Research On Self-Localization Algorithm Of Wireless Sensor Network

Wireless sensor networks, a novel technology about acquiring and processing information, have been proposed for a multitude of diverse applications, for example, external goal of positioning and tracking, providing space named for the network, reporting the quality of network coverage, realizing the topology of the network by self-configuration, and so on, but self-localization is the basis of these applications.Despite the intensive deployment of wireless sensor networks is one of the characteristics, but there are always some degree of connectivity or up to the lower unknown nodes. How to maximize the positioning accuracy of these nodes is a problem in WSNs. So in this paper the self-localization problems have been studied under the conditions of low and medium connectivity in random topology.Now most algorithms with larger positioning errors, and vulnerable to changes in low-density of random network topology, this paper provides the WSADH algorithm based on the DV-HOP. Assumptions using radio transmission model of free space, the WSADH raises the weighted average thinking, using the hops to determine the weight ratio to calculate the average distance each jump, and the choosing beacon node strategy selects three optimal beacons to calculate the positions of unknown nodes using trilateration, which is as the initial phase of the estimated position in iterative refinement.So enhancing the stability of algorithms, reducing the number of iterative and improving the positioning accuracy under the lower density.The WSADH algorithm introduces two floodings the same as DV-HOP. In order to reduce communication costs, this article raises a new positioning algorithm, the DADHL named based on the density of nodes. It accords the Kleinrock-Silvester formula to estimate each jump distance, and references the weighted average thinking and choices beacon strategy.The DADHL algorithm integrates the shortest path and the estimated distance into one flooding with a smaller communication overhead, and uses the iterative refinement further improves the positioning accuracy.Finally, using OMNeT++ simulation tool to evaluate the WSADH and DADHL, the simulation results show that the WSADH and DADHL increase the positioning accuracy, and achieve the expected results.