Research On Mobile Node Localization Approaches Based On Artificial Bee Colony In WSNs

Wireless Sensor Networks(WSNs) consist of a self-organizing network which make up with a lot of low-cost tiny sensor nodes that can perform sensing, computing and communicating. The nodes in WSNs can work in cooperation to transmit the processed information to the remote monitor. As one of the key technologies of WSNs, localization technology has great effect to improve the performance and value of WSNs. At present, there are a large mounts of researches about the static localization in WSNs, but the research of “some or all nodes can randomly move” is relatively small. Because of the mobility of nodes, it increases the difficulty of the localization of nodes, and purely static network localization has been unable to adapt the dynamic needs. Although the mobile nodes make the process of localization complicated, the mobility of nodes can improve the positioning accuracy, positioning coverage and reduce positioning time, energy consumption and so on.Firstly, this dissertation researches the overview, prospects and mobile nodes localization problems of WSNs, and summarizes the meaning of the researches. Then through the analysis of the related dissertations at home and abroad, it researches the mobile localization algorithms and has a deep research of theories, methods and protocols. On the basis of the research, it proposes the mobile beacon path optimization algorithm and Monte Carlo Localization algorithm based on Artificial Bee Colony algorithm. At the same time, compared with the existing algorithms in the dissertations, this dissertation designs the simulation experiment and evaluates the performance of the modified algorithms.There are some shorts of mobile beacon nodes to locate with long time and lack of the localization accuracy in WSNs. So, this dissertation proposes the mobile beacon path optimization algorithm based on Artificial Bee Colony algorithm.Monte Carlo Localization algorithm in WSNs requires a lot of samples in order to better estimate the posterior density, due to the value of the density distribution in the posterior small number of samples in a larger area. Let Artificial Bee Colony algorithm work with Monte Carlo Localization algorithm to optimize the sample, and makes the samples move to the direction that the posterior density has greater values and then to overcome that Monte Carlo localization algorithm needs large number of samples to get better results.In summary, this dissertation makes a deep research on the localization technology in Wireless Sensor Networks, put forward two appropriate improvement algorithms, and experimental results demonstrate that the lacalization algorithms in this dissertation make obvious progress than the existent ones both on the precise and on the efficiency.