Mani Williams

Visualization of wireless sensor networks using ZigBee's Received Signal Strength Indicator (RSSI) for indoor localization and tracking

With the increasing popularity of Wireless Sensor Networks (WSN), indoor localization has become a key research challenge, since it is crucial to locate the sensors in order to analyze the sensor data in their spatial and temporal contexts. The paper presents a novel method to visualize the position and trajectory of a dynamic WSN using ZigBees Received Signal Strength Indicator (RSSI) in a map-based visualization, deployed on smart phones. Existing research has demonstrated the use of RSSI to estimate the position of ZigBee tags (transmitters). The contribution of this paper is to integrate the use of sensor cloud services for managing spatio-temporal data streams and a smartphone app for visualizing the dynamic spatial and temporal aspects of the readings from wireless sensor networks.

Graph mining indoor tracking data for social interaction analysis

With the advancement in wireless sensor networks (WSN) researchers in social network analysis (SNA) now have access to larger and more complex datasets that describe human interactions in the physical space. Studies in WSN thrive on accuracy and robustness whereas SNA operates on a higher level of data abstraction. Graph mining is a bridge between these two fields. This paper investigates two approaches to graph mining and compares their efficiency and appropriateness as the input systems for a social interaction analysis process.

An accuracy enhancement algorithm for fingerprinting method

Fingerprinting is the prevailing positioning method for location based service (LBS) and indoor positioning applications when compared with other methods such as cell of origin (CoO) and trilateration. It is especially more suitable for complicated indoor environments. However, higher positioning accuracy is still expected for it to match the capabilities of other mature techniques such as GPS. This paper presents a new algorithm for improving the positioning accuracy of the Nearest Neighbour (NN) algorithm from a Wi-Fi-based fingerprinting method. The new algorithm initially used the NN algorithm to identify the initial position estimate of the user being tracked. Then two distance correction values in two roughly perpendicular directions were calculated by the path loss model based on the two signal strength indicator (RSSI) values observed. The errors from the path loss model were eliminated through differencing two calculated distances which were derived from a similar environment. The new algorithm was tested and the results evaluated against that of the NN algorithm. The preliminary results from 24 test points showed that the positioning accuracy of the new approach has improved consistently and the root mean square accuracy improved to 3.4 m from 3.8 m with the NN algorithm.