Routing represents a pivotal concern in the context of Wireless Sensor Networks (WSN) owing to its divergence from traditional network routing paradigms. The inherent dynamism of the WSN environment, coupled with the scarcity of available resources, engenders considerable challenges for industry and academia alike in devising efficient routing strategies. Addressing these challenges, a viable recourse lies in applying heuristic search methodologies to ascertain the most optimal path in WSNs. Ant Colony Optimization (ACO) is a well-established heuristic algorithm that has demonstrated notable advancements in routing contexts. This paper introduces a modify routing protocols based on Ant colony optimization. In these protocols, we incorporate the inverse of the distance between nodes and their neighbours in the probability equations of ACO along with considering pheromone levels and residual energy. These formulation modifications facilitate the selection of the most suitable candidate for the subsequent hop, effectively minimizing the average energy consumption across all nodes in each iteration. Furthermore, in this protocol, we iteratively fine-tune ACO's parameter values based on the outcomes of several experimental trials. The experimental analysis is conducted through a diverse set of network topologies, and the results are subjected to comparison against well-established ACO algorithm and routing protocols. The efficacy of the proposed protocol is assessed based on various performance metrics, encompassing throughput, energy consumption, network lifetime, energy consumption, the extent of data transferred over the network, and the length of paths traversed by packets. These metrics collectively provide a comprehensive evaluation of the performance attainments of the routing protocols.

翻译：路由是无线传感器网络（WSN）中的一个关键问题，因其与传统网络路由范式存在显著差异。WSN环境的固有动态性，加之可用资源的稀缺性，给工业界和学术界设计高效路由策略带来了巨大挑战。为应对这些挑战，一个可行的途径是采用启发式搜索方法来确定WSN中的最优路径。蚁群优化（ACO）是一种成熟的启发式算法，在路由领域已展现出显著进展。本文提出了一种基于蚁群优化的改进路由协议。在这些协议中，我们将节点与其邻居间距离的倒数引入ACO的概率方程，同时考虑信息素水平和剩余能量。这些公式修改有助于选择最适合下一跳的候选节点，从而有效最小化每次迭代中所有节点的平均能耗。此外，在该协议中，我们根据多次实验测试结果迭代调整ACO的参数值。通过多种网络拓扑结构进行实验分析，并将结果与成熟的ACO算法及路由协议进行对比。基于吞吐量、能耗、网络生命周期、网络内传输数据量以及数据包路径长度等多项性能指标评估所提协议的有效性。这些指标共同提供了路由协议性能成就的全面评估。