Distributed Algorithms and Sensor Network Optimization

In distributed inference over a network, each agent makes an observation and sends a summary of its observation to a fusion center or another agent. The goal of the network is to cooperatively make a decision from a given set of hypotheses, based on the agent messages. Various important applications in environment monitoring, intrusion detection, cognitive radio systems, social networks, and big data analytics can be formulated as distributed inference problems or have subroutines that involve distributed learning. In this work, we develop inference methods to achieve asymptotically optimal performance, to optimize the trade-off between inference performance and energy consumption, and to achieve implementations on low-cost embedded platforms. 

For more information, you may contact our Professor Tay Wee Peng.

Back to Research Areas