Research On Location Of Wireless Sensor Mobile Node In WSN

WSN (Wireless Sensor Networks, WSN) as a new node access to information technology, has become a hot field of wireless content and its applications involve many aspects of people’s daily lives in the 21st century. With the advancement of technology and improvement of people’s living standards, the location information needs for people gradually shift to obtain information on the mobile nodes from the information on fixed nodes, node localization technology research in the static sensor networks gradually transformed into the dynamic sensor networks. Localization of mobile nodes in wireless sensor network is becoming popular in the field of technology.Due to the practical needs, the sensor nodes is not always static in wireless sensor networks,mobile nodes can be able to gather more useful location information, and can reduce the number of nodes, and can save energy networks consumption, but the mobile nodes can make the real-time positioning accuracy of the data more difficult. In this paper, the study is focus on how to improve the accuracy of mobile nodes’location in wireless sensor networks.This paper discusses the background and significance of the topic, overviews the current situation and the overall development of wireless sensor network, and then reviews the wireless sensor network node localization concepts and study of the classic node localization algorithm in the wireless sensor networks.This paper is focused on high-precision positioning of the mobile nodes in wireless sensor network. In the process of research, the study mainly based on the Monte Carlo method, and improved and perfected the communication function of the beacon nodes (anchor nodes), in the other words, the anchor node structure is loaded antenna modules to increase the node information acquisition and transmission channels,then establish a network model, and propose a precise positioning algorithm based on the cross area of the mobile node. The nature of the algorithm is to reduce the estimated area where the mobile node is to maximize the mobile node positioning accuracy goals. Through simulation analysis, the algorithm comparing with other related algorithms, has high positioning accuracy and is capable of fast convergence, while, due to the reduction for the sampling area of the mobile node, it makes sampling rate increasing, therefore, to a certain extent,the algorithm improves the effectiveness of the network energy.