Weighted Rendezvous Planning on Q-Learning Based Adaptive Zone Partition with PSO Based Optimal Path Selection

Nowadays, wireless sensor network (WSN) has emerged as the most developed research area. Different research have been demonstrated for reducing the sensor nodes’ energy consumption with mobile sink in WSN. But, such approaches were dependent on the path selected by the mobile sink since all sensed data should be gathered within the given time constraint. Therefore, in this article, the issue of an optimal path selection is solved when multiple mobile sinks are considered in WSN. In the initial stage, Q-learning based Adaptive Zone Partition method is applied to split the network into smaller zones. In each zone, the location and residual energy of nodes are transmitted to the mobile sinks through Mobile Anchor. Moreover, Weighted Rendezvous Planning is proposed to assign a weight to every node according to its hop distance. The collected data packets are transmitted to the mobile sink node within the given delay bound by means of a designated set of rendezvous points (RP). Then, an optimal path from RP to mobile sink is selected utilizing the particle swarm optimization algorithm which is applied during routing process. Experimental results demonstrated the effectiveness of the proposed approach where the network lifetime is increased by the reduction of energy consumption in multihop transmission.