Distributed Encoding Algorithm for Source Localization in Sensor Networks

We consider sensor-based distributed source localization applications, where sensors transmit quantized data to a fusion node, which then produces an estimate of the source location. For this application, the goal is to minimize the amount of information that the sensor nodes have to exchange in order to attain a certain source localization accuracy. We propose a distributed encoding algorithm that is applied after quantization and achieves significant rate savings by merging quantization bins. The bin-merging technique exploits the fact that certain combinations of quantization bins at each node cannot occur because the corresponding spatial regions have an empty intersection. We apply the algorithm to a system where an acoustic amplitude sensor model is employed at each node for source localization. Our experiments demonstrate significant rate savings (e.g., over 30%, 5 nodes, and 4 bits per node) when our novel bin-merging algorithms are used.