Energy efficient routing with adaptive data fusion in sensor networks

While in-network data fusion can reduce data redundancy and hence curtail network load, the fusion process itself may introduce significant energy consumption for emerging wireless sensor networks with vectorial data. Therefore, fusion-driven routing protocols for sensor networks cannot optimize over communication cost only -- fusion cost must also be accounted for. Towards this end, we design a novel routing algorithm, called Adaptive Fusion Steiner Tree (AFST), for energy efficient data gathering in sensor networks that jointly optimizes over the costs for data transmission and data fusion. Furthermore, AFST evaluates the benefit and cost of data fusion along information routes and adaptively adjusts whether fusion shall be performed. Analytically and experimentally, we show that AFST achieves better performance than existing algorithms including SLT, MFST, and SPT.