Abstract
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.
Highlights
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]
Storing the entire history of received signal strength (RSS) data is not recommended in WSN applications due to the increasing memory requirements
It is shown that when the proposed scheme is employed, the positioning error can be significantly reduced when there are only a few reference points. This is due to the fact that high-confidence trustworthy data are processed with a heavy weighting and the localization result is not misled. This implies that the proposed outlier detection scheme meets the requirement of the resource-limited environment of WSN
Summary
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]. To establish a low-cost, 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. Outliers to sensor data need to be detected and precluded in the signal processing stage; for otherwise the results are prone to significant errors. The paper is dedicated to address the outlier detection problem for the localization in a WSN-based indoor environment. The paper proposes an outlier detection scheme to cope with unreliable measurement data. The scheme is applied at the database construction phase for the establishment of a reliable database It is used at the localization/tracking phase to detect and remove unreliable measurement.
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