Device-Free Indoor Localization Based on Data Mining Classification Algorithms

Indoor localization is used in many applications such as security, health care, location-based services, and social networking. In traditional localization systems, a target person carries a radio device or sensor and the location of this device is taken as the location of the target person. However, there are situations in which a person does not carry a device. In such cases, device free localization (DFL) is the best solution. In this paper, we propose a radio frequency (RF)based DFL system using data mining classification algorithms. ZigBee nodes are deployed at the sides of a rectangular area and the area is divided into square grids. First, a model is developed for each classifier by collecting a received signal strength indicator (RSSI) when a person stands at the center of grids. The RSSI of each RF link is taken as an attribute for classifiers. Second, an online dataset is used to test the trained classifiers. RF links that contribute less for classification are removed from the attribute list. We also analyze the effect of ZigBee and WiFi interference on ZigBee-based DFL systems. Among five data mining classifiers, k-nearest neighbors and support vector machine using sequential minimal optimization achieve a classification accuracy of above 90%.