Optimization and resource management in wireless sensor networks

Abstract

In recent years, there has been a rapid expansion in the development and use of low-power, low-cost wireless modules with sensing, computing, and communication functionality. A wireless sensor network (WSN) is a group of these devices networked together wirelessly. Wireless sensor networks have found widespread application in infrastructure, environmental, and human health monitoring, surveillance, and disaster management. While there are many interesting problems within the WSN framework, we address the challenge of energy availability in a WSN tasked with a cooperative objective. We develop approximation algorithms and execute an analysis of concave utility maximization in resource constrained systems. Our analysis motivates a unique algorithm which we apply to resource management in WSNs. We also investigate energy harvesting as a way of improving system lifetime. We then analyze the effect of using these limited and stochastically available communication resources on the convergence of decentralized optimization techniques. The main contributions of this research are: (1) new optimization formulations which explicitly consider the energy states of a WSN executing a cooperative task; (2) several analytical insights regarding the distributed optimization of resource constrained systems; (3) a varied set of algorithmic solutions, some novel to this work and others based on extensions of existing techniques; and (4) an analysis of the effect of using stochastic resources (e.g., energy harvesting) on the performance of decentralized optimization methods. Throughout this work, we apply our developments to distribution estimation and rate maximization. The simulation results obtained help to provide verification of algorithm performance. This research provides valuable intuition concerning the trade-offs between energy-conservation and system performance in WSNs.