Toward Location-Enabled IoT (LE-IoT): IoT Positioning Techniques, Error Sources, and Error Mitigation

Abstract

Localization techniques are becoming key to add location context to Internet of Things (IoT) data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) and 5G technologies have become strong candidates for mass-market localization applications. However, various error sources 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 sourceslocalization algorithms -localization error sources and mitigation -localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-theart 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.

Indoor navigation: state of the art and future trends

“…By contrast, it is more challenging to use such methods in complex indoor and urban areas due to the existence of error sources such as multipath, NLoS conditions, and human-body effects. The survey paper (Li et al 2020a) has a detailed description of the error sources for position-fixing methods. It is difficult to alleviate the device-, signal-propagation-, and base-stationrelated error sources by the position-fixing technique itself.…”

“…Meanwhile, the newly-emerged IoT signals and the Low-Power Wide-Area Network (LPWAN, e.g., longrange (LoRa), Narrow Band-IoT (NB-IoT), Sigfox, and Long Term Evolution for Machines (LTE-M) have the advantages such as long-range, low-cost, low-powerconsumption, and massive connections (Li et al 2020a). Figure 3 demonstrates the communication ranges of 5G and LPWAN signals, with a comparison with other wireless technologies.…”

“…The accuracy ranges from 100-m-level to centimeter-level, depending on the base station deployment density and the type of measurement used. The survey paper (Li et al 2020a) provides a systematic review of 5G and LPWAN standardizations, PLAN techniques, error sources, and mitigation. In particular, it summarizes the PLAN errors by end-device-related errors, environment-related errors, base-station-related errors, and data-related errors.…”

“…An INS derives motion states by using angular-rate and linear specific-force measurements from gyros and accelerometers, respectively (Li et al 2020a) it is important to select a proper type of inertial sensors according to application requirements. The INS can provide autonomous PLAN solutions, which means it does not require the reception of external signals or the interaction with external environments.…”

Effect Evaluation of Spatial Characteristics on Map Matching-Based Indoor Positioning

“…While the Global Navigation Satellite Systems (GNSSs), such as GPS and Beidou, have been successfully applied in plenty of tasks, it does not work well in indoor environments since the satellite signals fail to pass through obstacles (e.g., buildings). To address the limitation of GNSS, a number of indoor positioning techniques have been proposed in recent years [ 1 , 2 , 3 , 4 , 5 , 6 ], including Wi-Fi, vision, light, and ultra-wideband (UWB), Bluetooth, and inertial sensors. However, each technique has its own drawbacks in terms of accuracy, coverage, cost, and power consumption.…”