Research Of Wireless Sensor Networks Node Localization Algorithm In Underground Complex Environment

With the rapid development of economy and in the situation of energy shortage, the industry of coal has got even more attention. For the mine's environment complex, bring tremendous security challenges to the produces, and threat to the life of the staff. Thus, localization and personnel monitoring of the underground mine began and has been developed rapidly. So this thesis deeply researched on node localization in wireless sensor networks (WSN) for underground complex environment.In recent years, wireless sensor networks have become research focus and node localization in wireless sensor network is the main supporting technologies. Node location in WSN has many different algorithms. Each algorithm has its own advantages and disadvantages, and has its own appropriate environment.This thesis first introduce radio wave propagation model, include three environments:free space, indoor multi-barrier environment and underground complex environment. It was modified combined with their actual measured values that the environmental factors in radio wave propagation model equation matrix of underground complex environment.Then we analysis the typical localization algorithms. Consider the algorithm complexity and localization accuracy of the algorithms. It is studied that the based on the received signal strength indicator (RSSI) of the localization algorithm. We elaborate the theory of localization algorithms, and implement the algorithm combined with radio wave propagation model in underground complex environment. Because the localization algorithm accuracy is not high, this thesis simulate the algorithm which chosen different parameters. We can see the accuracy of the localization algorithm is closely related to the algorithm iterations and the place of the reference node. After simulation we can see if we select the appropriate number of iterations and the place of the reference node, the algorithm accuracy improve under the premise of little change in the complexity of the algorithm. Finally, we summarize the work in this thesis, and prospect the future work.