This paper deals with the sink placement and anycast routing to increase the lifetime of multi-sink wireless sensor networks. Two algorithms are proposed, namely “Multi-sink Placement and Anycast Routing (MPAR)” and “Extended Multi-sink Placement and Anycast Routing (EMPAR)”, to jointly address the problems of clustering, multi-sink placement, and load-balanced anycast routing. MPAR and EMPAR rely on a two-level architecture in which sensors are clustered at the lower level. Each sensor transmits its data to the corresponding Cluster Head (CH) via a load-balanced data aggregation routing tree. At the upper level, both schemes use a modified particle swarm optimization algorithm to determine the best location of sinks. For each sink, a high-level anycast routing tree is developed using the ant colony optimization algorithm. Each anycast tree uses the hybrid Compressive Sensing (CS) method to forward the aggregated data from CHs to sinks. Extensive simulations are conducted to illustrate the efficiency of the proposed algorithms in terms of energy consumption, energy consumption variance, and network lifetime. The results show that EMPAR has a better performance than MPAR due to its CH selection strategy. As an advantage, EMPAR considers both remaining energy and distance criteria along with a rest factor to select the best CH for each cluster. For an average number of clusters, EMPAR reduces the energy consumption by 5.98% and 12.20%, respectively, compared to the MPAR algorithm and the energy-aware CS-based data aggregation algorithm. It also increases the network lifetime in comparison with the aforementioned algorithms by 12.26% and 30.38%, respectively.

本文讨论接收器放置和任播路由，以延长多接收器无线传感器网络的寿命。提出了两种算法，即“多接收器放置和任播路由（MPAR）”和“扩展多接收器放置和任播路由（EMPAR）”，以共同解决集群，多接收器放置和负载平衡的问题。任播路由。MPAR和EMPAR依赖于两级体系结构，其中传感器聚集在较低级。每个传感器都通过负载平衡数据聚合路由树将其数据传输到相应的簇头（CH）。在较高级别，这两种方案都使用改进的粒子群优化算法来确定汇的最佳位置。对于每个接收器，使用蚁群优化算法开发高级选播路由树。每个任播树都使用混合压缩感知（CS）方法将聚合数据从CH转发到接收器。进行了广泛的仿真，以从能耗，能耗方差和网络寿命方面说明所提出算法的效率。结果表明，EMPAR由于其CH选择策略而具有比MPAR更好的性能。作为优势，EMPAR会同时考虑剩余能量和距离标准以及休息因子，以为每个群集选择最佳CH。与MPAR算法和基于能量感知的CS数据聚合算法相比，对于平均集群数，EMPAR分别降低了5.98％和12.20％的能耗。与上述算法相比，它还可以将网络寿命分别增加12.26％和30.38％。