Efficient distributed information fusion using value of information based censoring

Abstract

In many distributed sensing applications, not all agents have valuable information at all times. Therefore, requiring all agents to communicate at all times can be resource intensive. In this work, the notion of Value of Information (VoI) is used to improve the efficiency of distributed sensing algorithms. Particularly, only agents with high VoI broadcast their measurements to the network, while others censor their measurements. New VoI realized data fusion algorithms are introduced, and an in depth analysis of the costs incurred by these algorithms and conventional distributed data fusion algorithms is presented. Numerical simulations are used to compare the performance of the VoI realized algorithms with traditional data fusion algorithms. A VoI based algorithm that adaptively adjusts the criterion for being informative is presented and shown to strike a good balance between reduced communication cost and increased accuracy.