Journal Article10.1002/ETT.2797
Constructing load-balanced virtual backbones in probabilistic wireless sensor networks via multi-objective genetic algorithm
Jing Selena He,Shouling Ji,Raheem Beyah,Ying Xie,Yingshu Li +4 more
- 01 Feb 2015
- Vol. 26, Iss: 2, pp 147-163
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TL;DR: This paper proposes a multi‐objective genetic algorithm to construct a load‐balanced VB under PNM and demonstrates that the proposed methods extend network lifetime by 69% on average compared with the existing state‐of‐the‐art approaches.
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Abstract: A connected dominating set CDS is used as a virtual backbone VB for efficient routing and broadcasting in wireless sensor networks WSNs Currently, almost all existing works focus on constructing minimum-sized CDS under the deterministic network model However, because of the existence of many probabilistic lossy links in WSNs, it is more practical to obtain a VB under the realistic probabilistic network model PNM Moreover, load-balance factor cannot be neglected when constructing a VB to prolong network lifetime Hence, in this paper, we propose a multi-objective genetic algorithm to construct a load-balanced VB under PNM Through simulations, we demonstrate that our proposed methods extend network lifetime by 69% on average compared with the existing state-of-the-art approaches Copyright © 2014 John Wiley & Sons, Ltd
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Citations
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Attacker Location Evaluation-Based Fake Source Scheduling for Source Location Privacy in Cyber-Physical Systems
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Two population-based optimization algorithms for minimum weight connected dominating set problem
TL;DR: These proposed algorithms are the first population-based algorithms to solve MWCDS problem on undirected graphs and compare the performance of the proposed algorithms with other greedy heuristics and brute force methods through extensive simulations.
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Reliable and energy efficient topology control in probabilistic Wireless Sensor Networks via multi-objective optimization
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TL;DR: The problem of constructing energy efficient CDS in WSNs while improving network reliability is addressed as a multi-objective optimization that simultaneously maximizes two contradictory parameters: reliability and energy efficiency.
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