Cluster-based Path Planning for Mobile Data Collectors in IoT sensor networks using Artificial Rabbits Optimization

The performance of Mobile Data Collector (MDC) assisted data collection in Internet of Things (IoT) bases sensor networks will be significantly impacted by the placement and clustering of sensors. In large-scale sensor networks, it is crucial and vital to create an effective path planning strategy for MDC. In this paper, Cluster-based Path Planning using Artificial Rabbits Optimization (CPP-ARO) algorithm is proposed for IoT sensor networks. Initially, the network is clustered and the cluster head (CH) is selected by applying ARO algorithm using the parameters node degree, node lifetime and node’s closeness centrality. In the next phase, MDC visiting schedule is determined based on the parameters traffic load, buffer occupancy rate, data collection latency and expected energy. Simulation results have shown that the proposed CPP-ARO algorithm attains maximum packet delivery ratio and energy efficiency with minimized delay and packet drops.