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<p>The rapid growth of Wireless Sensor Networks (WSNs) has led to the development of energy-efficient strategies to meet the requirements for extended network lifetime and reliable data transmission. Conventional energy optimization techniques face several limitations, including uneven energy depletion, premature node exhaustion, and poorly enforced routing, which can result in network partitioning and performance degradation. Many traditional optimization strategies, including deterministic and heuristic-based models, often struggle to adapt to changing network conditions, causing suboptimal energy consumption. This work explores nature-inspired metaheuristic algorithms to optimize the lifetime of WSNs. A thorough review of the available candidate strategies was conducted on the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), and Firefly Algorithm (FA). The models were evaluated for adaptability, convergence rate, and computational efficiency. GA effectively performs global searches using evolutionary principles, while PSO ensures rapid convergence through swarm intelligence. ACO optimizes paths by mimicking ants’ pheromone-based communication. GWO balances exploration and exploitation, while FA excels in multi-objective optimization due to its adaptive natural processes. These nature-inspired algorithms work together to enhance clustering, routing, and load balancing in WSNs. Simulation results demonstrate that these methods significantly reduce energy consumption, increase network lifetime, and ensure a higher packet delivery ratio compared to traditional methods. Thus, this work advances WSN optimization by establishing a hybrid and adaptable framework, promoting the development of next-generation intelligent sensor networks reliable in diverse scenarios.</p>

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Nature-Inspired Metaheuristic Strategies in Energy-Efficient Wireless Sensor Networks for Enhancing Network Lifetime: A Review

  • 期刊论文

  • 发表日期：

    2026年4月22日

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  • Anjana Koyalil,
  • Sivacoumar Rajalingam

摘要

The rapid growth of Wireless Sensor Networks (WSNs) has led to the development of energy-efficient strategies to meet the requirements for extended network lifetime and reliable data transmission. Conventional energy optimization techniques face several limitations, including uneven energy depletion, premature node exhaustion, and poorly enforced routing, which can result in network partitioning and performance degradation. Many traditional optimization strategies, including deterministic and heuristic-based models, often struggle to adapt to changing network conditions, causing suboptimal energy consumption. This work explores nature-inspired metaheuristic algorithms to optimize the lifetime of WSNs. A thorough review of the available candidate strategies was conducted on the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), and Firefly Algorithm (FA). The models were evaluated for adaptability, convergence rate, and computational efficiency. GA effectively performs global searches using evolutionary principles, while PSO ensures rapid convergence through swarm intelligence. ACO optimizes paths by mimicking ants’ pheromone-based communication. GWO balances exploration and exploitation, while FA excels in multi-objective optimization due to its adaptive natural processes. These nature-inspired algorithms work together to enhance clustering, routing, and load balancing in WSNs. Simulation results demonstrate that these methods significantly reduce energy consumption, increase network lifetime, and ensure a higher packet delivery ratio compared to traditional methods. Thus, this work advances WSN optimization by establishing a hybrid and adaptable framework, promoting the development of next-generation intelligent sensor networks reliable in diverse scenarios.