Distributed Data Aggregation Using Slepian-Wolf Coding in Cluster-Based Wireless Sensor Networks

In this paper, we study the major problems in applying Slepian-Wolf coding for data aggregation in cluster-based wireless sensor networks (WSNs). We first consider the clustered Slepian-Wolf coding (CSWC) problem, which aims at selecting a set of disjoint potential clusters to cover the whole network such that the global compression gain of Slepian-Wolf coding is maximized, and propose a distributed optimal-compression clustering (DOC) protocol to solve the problem. Under a cluster hierarchy constructed by the DOC protocol, we then consider the optimal intracluster rate-allocation problem. We prove that there exists an optimization algorithm that can find an optimal rate allocation within each cluster to minimize the intracluster communication cost and present an intracluster coding protocol to locally perform Slepian-Wolf coding within a single cluster. Furthermore, we propose a low-complexity joint-coding scheme that combines CSWC with intercluster explicit entropy coding to further reduce data redundancy caused by the possible spatial correlation between different clusters.