Abstract
Location service is one of the primary services in smart automated systems of Internet of Things (IoT). For various location-based services, accurate localization has become a key issue. Recently, research on IoT localization systems for smart buildings has been attracting increasing attention. In this paper, we propose a novel localization approach that utilizes the neighbor relative received signal strength to build the fingerprint database and adopts a Markov-chain prediction model to assist positioning. The approach is called the novel localization method (LNM) in short. In the proposed LNM scheme, the history data of the pedestrian's locations are analyzed to further lower the unpredictable signal fluctuations in a smart building environment, meanwhile enabling calibration-free positioning for various devices. The performance evaluation conducted in a realistic environment shows that the presented method demonstrates superior localization performance compared with well-known existing schemes, especially when the problems of device heterogeneity and WiFi signals fluctuation exist.
Note to Practitioners-This paper was motivated by the problem of developing Internet of Things (IoT) localization systems for smart buildings but it also applies to other IoT applications that have location-based service ability. Existing approaches to design such systems generally utilize the received signal strength (RSS) from WiFi to build fingerprint for obtaining user's position. This paper suggests a novel technique, named novel localization method (LNM), that uses neighbor relative (NR) signal fingerprint and Markov chain for localization in smart buildings. NR-RSS is used as the fingerprint data to build radio map instead of absolute RSS. Meanwhile, Markov-chain model is applied to conduct the mobile device's trajectory analysis. In this paper, we evaluate LNM on different mobile devices with various system parameters. Then we show how the location of mobile device can be accurately computed against device heterogeneity and environmental dynamics. Extensive physical experiments suggest that LNM is feasible and reliable although it has not yet been evaluated on non-Android devices. In future research, we will address the design of IoT localization that has a wide variety of smart objects equipped with heterogeneous communication medium.