Coverage-awareness Scheduling Protocols for Wireless Sensor Networks

Abstract

The coverage and energy issues are the fundamental problems which prevent the development of wireless sensor networks. In order to accurately evaluate the monitoring quality (coverage), one needs to model the interactive of sensors, phenomenons and the environment. Furthermore, in collaborative with scheduling algorithm and computer optimization, protocols can improve the overall monitoring quality and prolong the lifetime of network. This thesis is an investigation of coverage problem and its relative applications in the wireless sensor networks. We first discuss the realistic of current boolean sensing model and propose an irregular sensing model used to determine the coverage in the area with obstacles. We then investigate a joint problem of maintaining the monitoring quality and extending the lifetime of network by using scheduling schemes. Since the scheduling problem is NP hard, genetic algorithm and Markov decision process are used to determine an achievable optimal result for the joint problem of coverage-preserving and lifetime-prolong. In order to avoid the cost of centralized or distributed scheduling algorithms, a localized coverage-preserving scheduling algorithm is proposed by exploring the construction process of Voronoi diagram. Besides exploring the coverage characteristic in a static wireless sensor network, we investigate the coverage problem when the mobile elements are introduced into network. We consider the single-hop mobile data gathering problem with the energy efficiency and data freshness concerns in a wireless sensor network where the connectivity cannot be maintained. We first investigate the upper/lower bound of the covering time for a single collector to cover the monitoring area. Through our investigation we show that for a bounded rectangle area a hexagon walk could explore the area more efficiently than a random walk when the edges of area are known. We then propose a virtual force mobile model (VFM) in which the energy consumption for data transmission is modeled as a virtual elastic force and used to guide of mobile collectors to move to optimal positions for energy saving.