Research On High-performance Localization Algorithms In Wireless Sensor Networks

In recent years, along with the advance of wireless technologies and the maturity of software and hardware, more and more researchers pay attention to the study of Wireless Sensor Networks (WSNs). As a novel information acquisition technology, and based on self-organized, dynamic and data-centric characteristics, the WSNs have a great potential for the applications of military and civil field, such as rescuing survivors, environmental monitoring and aftershocks detection, in disaster areas. Especially, because of the data-centric characteristic, the design of algorithm and the development of application in the WSNs, are based on the reliable and high-efficiency data which is from sensor nodes and surroundings. As a key information of a sensor node, the location of this node is important for many location-based applications and services.This thesis focuses on high-performance data collection and high-precision localization with the collected high-performance data, based on theoretical analyses and experimental evaluations. The thesis proposes a proportionally fair data collection algorithm based on the analyses and studies for high-performance and fair data collection issues. Moreover, by studying the impact of parameter configuration in dynamic networks on localization accuracy, the thesis gets a useful observation:there is relationship between the localization accuracy and the data which is used to calculate the location of a sensor node (with different configuration of various parameters in dynamic networks, the data collected for localization is different). Furthermore, based on the observation and optimized parameter configuration ("optimized configuration" of various parameters can improve the performance of data collection and the pertinency of collected data with localization to obtain the high accuracy of location calculation), the thesis proposes high-precision localization algorithms. The contributions are shown as follows:(1) In wireless sensor networks, the efficiency of data collection and the fairness of data collection are important research issues. By analyzing "funneling effect" and excavating the relationship between the efficiency of data collection and the fairness of data collection, the thesis proposes a high-performance and proportionally fair data collection algorithm. This algorithm achieves the fairness of data collection and improves the throughput of network.(2) The thesis analyzes and studies the localization issue of duty-cycled dynamic network-s in detail. And then, the thesis proposes a high-performance dynamic localization algorithm based on using the optimized parameter configuration of dynamic network ("optimized parame-ter configuration" improves the pertinence and effectiveness of data collection with the location calculations of sensor nodes). By comparing with existing high-precision localization algorithm-s, our algorithm shows improved localization accuracy.(3) The thesis studies mobile-data-assisted localization algorithms. And then the thesis for-mulates the localization of a node as a posterior-probability-based location estimation by analyz-ing the impact of various mobility parameters on mobile-data-assisted localization algorithms, based on the distance measurement between mobile node and the node, using the observation model of mobile node. According to the comparison results with several other classical or up-to-date localization algorithms, our algorithm outperforms other algorithms in dynamic network environment with different parameter configurations.(4) Based on the impact of mobility parameters on localization, the thesis proposes a high-performance prior-data-based indoor localization algorithm, LuPI, which exploits the easy avail-ability of RSS (Received Signal Strength) information. LuPI uses the RSS variation between sampling positions to build the prior information data base. And then based on the RSS varia-tion data base, each mobile node can calculate their relative locations. The thesis implements the prototype system of LuPI with smart phones and WiFi routers. Extensive experiments are conducted. Compared with LiFS (Locating in Fingerprint Space) algorithm, LuPI improves the localization accuracy of mobile node in dynamic network environment. In addition, the accuracy of LuPI is impressive in rooms.