Yuan Hao, Chen Qiang, Li Hongbing, Zeng Die, Wu Tianwen, Wang Yuning, Zhang Wei
Chongqing Key Laboratory of Geological Environmental Monitoring and Disaster Early Warning in the Three Gorges Reservoir Area, Chongqing Three Gorges University, Chongqing 404120, China.
Internet of Things and Intelligent Control Technology Chongqing Engineering Research Center, Chongqing Three Gorges University, Chongqing 404120, China.
Math Biosci Eng. 2024 Feb 28;21(3):4587-4625. doi: 10.3934/mbe.2024202.
Cluster routing is a critical routing approach in wireless sensor networks (WSNs). However, the uneven distribution of selected cluster head nodes and impractical data transmission paths can result in uneven depletion of network energy. For this purpose, we introduce a new routing strategy for clustered wireless sensor networks that utilizes an improved beluga whale optimization algorithm, called tCBWO-DPR. In the selection process of cluster heads, we introduce a new excitation function to evaluate and select more suitable candidate cluster heads by establishing the correlation between the energy of node and the positional relationship of nodes. In addition, the beluga whale optimization (BWO) algorithm has been improved by incorporating the cosine factor and t-distribution to enhance its local and global search capabilities, as well as to improve its convergence speed and ability. For the data transmission path, we use Prim's algorithm to construct a spanning tree and introduce DPR for determining the optimal route between cluster heads based on the correlation distances of cluster heads. This effectively shortens the data transmission path and enhances network stability. Simulation results show that the improved beluga whale optimization based algorithm can effectively improve the survival cycle and reduce the average energy consumption of the network.
簇路由是无线传感器网络（WSN）中的一种关键路由方法。然而，所选簇头节点的不均匀分布以及不切实际的数据传输路径可能导致网络能量的不均匀消耗。为此，我们为簇状无线传感器网络引入了一种新的路由策略，该策略利用一种改进的白鲸优化算法，称为tCBWO-DPR。在簇头的选择过程中，我们引入了一种新的激励函数，通过建立节点能量与节点位置关系之间的相关性来评估和选择更合适的候选簇头。此外，通过结合余弦因子和t分布对白鲸优化（BWO）算法进行了改进，以增强其局部和全局搜索能力，以及提高其收敛速度和能力。对于数据传输路径，我们使用普里姆算法构建生成树，并引入DPR以基于簇头的相关距离确定簇头之间的最优路由。这有效地缩短了数据传输路径并增强了网络稳定性。仿真结果表明，基于改进白鲸优化的算法可以有效地提高网络的生存周期并降低平均能量消耗。
