EDTP: Energy-Density-based Trajectory Planning for Wireless Sensor Networks With Mobile Collectors

Mobile collectors (MCs) have been utilized to enhance the performance of wireless sensor networks (WSNs) in recent years. However, there are major limitations, particularly in latency-bound applications. The traveling paths taken by MCs have a significant impact on the time required for data collection. To regulate the paths of MCs, we propose a hybrid approach that MCs only visit cluster heads (CHs), and nodes that are not CHs forward data to the nearest CHs through a single-hopping. The energy difference and density of global sensor nodes are introduced to select CHs. To shorten the trajectory of MCs, the K-Dimension Tree Grouping algorithm (KDTG) is proposed to group CHs with the highest geographical similarity. The simulation results verify that the proposed algorithm has a broad range of applicability and robust stability, making it an asset for WSNs of varying scales and application scenarios.