A differential moth flame optimization algorithm for mobile sink trajectory

A popular data acquisition technique for Wireless Sensor Networks (WSNs) is usage of static sink. However, this results in hot-spot or sink-hole problem as the sensor nodes near the sink die as they disseminate the data of the entire network to the sink node. In this work, in order to alleviate these problems, mobile sink (MS) is used. However, designing an optimal trajectory for mobile sink traversal is a complex problem. Further, instead of constrained sensor nodes, relay nodes (RNs) are used to cluster the data sensed. These RNs are deployed using the proposed meta-heuristic Differential Moth Flame Optimization (DMFO) algorithm. Also, a traversal strategy for the MS is proposed in order to collect the sensed data. The proposed strategy is an improvement to most of the existing strategies that use Traveling Salesman Problem (TSP) solver with exponential complexity for sink movement. Extensive simulations are carried out and the results are analyzed for various network scenarios over different performance metrics.