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.
1) The document proposes an NSGA-III based energy efficient clustering and tree-based routing protocol for wireless sensor networks.
2) It forms clusters based on remaining energy of nodes initially, then uses NSGA-III to improve inter-cluster data aggregation and select the shortest path between cluster heads and the sink.
3) Simulation results show the proposed protocol significantly improves network lifetime, throughput, and residual energy over other techniques.
The document presents the outline of a research project on performance evaluation of secure data transmission in wireless sensor networks using IEEE 802.11x standards. The research aims to enhance network lifetime by designing an energy-efficient clustering approach and data aggregation technique. It involves developing a cluster head selection algorithm using genetic algorithms, designing a broadcast tree construction protocol for data transmission, and implementing hash-based authentication. The research will be conducted in phases involving literature review, methodology development, implementation, and performance evaluation. The expected outcomes include reduced data transmission time and improved quality of service through increased network lifetime.
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
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.
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.
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.
Extending the longevity, is a significant job to be accomplished by these sensor networks. The traditional routing protocols could not be applied here, due to its nodes powered by batteries. Nodes are often clustered in to non-overlapping clusters, so as to provide energy efficiency. A concise overview on clustering processes, within wireless sensor networks is given in this paper. But it is difficult to replace the deceased batteries of the sensor nodes. A distinctive sensor node consumes much of its energy during wireless communication. This research work suggests the development of a hierarchical distributed clustering mechanism, which gives improved performance over the existing clustering algorithm LEACH. The two hiding concepts behind the proposed scheme are the hierarchical distributed clustering mechanism and the concept of threshold. Energy utilization is significantly reduced, thereby greatly prolonging the lifetime of the sensor nodes.
Coverage and Connectivity Aware Neural Network Based Energy Efficient Routing...graphhoc
There are many challenges when designing and deploying wireless sensor networks (WSNs). One of the key challenges is how to make full use of the limited energy to prolong the lifetime of the network, because energy is a valuable resource in WSNs. The status of energy consumption should be continuously monitored after network deployment. In this paper, we propose coverage and connectivity aware neural network based energy efficient routing in WSN with the objective of maximizing the network lifetime. In the proposed scheme, the problem is formulated as linear programming (LP) with coverage and connectivity aware constraints. Cluster head selection is proposed using adaptive learning in neural networks followed by coverage and connectivity aware routing with data transmission. The proposed scheme is compared with existing schemes with respect to the parameters such as number of alive nodes, packet delivery fraction, and node residual energy. The simulation results show that the proposed scheme can be used in wide area of applications in WSNs.
1) The document proposes an NSGA-III based energy efficient clustering and tree-based routing protocol for wireless sensor networks.
2) It forms clusters based on remaining energy of nodes initially, then uses NSGA-III to improve inter-cluster data aggregation and select the shortest path between cluster heads and the sink.
3) Simulation results show the proposed protocol significantly improves network lifetime, throughput, and residual energy over other techniques.
The document presents the outline of a research project on performance evaluation of secure data transmission in wireless sensor networks using IEEE 802.11x standards. The research aims to enhance network lifetime by designing an energy-efficient clustering approach and data aggregation technique. It involves developing a cluster head selection algorithm using genetic algorithms, designing a broadcast tree construction protocol for data transmission, and implementing hash-based authentication. The research will be conducted in phases involving literature review, methodology development, implementation, and performance evaluation. The expected outcomes include reduced data transmission time and improved quality of service through increased network lifetime.
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
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.
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.
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.
Extending the longevity, is a significant job to be accomplished by these sensor networks. The traditional routing protocols could not be applied here, due to its nodes powered by batteries. Nodes are often clustered in to non-overlapping clusters, so as to provide energy efficiency. A concise overview on clustering processes, within wireless sensor networks is given in this paper. But it is difficult to replace the deceased batteries of the sensor nodes. A distinctive sensor node consumes much of its energy during wireless communication. This research work suggests the development of a hierarchical distributed clustering mechanism, which gives improved performance over the existing clustering algorithm LEACH. The two hiding concepts behind the proposed scheme are the hierarchical distributed clustering mechanism and the concept of threshold. Energy utilization is significantly reduced, thereby greatly prolonging the lifetime of the sensor nodes.
Coverage and Connectivity Aware Neural Network Based Energy Efficient Routing...graphhoc
There are many challenges when designing and deploying wireless sensor networks (WSNs). One of the key challenges is how to make full use of the limited energy to prolong the lifetime of the network, because energy is a valuable resource in WSNs. The status of energy consumption should be continuously monitored after network deployment. In this paper, we propose coverage and connectivity aware neural network based energy efficient routing in WSN with the objective of maximizing the network lifetime. In the proposed scheme, the problem is formulated as linear programming (LP) with coverage and connectivity aware constraints. Cluster head selection is proposed using adaptive learning in neural networks followed by coverage and connectivity aware routing with data transmission. The proposed scheme is compared with existing schemes with respect to the parameters such as number of alive nodes, packet delivery fraction, and node residual energy. The simulation results show that the proposed scheme can be used in wide area of applications in WSNs.
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.
WEIGHTED DYNAMIC DISTRIBUTED CLUSTERING PROTOCOL FOR HETEROGENEOUS WIRELESS S...ijwmn
This document describes a new clustering protocol called WDDC (Weighted Dynamic Distributed Clustering) for heterogeneous wireless sensor networks. WDDC selects cluster heads based on the ratio of a node's residual energy to the average network energy, and also considers the distance between nodes and the base station. WDDC divides the network lifetime into two zones and changes its behavior dynamically between the zones. Simulation results show WDDC outperforms other clustering protocols like SEP and DEEC in terms of energy efficiency and extending network lifetime.
QoS Framework for a Multi-stack based Heterogeneous Wireless Sensor Network IJECEIAES
Wireless sensor nodes consist of a collection of sensor nodes with constrained resources in terms of processing power and battery energy. Wireless sensors networks are used increasingly in many industrial and consumer applications. Sensors detect events and send via multi hop routing to the sink node for processing the event. The routing path is established through proactive or reactive routing protocols. To improve the performance of the Wireless Sensor Networks, multi stack architecture is addressed. But the multi stack architecture has many problems with respect to life time, routing loop and QOS. In this work we propose a solution to address all these three problems of life time, routing loop and QOS in case of multi stack architecture.
The document discusses energy efficient routing protocols for clustered wireless sensor networks. It provides an overview of wireless sensor networks and discusses how clustering is commonly used to improve energy efficiency and scalability. The document reviews several existing clustering-based routing protocols and analyzes their approaches for prolonging network lifetime by minimizing energy consumption in wireless sensor networks.
Maximizing Lifetime of Homogeneous Wireless Sensor Network through Energy Eff...CSCJournals
The objective of this paper is to develop a mechanism to increase the lifetime of homogeneous wireless sensor networks (WSNs) through minimizing long range communication, efficient data delivery and energy balancing. Energy efficiency is a very important issue for sensor nodes which affects the lifetime of sensor networks. To achieve energy balancing and maximizing network lifetime we divided the whole network into different clusters. In cluster based architecture, the role of aggregator node is very crucial because of extra processing and long range communication. Once the aggregator node becomes non functional, it affects the whole cluster. We introduced a candidate cluster head node on the basis of node density. We proposed a modified cluster based WSN architecture by introducing a server node (SN) that is rich in terms of resources. This server node (SN) takes the responsibility of transmitting data to the base station over longer distances from the cluster head. We proposed cluster head selection algorithm based on residual energy, distance, reliability and degree of mobility. The proposed method can save overall energy consumption and extend the lifetime of the sensor network and also addresses robustness against even/uneven node deployment.
This document summarizes research on topology control techniques in wireless sensor networks. It first discusses how topology control aims to reduce energy consumption while maintaining network connectivity by regulating nodes' transmission power. It then reviews several existing topology control algorithms proposed in other papers. These algorithms distribute transmission power control to maximize network lifetime. Finally, the document concludes that many topology control algorithms have been developed to achieve energy efficient routing, but implementing them on real-world testbeds poses challenges.
Clustering provides an effective method for
extending the lifetime of a wireless sensor network. Current
clustering methods selecting cluster heads with more residual
energy, and rotating cluster heads periodically to distribute the
energy consumption among nodes in each cluster. However,
they rarely consider the hot spot problem in multi hop sensor
networks. When cluster heads forward their data to the base
station, the cluster heads closer to the base station are heavily
burdened with traffic and tend to die much faster. To mitigate
the hot spot problem, we propose a Novel Energy Efficient
Unequal Clustering Routing (NEEUC) protocol. It uses residual
energy and groupsthe nodesinto clusters of unequal layers
A NOVEL APPROACH FOR ENERGY EFFICIENT HIERARCHY BASED ROUTING IN SENSOR NETWO...cscpconf
Wireless sensor network (WSN) is the collection of many micro-sensor nodes, connecting each other by a
wireless medium. WSN exhibits different approaches to provide reliable sensing of the environment,
detecting and reporting events. In this paper, we have proposed an algorithm for hierarchy based protocols
of wireless sensor networks, which consist of two groups of sensor nodes in a single cluster node. Each
cluster consists of a three cluster head. The event driven data sensing mechanism is used in this paper and
this sensed data is transmitted to the master section head. Hence efficient way of data transmission is possible with larger group of nodes. In this approach, using hierarchy based protocols; the lifetime of the sensor network is increased.
1) The document proposes a three-tier architecture for wireless sensor networks using a genetic algorithm based hierarchical cooperative technique (GAHCT) to select cluster heads and super heads.
2) GAHCT uses factors like residual energy, bandwidth, and memory capacity to select cluster heads in the first tier, super heads in the third tier, with cluster slaves making up the second tier.
3) Simulation results show that GAHCT improves network lifetime and reduces total energy consumption compared to single-tier and two-tier architectures by creating a more efficient network topology.
The document describes localized, self-organizing approaches for constructing energy-efficient data aggregation trees in sensor networks. It proposes Localized Power-Efficient Data Aggregation Protocols (L-PEDAPs) that use localized structures like LMST and RNG to approximate a minimum spanning tree. L-PEDAP then constructs an actual routing tree over these structures using localized parent selection strategies. Simulation results show L-PEDAP can achieve close to 90% of a theoretical upper bound on network lifetime derived in the paper, outperforming centralized solutions while meeting requirements like distributed operation, scalability, and robustness to failures.
34 9141 it ns2-tentative route selection approach for edit septianIAESIJEECS
Wireless Sensor Networks (WSNs) assume a crucial part in the field of mechanization and control where detecting of data is the initial step before any automated job could be performed. So as to encourage such perpetual assignments with less vitality utilization proportion, clustering is consolidated everywhere to upgrade the system lifetime. Unequal Cluster-based Routing (UCR) [7] is a standout amongst the most productive answers for draw out the system lifetime and to take care of the hotspot issue that is generally found in equivalent clustering method. In this paper, we propose Tentative Route (TRS) Selection approach for irregular Clustered Wireless Sensor Networks that facilitates in decision an efficient next relay to send the data cumulative by Cluster Heads to the Base Station. Simulation analysis is achieved using the network simulator to demonstrate the effectiveness of the TRS method.
ENERGY EFFICIENT HIERARCHICAL CLUSTER HEAD ELECTION USING EXPONENTIAL DECAY F...ijwmn
This document summarizes an article that proposes an improved algorithm for selecting cluster heads in wireless sensor networks. The algorithm uses an exponential decay function to predict the average energy of sensor nodes and selects cluster heads based on both the probabilistic LEACH algorithm and predicted energy levels. The algorithm was tested in MATLAB simulations of a homogeneous sensor network and showed improvements in stability, average energy dissipation per round, and lifespan over the baseline LEACH protocol.
Congestion in Wireless Sensor Networks has negative impact on the Quality of Service.
Congestion effects the performance metrics, namely throughput and per-packet energy
consumption, network lifetime and packet delivery ratio. Reducing congestion allows better
utilization of the network resources and thus enhances the Quality of Service metrics of the
network. Traffic Aware Dynamic Routing to Alleviate Congestion in Wireless Sensor Networks
reduces congestion by considering one hop neighbor routing in the network. This paper
proposed an algorithm for Quality of Service Based Traffic-Aware Data forwarding for
congestion control in wireless sensor networks based on two hop neighbor information. On
detection of congestion, the algorithm forwards data packets around the congestion areas by
spreading the excessive packets through multiple paths. The path with light load or under
loaded nodes is efficiently utilized whenever congestion occurs. The main aspect of the
algorithm is to build path to the destination using two independent potential fields depth and
queue length. Queue length field solves the traffic-aware problem. Depth field creates a
backbone to forward packets to the sink. Both fields are combined to yield a hybrid potential
field to make dynamic decision for data forwarding. Network Simulator used for simulating the
algorithm is NS2. The proposed algorithm performs better.
SLGC: A New Cluster Routing Algorithm in Wireless Sensor Network for Decrease...IJCSEA Journal
Decrease energy consumption and maximizing network lifetime are important parameters in designing and protocols for wireless sensor network (WSN).Clustering is one of the efficient methods in energy consumption by Cluster-Head in WSN. Besides, CH can process and aggregate data sent by cluster members, thus reducing network traffic for sending data to sink. In this paper presents a new cluster routing algorithm by dividing network into grids. In each grid computes the center-gravity and threshold of energy for selecting the node that has the best condition base on these parameters in grid for selecting Cluster-Head in current round, also SLGC selecting Cluster-Heads for next rounds thereby this CHs reduce the volume of controlling messages for next rounds and inform nodes for sending data into CH of respective round. This algorithm prolong network lifetime and decrease energy consumption by selecting CH in grid and sending data of grid to sink by this CH. Result of simulation shows that SLGC algorithm in comparison with the previous clustering algorithm has maximizing network lifetime and decrease energy consumption in network.
Clustering and data aggregation scheme in underwater wireless acoustic sensor...TELKOMNIKA JOURNAL
Underwater Wireless Acoustic Sensor Networks (UWASNs) are creating attentiveness in
researchers due to its wide area of applications. To extract the data from underwater and transmit to
watersurface, numerous clustering and data aggregation schemes are employed. The main objectives of
clustering and data aggregation schemes are to decrease the consumption of energy and prolong the
lifetime of the network. In this paper, we focus on initial clustering of sensor nodes based on their
geographical locations using fuzzy logic. The probability of degree of belongingness of a sensor node to its
cluster, along with number of clusters is analysed and discussed. Based on the energy and distance the
cluster head nodes are determined. Finally using using similarity function data aggregation is analysed and
discussed. The proposed scheme is simulated in MATLAB and compared with LEACH algorithm.
The simulation results indicate that the proposed scheme performs better in maximizing network lifetime
and minimizing energy consumption.
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.
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.
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.
The novel applications of sensor networks impose some requirements in wireless sensor network design. With the energy efficiency and lifetime awareness, the throughput and network delayalso required to support emerging applications of sensor networks. In this paper, we propose
throughput and network delay aware intra-cluster routing protocol. We introduce the back-up links in the intra-cluster communication path. The link throughput, communication delay, packet loss ratio, interference, residual energy and node distance are the considered factors in finding efficient path of data communication among the sensor nodes within the cluster. The
simulation result shows the higher throughput and lower average packet delay rate for the proposed routing protocol than the existing benchmarks. The proposed routing protocol also shows energy efficiency and lifetime awareness with better connectivity rate.
Optimal Coverage Path Planningin a Wireless Sensor Network for Intelligent Tr...IJCNCJournal
With the enhancement of the intelligent and communication technology, an intelligent transportation plays a vital role to facilitate an essential service to many people, allowing them to travel quickly and conveniently from place to place. Wireless sensor networks (WSNs) are well-known for their ability to detect physical significant barriers due to their diverse movement, self-organizing capabilities, and the integration of this mobile node on the intelligent transportation system to gather data in WSN contexts is becoming more and more popular as these vehicles proliferate. Although these mobile devices might enhance network performance, however it is difficult to design a suitable transportation path with the limited energy resources with network connectivity. To solve this problem, we have proposed a novel itinerary planning schema data gatherer (IPS-DG) model. Furthermore, we use the path planning module (PPM) which finds the transportation path to travel the shortest distance. We have compared our results under different aspect such as life span, energy consumption, and path length with Low Energy Adaptive Clustering Hierarchy (LEACH), Multi-Hop Weighted Revenue (MWR), Single-Hop Data Gathering Procedure (SHDGP). Our model outperforms in terms of energy usage, shortest path, and longest life span of with LEACH, MWR, SHDGP routing protocols.
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.
WEIGHTED DYNAMIC DISTRIBUTED CLUSTERING PROTOCOL FOR HETEROGENEOUS WIRELESS S...ijwmn
This document describes a new clustering protocol called WDDC (Weighted Dynamic Distributed Clustering) for heterogeneous wireless sensor networks. WDDC selects cluster heads based on the ratio of a node's residual energy to the average network energy, and also considers the distance between nodes and the base station. WDDC divides the network lifetime into two zones and changes its behavior dynamically between the zones. Simulation results show WDDC outperforms other clustering protocols like SEP and DEEC in terms of energy efficiency and extending network lifetime.
QoS Framework for a Multi-stack based Heterogeneous Wireless Sensor Network IJECEIAES
Wireless sensor nodes consist of a collection of sensor nodes with constrained resources in terms of processing power and battery energy. Wireless sensors networks are used increasingly in many industrial and consumer applications. Sensors detect events and send via multi hop routing to the sink node for processing the event. The routing path is established through proactive or reactive routing protocols. To improve the performance of the Wireless Sensor Networks, multi stack architecture is addressed. But the multi stack architecture has many problems with respect to life time, routing loop and QOS. In this work we propose a solution to address all these three problems of life time, routing loop and QOS in case of multi stack architecture.
The document discusses energy efficient routing protocols for clustered wireless sensor networks. It provides an overview of wireless sensor networks and discusses how clustering is commonly used to improve energy efficiency and scalability. The document reviews several existing clustering-based routing protocols and analyzes their approaches for prolonging network lifetime by minimizing energy consumption in wireless sensor networks.
Maximizing Lifetime of Homogeneous Wireless Sensor Network through Energy Eff...CSCJournals
The objective of this paper is to develop a mechanism to increase the lifetime of homogeneous wireless sensor networks (WSNs) through minimizing long range communication, efficient data delivery and energy balancing. Energy efficiency is a very important issue for sensor nodes which affects the lifetime of sensor networks. To achieve energy balancing and maximizing network lifetime we divided the whole network into different clusters. In cluster based architecture, the role of aggregator node is very crucial because of extra processing and long range communication. Once the aggregator node becomes non functional, it affects the whole cluster. We introduced a candidate cluster head node on the basis of node density. We proposed a modified cluster based WSN architecture by introducing a server node (SN) that is rich in terms of resources. This server node (SN) takes the responsibility of transmitting data to the base station over longer distances from the cluster head. We proposed cluster head selection algorithm based on residual energy, distance, reliability and degree of mobility. The proposed method can save overall energy consumption and extend the lifetime of the sensor network and also addresses robustness against even/uneven node deployment.
This document summarizes research on topology control techniques in wireless sensor networks. It first discusses how topology control aims to reduce energy consumption while maintaining network connectivity by regulating nodes' transmission power. It then reviews several existing topology control algorithms proposed in other papers. These algorithms distribute transmission power control to maximize network lifetime. Finally, the document concludes that many topology control algorithms have been developed to achieve energy efficient routing, but implementing them on real-world testbeds poses challenges.
Clustering provides an effective method for
extending the lifetime of a wireless sensor network. Current
clustering methods selecting cluster heads with more residual
energy, and rotating cluster heads periodically to distribute the
energy consumption among nodes in each cluster. However,
they rarely consider the hot spot problem in multi hop sensor
networks. When cluster heads forward their data to the base
station, the cluster heads closer to the base station are heavily
burdened with traffic and tend to die much faster. To mitigate
the hot spot problem, we propose a Novel Energy Efficient
Unequal Clustering Routing (NEEUC) protocol. It uses residual
energy and groupsthe nodesinto clusters of unequal layers
A NOVEL APPROACH FOR ENERGY EFFICIENT HIERARCHY BASED ROUTING IN SENSOR NETWO...cscpconf
Wireless sensor network (WSN) is the collection of many micro-sensor nodes, connecting each other by a
wireless medium. WSN exhibits different approaches to provide reliable sensing of the environment,
detecting and reporting events. In this paper, we have proposed an algorithm for hierarchy based protocols
of wireless sensor networks, which consist of two groups of sensor nodes in a single cluster node. Each
cluster consists of a three cluster head. The event driven data sensing mechanism is used in this paper and
this sensed data is transmitted to the master section head. Hence efficient way of data transmission is possible with larger group of nodes. In this approach, using hierarchy based protocols; the lifetime of the sensor network is increased.
1) The document proposes a three-tier architecture for wireless sensor networks using a genetic algorithm based hierarchical cooperative technique (GAHCT) to select cluster heads and super heads.
2) GAHCT uses factors like residual energy, bandwidth, and memory capacity to select cluster heads in the first tier, super heads in the third tier, with cluster slaves making up the second tier.
3) Simulation results show that GAHCT improves network lifetime and reduces total energy consumption compared to single-tier and two-tier architectures by creating a more efficient network topology.
The document describes localized, self-organizing approaches for constructing energy-efficient data aggregation trees in sensor networks. It proposes Localized Power-Efficient Data Aggregation Protocols (L-PEDAPs) that use localized structures like LMST and RNG to approximate a minimum spanning tree. L-PEDAP then constructs an actual routing tree over these structures using localized parent selection strategies. Simulation results show L-PEDAP can achieve close to 90% of a theoretical upper bound on network lifetime derived in the paper, outperforming centralized solutions while meeting requirements like distributed operation, scalability, and robustness to failures.
34 9141 it ns2-tentative route selection approach for edit septianIAESIJEECS
Wireless Sensor Networks (WSNs) assume a crucial part in the field of mechanization and control where detecting of data is the initial step before any automated job could be performed. So as to encourage such perpetual assignments with less vitality utilization proportion, clustering is consolidated everywhere to upgrade the system lifetime. Unequal Cluster-based Routing (UCR) [7] is a standout amongst the most productive answers for draw out the system lifetime and to take care of the hotspot issue that is generally found in equivalent clustering method. In this paper, we propose Tentative Route (TRS) Selection approach for irregular Clustered Wireless Sensor Networks that facilitates in decision an efficient next relay to send the data cumulative by Cluster Heads to the Base Station. Simulation analysis is achieved using the network simulator to demonstrate the effectiveness of the TRS method.
ENERGY EFFICIENT HIERARCHICAL CLUSTER HEAD ELECTION USING EXPONENTIAL DECAY F...ijwmn
This document summarizes an article that proposes an improved algorithm for selecting cluster heads in wireless sensor networks. The algorithm uses an exponential decay function to predict the average energy of sensor nodes and selects cluster heads based on both the probabilistic LEACH algorithm and predicted energy levels. The algorithm was tested in MATLAB simulations of a homogeneous sensor network and showed improvements in stability, average energy dissipation per round, and lifespan over the baseline LEACH protocol.
Congestion in Wireless Sensor Networks has negative impact on the Quality of Service.
Congestion effects the performance metrics, namely throughput and per-packet energy
consumption, network lifetime and packet delivery ratio. Reducing congestion allows better
utilization of the network resources and thus enhances the Quality of Service metrics of the
network. Traffic Aware Dynamic Routing to Alleviate Congestion in Wireless Sensor Networks
reduces congestion by considering one hop neighbor routing in the network. This paper
proposed an algorithm for Quality of Service Based Traffic-Aware Data forwarding for
congestion control in wireless sensor networks based on two hop neighbor information. On
detection of congestion, the algorithm forwards data packets around the congestion areas by
spreading the excessive packets through multiple paths. The path with light load or under
loaded nodes is efficiently utilized whenever congestion occurs. The main aspect of the
algorithm is to build path to the destination using two independent potential fields depth and
queue length. Queue length field solves the traffic-aware problem. Depth field creates a
backbone to forward packets to the sink. Both fields are combined to yield a hybrid potential
field to make dynamic decision for data forwarding. Network Simulator used for simulating the
algorithm is NS2. The proposed algorithm performs better.
SLGC: A New Cluster Routing Algorithm in Wireless Sensor Network for Decrease...IJCSEA Journal
Decrease energy consumption and maximizing network lifetime are important parameters in designing and protocols for wireless sensor network (WSN).Clustering is one of the efficient methods in energy consumption by Cluster-Head in WSN. Besides, CH can process and aggregate data sent by cluster members, thus reducing network traffic for sending data to sink. In this paper presents a new cluster routing algorithm by dividing network into grids. In each grid computes the center-gravity and threshold of energy for selecting the node that has the best condition base on these parameters in grid for selecting Cluster-Head in current round, also SLGC selecting Cluster-Heads for next rounds thereby this CHs reduce the volume of controlling messages for next rounds and inform nodes for sending data into CH of respective round. This algorithm prolong network lifetime and decrease energy consumption by selecting CH in grid and sending data of grid to sink by this CH. Result of simulation shows that SLGC algorithm in comparison with the previous clustering algorithm has maximizing network lifetime and decrease energy consumption in network.
Clustering and data aggregation scheme in underwater wireless acoustic sensor...TELKOMNIKA JOURNAL
Underwater Wireless Acoustic Sensor Networks (UWASNs) are creating attentiveness in
researchers due to its wide area of applications. To extract the data from underwater and transmit to
watersurface, numerous clustering and data aggregation schemes are employed. The main objectives of
clustering and data aggregation schemes are to decrease the consumption of energy and prolong the
lifetime of the network. In this paper, we focus on initial clustering of sensor nodes based on their
geographical locations using fuzzy logic. The probability of degree of belongingness of a sensor node to its
cluster, along with number of clusters is analysed and discussed. Based on the energy and distance the
cluster head nodes are determined. Finally using using similarity function data aggregation is analysed and
discussed. The proposed scheme is simulated in MATLAB and compared with LEACH algorithm.
The simulation results indicate that the proposed scheme performs better in maximizing network lifetime
and minimizing energy consumption.
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.
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.
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.
The novel applications of sensor networks impose some requirements in wireless sensor network design. With the energy efficiency and lifetime awareness, the throughput and network delayalso required to support emerging applications of sensor networks. In this paper, we propose
throughput and network delay aware intra-cluster routing protocol. We introduce the back-up links in the intra-cluster communication path. The link throughput, communication delay, packet loss ratio, interference, residual energy and node distance are the considered factors in finding efficient path of data communication among the sensor nodes within the cluster. The
simulation result shows the higher throughput and lower average packet delay rate for the proposed routing protocol than the existing benchmarks. The proposed routing protocol also shows energy efficiency and lifetime awareness with better connectivity rate.
Optimal Coverage Path Planningin a Wireless Sensor Network for Intelligent Tr...IJCNCJournal
With the enhancement of the intelligent and communication technology, an intelligent transportation plays a vital role to facilitate an essential service to many people, allowing them to travel quickly and conveniently from place to place. Wireless sensor networks (WSNs) are well-known for their ability to detect physical significant barriers due to their diverse movement, self-organizing capabilities, and the integration of this mobile node on the intelligent transportation system to gather data in WSN contexts is becoming more and more popular as these vehicles proliferate. Although these mobile devices might enhance network performance, however it is difficult to design a suitable transportation path with the limited energy resources with network connectivity. To solve this problem, we have proposed a novel itinerary planning schema data gatherer (IPS-DG) model. Furthermore, we use the path planning module (PPM) which finds the transportation path to travel the shortest distance. We have compared our results under different aspect such as life span, energy consumption, and path length with Low Energy Adaptive Clustering Hierarchy (LEACH), Multi-Hop Weighted Revenue (MWR), Single-Hop Data Gathering Procedure (SHDGP). Our model outperforms in terms of energy usage, shortest path, and longest life span of with LEACH, MWR, SHDGP routing protocols.
Optimal Coverage Path Planning in a Wireless Sensor Network for Intelligent T...IJCNCJournal
With the enhancement of the intelligent and communication technology, an intelligent transportation plays a vital role to facilitate an essential service to many people, allowing them to travel quickly and conveniently from place to place. Wireless sensor networks (WSNs) are well-known for their ability to detect physical significant barriers due to their diverse movement, self-organizing capabilities, and the integration of this mobile node on the intelligent transportation system to gather data in WSN contexts is becoming more and more popular as these vehicles proliferate. Although these mobile devices might enhance network performance, however it is difficult to design a suitable transportation path with the limited energy resources with network connectivity. To solve this problem, we have proposed a novel itinerary planning schema data gatherer (IPS-DG) model. Furthermore, we use the path planning module (PPM) which finds the transportation path to travel the shortest distance. We have compared our results under different aspect such as life span, energy consumption, and path length with Low Energy Adaptive Clustering Hierarchy (LEACH), Multi-Hop Weighted Revenue (MWR), Single-Hop Data Gathering Procedure (SHDGP). Our model outperforms in terms of energy usage, shortest path, and longest life span of with LEACH, MWR, SHDGP routing protocols.
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)
DATA GATHERING ALGORITHMS FOR WIRELESS SENSOR NETWORKS: A SURVEYijasuc
Recent developments in processor, memory and radio technology have enabled wireless sensor networks
which are deployed to collect useful information from an area of interest. The sensed data must be
gathered and transmitted to a base station where it is further processed for end-user queries. Since the
network consists of low-cost nodes with limited battery power, power efficient methods must be employed
for data gathering and aggregation in order to achieve long network lifetimes. In an environment where in
a round of communication each of the sensor nodes has data to send to a base station, it is important to
minimize the total energy consumed by the system in a round so that the system lifetime is maximized. With
the use of data fusion and aggregation techniques, while minimizing the total energy per round, if power
consumption per node can be balanced as well, a near optimal data gathering and routing scheme can be
achieved in terms of network lifetime. Several application specific sensor network data gathering protocols
have been proposed in research literatures. However, most of the proposed algorithms have been some
attention to the related network lifetime and saving energy are two critical issues for wireless sensor
networks. In this paper we have explored general network lifetime in wireless sensor networks and made an
extensive study to categorize available data gathering techniques and analyze possible network lifetime on
them.
MULTICASTING BASED ENHANCED PROACTIVE SOURCE ROUTING IN MANETSIJCNCJournal
Mobile Ad-hoc Network (MANET) is an accumulation of movable nodes organizing a irregular topology without centralized administration. In a MANET, multicasting is a significant technique for utilizing data communication system. Multicasting based enhanced proactive source routing is proposed in this paper for Mobile Ad hoc Networks. It explains an innovative multicasting algorithm that considers the transmission energy and residual energy while forwarding the data packets. It improves the network throughput and raises the network lifetimes. Simulation analysis is carried in this proposed system and this method shows improved performance over the existing system.
This document discusses improving the performance of mobile wireless sensor networks using a modified DBSCAN clustering algorithm. It first provides background on wireless sensor networks and discusses challenges related to mobility. It then reviews several existing works related to clustering, mobility, and extending network lifetime. The paper proposes using a modified DBSCAN algorithm that takes into account mobility, remaining energy, and distance to base station to select cluster heads. It evaluates the performance of this approach based on throughput, delay, and packet delivery ratio, finding improvements over other methods.
Energy efficient routing in wireless sensor network based on mobile sink guid...IJECEIAES
In wireless sensor networks (WSNs), the minimization of usage of energy in the sensor nodes is a key task. Three salient functions are performed by WSNs’ sensor nodes namely data sensing, transmitting and relaying. Routing technique is one of the methods to enhance the sensor nodes battery lifetime. Energy optimization is done by using one of the heuristic routing methods for data sensing and transmission. To enhance the energy optimization mainly concentrated on data relaying. In this work stochastic hill climbing is adapted. The proposed solution for data relaying utilizes geographical routing and mobile sink technique. The sink collects the data from cluster heads and movement of the sink is routed by stochastic hill climbing. Experimentation is done on the network simulator 2 Platform. The existing routing techniques like threshold sensitive energy efficient sensor network, energy-efficient low duty cycle, and adaptive clustering protocol are compared with the obtained results of chosen algorithm. The proposed work shows promising results with respect to lifetime, average energy of nodes and packet delivery ratio.
Energy Efficient Grid based Routing Algorithm using Closeness Centrality and ...IRJET Journal
This document proposes an energy efficient routing algorithm for wireless sensor networks that uses grid clustering, closeness centrality for cluster head selection, and bacterial foraging optimization (BFO) for routing. Grid clustering is used to divide the sensor network area into grids, with each grid forming a cluster. Closeness centrality, which is based on node distance, is used to select cluster heads at optimal locations to minimize energy consumption. BFO, a nature-inspired optimization algorithm, is used for routing by considering energy and distance in its fitness function. Simulation results show the proposed algorithm outperforms existing algorithms in terms of network lifetime and stability.
A NODE DEPLOYMENT MODEL WITH VARIABLE TRANSMISSION DISTANCE FOR WIRELESS SENS...ijwmn
The deployment of network nodes is essential to ensure the wireless sensor network's regular operation and affects the multiple network performance metrics, such as connectivity, coverage, lifetime, and cost. This paper focuses on the problem of minimizing network costs while meeting network requirements, and proposes a corona-based deployment method by using the variable transmission distance sensor. Based on the analysis of node energy consumption and network cost, an optimization model to minimize Cost Per Unit Area is given. The transmission distances and initial energy of the sensors are obtained by solving the model. The optimization model is improved to ensure the energy consumption balance of nodes in the same corona. Based on these parameters, the process of network node deployment is given. Deploying the
network through this method will greatly reduce network costs.
The document proposes a clustering-based approach to dynamically allocate bandwidth in wireless networks. It extracts student data from a university's course timetable to predict user distributions over time. It then applies K-means clustering to group buildings into wireless nodes based on expected user loads. This clusters student devices and allows wireless nodes to adapt their bandwidth allocation according to predicted user demands at different times. The approach is tested on a university campus network, extracting student data to predict building loads and applying K-means clustering to allocate optimal bandwidth across wireless nodes over time.
International Journal of Advanced Smart Sensor Network Systems ( IJASSN )ijassn
With the availability of low cost, short range sensor technology along with advances in wireless networking, sensor networks has become a hot topic of discussion. The International Journal of Advanced Smart Sensor Network Systems is an open access peer-reviewed journal which focuses on applied research and applications of sensor networks. While sensor networks provide ample opportunities to provide various services, its effective deployment in large scale is still challenging due to various factors. This journal provides a forum that impacts the development of high performance computing solutions to problems arising due to the complexities of sensor network systems. It also acts as a path to exchange novel ideas about impacts of sensor networks research.
Design and implementation of grid based clustering in WSN using dynamic sink ...journalBEEI
A wireless sensor networks (WSNs) play a significant application, especially in the monitored remoting environmental, which enables by the availability of sensors which are cheaper, smaller, and intelligent. The equipment of such sensors be with wireless interfaces, which a communication with other sensors occurs for creating a network, that contains many distributed nodes. The closest nodes to the sink are exploited at an enormous traffic load while the data from the whole regions are forwarded between them to reach the sink. This result in exhausting their energy quickly and partitioning the network. This is solved by changing the sink node position in Grid based clustering technique, which considers the optimal method for this purpose. A simulation with MATLAB can be applied for grid based clustering technique to evaluate the performance of WSN. The expected results deal with outperforms in throughput, reducing energy consumption and increasing residual energy, in addition to prolong the network lifetime of the sensor network.
Energy efficient clustering in heterogeneousIJCNCJournal
Cluster head election is a key technique used to reduce energy consumption and enhancing the throughput
of wireless sensor networks. In this paper, a new energy efficient clustering (E2C) protocol for
heterogeneous wireless sensor networks is proposed. Cluster head is elected based on the predicted
residual energy of sensors, optimal probability of a sensor to become a cluster head, and its degree of
connectivity as the parameters. The probability threshold to compete for the role of cluster head is derived.
The probability threshold has been extended for multi-levels energy heterogeneity in the network. The
proposed E2C protocol is simulated in MATLAB. Results obtained in the simulationshowthat performance
of the proposed E2Cprotocol is betterthan stable election protocol (SEP), and distributed energy efficient
clustering (DEEC) protocol in terms of energy consumption, throughput, and network lifetime.
Similar to Greedy Cluster Based Routing for Wireless Sensor Networks (20)
Home security is of paramount importance in today's world, where we rely more on technology, home
security is crucial. Using technology to make homes safer and easier to control from anywhere is
important. Home security is important for the occupant’s safety. In this paper, we came up with a low cost,
AI based model home security system. The system has a user-friendly interface, allowing users to start
model training and face detection with simple keyboard commands. Our goal is to introduce an innovative
home security system using facial recognition technology. Unlike traditional systems, this system trains
and saves images of friends and family members. The system scans this folder to recognize familiar faces
and provides real-time monitoring. If an unfamiliar face is detected, it promptly sends an email alert,
ensuring a proactive response to potential security threats.
In the era of data-driven warfare, the integration of big data and machine learning (ML) techniques has
become paramount for enhancing defence capabilities. This research report delves into the applications of
big data and ML in the defence sector, exploring their potential to revolutionize intelligence gathering,
strategic decision-making, and operational efficiency. By leveraging vast amounts of data and advanced
algorithms, these technologies offer unprecedented opportunities for threat detection, predictive analysis,
and optimized resource allocation. However, their adoption also raises critical concerns regarding data
privacy, ethical implications, and the potential for misuse. This report aims to provide a comprehensive
understanding of the current state of big data and ML in defence, while examining the challenges and
ethical considerations that must be addressed to ensure responsible and effective implementation.
Cloud Computing, being one of the most recent innovative developments of the IT world, has been
instrumental not just to the success of SMEs but, through their productivity and innovative contribution to
the economy, has even made a remarkable contribution to the economic growth of the United States. To
this end, the study focuses on how cloud computing technology has impacted economic growth through
SMEs in the United States. Relevant literature connected to the variables of interest in this study was
reviewed, and secondary data was generated and utilized in the analysis section of this paper. The findings
of this paper revealed that there have been meaningful contributions that the usage of virtualization has
made in the commercial dealings of small firms in the United States, and this has also been reflected in the
economic growth of the country. This paper further revealed that as important as cloud-based software is,
some SMEs are still skeptical about how it can help improve their business and increase their bottom line
and hence have failed to adopt it. Apart from the SMEs, some notable large firms in different industries,
including information and educational services, have adopted cloud computing technology and hence
contributed to the economic growth of the United States. Lastly, findings from our inferential statistics
revealed that no discernible change has occurred in innovation between small and big businesses in the
adoption of cloud computing. Both categories of businesses adopt cloud computing in the same way, and
their contribution to the American economy has no significant difference in the usage of virtualization.
Energy-constrained Wireless Sensor Networks (WSNs) have garnered significant research interest in
recent years. Multiple-Input Multiple-Output (MIMO), or Cooperative MIMO, represents a specialized
application of MIMO technology within WSNs. This approach operates effectively, especially in
challenging and resource-constrained environments. By facilitating collaboration among sensor nodes,
Cooperative MIMO enhances reliability, coverage, and energy efficiency in WSN deployments.
Consequently, MIMO finds application in diverse WSN scenarios, spanning environmental monitoring,
industrial automation, and healthcare applications.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication. IJCSIT publishes original research papers and review papers, as well as auxiliary material such as: research papers, case studies, technical reports etc.
With growing, Car parking increases with the number of car users. With the increased use of smartphones
and their applications, users prefer mobile phone-based solutions. This paper proposes the Smart Parking
Management System (SPMS) that depends on Arduino parts, Android applications, and based on IoT. This
gave the client the ability to check available parking spaces and reserve a parking spot. IR sensors are
utilized to know if a car park space is allowed. Its area data are transmitted using the WI-FI module to the
server and are recovered by the mobile application which offers many options attractively and with no cost
to users and lets the user check reservation details. With IoT technology, the smart parking system can be
connected wirelessly to easily track available locations.
Welcome to AIRCC's International Journal of Computer Science and Information Technology (IJCSIT), your gateway to the latest advancements in the dynamic fields of Computer Science and Information Systems.
Computer-Assisted Language Learning (CALL) are computer-based tutoring systems that deal with
linguistic skills. Adding intelligence in such systems is mainly based on using Natural Language
Processing (NLP) tools to diagnose student errors, especially in language grammar. However, most such
systems do not consider the modeling of student competence in linguistic skills, especially for the Arabic
language. In this paper, we will deal with basic grammar concepts of the Arabic language taught for the
fourth grade of the elementary school in Egypt. This is through Arabic Grammar Trainer (AGTrainer)
which is an Intelligent CALL. The implemented system (AGTrainer) trains the students through different
questions that deal with the different concepts and have different difficulty levels. Constraint-based student
modeling (CBSM) technique is used as a short-term student model. CBSM is used to define in small grain
level the different grammar skills through the defined skill structures. The main contribution of this paper
is the hierarchal representation of the system's basic grammar skills as domain knowledge. That
representation is used as a mechanism for efficiently checking constraints to model the student knowledge
and diagnose the student errors and identify their cause. In addition, satisfying constraints and the number
of trails the student takes for answering each question and fuzzy logic decision system are used to
determine the student learning level for each lesson as a long-term model. The results of the evaluation
showed the system's effectiveness in learning in addition to the satisfaction of students and teachers with its
features and abilities.
In the realm of computer security, the importance of efficient and reliable user authentication methods has
become increasingly critical. This paper examines the potential of mouse movement dynamics as a
consistent metric for continuous authentication. By analysing user mouse movement patterns in two
contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive
behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond
conventional methodologies by employing a range of machine learning models. These models are carefully
selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as
reflected in their mouse movements. This multifaceted approach allows for a more nuanced and
comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
enhance computer security and highlights the potential of leveraging user behavior, specifically mouse
dynamics, in developing robust authentication systems.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This research aims to further understanding in the field of continuous authentication using behavioural
biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing
Minecraft with a Samsung Tablet, each for a duration of 15 minutes. Utilizing this dataset, we employed
machine learning (ML) binary classifiers, being Random Forest (RF), K-Nearest Neighbors (KNN), and
Support Vector Classifier (SVC), to determine the authenticity of specific user actions. Our most robust
model was SVC, which achieved an average accuracy of approximately 90%, demonstrating that touch
dynamics can effectively distinguish users. However, further studies are needed to make it viable option
for authentication systems. You can access our dataset at the following
link:https://github.com/AuthenTech2023/authentech-repo
This paper discusses the capabilities and limitations of GPT-3 (0), a state-of-the-art language model, in the
context of text understanding. We begin by describing the architecture and training process of GPT-3, and
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tasks, such as language translation, question-answering, and text completion. Throughout this research
project, a summarizing tool was also created to help us retrieve content from any types of document,
specifically IELTS (0) Reading Test data in this project. We also aimed to improve the accuracy of the
summarizing, as well as question-answering capabilities of GPT-3 (0) via long text
In the realm of computer security, the importance of efficient and reliable user authentication methods has
become increasingly critical. This paper examines the potential of mouse movement dynamics as a
consistent metric for continuous authentication. By analysing user mouse movement patterns in two
contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive
behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond
conventional methodologies by employing a range of machine learning models. These models are carefully
selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as
reflected in their mouse movements. This multifaceted approach allows for a more nuanced and
comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
enhance computer security and highlights the potential of leveraging user behavior, specifically mouse
dynamics, in developing robust authentication systems.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
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The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
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The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
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Greedy Cluster Based Routing for Wireless Sensor Networks
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
DOI:10.5121/ijcsit.2017.9208 85
GREEDY CLUSTER BASED ROUTING FOR WIRELESS
SENSOR NETWORKS
M. Parthasarathi 1
and Dr. Karthikeyani Vajravel 2
1
Lecturer, Department of MCA, KSR College of Arts and Science,
Tiruchengode, Namakkal – Dt, India
2
Asst. Professor, Department of Computer science,
Thiruvalluvar Govt Arts college, Rasipuram, Namakkal Dt., India
ABSTRACT
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.
KEYWORDS
Wireless Sensor Network, Cluster-based Routing, Greedy route, Cluster head, dynamic routing.
1. INTRODUCTION
In the recent past, several routing algorithms have been designed to improve the network lifetime
in wireless sensor networks. In WSN, a multiple sensor nodes are distributed through the
network. The data collected by the sensor nodes in WSN, are transmitted to a sink node.
Therefore, the position of the sink node provides the great importance on the energy consumption
and improving the lifetime of network. The mechanism adopted in clustering is highly significant
in saving energy resources for network activities and has become promising area in the field of
research.
Figure 1 Wireless sensor networkS
Sink node
2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
86
As shown in figure 1, the data collected by the source node are sent to sink node to forward the
required destination node in WSN. Therefore the energy consumption and network lifetime is the
major concern in the WSN.
Two-tier Particle Swarm Optimization for Clustering and Routing (TPSO-CR) [1] with a novel
particle encoding scheme and fitness function identified the optimal routing tree to improve the
packet delivery rate. However, network lifetime remained unaddressed. Energy Delay Index for
Trade-off (EDIT) [2] was designed with the objective of improving the network lifetime with the
help of Euclidean distance. However, the successful data delivery was not measured in terms of
throughput. Another method to extend the network lifetime by using the fuzzy inference engine
to select the super cluster head was presented in [3]. However, it increased the overhead.
Designing scalable routing in WSN with prolonged network lifetime is considered to be the most
challenging task. In [4], a grid based method using upper Bollinger band and lower Bollinger
band was designed aiming at improving the network lifetime. Clustering and multi-hop routing
algorithms [5] was designed with the purview of improving network lifetime using inter-cluster
and intra-cluster transmission tree.
Though many research works were conducted to improve network lifetime but at the cost of data
being transmitted. To improve network lifetime and the data transmission rate, in [6], Rule
Driven Multi-path Routing algorithm was designed for building cluster and transmitting data in
an efficient manner.
In Wireless Sensor Networks (WSNs), one of the most influential factors next to the network
lifetime is the energy efficiency. In [7], Energy Efficiency Semi Static Clustering (EESSC) based
on Hierarchical Agglomerative Clustering (HAC) was designed to improve the WSN’s energy
efficiency. In [8], honey bee swarm intelligent based approach was designed to increase the
amount of packets delivered to the base station.
An extended version including the fuzzy model was investigated in [9] for generating balanced
clusters and prolonging the network lifetime was designed. Another Particle Swarm Optimization
approach was presented in [10] to increase the total data packets to be delivered to the base
station.
Energy conservation and fault tolerance plays major role in the deployment of a wireless sensor
network (WSN). Therefore the design of clustering and routing algorithms should incorporate
both these issues to prolong the network lifetime. In [11], a distributed clustering and routing
algorithms jointly referred as Distributed Fault-tolerant Clustering and Routing (DFCR) was
designed to prove the energy efficiency in case of fault tolerant. An integrated topology control
and routing algorithm based on Mixed Integer Linear Programming model [12] was designed to
optimally select the cluster head node.
Due to the limited energy possessed by the sensor nodes, increasing the network lifetime in WSN
is a challenging task. This criticality increases with the increase in the density and topology
change of network because of more data collections and packet transmissions. In [13], a
clustering protocol based on Fan Shaped clustering was designed to improve both the energy
saving and packet collection rate. In [14], adaptive clustering habit scheme was designed for
wireless network to minimize the overall consumption of energy.
In [15], Clustering algorithm based on Ant Colony Optimization was investigated to manage
inter-cluster and intra-cluster communication, varying the grid size of the network. In [16],
cluster-based multi-path routing protocol was designed giving higher responsibility to the sink
node for wireless sensor networks. A novel layer-based hybrid algorithm [17] for cluster head and
3. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
87
cluster member selection was performed by way of information exchange between the
neighbouring nodes until the cluster head node was selected.
Recent advances in miniaturization in the field of wireless communications have resulted in the
making of micro sensors with limited processing and communicating capabilities. In [18], Energy
Efficient Multilayered Architecture (EEMA) was designed to not only improve the network
lifetime but also to reduce the routing delay. Another method based on Adaboost algorithm was
investigated in [19] to minimize routing delay. Link cost function [20] was used to improve the
packet delivery ratio and reduce energy consumption in WSN.
Based on the reviews, the existing methods has the limitations such as lack of network lifetime,
reduced the delivery ratio and higher overhead. In order to overcome such kind of issues in WSN,
a novel method is Greedy Cluster-based Routing (GCR) technique is introduced.
The contribution of the research paper is organized as follows; a novel Greedy Cluster-based
Routing (GCR) technique is developed to extend the network lifetime in WSN. The GCR uses
random timer for selecting the cluster head node. After that, Neighbour Node selection is carried
out through Mass Proportion value for achieving the dynamic routing thereby improving network
lifetime. Finally, GCR performs local route search using greedy strategy for selecting the
neighbouring optimal node to forward data packet that is close to the destination node and it has
sufficient energy for routing. This helps to reduce energy consumption during data forwarding.
The remainder of this paper is organized as follows. Section 2 provides an overview of Greedy
Cluster-based Routing, with the aid of a network model. In Section 3, experimental results are
presented and detailed discussion is included in Section 4. Finally, Section 5 concludes the work.
2. GREEDY CLUSTER-BASED ROUTING FOR WSN
In this section, an overview of the Greedy Cluster-based Routing (GCR) is presented starting with
a design of network model to improve the routing efficiency in terms of network lifetime,
reducing the energy consumption during data forwarding.
2.1Network model
Let us consider a wireless network with number of sensor nodes that are uniformly deployed in
‘ ∗ ’ square area represented as a simple digraph ‘ = , ’ with transmission range ‘ ’.
The network consists of ‘ ’ wireless sensor nodes represented as ‘ = , , … , ’ and
‘ = , , … , ’ representing the set of communication links between the sensor nodes.
Suppose an adjacent pair ‘ , ’, then ‘ , ’ represents that both sensor nodes ‘ and ‘ ’
lie within the transmission range ‘ ’ and is expressed as given below.
= , | , ≤ , !ℎ # , ∈ (1)
2.2 Problem definition
The cluster head node in wireless network based on the design of cluster consumes more energy
during intra-cluster and inter-cluster communication. The energy consumption during intra-
cluster communication is highly influenced by the cluster size, where the energy consumption
increases with number of sensor nodes in cluster. As the sensor node in the wireless sensor
networks has limited transmission range, multi-hop design is used for data transmission. During
data transmission phase, cluster heads act as intermediate nodes to distribute data between source
and destination in WSN. During the inter-cluster communication, the cluster heads nearer to the
4. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
88
neighbourhood of base station overcomes heavy traffic and expires faster than far away cluster
heads. This process improves the control overhead. In order to overcome such kind of issues a
Greedy Cluster Based Routing Protocol is designed.
2.3 Greedy Cluster-based Routing
The design of Greedy Cluster-based Routing (GCR) method is carried out aiming at improving
the network lifetime in WSN. Specifically, there are three parameters such as energy
consumption, network lifetime and throughput are considerably affects the network performance.
Initially, the Cluster Based Routing Protocol is applied to improve the network lifetime through
the arbitrary timer. Secondly, Mass Proportion-based Neighbour Node selection measure is
performed for selecting the neighbouring node improving the throughput. Finally, Greedy Local
route search algorithm used for reducing the energy consumption in WSN. The architecture
diagram of the GCR) method is shown in figure 1.
Figure 2 Architecture diagram of Greedy Cluster-based Routing (GCR) method
Figure 2 shows the architecture diagram of the Greedy Cluster-based Routing (GCR) method.
The GCR method performs efficient transmission and improves the network lifetime in order to
obtain the higher throughput and reduce the energy consumption in WSN. The description about
GCR method is explained in below two subsections.
2.4 Cluster Based Routing Protocol
The Cluster Based Routing Protocol (CBRP) in the GCR uses an arbitary timer during data
packets forwarding to minimize the control overhead via the location information of the sensor
nodes in the network. The CBRP uses random timer for selecting the cluster head node. During
network initialization stage, the base station broadcasts a beacon message to all the sensor nodes
in the network. To find the neighbour nodes, the GCR uses maximum mass proportion value
which is discussed in the next section. During data packets forwarding, each node initiates a timer
and is expressed as given below.
% = ∑
' ( )*,+)
**,
- (2)
WSN
Cluster Based Routing Protocol
uses arbitrary timer
Mass Proportion-based
Neighbour Node selection
Greedy Local route search
algorithm
Improved network lifetime and minimum energy
consumption
5. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
89
. =
/
0
(3)
From (2), ‘ 12, 31 ’ corresponds to the distance between the source node ‘12’ and the base
station ‘31’ with a total of ‘22 ’ neighbouring nodes. From (3), ‘.’ represents the timer whereas
‘4’ corresponds to the timer value between ‘[0, 1]’. With the aid of ‘%’, each sensor node has
more neighbouring nodes with minimum distance to the base station is identified. Figure 1 shows
the construction of cluster based routing protocol.
Figure 3 Cluster Based Routing Protocol
The sensor node completes the timer ‘.’ is the cluster head ‘9:’, node for that cluster. As shown
in the figure 3, the gray circle completes the timer forms the cluster head node. The remaining
sensor node enters the cluster by sending a join message to the cluster head node and is expressed
as given below.
∑ 1;
- = ∑ 9<=> [9:]?- (4)
Once the cluster formation is accomplished then the each sensor nodes measures the arbitrary
timer in order to participate in the cluster head selection process. The arbitrary timer ‘@.’ is
expressed as given below.
A = B ∗ C (5)
@. = ∑ D1 −
F
**,
G- (6)
From (6), the arbitrary timer ‘@.’ is obtained using the total energy ‘ B’, ‘ C’, the utilized energy
and the remaining neighbouring nodes ‘22 ’ respectively. Figure 2 shows the cluster based
routing algorithm.
Input: Source node ‘12’, Destination node ‘ 2’, Sensor node ‘1 = 1 , 1 , . . , 1 ’,
Neighbour node ‘ 22 = 22 , 22 , … , 22 ’, Data Packets
Cluster head sensor nodes (non cluster head node)
B
6. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
90
‘ I = I , I , … , I ’
Output: Improved network lifetime
Step 1: Begin
Step 2: Repeat
Step 3: For each Source node ‘12’ with Destination node ‘ 2’ and Data
Packets ‘ I ’
Step 4: Measure timer for data packet forwarding using (3)
Step 5: Assign higher neighbouring nodes with minimum distance to be
cluster head node
Step 6: Measure non cluster head node using (4)
Step 7: Measure arbitrary timer using (5)
Step 8 : End for
Step 9: Until (all nodes are assigned with either cluster head or non cluster head
node)
Step 10: End
Figure 4 Cluster based routing algorithm
As shown in figure 4, each sensor node starts its timer using its local information to participate in
the cluster head selection process. During cluster head selection process, sensor nodes uses
energy consumption cost ‘A’ as the influential factor. The neighbourhood node selection and the
total remaining neighbouring nodes play an important role in selecting a cluster head for the
specific cluster. The neighbourhood node selection is performed by applying mass proportion
value that is discussed in the next section. The GCR uses only local information to select cluster
heads and reducing the control overhead. This helps to improve the network lifetime.
2.5 Mass Proportion-based Neighbour Node selection
The GCR employed for neighbour node selection based on the mass proportion value. Let us
assume that each sensor node initially has single sensed information (i.e. data packets) to be sent
to the base station in the network and each sensor node attracts its neighbouring nodes with mass
proportion value. Figure 4 shows the Neighbour Node selection based on Mass Proportion value.
Figure 5 Neighbour node selections
Let us consider the sensor nodes ‘1 ’ and ‘1 ’ in the figure 3. As shown in the figure 5, the
neighbour nodes of ‘1 ’ is selected within one hop away from node ‘1 ’. Here, the gray circles are
the neighbours of sensor node ‘1 ’. On the other hand, dark gray circles form the neighbours of
the neighbouring node ‘1 ’. After that, the mass proportion value between sensor node ‘1 ’ and
1
1;
1J
1K
1L
1
7. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
91
‘1 ’, ‘MI ’ is directly proportional to the mass of the sensor node and the mass of the
neighbouring sensor nodes and inversely proportional to the square of the distance between them.
The mass proportion value is as expressed below.
MI = N
O,∗ OP
' ( ),,)P
Q (7)
From (7), ‘N’ symbolizes the constant, ‘M ’ and ‘M ’ represents the mass of the sensor node ‘1 ’
and ‘1 ’ with the distance represented by ‘ 1 , 1 ’ respectively. Figure 3 illustrates the basic
idea of neighbour node selection applied in the proposed GCR method. The sensor node ‘1 ’ is
attracted by all its neighbours in different directions and the mass value for all the neighbouring
sensor nodes are different. Therefore, the major concern is in selecting the neighbour of the ‘1 ’
node.
In the proposed method, the neighbour with the maximum mass proportion value is selected as
the next neighbouring node and is expressed as given below.
∑ 1 → 22;
- = M@A MI (8)
Due to the diversified nature of the network and the changing topology, the energy of each sensor
node varies and their mass proportion value also varied. In this way, dynamic routing is achieved
by measuring different mass proportion value for several sensor nodes thereby improving the rate
of network lifetime.
2.6 Local route search algorithm
Finally, a local route search algorithm called greedy forwarding strategy is designed with the
obtained neighbouring nodes through which the data packets are transmitted. The local route
search algorithm based on greedy forwarding uses local information with the objective of brining
the data packets closer to the destination.
As the GCR applies greedy strategy for local route search between the source and destination, a
function that describes which neighbouring nodes is the most optimal node. Obviously, the GCR
selects the optimal node to forward data packet which is both close to the destination node and
has enough energy for routing. This is obtained by the expression given below.
S = ∑ 22 MT2 < , , … , ?U[ 1 , 31 ]- (9)
The Greedy Strategy-based Local Route Search algorithm is given in figure 4.
Input: Source node ‘12’, Destination node ‘ 2’, Sensor node ‘1 = 1 , 1 , . . , 1 ’,
Neighbour node ‘22 = 22 , 22 , … , 22 ’, Data Packets ‘ I = I , I , … , I ’,
Base Station ‘31’
Output :Optimal energy consumption
Begin
For each Source node ‘12’ with Destination node ‘ 2’ and Data Packets ‘ I ’
For sensor nodes ‘1 ’ and ‘1 ’
Measure the mass proportion value using (7)
Obtain neighbour node with maximum mass proportion value using (8)
Measure the function (9) for obtaining optimal neighbouring node
Forward data packets through the obtained optimal neighbouring node
8. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
92
End for
End for
End
Figure 6Greedy Local route search algorithm
Figure 6 shows the Greedy Local route search algorithm. Initially, the mass proportional value is
obtained. With the obtained mass proportional value, the neighbour node with maximum mass
proportion value is obtained. Followed by, the optimal neighbour node is measured based on the
minimum energy and minimum distance between the sensor node and base station through which
the data packet is transmitted. This in turn ensures minimum energy consumption for improving
the network lifetime.
3. EXPERIMENTAL SETTINGS
In this section, the performance of GCR is evaluated through theoretical analysis and simulation
study, including energy consumption, network lifetime with respect to different number of sensor
nodes. The results of the metrics of GCR method is compared against the existing methods such
as TPSO-CR (Two-tier Particle Swarm Optimization for Clustering and Routing) [1] and EDIT
Energy Delay Index for Trade-off [2] respectively. The performance evaluations are based on the
simulations of 70 sensor nodes that form a wireless sensor network over a rectangular space 1000
m * 1000m. Each node’s radio propagation range is 300m and data rate is 1Mbps. Table 1 lists
the set of input parameter and evaluates performance of GCR method via simulation that
generates traffic for every 10 m/s.
The nodes are distributed in an area using Random Way point model for simulation, whereas the
link layer provides the link between two sensor nodes and the design of link is multi direction.
The base station collects the data packets of range 9 – 63 and forwards the data packets to the
base station with each data packet size differing from 100 KB to 512 KB. The simulation time
varies from 500 simulation seconds to 1500 simulation seconds.
Table 1 Simulation parameters
Parameters Values
Area of sensing field 1000m * 1000m
Number of sensor nodes 70
Number of data packets 8, 16, 24, 32, 40, 48, 56
Simulation time 50s
Bandwidth 1Mbps
Propagation range 300m
Simulation runs 7
4. DISCUSSION
To validate the analysis of the proposed Greedy Cluster-based Routing (GCR) in Wireless Sensor
Network with existing TPSO-CR (Two-tier Particle Swarm Optimization for Clustering and
Routing) [1] and EDIT Energy Delay Index for Trade-off [2], simulation results under NS2 are
presented. The parameters of the GCR method are chosen as provided in the experiment section.
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4.1.Scenario 1: Energy consumption
Energy consumption between cluster head is measured using the energy consumed by a single
sensor node with respect to the total sensor nodes in WSN. The mathematical formulation for
energy consumption is as given below.
9 = ∑ #VW 1 ∗ .>XYN 1- (10)
From (10), the energy consumption ‘ 9’ between cluster head is obtained by the product of the
energy for single node ‘ #VW 1 ’ and total sensor nodes ‘.>XYN 1 ’ in the network. The
energy consumption is measured in terms of Joules.
Table 2 Performance evaluation of energy consumption
Sensor node
density
Energy consumption (J)
GCR TPSO-CR EDIT
10 51 58 60
20 62 75 80
30 71 83 86
40 83 91 95
50 89 108 113
60 98 115 120
70 105 120 129
As listed in table 2, GCR method measures the energy consumption between cluster head in WSN
with respect to node density in the range of 10 to 70 sensor nodes. It is measured in terms of
Joules (J). The energy consumption between cluster head in WSN using GCR method offers
comparable values than the state-of-the-art methods.
Figure 7 Measure of energy consumption with respect to sensor node density
Figure 7 presents the variation of energy consumption with respect to node density in wireless
sensor network. All the results provided in figure 5 confirm that the proposed GCR method
significantly outperforms the other two methods, TPSO-CR [1] and EDIT [2]. The energy
consumption between cluster head is reduced in the GCR method using the cluster-based routing
algorithm. With the application of cluster-based routing algorithm, arbitrary timer is applied to
decide upon the cluster head node among the other sensor nodes in the network. The proposed
0
20
40
60
80
100
120
140
10 20 30 40 50 60 70
Energyconsumption(J)
Sensor node density
GCR
TPSO-CR
EDIT
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GCR selects an optimal node through the local route search algorithm to forward data packet. The
optimal nodes are selected based on nearer to destination node and has minimum energy.
Therefore, GCR method obtains an optimal node for efficient packet transmission thereby
enhancing the network lifetime with minimum energy consumption. The energy consumption is
reduced by 16.31% compared to existing TPSO-CR [1]. As a result, energy consumption is
reduced in the GCR method using the cluster based routing algorithm. Moreover, using the
arbitrary timer, the sensor nodes participates in the cluster head selection process, uses only local
information, further reduces the energy consumption between cluster head by 22.07% compared
to existing EDIT [2].
4.2.Scenario 2: Network lifetime
The lifetime of the network is determined by the number of sensor nodes in the network in WSN.
The network lifetime is expressed as given below.
2Z = D
)[]^__^
`aBJbc
G ∗ 100 (11)
From (11), the network lifetime ‘2Z’ is measured using the total number of sensor nodes
‘.>XYN)’ in the network and the sensor node addressed ‘1JLLde((eL’in WSN. Higher the network
lifetime, more efficient the method is said to be and is measured in terms of percentage (%).
Table 3 Performance evaluation of network lifetime
Sensor node
density
Network lifetime (%)
GCR TPSO-CR EDIT
10 85.14 82.28 75.15
20 89.16 86.76 79.20
30 97.23 94.15 87.76
40 82.14 79.81 70.23
50 85.28 82.19 75.14
60 88.18 85.66 78.32
70 93.14 90.29 83.14
The targeting results of network lifetime using GCR method with two state-of-the-art methods
[1], [2] in table 3 presented for comparison based on the node density in wireless sensor network.
Figure 8 Measure of network lifetime with varied sensor nodes
0
20
40
60
80
100
120
10 20 30 40 50 60 70
Networklifetime(%)
Sensor node density
GCR
TPSO-CR
EDIT
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From figure 8, it is evident that the network lifetime is improved using the proposed GCR method
than the existing methods. This is because; the GCR method uses cluster based routing algorithm
uses arbitrary timer. In WSN, each sensor nodes starts with timer and its local information to
contribute in the cluster head selection process. The node which has minimum energy
consumption cost is selected during cluster head selection process. For transmitting a packet to
destination, the neighbourhood node selection is essential. Therefore this process is obtained
through applying mass proportion value. The GCR method uses only local information for
choosing the cluster heads. Through this cluster head, the transmission is carried out effectively
and reducing the control overhead. With the application of mass proportion value, neighbour
node detection is performed in an efficient manner at different time intervals resulting in the
improvement of network lifetime. At the same time, in GCR method, not only the mass
proportion value is obtained for the sensor and neighbouring nodes but also the minimum
distance between them is considered. This in turn increases the network lifetime using GCR
method by 3.08% compared to TPSO-CR [1] and 11.56% compared to EDIT [2] respectively.
4.3 Scenario: Throughput
Throughput measures the rate of successful data packets delivery over a period of time interval in
WSN. Therefore, throughput rate is the ratio of data packets sent by the source node and the data
packets received by the destination node. It is measured in terms of percentage (%) and is
formulated as given below.
. =
'f]
'f_
∗ 100 (12)
From (12), the rate of throughput ‘.’ is measured using the data packets sent ‘ I(’ and the data
packets received ‘ Id’. Higher the data packets being received, more efficient the method is said
to be. To better understand the effectiveness of the proposed GCR method, extensive
experimental results are reported in table 4 with respect to data packets sent.
Table 4 Performance evaluation of throughput
Data packets Throughput
GCR TPSO-CR EDIT
8 85.28 81.23 73.21
16 87.14 83.19 76.29
24 89.38 85.15 77.29
32 92.23 88.20 81.32
40 84.19 80.20 73.78
48 87.32 83.18 78.14
56 90.14 86.67 80.29
NS2 simulator is used to experiment throughput rate by analyzing the result using table and graph
values. Results are presented for different number of data packets and the results reported here
confirm that with the increase in the number of data packets, the rate of throughput also gets
increased. Though, the increase is not linear. This is due to the sensor node movements at
different time periods.
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Figure 9 Measure of throughput
Figure 9 shows the rate of throughput with respect to different number of data packets with size
in the range of 100KB to 512KB in wireless sensor network considered for experimental purpose.
As shown in figure, the proposed GCR method performs relatively well when compared to two
other existing methods TPSO-CR [1] and EDIT [2]. This is because; the rate of throughput is
improved in the GCR method by applying Greedy strategy through local route search model. By
applying the Greedy Local Route Search algorithm, the neighbouring node is selected based on
the maximum mass proportional value. With the higher mass proportional value of the node, a
function with sensor nodes consuming minimum energy and minimum distance between the
nodes are selected for data packet forwarding. This helps in increasing the rate of throughput by
4.53% compared to TPSO-CR [1]. With obtained optimal neighbour node using the function in
GCR method, the rate of throughput is improved in GCR method by 12.25% compared to EDIT
[2] respectively.
5. CONCLUSION
In this paper, Greedy Cluster-based Routing (GCR) method is developed based on the Cluster-
based Routing Protocol and Mass Proportion-based Neighbour Node selection with Greedy
strategy for wireless sensor network. The GCR method minimizes the energy consumption
between cluster head and therefore improves the network lifetime. The GCR method uses cluster-
based routing algorithm in a dynamic manner, it reduces the energy consumption between cluster
head in WSN through arbitrary timer. As a result, the proposed cluster-based routing algorithm
selects the cluster head in an efficient manner and helps in minimizing the energy consumption
between cluster head and therefore improving the efficiency of the system and the overall
network. By applying the Mass Proportion-based Neighbour Node selection with the maximum
proportion value, improves the network lifetime in WSN. Finally, the greedy strategy based local
route search algorithm is considered in a significant manner and therefore improving the rate of
throughput. Different sensor nodes with varied packet sizes on WSN using GCR method analyze
the routing efficiency to significantly improve the energy savings and therefore the network
lifetime in WSN. A series of simulation results are performed to test the energy consumption,
network lifetime and throughput rate. The simulation results show that GCR method offers better
performance with an improvement of network lifetime by 7.32% and improves the throughput
rate by 8.39% compared to TPSO-CR [1] and EDIT [2] respectively.
0
10
20
30
40
50
60
70
80
90
100
8 16 24 32 40 48 56
Throughput(%)
Data packets
GCR
TPSO-CR
EDIT
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AUTHORS
M. PARTHASARATHI, received the B.Sc.,(Physics), M.Sc (CS) fromBharathidasan
University, Trichy, in 1992 - 1998 and ME in computer science Engineering in Anna
University, Coimbatore in 2008. Currently,He is pursuing the Ph.D. degree in Computer
Science in MS university,Tirunelveli. His research interests wireless networks. He has
more than 17 years teaching experience in various colleges. Now currently he is working
in KSR college of Arts and Science, Tiruchengode from 2005 to till date.
Dr. KARTHIKEYANI V was born in Tamil Nadu, India in 1972. She is working as an
Assistant Professor in Department of Computer Science at Govt. Arts College, Rasipuram,
Tamilnadu, India. She was awarded Doctoral degree from Periyar Universtiy, Salem,
Tamilnadu, India. She has published 33 National and International Journals and presented
several papers in International and National Conferences. She has 21 years of teaching
experience. Her areas of interests are Image Processing, Computer Graphics, Multimedia, Data Mining and
Web Mining. She is a life member in Computer Society of India (CSI), ISTE (Indian Society for Technical
Education), ACM-CSTA (Computer Science Teacher Association).