multilevel queue scheduling algorithms.
Moreover, these algorithms are not dynamic to the changing
requirements of WSN applications since their scheduling policies
are predetermined.
In the Advanced Multilevel Priority packet scheduling
scheme, each node except those at the last level has three levels of
priority queues. According to the priority of the packet and
availability of the queue, node will schedule the packet for
transmission. Due to separated queue availability, packet
transmission delay is reduced. Due to reduction in packet
transmission delay, node can goes into sleep mode as soon as
possible. And Expired packets are deleted at the particular node
at itself before reaching the base station, so that processing
burden on the node is reduced. Thus, energy of the node is saved.
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.
This paper considers a heterogeneous network of energy constrained sensors deployed over a region. Each
Normal sensor node in a network is systematically gathering and transmitting sensed data to the clusterhead,
and then cluster head sending data to a base station (via intermediate cluster- heads). This paper
focuses on reducing the energy consumption and hence improving lifetime of wireless sensor Networks.
Clustering sensor node is an effective topology for the energy constrained networks. So energy saving
algorithm has been developed in which clusters are formed considering a subset of high energy nodes as a
cluster-head and another subset of powerful nodes is ask to go to sleep. When Cluster heads deplete their
energy another subset of nodes becomes active and acts as a cluster head. Proposed approach is
implemented in MATLAB, Simulation results shows that it can prolong the network lifetime than LEACH
protocol, and achieves better performance than the existing clustering algorithms such as LEACH.
INCREASING WIRELESS SENSOR NETWORKS LIFETIME WITH NEW METHODijwmn
One of the most important issues in Wireless Sensor Networks (WSNs) is severe energy restrictions. As the
performance of Sensor Networks is strongly dependence to the network lifetime, researchers seek a way to
use node energy supply effectively and increasing network lifetime. As a consequence, it is crucial to use
routing algorithms result in decrease energy consumption and better bandwidth utilization. The purpose of
this paper is to increase Wireless Sensor Networks lifetime using LEACH-algorithm. So before clustering
Network environment, it is divided into two virtual layers (using distance between sensor nodes and base
station) and then regarding to sensors position in each of two layers, residual energy of sensor and
distance from base station is used in clustering. In this article, we compare proposed algorithm with wellknown LEACH and ELEACH algorithms in homogenous environment (with equal energy for all sensors)
and heterogeneous one (energy of half of sensors get doubled), also for static and dynamic situation of base
station. Results show that our proposed algorithm delivers improved performance.
Data Dissemination in Wireless Sensor Networks: A State-of-the Art SurveyCSCJournals
A wireless sensor network is a network of tiny nodes with wireless sensing capacity for data collection processing and further communicating with the Base Station this paper discusses the overall mechanism of data dissemination right from data collection at the sensor nodes, clustering of sensor nodes, data aggregation at the cluster heads and disseminating data to the Base Station the overall motive of the paper is to conserve energy so that lifetime of the network is extended this paper highlights the existing algorithms and open research gaps in efficient data dissemination.
WIRELESS SENSOR NETWORK CLUSTERING USING PARTICLES SWARM OPTIMIZATION FOR RED...IJMIT JOURNAL
Wireless sensor networks (WSN) is composed of a large number of small nodes with limited functionality. The most important issue in this type of networks is energy constraints. In this area several researches have been done from which clustering is one of the most effective solutions. The goal of clustering is to divide network into sections each of which has a cluster head (CH). The task of cluster heads collection, data aggregation and transmission to the base station is undertaken. In this paper, we introduce a new approach for clustering sensor networks based on Particle Swarm Optimization (PSO) algorithm using the optimal fitness function, which aims to extend network lifetime. The parameters used in this algorithm are residual energy density, the distance from the base station, intra-cluster distance from the cluster head. Simulation results show that the proposed method is more effective compared to protocols such as (LEACH, CHEF, PSO-MV) in terms of network lifetime and energy consumption.
WEIGHTED DYNAMIC DISTRIBUTED CLUSTERING PROTOCOL FOR HETEROGENEOUS WIRELESS S...ijwmn
In wireless sensor networks (WSN), conserving energy and increasing lifetime of the network are a critical issue that has been addressed by substantial research works. The clustering technique has been proven particularly energy-efficient in WSN. The nodes form groups (clusters) that include one cluster head and member clusters. Cluster heads (CHs) are able to process, filter, gather the data sent by sensors
belonging to their cluster and send it to the base station. Many routing protocols which have been proposed are based on heterogeneity and use the clustering scheme such as SEP and DEEC. In this paper we introduce a new approach called WDDC in which cluster heads are chosen on the basis
of probability of ratio of residual energy and average energy of the network. It also takes into consideration distances between nodes and the base station to favor near nodes with more energy to be cluster heads. Furthermore, WDDC is dynamic; it divides network lifetime in two zones in which it changes its behavior. Simulation results show that our approach performs better than the other distributed clustering protocols such as SEP and DEEC in terms of energy efficiency and lifetime of the network.
A Fault tolerant system based on Genetic Algorithm for Target Tracking in Wir...Editor IJCATR
In this paper, we explored the possibility of using Genetic Algorithm (GA) being used in Wireless Sensor Networks in general with
specific emphasize on Fault tolerance. In Wireless sensor networks, usually sensor and sink nodes are separated by long communication
distance and hence to optimize the energy, we are using clustering approach. Here we are employing improved K-means clustering algorithm to
form the cluster and GA to find optimal use of sensor nodes and recover from fault as quickly as possible so that target detection won’t be
disrupted. This technique is simulated using Matlab software to check energy consumption and lifetime of the network. Based on the
simulation results, we concluded that this model shows significant improvement in energy consumption rate and network lifetime than other
method such as Traditional clustering or Simulated Annealing
A QoI Based Energy Efficient Clustering for Dense Wireless Sensor Networkijassn
In a wireless sensor network Quality of Information (QoI), Energy Efficiency, Redundant data avoidance,
congestion control are the important metrics that affect the performance of wireless sensor network. As
many approaches were proposed to increase the performance of a wireless sensor network among them
clustering is one of the efficient approaches in sensor network. Many clustering algorithms concentrate
mainly on power Optimization like FSCH, LEACH, and EELBCRP. There is necessity of the above
metrics in wireless sensor network where nodes are densely deployed in a given network area. As the nodes
are deployed densely there is maximum possibility of nodes appear in the sensing region of other nodes. So
there exists an option that nodes have to send the information that is already reached the base station by its
own cluster members or by members of other clusters. This mechanism will affect the QoI, Energy factor
and congestion control of the wireless sensor networks. Even though clustering uses TDMA (Time Division
Multiple Access) for avoiding congestion control for intra clustering data transmission, but it may fail in
some critical situation. This paper proposed a energy efficient clustering which avoid data redundancy in a
dense sensor network until the network becomes sparse and hence uses the TDMA efficiently during high
density of the nodes.
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)
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.
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
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.
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
Energy aware clustering protocol (eacp)IJCNCJournal
Energy saving to prolong the network life is an important design issue while developing a new routing
protocol for wireless sensor network. Clustering is a key technique for this and helps in maximizing the
network lifetime and scalability. Most of the routing and data dissemination protocols of WSN assume a
homogeneous network architecture, in which all sensors have the same capabilities in terms of battery
power, communication, sensing, storage, and processing. Recently, there has been an interest in
heterogeneous sensor networks, especially for real deployments. This research paper has proposed a new
energy aware clustering protocol (EACP) for heterogeneous wireless sensor networks. Heterogeneity is
introduced in EACP by using two types of nodes: normal and advanced. In EACP cluster heads for normal
nodes are elected with the help of a probability scheme based on residual and average energy of the
normal nodes. This will ensure that only the high residual normal nodes can become the cluster head in a
round. Advanced nodes use a separate probability based scheme for cluster head election and they will
further act as a gateway for normal cluster heads and transmit their data load to base station when they
are not doing the duty of a cluster head. Finally a sleep state is suggested for some sensor nodes during
cluster formation phase to save network energy. The performance of EACP is compared with SEP and
simulation result shows the better result for stability period, network life and energy saving than SEP.
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.
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 DATA COMMUNICATION APPROACH IN WIRELESS SENSOR NETWORKSijassn
Wireless sensor network has a vast variety of applications. The adoption of energy efficient cluster-based configuration has many untapped desirable benefits for the WSNs. The limitation of energy in a sensor node creates challenges for routing in WSNs. The research work presents the organized and detailed description of energy conservation method for WSNs. In the proposed method reclustering and multihop data transmission processes are utilized for data reporting to base station by sensor node. The accurate use of energy in WSNs is the main challenge for exploiting the network to the full extent. The main aim of the proposed method is that by evenly distributing the energy all over the sensor nodes and by reducing the total energy dissipation, the lifetime of the network is enhanced, so that the node will remain alive for longer times inside the cluster. The result shows that the proposed clustering approach has higher stable region and network life time than Topology-Controlled Adaptive Clustering (TCAC) and Low-Energy Adaptive Clustering Hierarchy (LEACH) for WSNs.
CUTTING DOWN ENERGY USAGE IN WIRELESS SENSOR NETWORKS USING DUTY CYCLE TECHNI...ijwmn
A wireless sensor network is composed of many sensor nodes, that have beengiven out in a
specific zoneandeach of them hadanability of collecting information from the environment and
sending collected data to the sink. The most significant issues in wireless sensor networks,
despite the recent progress is the trouble of the severe limitations of energy resources.Since that
in different applications of sensor nets, we could throw a static or mobile sink, then all aspects of
such networks should be planned with an awareness of energy.One of the most significant topics
related to these networks, is routing. One of the most widely used and efficient methods of
routing isa hierarchy (based on clustering) method.
CUTTING DOWN ENERGY USAGE IN WIRELESS SENSOR NETWORKS USING DUTY CYCLE TECHNI...ijwmn
A wireless sensor network is composed of many sensor nodes, that have beengiven out in a
specific zoneandeach of them hadanability of collecting information from the environment and
sending collected data to the sink. The most significant issues in wireless sensor networks,
despite the recent progress is the trouble of the severe limitations of energy resources.Since that
in different applications of sensor nets, we could throw a static or mobile sink, then all aspects of
such networks should be planned with an awareness of energy.One of the most significant topics
related to these networks, is routing. One of the most widely used and efficient methods of
routing isa hierarchy (based on clustering) method.
In The present study with the objective of cutting down energy consumption and persistence of
network coverage, we have offered a novel algorithm based on clustering algorithms and multihop routing.To achieve this goal, first, we layer the network environment based on the size of the
network.We will identify the optimal number of cluster heads and every cluster head based on
the mechanism of topology control will start to accept members.Likewise, we set the first layer
as gate layer and subsequently identifying the gate’s nodes, we’d turn away half of the sensors
and then stop using energy and the remaining nodes in this layer will join the gate’s nodes
because they hold a critical part in bettering the functioning of the system. Cluster heads off
following layers send the information to cluster heads in the above layer until sent data will be
sent to gate’s nodes and finally will be sent to sink. We have tested the proposed algorithm in
two situations 1) when the sink is off and 2)when a sink is on and simulation data shows that
proposed algorithm has better performance in terms of the life span of a network than LEACH
and ELEACH protocols.
INCREASING WIRELESS SENSOR NETWORKS LIFETIME WITH NEW METHODijwmn
One of the most important issues in Wireless Sensor Networks (WSNs) is severe energy restrictions. As the
performance of Sensor Networks is strongly dependence to the network lifetime, researchers seek a way to
use node energy supply effectively and increasing network lifetime. As a consequence, it is crucial to use
routing algorithms result in decrease energy consumption and better bandwidth utilization. The purpose of
this paper is to increase Wireless Sensor Networks lifetime using LEACH-algorithm. So before clustering
Network environment, it is divided into two virtual layers (using distance between sensor nodes and base
station) and then regarding to sensors position in each of two layers, residual energy of sensor and
distance from base station is used in clustering. In this article, we compare proposed algorithm with wellknown LEACH and ELEACH algorithms in homogenous environment (with equal energy for all sensors)
and heterogeneous one (energy of half of sensors get doubled), also for static and dynamic situation of base
station. Results show that our proposed algorithm delivers improved performance.
Data Dissemination in Wireless Sensor Networks: A State-of-the Art SurveyCSCJournals
A wireless sensor network is a network of tiny nodes with wireless sensing capacity for data collection processing and further communicating with the Base Station this paper discusses the overall mechanism of data dissemination right from data collection at the sensor nodes, clustering of sensor nodes, data aggregation at the cluster heads and disseminating data to the Base Station the overall motive of the paper is to conserve energy so that lifetime of the network is extended this paper highlights the existing algorithms and open research gaps in efficient data dissemination.
WIRELESS SENSOR NETWORK CLUSTERING USING PARTICLES SWARM OPTIMIZATION FOR RED...IJMIT JOURNAL
Wireless sensor networks (WSN) is composed of a large number of small nodes with limited functionality. The most important issue in this type of networks is energy constraints. In this area several researches have been done from which clustering is one of the most effective solutions. The goal of clustering is to divide network into sections each of which has a cluster head (CH). The task of cluster heads collection, data aggregation and transmission to the base station is undertaken. In this paper, we introduce a new approach for clustering sensor networks based on Particle Swarm Optimization (PSO) algorithm using the optimal fitness function, which aims to extend network lifetime. The parameters used in this algorithm are residual energy density, the distance from the base station, intra-cluster distance from the cluster head. Simulation results show that the proposed method is more effective compared to protocols such as (LEACH, CHEF, PSO-MV) in terms of network lifetime and energy consumption.
WEIGHTED DYNAMIC DISTRIBUTED CLUSTERING PROTOCOL FOR HETEROGENEOUS WIRELESS S...ijwmn
In wireless sensor networks (WSN), conserving energy and increasing lifetime of the network are a critical issue that has been addressed by substantial research works. The clustering technique has been proven particularly energy-efficient in WSN. The nodes form groups (clusters) that include one cluster head and member clusters. Cluster heads (CHs) are able to process, filter, gather the data sent by sensors
belonging to their cluster and send it to the base station. Many routing protocols which have been proposed are based on heterogeneity and use the clustering scheme such as SEP and DEEC. In this paper we introduce a new approach called WDDC in which cluster heads are chosen on the basis
of probability of ratio of residual energy and average energy of the network. It also takes into consideration distances between nodes and the base station to favor near nodes with more energy to be cluster heads. Furthermore, WDDC is dynamic; it divides network lifetime in two zones in which it changes its behavior. Simulation results show that our approach performs better than the other distributed clustering protocols such as SEP and DEEC in terms of energy efficiency and lifetime of the network.
A Fault tolerant system based on Genetic Algorithm for Target Tracking in Wir...Editor IJCATR
In this paper, we explored the possibility of using Genetic Algorithm (GA) being used in Wireless Sensor Networks in general with
specific emphasize on Fault tolerance. In Wireless sensor networks, usually sensor and sink nodes are separated by long communication
distance and hence to optimize the energy, we are using clustering approach. Here we are employing improved K-means clustering algorithm to
form the cluster and GA to find optimal use of sensor nodes and recover from fault as quickly as possible so that target detection won’t be
disrupted. This technique is simulated using Matlab software to check energy consumption and lifetime of the network. Based on the
simulation results, we concluded that this model shows significant improvement in energy consumption rate and network lifetime than other
method such as Traditional clustering or Simulated Annealing
A QoI Based Energy Efficient Clustering for Dense Wireless Sensor Networkijassn
In a wireless sensor network Quality of Information (QoI), Energy Efficiency, Redundant data avoidance,
congestion control are the important metrics that affect the performance of wireless sensor network. As
many approaches were proposed to increase the performance of a wireless sensor network among them
clustering is one of the efficient approaches in sensor network. Many clustering algorithms concentrate
mainly on power Optimization like FSCH, LEACH, and EELBCRP. There is necessity of the above
metrics in wireless sensor network where nodes are densely deployed in a given network area. As the nodes
are deployed densely there is maximum possibility of nodes appear in the sensing region of other nodes. So
there exists an option that nodes have to send the information that is already reached the base station by its
own cluster members or by members of other clusters. This mechanism will affect the QoI, Energy factor
and congestion control of the wireless sensor networks. Even though clustering uses TDMA (Time Division
Multiple Access) for avoiding congestion control for intra clustering data transmission, but it may fail in
some critical situation. This paper proposed a energy efficient clustering which avoid data redundancy in a
dense sensor network until the network becomes sparse and hence uses the TDMA efficiently during high
density of the nodes.
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)
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.
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
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.
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
Energy aware clustering protocol (eacp)IJCNCJournal
Energy saving to prolong the network life is an important design issue while developing a new routing
protocol for wireless sensor network. Clustering is a key technique for this and helps in maximizing the
network lifetime and scalability. Most of the routing and data dissemination protocols of WSN assume a
homogeneous network architecture, in which all sensors have the same capabilities in terms of battery
power, communication, sensing, storage, and processing. Recently, there has been an interest in
heterogeneous sensor networks, especially for real deployments. This research paper has proposed a new
energy aware clustering protocol (EACP) for heterogeneous wireless sensor networks. Heterogeneity is
introduced in EACP by using two types of nodes: normal and advanced. In EACP cluster heads for normal
nodes are elected with the help of a probability scheme based on residual and average energy of the
normal nodes. This will ensure that only the high residual normal nodes can become the cluster head in a
round. Advanced nodes use a separate probability based scheme for cluster head election and they will
further act as a gateway for normal cluster heads and transmit their data load to base station when they
are not doing the duty of a cluster head. Finally a sleep state is suggested for some sensor nodes during
cluster formation phase to save network energy. The performance of EACP is compared with SEP and
simulation result shows the better result for stability period, network life and energy saving than SEP.
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.
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 DATA COMMUNICATION APPROACH IN WIRELESS SENSOR NETWORKSijassn
Wireless sensor network has a vast variety of applications. The adoption of energy efficient cluster-based configuration has many untapped desirable benefits for the WSNs. The limitation of energy in a sensor node creates challenges for routing in WSNs. The research work presents the organized and detailed description of energy conservation method for WSNs. In the proposed method reclustering and multihop data transmission processes are utilized for data reporting to base station by sensor node. The accurate use of energy in WSNs is the main challenge for exploiting the network to the full extent. The main aim of the proposed method is that by evenly distributing the energy all over the sensor nodes and by reducing the total energy dissipation, the lifetime of the network is enhanced, so that the node will remain alive for longer times inside the cluster. The result shows that the proposed clustering approach has higher stable region and network life time than Topology-Controlled Adaptive Clustering (TCAC) and Low-Energy Adaptive Clustering Hierarchy (LEACH) for WSNs.
CUTTING DOWN ENERGY USAGE IN WIRELESS SENSOR NETWORKS USING DUTY CYCLE TECHNI...ijwmn
A wireless sensor network is composed of many sensor nodes, that have beengiven out in a
specific zoneandeach of them hadanability of collecting information from the environment and
sending collected data to the sink. The most significant issues in wireless sensor networks,
despite the recent progress is the trouble of the severe limitations of energy resources.Since that
in different applications of sensor nets, we could throw a static or mobile sink, then all aspects of
such networks should be planned with an awareness of energy.One of the most significant topics
related to these networks, is routing. One of the most widely used and efficient methods of
routing isa hierarchy (based on clustering) method.
CUTTING DOWN ENERGY USAGE IN WIRELESS SENSOR NETWORKS USING DUTY CYCLE TECHNI...ijwmn
A wireless sensor network is composed of many sensor nodes, that have beengiven out in a
specific zoneandeach of them hadanability of collecting information from the environment and
sending collected data to the sink. The most significant issues in wireless sensor networks,
despite the recent progress is the trouble of the severe limitations of energy resources.Since that
in different applications of sensor nets, we could throw a static or mobile sink, then all aspects of
such networks should be planned with an awareness of energy.One of the most significant topics
related to these networks, is routing. One of the most widely used and efficient methods of
routing isa hierarchy (based on clustering) method.
In The present study with the objective of cutting down energy consumption and persistence of
network coverage, we have offered a novel algorithm based on clustering algorithms and multihop routing.To achieve this goal, first, we layer the network environment based on the size of the
network.We will identify the optimal number of cluster heads and every cluster head based on
the mechanism of topology control will start to accept members.Likewise, we set the first layer
as gate layer and subsequently identifying the gate’s nodes, we’d turn away half of the sensors
and then stop using energy and the remaining nodes in this layer will join the gate’s nodes
because they hold a critical part in bettering the functioning of the system. Cluster heads off
following layers send the information to cluster heads in the above layer until sent data will be
sent to gate’s nodes and finally will be sent to sink. We have tested the proposed algorithm in
two situations 1) when the sink is off and 2)when a sink is on and simulation data shows that
proposed algorithm has better performance in terms of the life span of a network than LEACH
and ELEACH protocols.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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 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
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
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. .
INCREASING WIRELESS SENSOR NETWORKS LIFETIME WITH NEW METHODijwmn
One of the most important issues in Wireless Sensor Networks (WSNs) is severe energy restrictions. As the
performance of Sensor Networks is strongly dependence to the network lifetime, researchers seek a way to
use node energy supply effectively and increasing network lifetime. As a consequence, it is crucial to use
routing algorithms result in decrease energy consumption and better bandwidth utilization. The purpose of
this paper is to increase Wireless Sensor Networks lifetime using LEACH-algorithm. So before clustering
Network environment, it is divided into two virtual layers (using distance between sensor nodes and base
station) and then regarding to sensors position in each of two layers, residual energy of sensor and
distance from base station is used in clustering. In this article, we compare proposed algorithm with wellknown LEACH and ELEACH algorithms in homogenous environment (with equal energy for all sensors)
and heterogeneous one (energy of half of sensors get doubled), also for static and dynamic situation of base
station. Results show that our proposed algorithm delivers improved performance.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A LOW-ENERGY DATA AGGREGATION PROTOCOL USING AN EMERGENCY EFFICIENT HYBRID ME...IJCNCJournal
Recent wireless sensor network focused on developing communication networks with minimal power and cost. To achieve this, several techniques have been developed to monitor a completely wireless sensor network. Generally, in the WSN network, communication is established between the source nodes and the destination node with an abundant number of hops, an activity which consumes much energy. The node existing between source and destination nodes consumes energy for transmission of data and maximize network lifetime. To overcome this issue, a new Emergency Efficient Hybrid Medium Access Control (EEHMAC) protocol is presented to reduce consumption of energy among a specific group of WSNs which will increase the network lifetime. The proposed model makes a residual battery is utilized for effective transmission of data with minimal power consumption. Compared with other models, the experimental results strongly showed that our model is not only able to reduce network lifetime but also to increase the overall network performance.
Energy Aware Talented Clustering with Compressive Sensing (TCCS) for Wireless...IJCNCJournal
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.
Similar to Maximizing Lifetime of Homogeneous Wireless Sensor Network through Energy Efficient Clustering Method (20)
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
TESDA TM1 REVIEWER FOR NATIONAL ASSESSMENT WRITTEN AND ORAL QUESTIONS WITH A...
Maximizing Lifetime of Homogeneous Wireless Sensor Network through Energy Efficient Clustering Method
1. Asfandyar Khan, Azween Abdullah, Nurul Hasan
International Journal of Computer Science and Security, Volume (3), Issue (6) 583
Maximizing Lifetime of Homogeneous Wireless Sensor Network
through Energy Efficient Clustering Method
Asfandyar Khan asfand43@yahoo.com
Computer & Information Sciences Department
Universiti Teknologi PETRONAS
Bandar Seri Iskander, 31750 Tronoh,
Perak, Malaysia
Azween Abdullah azweenabdullah@petronas.com.my
Computer & Information Sciences Department
Universiti Teknologi PETRONAS
Bandar Seri Iskander, 31750 Tronoh,
Perak, Malaysia
Nurul Hasan nurul_hasan@petronas.com.my
Chemical Engineering Department
Universiti Teknologi PETRONAS
Bandar Seri Iskander, 31750 Tronoh, Perak, Malaysia
Abstract
The main purpose of this paper is to develop a mechanism to increase the
lifetime of homogeneous sensor nodes by controlling long distance
communication, energy balancing and efficient delivery of information. 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 introduce a candidate cluster head node on the
basis of node density. We introduce a modified cluster based model by using
special nodes called server nodes (SN) that is powerful in term of resources.
These server nodes are responsible for transmitting the data from cluster head to
the base station. Our proposed algorithm for cluster head selection based on
residual energy, distance, reliability and degree of mobility. The proposed
architecture is more scalable and proposed algorithm is robustness against
even/uneven node deployment.
Keywords: Server node (SN), cluster head (CH), Network lifetime.
1. INTRODUCTION
A Wireless sensor network (WSN) is composed of large numbers of tiny low powered sensor
nodes and one or more multiple base stations (sinks). These tiny sensor nodes consist of
sensing, data processing and communication components. The sensor nodes sense, measure
2. Asfandyar Khan, Azween Abdullah, Nurul Hasan
International Journal of Computer Science and Security, Volume (3), Issue (6) 584
and collect ambient environment conditions, they have the ability to process the data and perform
simple computation and send the processed information to the base station either directly or
through some intermediate point called gateway. Gateway can be used for fusion and removing
the anomalies and to get some conclusion from the collected data over a period of time. Wide
range of application can be found in [1, 2].
Sensor nodes are resource constrained in term of energy, processor and memory and low range
communication and bandwidth. Limited battery power is used to operate the sensor nodes and is
very difficult to replace or recharge it, when the nodes die. This will affect the network
performance. Energy conservation and harvesting increase lifetime of the network. Optimize the
communication range and minimize the energy usage, we need to conserve the energy of sensor
nodes [1, 2].Sensor nodes are deployed to gather information and desired that all the nodes
works continuously and transmit information as long as possible. This address the lifetime
problem in wireless sensor networks. Sensor nodes spend their energy during transmitting the
data, receiving and relaying packets. Hence, designing routing algorithms that maximize the life
time until the first battery expires is an important consideration.
Designing energy aware algorithms increase the lifetime of sensor nodes. In some applications
the network size is larger required scalable architectures. Energy conservation in wireless sensor
networks has been the primary objective, but however, this constrain is not the only consideration
for efficient working of wireless sensor networks. There are other objectives like scalable
architecture, routing and latency. In most of the applications of wireless sensor networks are
envisioned to handled critical scenarios where data retrieval time is critical, i.e., delivering
information of each individual node as fast as possible to the base station become an important
issue. It is important to guarantee that information can be successfully received to the base
station the first time instead of being retransmitted.
In wireless sensor network data gathering and routing are challenging tasks due to their dynamic
and unique properties. Many routing protocols are developed, but among those protocols cluster
based routing protocols are energy efficient, scalable and prolong the network lifetime [3, 4].In the
event detection environment nodes are idle most of the time and active at the time when the
event occur. Sensor nodes periodically send the gather information to the base station. Routing
is an important issue in data gathering sensor network, while on the other hand sleep-wake
synchronization are the key issues for event detection sensor networks.
In the clustered environment, the data gathered by each sensor is communicated either by single
hop or multihop to base station. In cluster based architectures, at times, each cluster has their
own leader node that collects the aggregated data from the non leader nodes and is responsible
for the data transmission to the base station. Clustering approach increases the network life time,
because each node do not directly communicate with the base station and hence overcome the
problem of long range communication among the sensors nodes. To improve the overall network
scalability, cluster based architecture can share the traffic load equally among all the nodes in
various clusters, due to this the end to end delay between the sensor nodes and command node
can be reduced [5]. Many critical issues associated with clustering architecture system because
of the non-uniformly distribution of the sensors in the field. Some cluster heads may be heavily
loaded than others, thus causing latency in communication, decreasing the life time of the
network and inadequate tracking of targets or events.
Self organization of the nodes with in cluster for a randomly deployed large number of sensors
was considered in recent years emphasizing the limited battery power and compact hardware
organization of each sensor module. To send the information from a high numbers of sensors
nodes to base station, it is necessary to be a cost effective and group all the nodes in cluster. It is
necessary to examine a list of metrics that determine the performance of a sensor network. In this
paper, we propose a strategy to select an efficient cluster head for each cluster on the basis of
different parameters. Our proposed strategy will be better in terms of data delivery and energy
balancing as shown in Fig 1.
The focus in this paper is to assess the role of strategic sensor node placement (clustering,
cluster head selection) on the data delivery of a network. In this regard, Section 2 discusses
related work. Section 3 describes the methodology, section 4 describes the analytical discussion,
and section 5 presents conclusion and future works.
3. Asfandyar Khan, Azween Abdullah, Nurul Hasan
International Journal of Computer Science and Security, Volume (3), Issue (6) 585
2. RELATED WORK
In wireless sensor network energy efficient communication is a matter of survival; as a result
research mainly focused towards energy efficient communication, energy conservation,
prolonging network lifetime. Various routing techniques have been introduced, but clustering
architecture is one of the most dominant, and scalable. In [6], node should become a cluster head
by calculating the optimal probability, in order to minimize network energy consumption has been
proposed. Heinzelman et al [7] introduced a hierarchical clustering algorithm for sensor networks
called LEACH. It uses distributed cluster formation for a randomized rotation of the cluster-head
role with the objective of reducing energy consumption that increases the network life time and
data delivery. LEACH uses TDMA/CDMA, MAC to reduce inter and intra cluster collisions. Uppu
et al [8] introduced a technique of backup heads to avoid re-clustering, secondary membership
heads to eliminate redundant transmission and optimum distance hopping to achieve network
efficiency in terms of life time and data delivery.
Balanced cluster architecture are used to maximize the life time of the network by forming
balanced cluster and minimize the total energy consumed in communication and also the number
of hop is not fixed and depends upon the spatial location of sensors[An energy constrained multi-
hop clustering algorithm].L. Ying et al [9] developed an algorithm for cluster head selection based
on node residual energy and required transmission energy. In [10], uneven load in network is
minimized by cluster size adaptation using cluster ID based routing scheme. Cluster head
maintain information of a node with maximum residual energy in its cluster. It ensures the location
of cluster head (CH) approximately at center of cluster. In case of uneven deployment, it is not
necessary that most of the nodes are in the center of the cluster. In this way, the distance
between CH and sensor nodes will be increased.
A cluster-based routing protocol called Energy Efficient Clustering Routing (EECR) [11] select
cluster head on the basis of weight value and leads to a more uniform distribution evenly among
all sensor nodes. In energy constrained case, the traffic pattern and remaining energy level
condition the routing scheme may be adoptive. Cluster heads selection is an NP-hard problem
[12]. Thus, in the literature, the solution is based on heuristic approaches. Efficient clustering
algorithms do not require regular topology re-construction and this will lead to regular information
exchange among the nodes in the network. HEED [13] is another distributed clustering
approach. In this approach, the cluster heads are selected periodically on the basis of two
parameters, the residual energy and cost incurred during the intra clustering communication.
In [14] some resource rich nodes called gateways are deployed in the network and performing
fusion to relate sensor reports. Member nodes of each cluster communicate with base station
only through gateway nodes. MECH [15] avoids the uneven member distribution of cluster and
reduced the long range communication between cluster head and base station. MECH suffer by
Energy consumption in each round.
TEEN [16] based on LEACH, the transmission is less frequently and the sensor nodes sense the
media contiguously. After cluster formation and cluster head selection process, CH broadcasts
two thresholds value, called hard threshold and soft threshold as the sense attributes. Hard
threshold is the minimum possible value of the sense attribute that triggers the nodes to switch on
its transmitter and transmit. Thus, the hard threshold reduces the number of transmission and
allows the nodes to transmit only when the sensed attribute is in the range of interest. The soft
threshold further reduces the number of transmissions when there is little or no change in the
value of sensed attributes. The number of packets transmission is controlled by setting the soft
threshold and hard threshold. However the main disadvantage of this scheme is that when
periodic reports are needed and if the threshold is not received, the user will not get any data
from the network at all.
4. Asfandyar Khan, Azween Abdullah, Nurul Hasan
International Journal of Computer Science and Security, Volume (3), Issue (6) 586
3. METHODOLGY
3.1 System Model and Problem Statement
The sensor nodes are highly resource constrained. Energy is one of the major issues of the
sensor nodes. In wireless sensor network most of the energy is consumed during transmission
and it is further increased with the distance, as energy consumption is directly proportional to the
square of the distance among the nodes. In order to minimize energy consumption and increase
lifetime of the network, we must keep the distance under consideration and it is possible by the
architecture design of the network and efficient routing schemes. Scalability is also another issue,
as they may contain hundreds or thousands of nodes and this issue are addressed in cluster
based architecture particularly in LEACH [7]. There are few areas in LEACH [7] that can be
improved and to make it more energy efficient and scalable. Avoiding the creation of new routing
table and selection of cluster head in each round significantly reduces the amount of energy
consumed. In cluster based architectures, cluster head are over loaded with long range
transmissions to the base station [10] and with additional processing responsibility of data
aggregation. Due to these responsibilities, cluster heads nodes are drained of their energy
quickly. It is unsuitable in the case of homogenous wireless sensor networks that cluster heads
are regular nodes but they communicate for longer distance with the base station and also the
cluster head that are near to the base station are drained of their energy quickly because of inter
cluster communication. This leads to energy imbalance among the clusters. For a broad analysis
of our proposed scheme, we have developed a system model based on the energy consumption
during transmission. In most of short range applications, the circuit energy consumption is higher
than transmission energy. Energy efficient communication techniques mainly focus on minimizing
the transmission energy, while in long range applications the transmission energy is dominant in
the total energy consumption. The transmission energy generally depends on the transmission
distance. The findings by [23] that different performance parameters are designed to minimize the
energy consumption and to prolong the network lifetime. These parameters are described as
follows:
3.1.1 Distance (D) to Base Station
“D” is the summation of all distances from sensor nodes to the BS. This distance is defined as
follows:
i.e.
Where “xis” is the sum of distance from the node “i” to the Base Station. For a larger network, try
to keep this distance minimum because most of the energy will be wasted. However, for a smaller
network the nodes near to base station directly send the information may be an acceptable
option.
3.1.2 Cluster Distance (C)
C is the summation of the distances from the member nodes to the cluster head (CH) and the
distance from the cluster head to the server node (SN). For a cluster with k member nodes, the
cluster distance C is defined as follows:
(1)
i.e.
(2)
5. Asfandyar Khan, Azween Abdullah, Nurul Hasan
International Journal of Computer Science and Security, Volume (3), Issue (6) 587
Where “xih” is the distance from node “i” to the cluster head (CH) and “xihsn” is the distance from
the cluster head to the server node (SN). For a cluster that has large number of spreads nodes,
the distance among the nodes ‘i’ to cluster head (CH) will be more and the energy consumption
will be higher. So keep the cluster size small to reduce energy dissipation and C should not be
too large. This metric will keep control the size of the clusters. The Equation.3 shows the total
distances “Tdist” from cluster member ‘i’ to cluster head and from cluster head (CH) to server node
(SN) and from server Node to Base station (BS).
3.1.3 Total Dissipated Energy (E)
The total dissipated energy “Etotal” shows the energy dissipated to transfer the aggregated
messages from the cluster to the BS. For a cluster with k member nodes, the total dissipated
energy can be calculated follows:
The fist part of Equation.4 show the energy consumed to transmit messages from member nodes
to the cluster head. The second part shows the energy consumed to transmit aggregated
messages by cluster head to server node SN to receive messages from the member nodes.
Finally, the fourth term “ETsnb” resents the energy needed to transmit from the SN to the BS.
3.1.4 Residual energy
The energy dissipated in previous round by the cluster head preferably les then residual energy
of a node. Equation for residual energy of node i is described in [17]. The individual node energy
(lifetime of node) can be calculated by the formula as presented in [18] is:
Where ‘Ti’ is the node lifetime, ‘Eb’ is the initial energy of the battery ‘eij’ is energy required for
transmission of one information unit, ‘qij’ is the rate at which information is generated at node i,
‘Si’ is the set of neighbor nodes of node i. Based on the node lifetime, the network lifetime can be
computed by:
Where ‘Tsys’ is the system life time, ‘N’ is the number of nodes in the network.
3.2 Proposed Cluster Based WSN Architecture
We have introduced a resource reachable node called server node (SN). It has the ability to cover
long transmission range. Server node (SN) is deployed in a location where all the nodes of each
cluster are easily reachable. If it is not reachable, it is recommended to add another server node
(SN). Due to extra processing capability sever node (SN) are responsible for selecting cluster
head from candidate nodes. The purpose of introducing SN is to closely monitor the operation of
sensor nodes in a cluster and command them for specific operations as shown in Fig 1.
(3)
(4)
Sij
b
qijeij
E
Ti (5)
qijeij
E
TiTsys
Sij
b
NiNi
maxmax (6)
6. Asfandyar Khan, Azween Abdullah, Nurul Hasan
International Journal of Computer Science and Security, Volume (3), Issue (6) 588
3.3 Cluster Formation and Node Deployment
The main purpose of clustering the sensor nodes is to form groups so as to reduce the overall
energy spent in aggregation, communicating the sensed data to the cluster head and base
station. There are various methods proposed for clustering, but K-mean [19] is found to be the
most efficient for clustering. Fig 1 shows the formation of different clusters and the assignment of
nodes to each cluster. K-mean clustering technique assigns nodes to the cluster having the
nearest centroid. K-mean algorithm uses Euclidean distance formula to calculate distance
between centroid and other objects Fig. 2.
FIGURE 1: Modified Cluster Based Architecture
Server NodeServer Node
Base Station
Long Range
Communication
Control
Center
(7)
FIGURE 2: 100 Nodes Random Network
100 Nodes Random Network
-100
-80
-60
-40
-20
0
20
40
60
80
100
-100 -80 -60 -40 -20 0 20 40 60 80 100
X-Coordicate
Y-Coordinate
7. Asfandyar Khan, Azween Abdullah, Nurul Hasan
International Journal of Computer Science and Security, Volume (3), Issue (6) 589
K-mean is computationally efficient and does not require the user to specify many parameters.
The number of clusters and the nodes assigned to each cluster is shown in Table 1.
Cluster Nodes
1 2
2 9
3 31
4 1
5 6
6 8
7 26
8 17
Valid 100
Missing 0
Table I: Number of Cases in each Cluster
Clusters
1 2 3 4 5 6 7 8
X
-34.00 99.00 91.00 -76.00 -34.00 54.00 34.00 9.00
Y
-78.00 100.00 22.00 22.00 -23.00 -90.00 -21.00 66.00
Table II:Cluster Centroids
Clusters Formation
0
1
2
3
4
5
6
7
8
9
10
0 10 20 30 40 50 60 70 80 90 100
Nodes
Clusters
FIGURE 3: Cluster Formation
8. Asfandyar Khan, Azween Abdullah, Nurul Hasan
International Journal of Computer Science and Security, Volume (3), Issue (6) 590
3.4 Selection of Candidate Cluster Head
The selection of cluster head becomes highly challenging when there is an uneven distribution of
sensor nodes in clusters. In order to make the cluster head (CH) selection algorithm more
accurate, we first identify the candidate sensor nodes for cluster head and then select the best
among them. In order to select candidate cluster heads from each cluster, we use the K-theorem.
The philosophy behind the K-theorem is to select a candidate CHs based on the bunch of sensor
nodes as there is an uneven distribution Fig. 4.
The server node (SN) set the value of ki for each cluster. The value of ki is relative to the node
density in a cluster and ratio (i.e. r) of the cluster heads in a WSN. It is the product of the number
of nodes in a cluster (i.e. ni) and ratio r. The value of r can vary from 0.01 to 0.99 but it should not
be more than 0.50. The lesser the value of ki, the greater the probability of getting a local optimal
is. The value of ki determines the ki number of best sensor nodes as candidate CHs.
For each sensor node deployed in the cluster, we choose its ki nearest neighbors based on
distance. The distance between sensor nodes can be calculated through received signal strength
indicator (RSSI) that is described in detail in [20] or any other localization technique [21, 22].
When the number of immediate (1-hop) neighbors is less than ki and distance is greater than the
transmission range then multihop route is preferred. Multihop route is preferred over direct
because of less energy consumption.
The server node (SN) adopts the procedure detailed below in order to select candidate CHs for
each cluster. The server node (SN) maintains a table for each cluster, listing all the sensor nodes
present in the cluster. It maintains ki nearest neighbors for each node and the frequency of
occurrence of each node is maintained in the table. The ordered list of sensor nodes based on
their frequency is shown in Table. III i.e. Si. The minimum frequency required in cluster i to be the
CH (i.e. Ki) is calculated based on weighted mean of frequencies and 1 is added for better result.
Weighted mean is calculated by product of each frequency of occurrence into number of sensor
nodes having that frequency. The value of Ki is rounded to the nearest integer if required. The
sensor nodes having frequency Ki or greater are identified as candidates for CH i.e. Ci. The best
candidates for cluster head would always be equals to value of ki i.e. 3 in this case.
FIGURE 4: Selection of Candidate Cluster Head
9. Asfandyar Khan, Azween Abdullah, Nurul Hasan
International Journal of Computer Science and Security, Volume (3), Issue (6) 591
Node ID
ki = 3
List of Terminals with its K-Nearest
Neighbor
Frequency of
Occurrence
1 1) 2, 3, 4 3
2 2) 1, 4, 5 4
3 3) 1, 4, 6 5
4 4) 2, 3, 6 6
5 5) 2, 4, 6 4
6 6) 3, 4, 5 7
7 7) 3, 9, 10 2
8 8) 5, 6, 9 2
9 9) 6, 8, 10 4
10 10) 6, 7, 9 3
Table III: List of nodes with their K-nearest neighbors and their frequency of occurrence
Sorting {Si = Ordered list of sensor nodes, where i is the frequency of occurrence}
S2 = (7, 8), S3 = (1, 10), S4 = (2, 5, 9), S5 = (3)
S6 = (4), S7 = (6)
Ki = [Weighted Mean] + 1
= [(2*2)+(3*2)+(4*3)+(5*1)+(6*1)+(7*1) / 10 ] + 1
= [(4 + 6 + 12 + 5 + 6 + 7) / 10] + 1
= [(40) / 10] + 1 4 + 1
Ki = 5
So, the best nodes for candidate cluster head in cluster are: Ci = {3, 4, 6} when ki = 3.
3.5 Cluster Head Selection
We describe a cluster head selection algorithm which is energy efficient in Fig.5. Suitable cluster-
head selection makes the network efficient and increases the life time and data delivery of the
networks.
10. Asfandyar Khan, Azween Abdullah, Nurul Hasan
International Journal of Computer Science and Security, Volume (3), Issue (6) 592
No
No
Yes
CH Aggregate and send to
SN
Begin
Initialization Phase
Cluster Formation
Yes
Select Candidate CHs
Select CHs within
Candidate CHs
Node wakeup, send/ receive data
to CH
Exchange Setup and
Maintenance messages
If R.E >= 2 (E.D) Nodes alive?
End
Round 1st
SN calculate RE & ED in
current round
CH create TDMA schedule
Candidate CHs
11. Asfandyar Khan, Azween Abdullah, Nurul Hasan
International Journal of Computer Science and Security, Volume (3), Issue (6) 593
4. ANALYTICAL DISCUSSION
In our methodology the idea is to distribute the load of long range transmission from CH to the
SN. This will conserve energy at CH and ultimately lead to increase in the life time of wireless
sensor network. The problem is when CHs are burdened with extra processing by gathering
information from non cluster heads and as a result the CHs are drained of their energy quickly.
There is no need to have inter-cluster multihop communication because each cluster head is able
to directly reach the SN. In this way, the CHs near the BS or SN does not have to take extra
responsibility of transmitting other clusters information. They just have to transmit their own data.
This will lead to energy balancing among the clusters that was previously a problem. The
modified architecture can be more scalable as we just need to add extra SN if network density or
distance is high.
The proposed algorithm describes the selection process of CHs. The benefit of using K-Theorem
is that it minimizes the communication and reduces the long range intra-cluster communication by
selecting the best nodes for cluster heads from the location where network density is higher.
5. CONSLUSION & FUTURE WORK
Energy efficiency is the most important design consideration for wireless sensor networks and its
optimum utilization is a challenge in its own regard. We achieved energy efficiency through
efficient cluster head selection algorithm. We divide sensor nodes into clusters through by using
K-Mean. Our model is simple, efficient and less costly and can scale well to large networks. The
findings of this research can be summarized as follows. We believe that this will not only minimize
the communication cost but will also increase the reliability of the network.
A good clustering technique is one that achieves an energy balance in the network and
maximum data delivery.
The main factor for energy balance and data delivery is the efficient clustering and cluster
head selection.
Optimization of data delivery depends upon the optimum level of energy of CH neighbor’s
nodes. Our work can be extended to analyze the performance for other parameters. i.e.
latency, throughput and efficient routing techniques.
6. REFERENCES
[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, “Wireless sensor networks: a
survey”, Computer Networks: The International Journal of Computer and
Telecommunications Networking”, v.38 n.4, p.393-422, 15 March 2002
[2] M. Younis, K. Akkaya, M. Eltoweissy, A. Wadaa, “On Handling QoS Traffic in Wireless
Sensor Networks”, Proceedings of the Proceedings of the 37th Annual Hawaii International
Conference on System Sciences (HICSS'04) - Track 9, p.90292.1, January 05-08, 2004
[3] K. Akkaya, M. F. Younis, “A survey on routing protocols for wireless sensor networks”, Ad
Hoc Networks”, 3(3): 325-349 (2005)
[4] A. A. Abbasi, M. F. Younis, “A survey on clustering algorithms for wireless sensor
networks”, Computer Communications: 30(14-15): 2826-2841 (2007)
[5] C. P. Low, C. F. J. M. Ng, N.H.Ang, ”Efficient Load-Balanced Clustering Algorithms for
Wireless Senser Networs”, Elsevier Computer communications”, pp 750-759,2007
[6] H. Yang, B. Sikdar, “Optimal Cluster Head Selection in the LEACH Architecture”, In the
proceedings of the 26th IEEE International Performance and Communications
Conference”, IPCCC2007, April 11-13, 2007, New Orleans, Louisiana, USA
FIGURE 5: Flow Chart of Cluster Head
12. Asfandyar Khan, Azween Abdullah, Nurul Hasan
International Journal of Computer Science and Security, Volume (3), Issue (6) 594
[7] W. Heinzelman, A. Chandrakasan, H. Balakrishnan, “Energy-efficient communication
protocols for wireless microsensor networks”, Proceedings of the Hawaii International
Conference on Systems Sciences, Jan. 2000
[8] N. Uppu, B. V. S. S. Subrahmanyam, R. Garimella, “An Energy Efficient Technique to
prolong Network Life Time of Ad Hoc Sensor Networks (ETPNL)”, IEEE Technical Review
Vol 25,2008
[9] L. Ying, Y. Haibin, “Energy Adaptive Cluster-Head Selection for Wireless Sensor
Networks”, In Proceedings of the 6th International Conference on Parallel and Distributed
Computing, Applications and Technologies (PDCAT’05)”, pp. 634--638, 2005
[10] I. Ahmed, M. Peng, W. Wang, ”A Unified Energy Efficient Cluster ID based Routing
Scheme for Wireless Sensor Networks-A more Realistic Analysis”, Proc of IEEE Third
International conference on Networking and Services (ICNS’07), pp-86, 2007
[11] L. D. Shu-song, W. Xiang-ming, ”An energy Efficient clustering routing algorithm for
wireless sensor networks”, The journal of China Universities of posts and
Telecommunications, Volume 13, Issue 3, September 2006
[12] S. Basagni, I. Chlamtac, A. Farago, “A Generalized Clustering Algorithm for Peer-to-Peer
Networks”, In: Workshop on lgorithmic Aspects of Com., Bologna, Italy (July 1997)
[13] O. Younis, S. Fahmy, Distributed Clustering for Scalable, Long-Lived Sensor Networks”,
Purdue University, Technical Report, CSD TR-03-026 (2003)
[14] G. Gupta, M. Younis, “Load-Balanced Clustering in Wireless Sensor Networks”,
In:Proceedings of the Int. Conference on Communication, Anchorage, AK (2003)
[15] R. S. Chang, C. J. Kuo, ‘‘ An energy efficient routing mechanism for wireless sensor
networks, ’’ Advanced Information Networking and Applications, 2006. AINA 2006. 20th
International Conference, Volume 2, 18-20 April 2006 Page(s):5 pp
[16] A. Manjeshwar, D. P. Agarwal, "TEEN: a routing protocol for enhanced e±ciency in
wireless sensor networks," In 1st International Workshop on Parallel and Distributed
Computing Issues in Wireless Networks and Mobile Computing, Apri 2001
[17] Tillapart. P., S. Thammarojsakul, T. Thumthawatworn, and P. Santiprabhob, “An Approach
to Hybrid Clustering and Routing in Wireless Sensor Networks”, Proc of IEEE Aerospace
Conference, 2005
[18] H. Kar, A. Willing, “Protocols and Architecture for Wireless Sensor Networks”, John Wiley
and sons, 2005
[19] D. Arthur, S. Vassilvitskii,” How Slow is the k-Means method?”, SCG’06, June 5–7, 2006,
Sedona, Arizona, USA. Copyright 2006 ACM 1595933409/06/0006
[20] A. A. Minhas, “Power Aware Routing Protocols for Wireless ad hoc Sensor Networks”, Ph.
D Thesis, Graz University of Technology, Graz, Austria, March 2007
[21] L. Hu, D. Evans, “Localization for mobile sensor networks”, ACM International Conference
on Mobile Computing and Networking, (MobiCom 2004)
[22] J. Hightower, G. Borriello, “Location systems for ubiquitous computing”, IEEE Computer 34
(8) (2001) 57–66
[23] A. Matin, “Energy-Efficient Intelligent hierarchical Cluster-Based Routing for Wireless Sensor
Networks”, MSc Thesis, Acadia University 2006