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

Algorithms for Autonomous Personal Navigation Systems

Personal positioning is a challenging topic in the area of navigation mainly because of the cost, size and power consumption constraints imposed on the hardware. Satellite based positioning techniques can meet the requirements for many applications, but cover well only outdoor environment. Problems like weak satellite signals make the positioning impossible indoors. Urban canyons are also difficult areas for GNSS based navigation because of large multipath errors and satellite signal outages. Many applications require seamless positioning in all environments. However, there is no overall solution for navigation in GNSS denied environment, which is reliable, accurate, cost effective and quickly installed. Recently developed systems for indoor positioning often require pre-installed infrastructure. Another approach is to use fully autonomous navigation systems based on self-contained sensors and street or indoor maps. This thesis is concerned with autonomous personal navigation devices, which do not rely on the reception of external information, like satellite or terrestrial signals. The three proposed algorithms can be integrated into personal navigation systems. The first algorithm computes positioning for a map aided navigation system designed for land vehicles traveling on road network. The novelty is in application of particle filtering to vehicle navigation using road network database. The second algorithm is aimed at map aided vehicle navigation indoors. The novelty is in the method for correction of position and heading. The third algorithm computes solution for pedestrian navigation system, which is based on body mounted inertial measurement unit and models of human gait

Fusion of non-visual and visual sensors for human tracking

Human tracking is an extensively researched yet still challenging area in the Computer Vision field, with a wide range of applications such as surveillance and healthcare. People may not be successfully tracked with merely the visual information in challenging cases such as long-term occlusion. Thus, we propose to combine information from other sensors with the surveillance cameras to persistently localize and track humans, which is becoming more promising with the pervasiveness of mobile devices such as cellphones, smart watches and smart glasses embedded with all kinds of sensors including accelerometers, gyroscopes, magnetometers, GPS, WiFi modules and so on. In this thesis, we firstly investigate the application of Inertial Measurement Unit (IMU) from mobile devices to human activity recognition and human tracking, we then develop novel persistent human tracking and indoor localization algorithms by the fusion of non-visual sensors and visual sensors, which not only overcomes the occlusion challenge in visual tracking, but also alleviates the calibration and drift problems in IMU tracking --Abstract, page iii

A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

Positioning algorithms for RFID-based multi-sensor indoor/outdoor positioning techniques

Position information has been very important. People need this information almost everywhere all the time. However, it is a challenging task to provide precise positions indoor/outdoor seamlessly. Outdoor positioning has been widely studied and accurate positions can usually be achieved by well developed GPS techniques. However, these techniques are difficult to be used indoor since GPS signals are too weak to be received. The alternative techniques, such as inertial sensors and radio-based pseudolites, can be used for indoor positioning but have limitations. For example, the inertial sensors suffer from drifting problems caused by the accumulating errors of measured acceleration and velocity and the radio-based techniques are prone to the obstructions and multipath effects of the transmitted signals. It is therefore necessary to develop improved methods for minimising the limitations of the current indoor positioning techniques and providing an adequately precise solution of the indoor positioning and seamless indoor/outdoor positioning. The main objectives of this research are to investigate and develop algorithms for the low-cost and portable indoor personal positioning system using Radio Frequency Identification (RFID) based multi-sensor techniques, such as integrating with Micro-Electro-Mechanical Systems (MEMS) Inertial Navigation System (INS) and/or GPS. A RFID probabilistic Cell of Origin (CoO) algorithm is developed, which is superior to the conventional CoO positioning algorithm in its positioning accuracy and continuity. Integration algorithms are also developed for RFID-based multi-sensor positioning techniques, which can provide metre-level positioning accuracy for dynamic personal positioning indoors. In addition, indoor/outdoor seamless positioning algorithms are investigated based on the iterated Reduced Sigma Point Kalman Filter (RSPKF) for RFID/MEMS INS/low-cost GPS integrated technique, which can provide metre-level positioning accuracy for personal positioning. 3-D GIS assisted personal positioning algorithms are also developed, including the map matching algorithm based on the probabilistic maps for personal positioning and the Site Specific (SISP) propagation model for efficiently generating the RFID signal strength distributions in location fingerprinting algorithms. Both static and dynamic indoor positioning experiments have been conducted using the RFID and RFID/MEMS INS integrated techniques. Metre-level positioning accuracy is achieved (e.g. 3.5m in rooms and 1.5m in stairways for static position, 4m for dynamic positioning and 1.7m using the GIS assisted positioning algorithms). Various indoor/outdoor experiments have been conducted using the RFID/MEMS INS/low-cost GPS integrated technique. It indicates that the techniques selected in this study, integrated with the low-cost GPS, can be used to provide continuous indoor/outdoor positions in approximately 4m accuracy with the iterated RSPKF. The results from the above experiments have demonstrated the improvements of integrating multiple sensors with RFID and utilizing the 3-D GIS data for personal positioning. The algorithms developed can be used in a portable RFID based multi-sensor positioning system to achieve metre-level accuracy in the indoor/outdoor environments. The proposed system has potential applications, such as tracking miners underground, monitoring athletes, locating first responders, guiding the disabled and providing other general location based services (LBS)

A Meta-Review of Indoor Positioning Systems

An accurate and reliable Indoor Positioning System (IPS) applicable to most indoor scenarios has been sought for many years. The number of technologies, techniques, and approaches in general used in IPS proposals is remarkable. Such diversity, coupled with the lack of strict and verifiable evaluations, leads to difficulties for appreciating the true value of most proposals. This paper provides a meta-review that performed a comprehensive compilation of 62 survey papers in the area of indoor positioning. The paper provides the reader with an introduction to IPS and the different technologies, techniques, and some methods commonly employed. The introduction is supported by consensus found in the selected surveys and referenced using them. Thus, the meta-review allows the reader to inspect the IPS current state at a glance and serve as a guide for the reader to easily find further details on each technology used in IPS. The analyses of the meta-review contributed with insights on the abundance and academic significance of published IPS proposals using the criterion of the number of citations. Moreover, 75 works are identified as relevant works in the research topic from a selection of about 4000 works cited in the analyzed surveys