Research On Self-Organizing Evolution Models And Key Techniques Of Wireless Sensor Networks

As an emerging information access technology, Wireless Sensor Networks (WSNs) is currently one of the hot spots in the research field of information science. It converges embedded computing, wireless communications, sensors and other multi-disciplinary technology. Research on evolving mechanisms and building corresponding self-organizing evolution model of wireless sensor networks, which are large scale and intensive deployment, can reproduce its true features of topology, which is conducive to the overall analysis and assessment of network performance, as well as design of network protocols. Currently, research on evolution model of wireless sensor networks is still in the exploratory stage, and its development is not yet ripe. On the one hand, more and more researchers devote their enthusiastic energy to explore and characterize the evolution model; on the other hand, the researches dedicate the practical application and the work efficiency of the evolution model. Based on the analysis of complex network theory and the dynamics of actual networks, new self-organizing evolution models and corresponding applications of wireless sensor network are designed. The main contributions of this dissertation are summerized as follows:(1) Based on research of the small-world evolution model, the broadcasting algorithm and the anycast routing algorithm of WSNs are proposed.Broadcasting and Anycast Routing are important operations and been widely used in wireless sensor networks. These networks are power constrained as nodes operate with limited battery power. Wireless sensor networks are spatial graphs that having much more clustered and much high path length characteristics. We investigate the small-world evolution model in WSNs.After considering energy efficient broadcasting and anycast routing in such networks, combined the two-radius evolution model and the heuristic search of ant algorithm, Small-World Power-aware Broadcasting Algorithm (SWPBA) with single-sink and Small-World Power-aware Anycast Rouing algorithm (SWPAR) with multi-sinks are proposed. Given different densities of network, simulation results show that SWPBA significantly improves life of networks, also reduces waiting time and power consumption, and SWPAR can effectively resolve the problems of energy holes, hottest path and sink bottlenecks.(2) A stochastic placement evolution model of WSNs by is proposed Power-Law topology has been proved to be an effective technique to improve fault tolerance. A power-law evolution model of wireless sensor networks by stochastic placement is proposed. In dynamic evolving process of WSNs, there are four types of events as follows: adding new nodes, nodes failure, adding new links and links failure. The dynamics of nodes and links are integrated into the evolving model consequently. By using continuum theory, the theoretical computing and simulated results show that the degree distribution of this model follows a power law. The scale-free properties revealed in this model display a tempting application foreground.(3) A local-world evolution nodel of WSNs is proposedMaking use of the mechanisms of link compensation and local-world preferential attachment, a local-world evolution nodel of WSNs is proposed, which is based on the classic S-R model and local-world model. We introduce behaviors in WSNs to the dynamics in this model. Using continuum theory and rate equation method, we get the expression of power law degree distribution with the exponent 3. This model has excellent robustness to random fault or failure of nodes, and provides new reference for constructing reliable topology of WSNs.(4) A clustering-based evolution of WSNs is proposedIn Wireless Sensor Networks (WSNs), clustering-based algorithms have been proved to be effective techniques to save energy and improve fault tolerance. In this paper, a scale-free evolving model for clustering-based WSNs with poisson growth behavior is presented, in which the nodes arrive as a Poisson process with rateλ. This model takes into account four types of evolving events: selection of cluster-head nodes, preferential attachment of non cluster-head nodes, failure of non cluster-head nodes, transition of non cluster-head nodes. By using continuum theory, the theoretical computing results show that the size distribution of cluster in this model follows a power law, and the simulation results are agreement with the theoretical analysis, which provide better compensation for modeling real sensor networks.