Research On Monte-Carlo Localization Algorithms For Mobile Wireless Sensor Networks

Localization of mobile nodes is one of the hot issues in wireless sensor networks. It is to get the position information of mobile unknown nodes by using information of some anchor nodes and some special mechanisms. There are still lots of shortcomings in this technology such as great impacted by circumstances, low localization accuracy, huge power costs and so on. So it is unsuitable for mobile wireless sensor networks. It is obviously that the technology of wireless sensor networks will go ahead very quickly and its applications will be wider and wider in the coming future. The research as described in this dissertation is of significance in both theoretical and application areas.Monte-Carlo localization technique is a particle filter combined with probabilistic models of robot perception and motion. It is able to solve complicated localization problem effectively and robustly, so it can also be used in localization of mobile wireless sensor networks. It reveals that Monte-Carlo localization technique can exploit mobility to improve the accuracy of localization and reduce the costs of localization.A large sample size is needed for Monte-Carlo localization algorithm in mobile wireless sensor networks due to too little samples locate in the regions where the value of posterior density distribution is large. A new localization algorithm named genetic Monte-Carlo localization is proposed. The crossover and mutation operations in genetic algorithm are introduced into Monte-Carlo localization algorithm to make samples move towards regions with large value of posterior density distribution, so the sample set of localization algorithm can represent the desired posterior density distribution better. Simulation results show that genetic Monte-Carlo localization algorithm needs fewer samples and is more precise and robust.Furthermore, by introducing the concept of anchor nodes'influence,a new localization algorithm named weighted sampling Monte-Carlo localization is proposed. Exploiting the mobility of nodes and the information of multi-hop anchor nodes, weighted sampling Monte-Carlo localization algorithm uses coefficients, which are decided by the influence of anchor nodes to unknown nodes, to prompt localization accuracy. Simulation study shows that weighted sampling Monte-Carlo localization algorithm is distributed, scalable and robust. It also exhibits fine performances on coverage of localization, which is suitable for the localization in large-scale mobile wireless sensor networks.