An improved MDS localisation algorithm for a WSN in a sub-surface mine

Wireless sensor networks have been successfully applied in a wide range of application domains. However, because of the properties of wireless signals, Wireless sensor networks applications in underground environments have been limited. In this paper, we present a Kalman-filter-based localisation algorithm for use in a Wireless sensor networks deployed in a sub-surface mine for environmental monitoring to identify the positions of a large number of miners, each carrying a wireless mobile node. To improve the positioning accuracy even when current measurements are not available, we enhance the estimates of the received signal strength indication (RSSI) signal intensity and range obtained from the Kalman filter by adjusting them using the elastic particle model. Then, we obtain the distance matrix of the WSN based on arrival of angle and the cosine theorem. Finally, we determine the final positions of all mobile nodes using a multidimensional scaling algorithm.