Efficient Path Planning for a Mobile Sink to Reliably Gather Data from Sensors with Diverse Sensing Rates and Limited Buffers

Wireless sensor networks are vulnerable to energy holes, where sensors close to a static sink are fast drained of their energy. Using a mobile sink (MS) can conquer this predicament and extend sensor lifetime. How to schedule a traveling path for the MS to efficiently gather data from sensors is critical in performance. Some studies select a subset of sensors as rendezvous points (RPs). Non-RP sensors send data to the nearest RPs and the MS visits RPs to retrieve data. However, these studies assume that sensors produce data with the same speed and have no limitation on buffer size. When the two assumptions are invalid, they may encounter serious packet loss due to buffer overflow at RPs. In the paper, we show that the path planning problem is NP-complete and propose an efficient path planning for reliable data gathering (EARTH) algorithm by relaxing these impractical assumptions. It forms a spanning tree to connect all sensors and then selects each RP based on hop count and distance in the tree and the amount of forwarding data from other sensors. An enhanced EARTH (eEARTH) algorithm is also developed to further reduce path length. Both EARTH and eEARTH incur less computational overhead and can flexibly recompute new paths when sensors change sensing rates. Simulation results verify that they can find short traveling paths for the MS to collect sensing data without packet loss, as compared with existing methods.