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A Review on Multi-objective Optimization in Wireless Sensor Networks Using Nature Inspired Meta-heuristic Algorithms

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Abstract

Wireless Sensor Networks (WSNs) is a tremendously growing field, wherein users can design their sensor-based applications, depending on the application requirement. Most practical challenges in WSNs involve several potentially conflicting objectives that must be met. Satisfying one objective leads to degradation in other objective’s performance( for example, if we focus on increasing network lifetime, latency may also increase, which is not desirable). Thus, it is very challenging to find trade-off amongst these conflicting optimization criterion. An updated overview of the research efforts have been undertaken to solve this challenge using Multi-objective Optimization (MOO) methods, particularly nature-inspired meta-heuristic MOO algorithms. This paper presents a systematic review of MOO techniques in WSNs. Besides, a study of applications of MOO is presented in diverse application domains, specifically in the area of WSNs. Furthermore, the integration of WSNs with MOO is studied to guide the researchers in future.

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Gunjan A Review on Multi-objective Optimization in Wireless Sensor Networks Using Nature Inspired Meta-heuristic Algorithms. Neural Process Lett 55, 2587–2611 (2023). https://doi.org/10.1007/s11063-022-10851-4

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