Numerical analysis of energy consumption and lifetime in complex sensor networks

Abstract

Many complex networks share a common feature that the nodes are resource-limited. Examples include the Internet which consists of finite-buffer network devices, cooperation networks which are composed of time-limited people, and wireless sensor networks which are constituted of self-powered devices. Therefore, studying resourcelimited networks is important from an engineering perspective. In the introduction part of this thesis, some theoretical models for generating complex networks and some important measures on networks and some overview of energy-saving techniques for sensor networks are reviewed. In the main part of the thesis, the relationship between energy consumption and time e ciency of sensor networks are studied. A comprehensive numerical study of sensor networks with five different structural topologies and four different global and local routing methods are presented. Their performances and costs are studied. The numerical results showed that networks with both scale-free and small-world topological features will have the longest lifetime by using the same routing methods. After that, the local routing method is studied deeply. The relationship between the weighting of the energy and the degree of sensor nodes by using the random-walk routing method is studied. Numerical results show that the size of a living network follows a pattern of exponential decrease in time. Based on their different behaviors, the networks are categorized into two types and a functional lifetime criterion to measure the performances of the networks is proposed. If the energy information is taken into account in the routing methods, the time of keeping the large portion of networks functional becomes longer. The findings provide references and guidelines for designing better sensor networks under various conditions for possible applications.

Date of Award	14 Feb 2014
Original language	English
Awarding Institution	City University of Hong Kong
Supervisor	Kai Hau Alan YEUNG (Supervisor) & Guanrong CHEN (Co-supervisor)