A Distributed Hybrid Event-Time-Driven Scheme for Optimization over Sensor Networks

Motivated by application to sensor networks, this paper studies distributed algorithms for solving optimization problems such as on sensor data gathering, network utility, and mobile vehicle localization. A type of distributed hybrid optimization algorithms based on the coordinate descent method is presented and analyzed. The proposed optimization algorithms differ from the existing ones since the hybrid driven scheme allows more choices of actuation time on sensor nodes. Applying the proposed algorithms, each sensor node is driven in a hybrid event-time manner, which removes the requirement of strict time synchronization, and allows a tradeoff between communications and computation performances. The convergence and optimality results are established for the hybrid optimization algorithms, and illustrative examples are given to verify the theoretical results, showing the tradeoff between communications and computation performances.