Algorithm To Improve The Accuracy Of DV-Hop Algorithm

The Wireless Sensor Network (WSN) is a new type of self-organizing multi-hop data centric networks and one of the hot research fields in recent years. Its unique features have aroused series of new research issues. Localization of nodes in the network is a import problem in these research issues, the localization information can help people get the position of the gathering data at the same time, in addition, some routing protocol or some application are based on the localization information, without the localization information, a lot of application of WSNs are empty.There are Range-Free algorithm and Rang-Based algorithm in Localization algorithm of WSN. Range-Based algorithms is useful, because them can provide very high accurate result, however in some situation, low accuracy of locate is acceptable, and people pay more attention on the cost of the system which is the backward of Range-Based algorithms, they need nodes equip some expensive hardware. Range-Free algorithms, appealing to people for their cost-efficiency, suffer from the precision problem. DV-Hop is a typical Range-Free algorithm which is the basic idea of many algorithms. But much research show there exists some problem in DV-Hop. This paper proposed two different type of algorithms to improve the accuracy of DV-Hop. The first algorithm is PAVC, this algorithm tries to take the advantages of the PAV algorithm to calibrate the average distance per hop then with the help of the new average distance per hop locate the position-unknown nodes. The PAVC actually can improve the accuracy of the DV-Hop, while the improvement is not obvious and according to the data of experiment we can see sometimes the PAVC has worse result than DV-Hop, means the PAVC is not stable. Some methods try to combine received signal strength indication (RSSI) with range-free localization algorithms to improve the accuracy, but RSSI is sensitive to the radio irregularity.Based on the well known RIM model, we come up with a the second approach, a new method of RSSI calibration, namely MRIRC, to mitigate the impact of radio irregularity. MRIRC divides nodes within a continuous angle into groups with the same level of RSSI deviation. By doing this, given an irregular deviation input, MRIRC can get a maximum angle (worst case), which guarantees that the nodes in the same group are in the same level of radio irregularity, thereby improving the accuracy of the distance estimations. We conduct simulations for large-scale sensor networks, and the results show that MRIRC achieves superior performance over the other two typical Range-Free algorithms. By doing the simulation we conclude that this idea has more obvious advantages than the improvement of the PAVC, and the improvement is more stable too.