ABSTRACT
The building blocks of a sensor network, often called “Motes,” are self–contained, battery-powered computers that measure light, sound, temperature, humidity, and other environmental variables. The wide range of applications of sensor networks are being envisioned in a number of areas, including geographical monitoring, inventory management, homeland security, and health care. This chapter introduces a powerful class of information fusion algorithms, which are based on formulating the sensor fusion problem as a “convex feasibility problem.” The chapter presents a general paradigm for formulating and solving distributed signal-processing problems in sensor network. The core of this paradigm is formulating the problem as a convex feasibility problem. The raw data collected by sensor nodes are processed locally to specify a convex feasible set to which the global solution must belong. Each sensor node in the network specifies its own feasible set and may update this set as it collects new data over time.
