Distributed Optimization Over Wireless Sensor Networks using Swarm Intelligence
In this work, we study how a group of sensor nodes in a wireless sensor network could collaborate with each other to perform complicated signal processing (e.g. estimation and tracking) and optimization tasks through local communication and distributed computation. We develop a distributed evolutionary optimization framework based on a swarm intelligence principle. During the optimization process, we only use and share local estimation results through communication links. This scheme can significantly reduce the communication energy cost and reach fast convergence. We use target localization as an example to evaluate the performance of the proposed distributed optimization algorithm. Our simulation results demonstrate that it outperforms existing distributed optimization algorithms, such as distributed gradient search.