Data aggregation using distributed lossy source coding in wireless sensor networks

In this paper, we study the application of distributed lossy source coding for data aggregation in cluster-based wireless sensor networks (WSNs). We consider a clustered lossy coding (CLC) problem, which aims to select a set of disjoint clusters to cover the whole network such that the total rate of encoded data generated by all clusters or nodes in the network is minimized, given the spatial correlation structure of the network and a couple of total and individual distortion constraints. To solve this problem, we first prove that the overall optimization problem can be decoupled into two independent optimization problems: an optimal clustering problem and an optimal distortion allocation problem. The first problem aims at constructing a clustered hierarchy to minimize the global network entropy without considering distortion allocation, while the second problem aims to optimally allocate a distortion to each sensor node under the given distortion constraints without considering, ode clustering. We then present a distributed optimal-compression clustering protocol to solve the first problem and use Lagrange multipliers to solve the second problem.