WiFi Access Points Line-of-Sight Detection for Indoor Positioning Using the Signal Round Trip Time

The emerging WiFi Round Trip Time measured by the IEEE 802.11mc standard promised sub-meter-level accuracy for WiFi-based indoor positioning systems, under the assumption of an ideal line-of-sight path to the user. However, most workplaces with furniture and complex interiors cause the wireless signals to reflect, attenuate, and diffract in different directions. Therefore, detecting the non-line-of-sight condition of WiFi Access Points is crucial for enhancing the performance of indoor positioning systems. To this end, we propose a novel feature selection algorithm for non-line-of-sight identification of the WiFi Access Points. Using the WiFi Received Signal Strength and Round Trip Time as inputs, our algorithm employs multi-scale selection and Machine Learning-based weighting methods to choose the most optimal feature sets. We evaluate the algorithm on a complex campus WiFi dataset to demonstrate a detection accuracy of 93% for all 13 Access Points using 34 out of 130 features and only 3 s of test samples at any given time. For individual Access Point line-of-sight identification, our algorithm achieved an accuracy of up to 98%. Finally, we make the dataset available publicly for further research

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

Crowd sourced self beacon mapping with isolated signal aware bluetooth low energy positioning

In the past few decades, there has been an increase in the demand for positioning and navigation systems in various fields. Location-based service (LBS) usage covers a range of different variations from advertising and navigation to social media. Positioning based on a global navigation satellite system (GNSS) is the commonly used technology for positioning nowadays. However, the GNSS has a limitation of needing the satellites to be in line-of-sight (LOS) to provide an accurate position. Given this limitation, several different approaches are employed for indoor positioning needs. Bluetooth low energy (BLE) is one of the wireless technologies used for indoor positioning. However, BLE is well-known for having unstable signals, which will affect an estimated distance. Moreover, unlike Wi-Fi, BLE is not commonly and widely used, and BLE beacons must thus be placed to enable a venue with BLE positioning. The need to deploy the beacons results in a lengthy process to place and record the position of each placed beacon. This thesis proposes several solutions to solve these problems. A filter based on a Fourier transform is proposed to stabilise a BLE signal to obtain a more reliable reading. This allows the BLE signals to be less affected by internal variation than unfiltered signal. An obstruction-aware algorithm is also proposed using a statistical approach, which allows for the detection of non-line-of-sight (NLOS). These proposed solutions allow for a more stable BLE signal, which will result in a more reliable estimation of distance using the signal. The proposed solutions will enable accurate distance estimation, which will translate into improved positioning accuracy. An improvement in 88% of the test points is demonstrated by implementing the proposed solutions. Furthermore, to reduce the calibration needed when deploying the BLE beacons, a beacon-mapping algorithm is proposed that can be used to determine the position of BLE beacons. The proposed algorithm is based on trilateration with added information about direction. It uses the received signal strength (RSS) and the estimated distance to determine the error range, and a direction line is drawn based on the estimated error range. Finally, to further reduce the calibration needed, a crowdsource approach is proposed. This approach is proposed alongside a complete system to map the location of unknown beacons. The proposed system uses three phases to determine the user location, determine the beacons’ position, and recalculate BLE scans that have insufficient number of known BLE beacons. Each beacon and user’s position determined is assigned a weight to represent the reliability of that position. This is important to ensure that the position generated from a more reliable source will be emphasised. The proposed system demonstrates that the beacon-mapping system can map beacons with a root mean squared error (RMSE) of 4.64 m and a mean of absolute error (MAE) of 4.28 m