The Research Of Localization Technologies Of DV-Hop In Wireless Sensor Networks

As the development of wireless communication and sensor technology, wireless sensor networks (WSN) are becoming a hot research field. Composed by a large number of micro sensors which have sensing capabilities, data processing capabilities and short range wireless communication capabilities, and completed by a wireless multi-hop network of self-organizing systems, WSN can sense, collect and process the information from the coverage area of the object, then sent to the observers. Currently, wireless sensor networks have broad application prospects in military, medical, rescue, traffic control, home automation, environmental monitoring, business and other fields.In the application of sensor networks, we must first determine the location of sensor nodes. Otherwise, data collection will be meaningless. So, positioning technologies which can accurately informed node position coordinates become an important part in sensor technology researches. Among a huge number of positioning algorithms technology, DV-Hop algorithm is a widely used algorithm for it’s easy to be implemented. However, subject to the constraints of the node degree distribution, positioning accuracy of DV-Hop algorithm is affected. Therefore, this thesis focuses on improving the accuracy of DV-Hop algorithm improvements. In this thesis, the development process and key technologies of wireless sensor networks were described, and the existing research of node localization algorithm for wireless sensor network were reviewed and summarized, the practical value of a classic node self-localization algorithm called DV-Hop were introduced, then, the deviation of positioning accuracy of DV-Hop algorithm were analysised, and the improvements of DV-Hop classic algorithms were proposed, which by using curve fitting method to calculate the curvature, filtering out the appropriate anchor nodes to participate the calculation. At last, simulated by MATLAB, the two algorithms were compared at the same uniformity degree. Experimental results showed that the improved algorithm had good performence.