Research Of Localization Algorithm Combining Range-based And Range-free Localization In WSN

With the rapid development of microelectromechanical systems（MEMS）andsensor technology, wireless sensor networks （WSN） gradually become a hotresearch area, and have shown a great prospect and high practical value in military,medical, environmental monitoring and many other areas. WSN is composed of vastamounts of cheap micro sensor nodes wirelessly, and these nodes collaborate witheach other and self-organize into a multi-hop network. It is a new way to obtain,transmit and process information. Node location of WNS is the basis to analysis theinformation acquired by sensors in most application areas, so the localizationalgorithm in WSN has become an important research field for researchers.In this paper, after researching all kinds of domestic and foreign WSN nodelocalization algorithm, we propose an improved algorithm based on DV-hop, which isa combination of range-based localization and range-free localization. ConventionalDV-hop algorithm has a low complexity, and can obtain node position without deviceto measure distance between nodes. However, the localization accuracy of DV-hop isquite low, and it can only be applied in evenly distributed network. The proposedalgorithm in this paper is easy to implement and has an excellent performance inevenly distributed network, which is inherited from DV-hop. What’s more, the newalgorithm extends application scenario of DV-hop, which means the new algorithmhas an excellent performance in unevenly distributed but high anchor ratio network.Meanwhile, the proposed method also comes with an optional iterative refinementstage, thus giving more room to improve the algorithm.Finally, after describing basic concepts and characteristics of OMNeT++platform, this thesis has realized DV-hop-Advanced algorithm on OMNeT++. It isproven that the algorithm is better than the conventional DV-hop. It can be applied notonly in evenly distributed network but also in unevenly distributed but high anchordensity network, which greatly expands application scenarios of the algorithm.