Process of mutation: An example.

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，已然成为重要的研究课题。在无线传感器网络应用中，借助聚类系统监测网络状态至关重要。不同领域的传感器协同工作，可提升本地化信息上报的精度。然而，靠近数据汇聚节点（data sink）的传感器节点能耗更高，进而引发热点问题。为应对上述挑战，本研究采用聚类与优化路由技术。信息聚合流程包含聚类操作，且可进一步细分为子聚类。每个聚类可包含一个簇首（Cluster Head, CH），或仅包含未配备簇首的传感器节点（Sensor Nodes, SN）。聚类机制本质上可优化簇首的运算性能，提升网络运行效率，并构建系统化的网络拓扑结构。该模型可同时支持多跳与单跳两种通信模式。本研究采用遗传算法（Genetic Algorithm, GA）进行簇首选择，并综合考量多项影响因素。尽管遗传算法具备较强的全局探索能力，但仍需进行有效的管控。为此，本研究引入草原犬群优化算法（Prairie Dog Optimization, PDO）以解决该不足。本文提出的热点缓解型遗传-草原犬群优化算法（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，尤其在热点规避方面效果显著。采用该算法后，网络生命周期可达20913毫秒，剩余能量达310焦耳。与现有技术的对比分析结果表明，本研究提出的方案性能更优。