The paper exploits the outlier detection techniques for wireless-sensor-network- (WSN-) based localization problem and proposes an outlier detection scheme to cope with noisy sensor data. The cheap and widely available measurement technique—received signal strength (RSS)—is usually taken into account in the indoor localization system, but the RSS measurements are known to be sensitive to the change of the environment. The paper develops an outlier detection scheme to deal with abnormal RSS data so as to obtain more reliable measurements for localization. The effectiveness of the proposed approach is verified experimentally in an indoor environment. 1. Introduction Advances in Microelectromechanical Systems (MEMSs), embedded technologies, wireless communication, digital and analog devices, and battery techniques make the wireless sensor networks (WSNs) a prominent and enabling technology in surveillance and exploration applications [1]. One significant attribute of the WSNs is the localization capability. The location information can remarkably enhance the contents of the gathered information in monitoring, tracking, and decision making applications. Indeed, in many applications such as surveillance, target tracking, and intrusion detection, the measurement data are meaningless without the location attributes. To establish a low-cost, easily implementable, and high-reliable indoor positioning capability, WSNs can utilize the received signal strength (RSS) measurements as the baseline for range determination and location estimation. Unfortunately, the propagation behavior of radio signal in indoor environments is complex, which makes the RSS-based indoor localization a challenging issue. Wireless positioning technology can be roughly divided into two categories: radio positioning, which includes GPS, RFID, WiFi, and ultra-wideband (UWB), and non-radio positioning, which includes video cameras (optical), infrared, ultrasound, and inertial systems [2, 3]. It is worthwhile to note that as each sensor has its limitations, a practical positioning system may often employ sensor fusion techniques to integrate the sensors to yield improved performance. To enhance the overall reliability and accuracy in localization, the basic problem in signal reliability of each sensor needs to be addressed. Although sensors can be well calibrated, each measurement is subject to measurement noise and systematic error. In particular, outliers to sensor data need to be detected and precluded in the signal processing stage; for otherwise the results are prone to significant errors.
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