Research And Application On Wireless Sensor Networks Localization Technology

Wireless Sensor Networks, WSN have greatly spread in the fields of military affairs, environment inspection, forest fireproofing, factories and collieries supervision. Many small, multi-function and low-cost nodes are placed in the special area or the circumstance in which human can not survive. These nodes compose a large, application-related and self-organized network which has a dynamic topology.The localization of nodes and targets is one of the WSN's applications, and it is the base of the numerous applications. Many applications of WSN such as supervision task distribution, route rules, overlay information, load equilibrium, topology control etc. depend on the nodes' location information. Therefore, the study for localization of WSN possesses great significance in the theory and practice.Due to the constraints of cost, size and complexity, there is always measure distance, localization accuracy and hardware complexity, and other contradictory issues in existing localization technologies.First, this paper proposes a localization algorithm based on the distance difference between nodes. The algorithm uses the RF packet only to obtain the distance differences between unknown node and beacon nodes. Then the algorithm figure out the node's coordinates by the improved trilateration to implement the long distance, controllable accuracy, low-cost localization.Second, this paper proposes a distributed Kalman algorithm to optimize the TDOA's error. The algorithm does the Kalman filtering algorithm with distance between unknown node and beacon nodes as the measurements. In this paper, we have done the emulation and simulation experiment of TDOA localization and distributed Kalman algorithm.Finally, this paper applies wireless sensor networks localization technology to a new concept Car Driving License Examination System. We have designed the WSN nodes especially for this examination system and transplanted TinyOS on the hardware platform. The examination system adopts TDOA algorithm which has been optimized by distributed Kalman algorithm to locate and track the examination car. With the car's profile the system can detect the fouls action such as hitting the bar, slopping over the lines.