Robust Distributed Optimization in Wireless Sensor Network

Abstract

Wireless sensor networks continue to get tremendous popularity, as evidenced by the increasing number of applications for these networks. The limiting factors of the sensor nodes, such as their finite energy supplies and their moderate processing abilities, as well as the unreliable wireless medium restrict the performance of wireless sensor networks. Energy efficient communication is a critical design objective for wireless sensor networks which are usually highly energy constrained. To achieve these goals, this thesis describes a distributed approach for solving several optimization problems in wireless sensor network.

The idea of distributed signal processing relies on the divide-and-conquer paradigm, which is often used in multiprocessor computers. According to the divide-and-conquer paradigm, a problem is divided into multiple sub-problems of smaller size. Every sensor solves each subproblem by using the same algorithm, and the solution to the original problem is obtained by combining the outputs from the different sensors. By designing appropriate communication protocols and collaborative computational schemes, sensors operate as distributed adaptive filters and generate the desired result. In an incremental mode of cooperation, information flows in a sequential manner from one node to the adjacent node. This mode of operation requires a cyclic pattern of collaboration among the nodes. In a diffusion implementation, on the other hand, each node communicates with all its neighbours as dictated by the network topology. READ FULL ABSTRACT IN THE DOCUMENT