Lifetime optimized hierarchical architecture for correlated data gathering in wireless sensor networks

In-network aggregation is essential for correlated data gathering in wireless sensor networks which are resource-constraint in terms of energy, computation and storage. In this paper, we consider the problem of building a minimum cost hierarchical architecture for correlated data gathering with in-network aggregation, which is formulated as a min-sum optimization problem. To solve the problem, we first develop a minimum-cost distributed algorithm which involves only simple message-passing rules. The algorithm is then tuned to be energy-aware so that high-energy sensor nodes are preferably selected to become cluster heads (CHs), which act as encoding and relaying nodes for the raw sensing data from their corresponding one-hop member nodes. After the cluster formation phase, joint-entropy coding technique with explicit communication (specifically, foreign coding) is applied at every CH to remove possible data redundancy (due to the spatial data correlation) for in-network aggregation. Simulations show that the network lifetime can be significantly extended using our minimum cost cluster-based approach.