Sensor Localization Algorithm Based On Weighted Least Square In Wireless Sensor Network

Wireless sensor network (WSN) is a new direction of the field of information science, it is the merging results of traditional disciplines and information technology. It combines microelectronic technology, embedded computing technology, modern networking and wireless communication technology, distributed information processing technology and other advanced technologies, real-time monitoring, sensing and gathering the information (such as sound, temperature, humidity, pressure, etc.) of the environmental and monitoring object in area of network monitoring, and processing the information collected, processed information through wireless means were sent to the information Collectors. Because of its huge prospects in environmental monitoring, industrial manufacturing, military defense, health care, traffic management and other fields, concerned by International academic and industry In recent years. However, it is the prerequisite for many applications to determine the location of Information gathering or to get the location of the message, because there will not be any practical significance,if the monitoring information is not combined with the specific environment, wireless sensor network node localization problem in wireless sensor network research and applications play an important role, it is one of the key technologies in wireless sensor networks, researching on the node localization problem has very important significance. This paper focuses on the self-localization algorithm for wireless sensor networks, Learning and study weighted least squares algorithm and a new weighted method was applied to the localization simulation.This is a significance work. The primary works are as follows:1. This paper has analyzed the implementation process of the classical Euclidean algorithm. The energy consumption of the original Euclidean algorithm and the improved Euclidean algorithm has been compared,and the two algorithms were simulated.2. Some Weighted least squares can not adapt to the impact caused by changes in the environment.Based on this problem,a new weighting method has been proposed in this paper.Weight has not need to been set in advance and it can reflect the the impact of environment on the positioning computation, reducing location error.3. The orphan nodes have be seen as anchor node after being localized.The new anchor node will been given different weights, the value of the weights reflect its credibility in further positioning calculation. According to the updated information, other nodes raise the localization accuracy by iterative refinements. Improved the problem that anchor ratio has a strong impact on localization error and coverage in Euclidean algorithm.