A Novel Method for Constructing a WIFI Positioning System with Efficient Manpower

Abstract

With the rapid development of WIFI technology, WIFI-based indoor positioning technology has been widely studied for location-based services. To solve the problems related to the signal strength database adopted in the widely used fingerprint positioning technology, we first introduce a new system framework in this paper, which includes a modified AP firmware and some cheap self-made WIFI sensor anchors. The periodically scanned reports regarding the neighboring APs and sensor anchors are sent to the positioning server and serve as the calibration points. Besides the calculation of correlations between the target points and the neighboring calibration points, we take full advantage of the important but easily overlooked feature that the signal attenuation model varies in different regions in the regression algorithm to get more accurate results. Thus, a novel method called RSSI Geography Weighted Regression (RGWR) is proposed to solve the fingerprint database construction problem. The average error of all the calibration points’ self-localization results will help to make the final decision of whether the database is the latest or has to be updated automatically. The effects of anchors on system performance are further researched to conclude that the anchors should be deployed at the locations that stand for the features of RSSI distributions. The proposed system is convenient for the establishment of practical positioning system and extensive experiments have been performed to validate that the proposed method is robust and manpower efficient.

Reference Device-Assisted Adaptive Location Fingerprinting

“…Besides, two peak distribution [14] and multimodal distribution [12] are also found. To overcome the limitations of samples over specific time periods, and to reduce the heavy data collection burden, crowd-sourcing-based methods [15,16] or reference device-based methods [2,3,4,5,6,7,8,9] provide alternatives. Our work focuses on the reference device-based manner.…”

“…On the one hand, RSSI measurements can be mapped to geographical distances [2], and trilateration is then applied to estimate locations. On the other hand, RSSI measurements can be used to estimate the signal strength profiles of APs [3,4,5,6,7,8,9], and location fingerprinting is then used for location estimation. In this paper, the discussions follow the latter approach.…”

“…In the solution, the Gaussian Process Regression (GPR) method was used to augment the Log distance path loss model to obtain AP signal strength profiles. Similarly, modified APs are also applied by Du et al [9]. With additional self-made sensor anchors, they use the Geography Weighted Regression (GWR) method to obtain a more accurate signal propagation model to construct radio maps.…”

“…All the mentioned factors can lead to an outdated radio map which deteriorates the location fingerprinting performance and therefore repeated site surveys or calibrations are needed from time to time. As a result, approaches that adapt to dynamic and complex environments are more applicable [2,3,4,5,6,7,8,9]. However, heterogeneity [10] of reference devices is an additional issue as for adaptive approaches which always rely on real-time or online RSSI samples measured by a group of reference devices or users’ devices.…”

Indoor Localization Using Semi-Supervised Manifold Alignment with Dimension Expansion

“…Therefore, various indoor localization alternatives are proposed to make up for the GNSS in indoor environments, like the Assistant Global Positioning System (A-GPS) [6], cellular networks [7], Wireless Local Area Networks (WLAN) [8], Bluetooth [9], Radio Frequency Identification (RFID) [10], Near Field Communication (NFC) [11], Visible Light Communications (VLC) [12], Infrared Radiation (IR) [13] and motion sensor-based [14] localization approaches. Among them, the WLAN-based localization approach is preferred due to the wide deployment of WLAN infrastructures [15].…”