CAPP: coverage aware topology adaptive path planning algorithm for data collection in wireless sensor networks.

Data collection is an important task in many mobile wireless sensor network (MWSN) applications. The energy of sensor nodes around the sink depletes rapidly due to transmitting large amounts of data from neighboring nodes. This problem can be mitigated through the use of intelligent mobile vehicles to collect the data. While traditional data collection methods focus on maximizing data acquisition or reducing network energy consumption, they do not take into account the actual sensor nodes' coverage of the region of interest (ROI). To the best of our knowledge, most research on data collection focuses on path planning for the mobile collector in a static environment. During the lifetime of the network, coverage holes may appear due to node energy depletion. We propose a coverage aware topology adaptive path planning algorithm (CAPP) for path planning for WSNs where all sensor nodes are coverage aware and respond by moving to better locations to improve coverage of the network and compensate for the failed nodes. First, the path planning algorithm determines the number of Stop Points (SPs) where it will stop to gather data. Then, Particle Swarm Optimization is used to find the best location for these SPs. Finally, the shortest path through these SPs is determined by Ant Colony Optimization. Through extensive simulation, we show that CAPP performs efficiently in data collection while also allowing the nodes to move for coverage hole repair. The result shows improvement in area coverage and reduced delay in data collection, with no increase in energy consumption. [ABSTRACT FROM AUTHOR]