Research Of Adaptive Node Localization Method In Dynamic Wireless Sensor Networks

Nowadays, with the development of MEMS, wireless communication, electric technology, especially the mobile robot technology, as a special kind of wireless sensor network, dynamic wireless sensor networks is not only widely used in industry, agriculture, medical and health care field, also has got remarkable achievements even in some harmful and dangerous areas, such as the military, space and environment detacts. And DWSN has become one of the most popular fields in the research field of wireless sensor networks. In DWSN, using mobile anchor node as a sensor node, not only greatly improves the network's self-organization and adaptability, but also makes WSN is an area of growing application. Therefore, the research of DWSN node has a very far-reaching significance.In DWSN, the normal operation of the sensor network is based on the location information of the nodes. Compared with the static network node localization, dynamic sensor network node localization is more complicated bacause of the mobility of sensor nodes. Now, The difficulty of current wireless sensor network localization is how to achieve the goals of low power consumption, low complexity and high positioning accuracy.This paper introduces and analyzes many kinds of positioning methods, emphatically the MCL algorithm. According to the corresponding researches and improvements of the MCL algorithm, this paper puts forward the adaptive node localization algorithm for dynamic sensor networks.The specific content and innovative points of this paper are as follows:(1) This paper summarizes the wireless sensor network technology. And then makes an in-depth study of DWSN positioning, especially the principle of the dynamic sensor network positioning, positioning performance indicators,classification, positioning features, representative positioning algorithm and model, which provide a basis for the improvement of the algorithm.(2) Aiming at the disadvantages of traditional MCL positioning algorithm, such as large amount of calculation, vulnerable to the environment, which lead to large positioning error, the paper improved MCL localization algorithm based on the research of MCL, MCB and EMCL location algorithm.(3) Verify the performance of the improved algorithm from multiple perspectives with MATLAB. Under the same conditions, we can compare the improved algorithm with the traditional MCL algorithm, MCB algorithm and EMCL algorithm. And, the comparison results show that the improved algorithm is superior.