On the scalability of hierarchical cooperation for dense sensor networks

In this paper we study the problem of information dissemination in dense multihop sensor networks characterized by highly correlated sample measurements. In particular, we investigate the benefits, and trade-offs, of exploiting correlations via cooperatively compressing the data as it hops around the network. First, we study two extreme cooperation strategies, namely no cooperation and network-wide cooperation. We show that network-wide cooperation achieves logarithmic growth rate for the transport traffic with the network size whereas the schedule length growth rate remains linear. Next, we analyze a two-phase cooperation strategy which localizes cooperation within regions of the network in an attempt to assess the performance of strategies bounded by the two aforementioned extremes. Finally, we extend two-phase cooperation to a multiphase hierarchical cooperation strategy where the number of phases depends on the number of nodes and the size of the cooperation set. The rationale behind this strategy is to achieve logarithmic scaling laws at the expense of more complexity in coordinating nodes' cooperation. In addition, hierarchical cooperation opens room for optimizing the transport traffic and schedule length for a given network size.