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<p>Mobile Wireless Sensor Networks (MWSNs) are an important component of the Internet of Things, consisting of hundreds to thousands of sensors linked through wireless channels. MWSNs extend the capabilities of traditional Wireless Sensor Networks (WSNs) by incorporating mobility in sensors, leading to more flexible data collection and broader coverage. Resource-constrained mobile sensors with dynamic topology face challenges that affect network performance and efficiency. Some of the challenging issues concerning MWSN include energy expenditure, connectivity, and scalability. This research proposes a Multiobjective-Adaptive Equilibrium Slime Mould Algorithm (M-AESMA) to perform an effective Cluster Head (CH) election, CH rotation, and route discovery, addressing the issues of increased energy expenditure and connectivity concerns in MWSN. The developed M-AESMA is a combination of the Equilibrium Optimizer (EO) and Slime Mould Algorithm (SMA), with an Adaptive Probability Threshold (APT) incorporated to achieve appropriate solutions during the searching process. A set of multiple objective functions comprising the interspace (spatial separation) between sensors &amp; CH, interspace between CH &amp; Mobile Sink (MS), Individual Node Neighborhood Count (INNC), Cluster Head Balancing Factor (CHBF), and Location Evaluation Factor (LEF), is used to optimize the M-AESMA for electing the CH. Furthermore, the M-AESMA is employed to discover the route to the BS from different sensor nodes according to node degree and residual energy. The incorporation of APT and EO into the M-AESMA enhances both exploitation and exploration, based on its dynamic adaptation to changes in the network. Thus, the proposed M-AESMA minimizes energy expenditure while enhancing data delivery in mobile environments. The M-AESMA is evaluated based on the metrics of alive node count, energy expenditure, life expectancy, data received at the MS, throughput, and Packet Loss Ratio (PLR). Existing methods, namely MOGWO, DE-ACO, and MSFBRR, are employed for comparison with M-AESMA. The alive node count of M-AESMA after 18,000 rounds is 218 nodes, which is higher than that of DE-ACO.</p>

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Multi objective-adaptive equilibrium slime mould algorithm to improve the life expectancy in mobile wireless sensor network

  • 期刊论文

  • 发表日期：

    2026年2月7日

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  • Tahira Mazumder,
  • B. V. R. Reddy,
  • Ashish Payal

摘要

Mobile Wireless Sensor Networks (MWSNs) are an important component of the Internet of Things, consisting of hundreds to thousands of sensors linked through wireless channels. MWSNs extend the capabilities of traditional Wireless Sensor Networks (WSNs) by incorporating mobility in sensors, leading to more flexible data collection and broader coverage. Resource-constrained mobile sensors with dynamic topology face challenges that affect network performance and efficiency. Some of the challenging issues concerning MWSN include energy expenditure, connectivity, and scalability. This research proposes a Multiobjective-Adaptive Equilibrium Slime Mould Algorithm (M-AESMA) to perform an effective Cluster Head (CH) election, CH rotation, and route discovery, addressing the issues of increased energy expenditure and connectivity concerns in MWSN. The developed M-AESMA is a combination of the Equilibrium Optimizer (EO) and Slime Mould Algorithm (SMA), with an Adaptive Probability Threshold (APT) incorporated to achieve appropriate solutions during the searching process. A set of multiple objective functions comprising the interspace (spatial separation) between sensors & CH, interspace between CH & Mobile Sink (MS), Individual Node Neighborhood Count (INNC), Cluster Head Balancing Factor (CHBF), and Location Evaluation Factor (LEF), is used to optimize the M-AESMA for electing the CH. Furthermore, the M-AESMA is employed to discover the route to the BS from different sensor nodes according to node degree and residual energy. The incorporation of APT and EO into the M-AESMA enhances both exploitation and exploration, based on its dynamic adaptation to changes in the network. Thus, the proposed M-AESMA minimizes energy expenditure while enhancing data delivery in mobile environments. The M-AESMA is evaluated based on the metrics of alive node count, energy expenditure, life expectancy, data received at the MS, throughput, and Packet Loss Ratio (PLR). Existing methods, namely MOGWO, DE-ACO, and MSFBRR, are employed for comparison with M-AESMA. The alive node count of M-AESMA after 18,000 rounds is 218 nodes, which is higher than that of DE-ACO.