Censoring for Bayesian Cooperative Positioning in Dense Wireless Networks

Cooperative positioning is a promising solution for location-enabled technologies in GPS-challenged environments. However, it suffers from high computational complexity and increased network traffic, compared to traditional positioning approaches. The computational complexity is related to the number of links considered during information fusion. The network traffic is dependent on how often devices share positional information with neighbors. For practical implementation of cooperative positioning, a low-complexity algorithm with reduced packet broadcasts is thus necessary. Our work is built on the insight that for precise positioning, not all the incoming information from neighboring devices is required, or even useful. We show that blocking selected broadcasts (transmit censoring) and discarding selected incoming information (receive censoring) based on a Cramér-Rao bound criterion, leads to an algorithm with reduced complexity and traffic, without significantly affecting accuracy and latency.