Zhongliang Deng

Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments

Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS). Due to the absence of satellite signal in Global Navigation Satellite System (GNSS), various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP), which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC), is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1) and the XiDan Joy City (Floors 1 and 2, as Test-bed 2), and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means.

Energy Balance Algorithm based on LEACH

For the weakness in the cluster head election of LEACH algorithm in Wireless Sensor Network(WSN),We propose an improved algorithm based on LEACH from point of view that regional energy balance of the network. Proposed algorithm can make the node that has more remaining energy to serve as the cluster head(CH). Meanwhile, when the sensor nodes choose the CHs, it will depend on not only the distance between the sensor nodes and the CHs, but also the capacity of the CHs to join. Simulation results show that the proposed algorithm is effective in balancing the network energy consumption and prolonging the network lifetime.