Three-dimensional Localization Algorithm For Wireless Sensor Networks

Wireless sensor networks(WSNs) is a multi-disciplinary technology, it contains a variety of technologies, for example wireless network technology, embedded technology, distributed information processing technology and micro electronic technology. With the development of three dimensional space wireless sensor network, it has been applied in many areas, such as the environment monitoring, poisonous gas detection, intelligent household environment and medical treatment system and so on.However, due to the particularity of the Wireless sensor networks, which still has many problems to be resolved, such as network routing, node fault diagnosis, the quality of service, low energy consumption, data compression, data fusion and data transmission, time synchronization and unknown node localization, etc.This thesis focuses on the problem of the network node location in three dimensional space. According to the motion state of the node, the problem can be classified into three categories, including the static node localization algorithm, the static anchor node localization algorithm and the mobile node localization algorithm. Three methods are briefly described as follows:(1) This paper discusses about AOA and Concentric Algorithm based three dimensional reduction node self-localization mechanisms (AC-RL) for WSN. In this scheme, unknown nodes measure the AOA information between anchor nodes and themselves by anchor nodes broadcast their location information. AOA information is utilized to calculate the unknown nodes in the projection area of the horizontal plane. Hence, the circles concentric positioning in three dimensional space is simplified to two dimensional space. Finally, it calculates the positions of unknown nodes according to the received information of them. The simulation results demonstrate that the AC-RL algorithm has advantages in positioning accuracy and node density.(2) This paper discusses about flying anchors and concentric based three-dimensional localization scheme (FAC) for WSN. The algorithm calculates the unknown node position according to the communication information of unknown node which is collected by mobile anchor nodes. FAC could achieve the localization with three auxiliary anchor point, which is less than the auxiliary anchor point of OU-3D. The simulation results demonstrate that the FAC algorithm has advantages in positioning accuracy, node density and the response time.(3) This paper proposes a FIMC localization algorithm based on monte carlo algorithm, this algorithm is divided into three stages:sampling stage, filtering stage, the resampling stage. In the sample stage, it uses the importance of fuzzification weights for sampling. In the filtering stage, it filters through multiple jump communication information between anchor nodes and unknown node. In the resampling stage, it uses the particle swarm optimization based on the resampling method, and then using the reverse fuzzification theory calculation the node position. The simulation results demonstrate that the FIMC algorithm has advantages in positioning accuracy, node density and the number of node positioning.