SEARCH: A Stochastic Election Approach for Heterogeneous Wireless Sensor Networks

Wang, Min-Yi; Ding, Jie; Chen, Wanpei; Guan, Weijun

doi:10.1109/lcomm.2015.2391100

Cited by 30 publications

(11 citation statements)

References 8 publications

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“…While CH is selected by BBKH, the difference in distance between the two parts, ie. from sensor nodes to CH, given by d and from CH to BS as given by b [42], must be less, so as to balance the energy dissipation in both the parts. Consider Eq.…”

Section: Fitness Functionmentioning

confidence: 99%

Biogeography-Based Krill Herd algorithm for energy efficient clustering in wireless sensor networks for structural health monitoring application

Senniappan¹,

Subramanian

2018

AIS

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Abstract. Civil buildings are prone to various kinds of damages. The detection of damages caused in a building at an early stage is essential in order to save the invaluable human life and significant belongings. Wireless sensor networks (WSN) help to detect damages caused to a building by sensing different factors, which affect civil structures. Energy efficiency of sensor nodes and network congestion are quite common issues in wireless sensor networks that affect the network performance. In this research work, the formation of energy efficient clusters mitigates congestion by considering the buffer occupancy level and fairness index of flows to improve the network lifetime. The proposed method uses Biogeography-Based Krill Herd (BBKH) algorithm for cluster head selection. BBKH based congestion mitigation outperforms other classical evolutionary optimizations and swarm intelligence algorithms like Genetic Algorithm, Particle Swarm Optimization (PSO) and Symbiotic Organisms Search (SOS). Compared with PSO, the network throughput has increased by 26.18% using BBKH. The network lifetime has increased by 42.11% using the proposed BBKH, compared to PSO. The extended lifetime of the network helps damage detection in civil structures for extensive periods.

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Section: Fitness Functionmentioning

confidence: 99%

Biogeography-Based Krill Herd algorithm for energy efficient clustering in wireless sensor networks for structural health monitoring application

Senniappan¹,

Subramanian

2018

AIS

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“…Because it is the interval over which all the nodes from the network are working against energy exertion without sacrifice [1,6,7,15,18,19,22,24,25,27,29,32,34,47,56,64].…”

Section: ) Stability Periodmentioning

confidence: 99%

A Review on Energy Efficient Clustering Protocol of Heterogeneous Wireless Sensor Network

Purkar¹,

R.S²

2017

IJET

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“…The energy consumed by a sensor is due to the capture, processing (switching energy and energy leaks), and communication (energy transmission and energy of receipt). The following Equation shows an antenna model and energy consumption rules to convey a message of L‐bits, a distance of d meters, the transmitter consumes

E_{T x} false(1, d false) = ({, \begin{matrix} array l E_{e l e c} + l ε_{f s} d^{2}, & array d ⩽ d_{0} \\ array l E_{e l e c} + l ε_{m p} d^{4}, & array d ⩾ d_{0} \end{matrix},)

where

d_{0} = \sqrt{\frac{ε_{f s}}{ε_{m p}}}

designates the threshold distance; E e l e c represents the energy consumption in the electronic system for sending or receiving a bit, ε m p d 4 and ε f s d 2 is the energy of the amplifier.…”

Section: Network and Radio Energy Dissipation Modelmentioning

confidence: 99%

Energy‐efficient routing protocol to improve energy consumption in wireless sensors networks

Elkamel

Cherif

2017

Int J Communication

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Summary The energy consumption is considered to be the major challenge in wireless sensor networks. In this paper, we shed light on a new approach to overcome the energy consumption problems. The objective of this work is to integrate an improved algorithm which is the K‐means to create a balanced energy on clusters and to use the Gaussian elimination algorithm during the election of cluster head that will guarantee the distribution of energy consumption. To address the problem of the optimal number of groups, we use Davies Bouldin index to increase the network lifetime. The simulation result shows that the proposed protocol extends the network lifetime compared to Leach, iMod‐Leach, leach‐C, and CELRP.

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SEARCH: A Stochastic Election Approach for Heterogeneous Wireless Sensor Networks

Cited by 30 publications

References 8 publications

Biogeography-Based Krill Herd algorithm for energy efficient clustering in wireless sensor networks for structural health monitoring application

Biogeography-Based Krill Herd algorithm for energy efficient clustering in wireless sensor networks for structural health monitoring application

A Review on Energy Efficient Clustering Protocol of Heterogeneous Wireless Sensor Network

Energy‐efficient routing protocol to improve energy consumption in wireless sensors networks

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