On the cooperation strategies for dense sensor networks

In this paper we characterize the scaling laws of the generated traffic and scheduling delays associated with the broadcast problem in dense multihop sensor networks where sample measurements are highly correlated. More specifically, we assess the benefits, and possibly the trade-offs, of exploiting sample correlations via cooperatively compressing the data as it hops around the network. First, we determine, with the aid of basic information theory, the transport traffic and schedule length growth rates under the no cooperation and network-wide cooperation extremes. We observe that network-wide cooperation significantly improves the transport traffic growth rate, without any degradation in the linear schedule length growth rate. Second, we propose a novel two-phase cooperation strategy that localizes cooperation within regions of the network in an attempt to optimize the schedule length for a given network size. We demonstrate the role of the cooperation set size in trading transport traffic for schedule length, or vice versa, and how the two extreme strategies turn out to be special cases of the two-phase cooperation framework.