Distributed Range-free Localization Methods In Mobile Wireless Sensor Networks

Wireless sensor networks have a wide range of potential application in military defense, industrial and agricultural control, urban management, bio-medical, environment monitoring, disaster relief, remote control of hazardous area. Location awareness is important for wireless sensor networks since many applications such as environment monitoring, vehicle tracking and mapping depend on the knowledge of the locations of sensor nodes. After monitoring an event, an important problem is the location where it occurred. Location information is an indispensable part of datum collected by sensor nodes, which means that monitoring information is usually meaningless without the location information. To determine the location of the event occurring or the locations of the sensor nodes which collect datum is one of the most basic functions of the wireless sensor networks, which is based on the accurate positioning of the sensor nodes themselves. Many applications of wireless sensor networks, such as undersea detection, tracking of animals, discovery of river channel and mapping, all depend on the mobility of sensor nodes. So localization in mobile wireless sensor networks has become one of the hot spots of research.Firstly, this paper introduces the structure, key technology, characteristic and applications of the wireless sensor networks, and the concept, classification and evaluation of the localization of nodes in the wireless sensor networks. Then this paper describes the existing localization methods of the localization of nodes in the wireless sensor networks, such as MCL, MCB, MMCL, Dual MCL, Mixture MCL, MSL*, MLS etc, and then compares their performance on the basis of the realization of those localization methods with Matlab.Secondly, this paper proposes two localization algorithms for mobile wireless sensor networks, which are called Weighted-MCB and EWMCB. In order to improve the localization accuracy, the two algorithms both use the information of the first-hop nodes to weight the samples in the sample set. Weighted-MCB uses the location of all the samples of the sample set of the first-hop nodes, and lets the samples, which have more possibility to the true location of the node, to have bigger influence factor in the location estimation of the node. However, the communication cost of the Weighted-MCB is big. So this paper also proposes the EWMCB algorithm. Through introducing the concept of"estimated location error"to estimate the error of the estimated location, the node only uses the estimated locations of the first-hop nodes (one location with one node) with high localization accuracy and its"estimated location error"to weight the samples of the node. In this way, EWMCB reduces the communication cost compared with Weighted-MCB, and EWMCB is more adaptable to the irregular radio range than Weighted-MCB.Thirdly, because in practical application obtaining the value of both the radio range r and the max speed vm ax is difficult, this paper proposes another localization algorithm called Min Initial r (MIR), which does not need to know the radio range r and the max speed vm ax, and could be used in both mobile and static wireless sensor networks. Among the existing localization algorithms, there is no algorithm which does not need both the radio range r and the max speed vm ax. We compare the MIR with Centroid and DV-hop, both of which does not need both the radio range r and the max speed vm ax, to explain the advantages of the MIR.Finally, we summarize this paper and propose the future research aspect.