article · Cluster Computing
This review examines the application of metaheuristic algorithms to address various challenges in wireless sensor networks (WSNs). WSNs face issues such as deployment, localisation, sink node placement, energy efficiency, and clustering, which negatively impact their limited energy and lifespan. Metaheuristic algorithms are effective in optimising these problems, which can be modelled as single or multi-objective optimisation tasks. The paper discusses existing literature on single and multi-objective metaheuristics, their evaluation criteria, WSN architectures, and their specific applications in WSN deployment, localisation, sink node placement, energy efficiency, and clustering. It also defines key terms, highlights ongoing difficulties, and outlines future research directions and challenges for improving WSN efficiency using these algorithms.
Wireless sensor networks are crucial for many applications but are limited by energy constraints and operational challenges. By reviewing how metaheuristic algorithms can optimise these networks, this work helps researchers and developers understand effective strategies to extend network lifespan and improve performance, leading to more reliable and sustainable sensor deployments.
This review identifies areas where metaheuristic algorithms can improve the operational efficiency and lifespan of wireless sensor networks. This could benefit organisations deploying WSNs for monitoring, data collection, or control systems by offering methods to optimise network design and resource use. As a literature review, this work provides foundational knowledge for further applied research and development rather than a direct, near-market application.
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Abstract Metaheuristic algorithms have wide applicability, particularly in wireless sensor networks (WSNs), due to their superior skill in solving and optimizing many issues in different domains. However, WSNs suffer from several issues, such as deployment, localization, sink node placement, energy efficiency, and clustering. Unfortunately, these issues negatively affect the already limited energy of the WSNs; therefore, the need to employ metaheuristic algorithms is inevitable to alleviate the harm imposed by these issues on the lifespan and performance of the network. Some associated issues regarding WSNs are modelled as single and multi-objective optimization issues. Single-objective issues have one optimal solution, and the other has multiple desirable solutions that compete, the so-called non-dominated solutions. Several optimization strategies based on metaheuristic algorithms are available to address various types of optimization concerns relating to WSN deployment, localization, sink node placement, energy efficiency, and clustering. This review reports and discusses the literature research on single and multi-objective metaheuristics and their evaluation criteria, WSN architectures and definitions, and applications of metaheuristics in WSN deployment, localization, sink node placement, energy efficiency, and clustering. It also proposes definitions for these terms and reports on some ongoing difficulties linked to these topics. Furthermore, this review outlines the open issues, challenge paths, and future trends that can be applied to metaheuristic algorithms (single and multi-objective) and WSN difficulties, as well as the significant efforts that are necessary to improve WSN efficiency.
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DOI: 10.1007/s10586-024-04619-9
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