Research On Range-free Localization Algorithm In Wireless Sensor Networks

Wireless Sensor Networks(WSNs) are multiple hops and self-organization networks, which are composed of a large number of sensor nodes with low power, at a low price. These sensor nodes are randomly deployed in a specific environment. In order to implement some different applications, they are able to collect all kinds of information in the environment, and send it to remote users. Node localization technology is one of the most important technologies in WSNs. In most of the applications, it makes no sense to gain environmental monitoring information without the related locations. As a result, the research on node localization technology is greatly critical in WSNs.Firstly, this paper summarizes the system structure, characteristics, key techniques and applications of WSNs. Secondly, node localization technology in WSNs is further studied. This paper mainly analyzes several typical localization algorithms, including their advantages, disadvantages and usage scenarios. At last, the thesis focuses on a range-free localization algorithm called localization algorithm using expected hop progress (LAEP), and mainly achieves the following results:This thesis analyzes the ambiguity in node localization when using LAEP. It is that adjacent nodes may be located at the close locations. In order to solve this problem, this paper put forward the concepts of computed distances and pseudo ranging distances with the corresponding calculation method. On the basis of this, two kinds of refinement algorithms are designed: refinement localization algorithm based on auxiliary correction points(RLAACP) and refinement localization algorithm based on vectors(RLAV). In RLAACP, according to the deviation of computed distances and pseudo ranging distances, each node calculates the coordinates of its auxiliary correction points. Then the centroid of all the auxiliary correction points is regarded as the node's refinement position. In RLAV, the location correction vector of each node is constructed by the differences between computed distances and pseudo ranging distances. The objective function is defined as the sum of the squares of the bias between computed distances and pseudo ranging distances, and we can obtain the new estimated location of each node in the direction of its location correction vector by making the objective function minimum. These two algorithms both further utilize the distance between neighbor nodes for refinement based on LAEP. The simulation results show that the proposed algorithms can greatly improve the location accuracy of the network.Because of the limited energy of WSNs, this thesis additionally discusses the energy consumption of the RLAV, which has faster convergence than RLAACP. Firstly, the energy consumption of the LAEP and the RLAV is analyzed through the corresponding energy consumption model. Secondly, from the perspective of reducing energy consumption, the RLAV is accordingly improved. Finally, the simulation results show that the improved RLAV has high practical value by weighing the positioning precision and energy consumption.