Book Description
Wireless sensor networks (WSNs) play an important role in applications such as environmental monitoring, source tracking, and health care,... In WSN, sensors have the ability to perform data sampling, distributed computing and information fusion. To perform such complex tasks, clock synchronization and localization are two fundamental and essential algorithms. WSNs have been widely studied in the past years, and the scientific literature reports many outcomes that make them applicable for some applications. For some others, research still needs to find solutions to some of the challenges posed by battery limitation, dynamicity, and low computing clock rate. With the aim of contributing to the research on WSN, this thesis proposes new algorithms for both clock synchronization and localization. For clock synchronization, sensors converge their local physical clock to perform data fusion. By applying the clock synchronization algorithm, sensors converge the time difference and therefore work at the same rate. In view of dynamicity, low computing and sparsity of WSN, a new pulse-coupled decentralized synchronization algorithm is proposed to improve the precision of the synchronization. The benefit of this kind of algorithm is that sensors only exchange zero-bit pulse instead of packets, so not only the communication is efficient but also robust to any failure of the sensors in the network. Localization of sensors has been widely studied. However, the quality and the accuracy of the localization still have a large room to improve. This thesis apply Leave-out Sign-dominant Correlated Regions (LSCR) algorithm to localization problem. With LSCR, one evaluates the accurate estimates of confidence regions with prescribed confidence levels, which provide not only the location but also the confidence of the estimation. In this thesis, several localization approaches are implemented and compared. The simulation result shows under mild assumptions, LSCR obtains competitive results compared to other methods.