Comparative analysis of the NL.

Wireless Sensor Networks (WSNs) consist of small, multifunctional nodes distributed across various locations to monitor and record parameters. These nodes store data and transmit signals for further processing, forming a crucial topic of study. Monitoring the network’s status in WSN applications using clustering systems is essential. Collaboration among sensors from various domains enhances the precision of localised information reporting. However, nodes closer to the data sink consume more energy, leading to hotspot challenges. To address these challenges, this research employs clustering and optimised routing techniques. The aggregation of information involves creating clusters, further divided into sub-clusters. Each cluster includes a Cluster Head (CH) or Sensor Nodes (SN) without a CH. Clustering inherently optimises CHs’ capabilities, enhances network activity, and establishes a systematic network topology. This model accommodates both multi-hop and single-hop systems. This research focuses on selecting CHs using a Genetic Algorithm (GA), considering various factors. While GA possesses strong exploration capabilities, it requires effective management. This research uses Prairie Dog Optimization (PDO) to overcome this challenge. The proposed Hotspot Mitigated Prairie with Genetic Algorithm (HM-PGA) significantly improves WSN performance, particularly in hotspot avoidance. With HM-PGA, it achieves a network lifetime of 20913 milliseconds and 310 joules of remaining energy. Comparative analysis with existing techniques demonstrates the superiority of the proposed approach.无线传感器网络（Wireless Sensor Networks, WSNs）由部署于不同区域的小型多功能节点组成，用于监测并记录各类环境参数。此类节点可存储数据并传输信号以供后续处理，无线传感器网络已然成为重要的研究课题。在无线传感器网络的应用场景中，借助分簇系统监测网络运行状态至关重要。不同传感节点间的协同协作可有效提升本地化信息上报的准确性。然而，距离数据汇聚节点（data sink）越近的传感器节点能耗越高，由此引发热点难题。为应对上述挑战，本研究采用分簇与优化路由技术相结合的方案。信息聚合过程需构建分簇结构，且各分簇可进一步划分为子分簇。每个分簇要么设有一个簇头（Cluster Head, CH），要么由未配置簇头的普通传感节点（Sensor Nodes, SN）构成。分簇机制本质上可优化簇头的运行性能，提升网络整体活跃度，并构建系统化的网络拓扑结构。该模型可同时适配多跳与单跳两种通信模式。本研究综合考量多种影响因素，采用遗传算法（Genetic Algorithm, GA）实现簇头选择。尽管遗传算法具备优异的全局探索能力，但仍需辅以有效的调度管控。为此，本研究引入土拨鼠优化算法（Prairie Dog Optimization, PDO）以解决该缺陷。本研究提出的热点缓解型土拨鼠-遗传混合算法（Hotspot Mitigated Prairie with Genetic Algorithm, HM-PGA）可显著提升无线传感器网络的整体性能，尤其在热点问题规避方面效果突出。采用该算法后，网络生命周期可达20913毫秒，剩余总能量达310焦耳。通过与现有同类技术的对比分析，可验证本研究提出方案的性能优越性。