Research Of Energy-based Multiple Source Localization Algorithm In Wireless Sensor Networks

Recently, wireless sensor network (WSN) which is considered as one of the ten most important technologies in 21st century has received a great deal of attention in the society. Source localization is one of its applications, including acoustic source localization, plume source localization and disaster rescuing such as fire and quake rescuing. So far, researches in literature mainly focus on single-source localization, however, targets always emerge in form of multiple sources. So research on multi-source localization has a significant practical meaning. In this thesis, energy-based multiple acoustic source and fire localization problems also sensor placement and sensor selection methods are addressed.The main research work and achievements of this thesis are as follows:The problem of energy-based multiple acoustic source localization is investigated. A multiple acoustic source localization algorithm is proposed. The basic idea of the method is to decompose the observed sensor data and consequently convert problem of multi-source localization into single source localization. The experimental results show that the algorithm can achieve satisfied localization accuracy with low computational complexity.Furthermore, the problem of distributed multi-source localization is researched. The forming of sensor node cycle is analyzed. Algorithm is run by each node and circles through the node cycle. The simulation results proved that compared with centralized way distributed localization algorithm has lower communication cost and satisfied localization accuracy.Moreover, the research of the problems of fire modeling and localization is carried out. A Gaussian multi-peak model is proposed. This model can well depict the real situation of fire. The fire locations and other parameters are estimated through adaptive sampling with sensor nodes using EM-MR algorithm. According to simulation results, the algorithm has high estimation accuracy and low computational complexity.In addition, sensor placement and selection strategies for localization are studied. Firstly, according to CRLB analysis, sensor placement strategy is proposed. Then based on the information theory, a heuristic method for sensor selection using Mahalanobis distance measurement and EKF algorithm is suggested. This method takes both information utility and communication cost into consideration and use EKF incrementally update the belief state. The method's efficiency can be proved by the experimental results.