Shang, F.; Jiang, Y.; Xiong, A.; Su, W.; He, L. A Node Localization Algorithm Based on Multi-Granularity Regional Division and the Lagrange Multiplier Method in Wireless Sensor Networks. Sensors2016, 16, 1934.
Shang, F.; Jiang, Y.; Xiong, A.; Su, W.; He, L. A Node Localization Algorithm Based on Multi-Granularity Regional Division and the Lagrange Multiplier Method in Wireless Sensor Networks. Sensors 2016, 16, 1934.
Shang, F.; Jiang, Y.; Xiong, A.; Su, W.; He, L. A Node Localization Algorithm Based on Multi-Granularity Regional Division and the Lagrange Multiplier Method in Wireless Sensor Networks. Sensors2016, 16, 1934.
Shang, F.; Jiang, Y.; Xiong, A.; Su, W.; He, L. A Node Localization Algorithm Based on Multi-Granularity Regional Division and the Lagrange Multiplier Method in Wireless Sensor Networks. Sensors 2016, 16, 1934.
Abstract
With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.
Keywords
WSN; RSSI; Voronoi diagram; vector similar degrees; Lagrange
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
