Minimum Cost Data Aggregation for Wireless Sensor Networks Computing Functions of Sensed Data

We consider a problem of minimum cost (energy) data aggregation in wireless sensor networks computing certain functions of sensed data. We use in-network aggregation such that data can be combined at the intermediate nodes en route to the sink. We consider two types of functions: firstly the summation-type which includes sum, mean, and weighted sum, and secondly the extreme-type which includes max and min. However for both types of functions the problem turns out to be NP-hard. We first show that, for sum and mean, there exist algorithms which can approximate the optimal cost by a factor logarithmic in the number of sources. For weighted sum we obtain a similar result for Gaussian sources. Next we reveal that the problem for extreme-type functions is intrinsically different from that for summation-type functions. We then propose a novel algorithm based on the crucial tradeoff in reducing costs between local aggregation of flows and finding a low cost path to the sink: the algorithm is shown to empirically find the best tradeoff point. We argue that the algorithm is applicable to many other similar types of problems. Simulation results show that significant cost savings can be achieved by the proposed algorithm.