Energy-Efficient and Coverage-Aware Clustering in Wireless Sensor Networks

Awad, Fahed; Taqieddin, Eyad; Seyam, Asmaa

doi:10.4236/wet.2012.33021

Cited by 26 publications

(15 citation statements)

References 25 publications

(38 reference statements)

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“…The work in [8] proposes an intelligent framework to cluster the WSN nodes and to optimize the locations of sensor nodes within WSN for extending sensing coverage and diminishing the energy depletion of the entire network.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of Enhanced AODV based Self-Organized Tree for Energy Balanced Routing in Wireless Sensor Networks

R*,

Patil

2020

IJITEE

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Wireless sensor networks (WSN) are gaining attention in numerous fields with the advent of embedded systems and IoT. Wireless sensors are deployed in environmental conditions where human intervention is less or eliminated. Since these are not human monitored, powering and maintaining the energy of the node is a challenging issue. The main research hotspot in WSN is energy consumption. As energy drains faster, the network lifetime also decreases. Self-Organizing Networks (SON) are just the solution for the above-discussed problem. Self-organizing networks can automatically configure themselves, find an optimalsolution, diagnose and self-heal to some extent. In this work, “Implementation of Enhanced AODV based Self-Organized Tree for Energy Balanced Routing in Wireless Sensor Networks” is introduced which uses self-organization to balance energy and thus reduce energy consumption. This protocol uses combination of number of neighboring nodes and residual energy as the criteria for efficient cluster head election to form a tree-based cluster structure. Threshold for residual energy and distance are defined to decide the path of the data transmission which is energy efficient. The improvement made in choosing robust parameters for cluster head election and efficient data transmission results in lesser energy consumption. The implementation of the proposed protocol is carried out in NS2 environment. The experiment is conducted by varying the node density as 20, 40 and 60 nodes and with two pause times 5ms, 10ms. The analysis of the result indicates that the new system consumes 17.6% less energy than the existing system. The routing load, network lifetime metrics show better values than the existing system.

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Section: Literature Reviewmentioning

confidence: 99%

Implementation of Enhanced AODV based Self-Organized Tree for Energy Balanced Routing in Wireless Sensor Networks

R*,

Patil

2020

IJITEE

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“…Some other protocols are also developed for heterogeneous type of WSNs. LEACH-VF (LEACH with Virtual Force), proposed by Awad et al [13] in 2012, uses the principles of virtual field force to locate the sensor nodes in each cluster. The approach is based on two key issues: sensing coverage and data transmission energy.…”

Section: A Related Workmentioning

confidence: 99%

A Swarm Optimization Based Power Aware Clustering Strategy for WSNs

Jangwan¹,

Negi²

2017

International Journal on Advanced Science, Engineering and Information Technology

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The technique of division of a wireless sensor network (WSN) into clusters has proved to most suitable for the reliable data communication inside the network. This approach also improves the throughput of the system along with other attributes such as rate of delivering data packet to the base station (BS) and overall energy dissipation of the sensor nodes in the network. This in turn results in the increased network lifetime. As the sensor nodes are operated by battery or some other source, this introduces a constraint in energy resource. Therefore, there is a strong need to develop a novel approach to overcome this constraint, since this phenomenon leads to the degradation of the network. The swarm intelligence approach is able to cope with all such pitfalls of WSNs. In this paper, we have presented a cluster-head (CH) selection technique which is based on swarm optimization with the main aim to increase the overall network lifetime. The proposed approach gives higher effects with regards to power utilization of nodes, data packets received at BS and stability period, and for this reason serves to be a higher performer as compared to Stable Election Protocol (SEP) and Enhance Threshold Sensitive Stable Election Protocol (ETSSEP). MATLAB simulation outcomes exhibit that the proposed clustering strategy outperforms the SEP and ETSSEP with regards to the above noted attributes.

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“…If the CH does not receive data from a sensor node, which previously belonged to its cluster, the CH assumes that the node has moved away. LEACH with Virtual Force (LEACH-VF) protocol was proposed in [12]. This protocol applies virtual field force (VFF) principles to determine the proper locations for both the sensors nodes and CHs in order to maximize the coverage area and minimize the consumed energy.…”

Section: Introductionmentioning

confidence: 99%

An efficient clustering approach using genetic algorithm and node mobility in wireless sensor networks

Banimelhem

Mowafi

Taqieddin

et al. 2014

2014 11th International Symposium on Wireless Communications Systems (ISWCS)

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Node clustering in wireless sensor networks helps in extending the network life time by reducing the nodes' communication energy and balancing their remaining energy. This paper presents a new genetic-based approach that improves the performance of the LEACH clustering protocol used in wireless sensor networks. The proposed approach utilizes the mobility feature of sensor nodes in order to reduce the communication distances between the cluster heads and the base station. In each round, new locations of the cluster heads are determined using a genetic algorithm. The simulation results demonstrate that the proposed approach outperforms LEACH in terms of network lifetime and average remaining energy.

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Energy-Efficient and Coverage-Aware Clustering in Wireless Sensor Networks

Cited by 26 publications

References 25 publications

Implementation of Enhanced AODV based Self-Organized Tree for Energy Balanced Routing in Wireless Sensor Networks

Implementation of Enhanced AODV based Self-Organized Tree for Energy Balanced Routing in Wireless Sensor Networks

A Swarm Optimization Based Power Aware Clustering Strategy for WSNs

An efficient clustering approach using genetic algorithm and node mobility in wireless sensor networks

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