Efficient Algorithms for Mobile Sink Aided Data Collection from Dedicated and Virtual Aggregation Nodes in Energy Harvesting Wireless Sensor Networks

We study the mobile data collection problem in an energy harvesting wireless sensor network (EH-WSN), where sensor nodes are densely deployed in a monitoring area and a mobile sink (MS) travels around the area to collect sensory data from the sensors. In order to optimize the network performance while achieving perpetual network operation, we propose efficient algorithms to dynamically schedule the MS for collecting data from sensors with different data generation rates. Specifically, in this paper, we propose an optimization framework that consists of three stages. We first deal with the reliable, stable, and energy neutral energy assignment for sensors. We then find a closed trajectory for the MS for sensory data collection that covers as many as aggregation nodes, and devise a decentralized algorithm to determine the data generation rate of each sensor and the data flow rate of each link to optimize the network performance. We also develop a fast heuristic algorithm for the problem. We finally evaluate the performance of the proposed algorithms through numerical experiments. The simulation results demonstrate that the proposed algorithms are efficient.