In a battery-powered sensor network, energy and communication bandwidth are both limited. Moreover, processing a sensor measurement locally often requires orders of magnitude less energy than communicating it to a distant node, yielding an interesting communication/computation tradeoff: whenever possible, the network should reduce the need for global communication at the expense of increased local processing and communication. A promising approach for reducing global communication is to perform signal processing to extract key information inside the sensor network in a distributed fashion, thus dramatically reducing global communication requirements without losing fidelity.
This project aims to develop a sensor network architecture whose communications hierarchy is aligned with the information flow of its computations. In particular, the research involves developing (1) a multi-overlay sensor network architecture that supports both multi-scale and proximity communication and computation; (2) new multiscale sensor data representations based on wavelet transforms; and (3) network services for sychronization and localization of network nodes. The research includes analysis, simulation, and a small-scale testbed of sensor nodes on the Rice University campus.
