Dynamic Localization In WSN And Its Application Research In Smart Campus

Mobile nodes localization is one of the key technologies of wireless sensor networks(WSN). In this paper, existing dynamic localization algorithms are summarized and classified. New mobile nodes localization algorithms are proposed respectively from the two localization algorithms classification system——range-free and range-based. Finally the construction of mobile nodes localization in smart campus will be elaborated in theory.One of the proposed localization algorithms is the improved algorithm of color localization for mobile nodes——Local Sampling and Filtering Color Dynamic Localization,LSF-CDL). The new algorithm uses the collected signals and adopts the overlapping signal region of beacon nodes which are able to communicate with the mobile nodes directly as the new local sampling area. Also the proportion factor of distance is used to weight the average hop distance which optimizes the calculation of hop distance in CDL. By comparing the RGB difference sequences, samples can be filtered out. And the absolute values of the samples’ difference sequences are used as the weighted standards to calculate the coordinates of the mobile node. The simulation results show that the proposed algorithm has good localization effect, which can obviously reduce the location error by more than 33% compared to the other classic algorithms such as Efficient Color-theory based Dynamic Localization(E-CDL) and Monte Carlo Localization(MCL).The other algorithm is that the signal strength resolution for dynamic localization based on sampling and filtering(SF-SSR). It’s based on the received signal strength(RSS) range measurement technology, and utilizes the sampling and filtering approach of MCL-kind particle filtering localization algorithms in mobile sensor networks(MSN) and integrates with the principle of strength resolution and composition in physics is proposed. In the produced rectangular coordinates, SF-SSR resolves the resultants the signal vectors between mobile node, beacon nodes, and samples and beacon nodes respectively. It samples from an error annulus, compares the signal resultant vectors of the samples with that of the mobile node, and then picks out the final samples whose resultant vectors’ mood are closest to that of the mobile node. SF-SSR takes the average value of those final samples’ coordinates as the mobile node’s location. Simulation results show that, under the same experiment conditions, the localization accuracy of SF-SSR is clearly higher than its counterparts and it needs no additional hardware.Mobile nodes localization in WSN is widely used in military, medical, family, logistics, education, and so on. Its application in education also belongs to a new field. This paper mainly discusses the important application of dynamic localization in education——the construction of smart campus. It briefly introduces the concept and core characteristics of smart campus, detailed introduces the role of dynamic localization in smart campus, mainly introduced two application examples of smart campus, campus life and smart classroom.Two mobile localization algorithms lay the foundation to develop the application of dynamic localization in smart campus. The stated theory of the application of dynamic localization in smart campus lays a theoretical foundation and provides scientific guidance.