Adaptive Data Fusion for Energy Efficient Routing in Wireless 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 and/or security requirements. Therefore, fusion-driven routing protocols for sensor networks cannot optimize over communication cost only—fusion cost must also be accounted for. In our prior work [2], while a randomized algorithm termed MFST is devised toward this end, it assumes that fusion shall be performed at any intersection node whenever data streams encounter. In this paper, we design a novel routing algorithm, called Adaptive Fusion Steiner Tree (AFST), for energy efficient data gathering. Not only does AFST jointly optimize over the costs for both data transmission and fusion, but also AFST evaluates the benefit and cost of data fusion along information routes and adaptively adjusts whether fusion shall be performed at a particular node. Analytically and experimentally, we show that AFST achieves better performance than existing algorithms, including SLT, SPT, and MFST.