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(LE-Iot): a Survey of Positioning Techniques, Error Sources IN PREPARATION FOR IEEE COMMUNICATIONS SURVEYS & TUTORIALS 1 Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation You Li, Member, IEEE, Yuan Zhuang, Member, IEEE, Xin Hu, Senior Member, IEEE, Zhouzheng Gao, Jia Hu, Long Chen, Senior Member, IEEE, Zhe He, Member, IEEE, Ling Pei, Member, IEEE, Kejie Chen, Maosong Wang, Xiaoji Niu, Ruizhi Chen, John Thompson, Fellow, IEEE, Fadhel Ghannouchi, Fellow, IEEE, and Naser El-Sheimy Abstract—The Internet of Things (IoT) has started to empower existing localization sensors. the future of many industrial and mass-market applications. Index Terms—Low-Power Wide-Area Networks; indoor navi- Localization techniques are becoming key to add location con- gation; LoRa; NB-IoT; Sigfox; LTE-M; 5G; machine learning; ar- text to IoT data without human perception and intervention. tificial intelligence; neural networks; vehicle positioning; wireless Meanwhile, the newly-emerged Low-Power Wide-Area Network communication; geo-spatial information; location data fusion; (LPWAN) technologies have advantages such as long range, multi-sensor integration. 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- I. INTRODUCTION market localization applications. However, there are various error HE Internet of Things (IoT) is shaping the future of many sources that have limited the localization performance by using such IoT signals. This paper reviews the IoT localization system T industrial and mass-market applications [1]. As a core through the following sequence: IoT localization system review - technology to acquire spatial IoT data, localization techniques localization data sources - localization algorithms - localization are both an important application scenario and a distinguished error sources and mitigation - localization performance evalu- feature for the next-generation IoT [2]. In particular, Location- ation. Compared to the related surveys, this paper has a more Enabled IoT (LE-IoT) is becoming key to add location context comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and to IoT data without human perception and intervention. mitigation, an original review on IoT localization performance This section answers three questions: (1) why is it necessary evaluation, and a more comprehensive review of IoT localization to review localization techniques for IoT systems; (2) what are applications, opportunities, and challenges. Thus, this survey the advantages and challenges for IoT-signal-based localiza- provides comprehensive guidance for peers who are interested tion; and (3) what are the differences between this survey and in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the the previous ones. Table I illustrates the notations and symbols that will be used in this survey. Y. Li, Z. He, F. Ghannouchi, and N. El-Sheimy are with Department of A. Localization Technologies and Applications Geomatics Engineering, University of Calgary ([email protected]; [email protected]; [email protected]; [email protected]). Y. As a military-to-civilian application, localization has been Zhuang and R. Chen are with the State Key Laboratory of Surveying, intensively researched and successfully commercialized in Mapping and Remote Sensing, Wuhan University ([email protected]; outdoor areas. Figure 1 demonstrates the timeline of location- [email protected]). X. Hu is with the School of Electronic arXiv:2004.03738v1 [cs.NI] 7 Apr 2020 Engineering, Beijing University of Posts and Telecommunications based services (LBS). The devices used in these applica- ([email protected]). Z. Gao is with the Department of Land Sciences, tions are actually “things” in the IoT. The main technologies China University of Geosciences (Beijing) ([email protected]). J. for these use cases are Global Navigation Satellite Systems Hu is with the Department of Computer Science, the University of Exeter ([email protected]). L. Chen is with the School of Data and Computer (GNSS) and Inertial Navigation Systems (INS) [3]. In con- Science, Sun Yat-sen University ([email protected]). L. Pei is trast, robust localization in indoor and urban areas is still an with the Shanghai Key laboratory of Navigation and Location Based open challenge [4]. The development of indoor localization Services, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University ([email protected]). K. Chen is with technologies has two directions: professional and mass-market the the Department of Earth and Space Sciences, Southern University applications. Professional applications (e.g., underground con- of Science and Technology ([email protected]). M. Wang is with struction and machine industry) are commonly implemented the College of Intelligence Science and Technology, National University of Defense Technology ([email protected]). X. Niu is with in small areas, require high (e.g., decimeter or centimeter the GNSS Research Center, Wuhan University ([email protected]). J. level) location accuracy; thus, they need specific network Thompson is with the School of Engineering, University of Edinburgh infrastructure, devices, and manpower. Furthermore, many ([email protected]). Corresponding author: Y. Zhuang. This paper is partly supported by the National Natural Science Foundation of China professional applications also require intellisense techniques (NSFC) Grants (No. 41804027, 61771135, and 61873163), the Natural for environment sensing. Sciences and Engineering Research Council of Canada (NSERC) Discovery In contrast, mass-market applications commonly do not Grants, NSERC CREATE Grants, NSERC Strategic Partnership Grants, the Canada Research Chair (CRC) Grants, and the Alberta Innovates Technology need high localization accuracy. Meter-level accuracy is al- Future (AITF) Grants ready within the human sensing range. However, mass-market IN PREPARATION FOR IEEE COMMUNICATIONS SURVEYS & TUTORIALS 2 Fig. 1. Timeline of location-based applications. Devices used in these applications are “things” in IoT applications are usually implemented in wide areas with mental signal feature maps. varying environments; meanwhile, these applications are not • Dead-reckoning (DR). Motion sensor (e.g., inertial sen- affordable for specific devices or manpower. In particular, for sor, magnetometer, and odometer) based DR can provide mass-market IoT applications, power consumption is new key autonomous outdoor/indoor localization solutions [18]. factor that should be considered. In general, mass-market IoT Nevertheless, it is challenging to obtain long-term accu- localization is more challenging due to the following factors: rate DR solutions with low-cost sensors because of the • Many IoT nodes (including user end-devices) cannot af- existence of sensor errors [10], the misalignment angles ford GNSS receivers due to their high power consumption between vehicle (e.g., human body and land vehicles) and and cost. device [20], and the requirement for position and heading • The complexity of environments. For example, the oc- initialization. currence of Non-Line-of-Sight (NLoS) [5], multipath [6], • Vision localization. Vision sensors (e.g., cameras and wide-area [7] and multi-floor [8] effects, and interference Light Detection and Ranging (LiDAR)) can provide high by moving objects and human bodies [9]. location accuracy when loop closures have been correctly • The necessity of using low-cost sensors which have detected [21]. Meanwhile, some previous issues, such as significant sensor errors. The sensor errors also change a large computational load, are being eliminated by mod- over time and are susceptible to environmental factors ern processors and wireless transmission technologies. (e.g., temperature [10]). However, the performance of vision localization systems • The variety of node motions, such as changes of speed is highly dependent on whether the measured features and orientation [11]. Also, it may be difficult to constrain are distinct in space and stable over time. It is difficult node motion with predefined paths [12]. to maintain accuracy in environments with indistinct or There are various types of localization technologies. Their reduplicative features (e.g., areas with glass or solid-color advantages and disadvantages are walls) [22]. • GNSS. Because of its capability to provide global In general, the existing technologies have their own advan- weather-independent positioning solutions, GNSS has tages and limitations [23]. Thus, it is difficult to generate low- been commercialized successfully. However, its perfor- cost but high-performance localization solutions through the mance can be degraded by signal outages, degradations, use of a stand-alone technology. Due to the complementary and multipath in indoor and urban environments [3]. characteristics of various technologies, multi-sensor integra- • Wireless localization. Localization using wireless signals tion has become a trend to achieve reliable, continuous, and can provide long-term location accuracy. However, its accurate outdoor/indoor seamless localization. performance is highly dependent on signal availability The Low-Power Wide-Area Network (LPWAN) and 5G and geometry [13]; meanwhile, its accuracy can be de- technologies have been used
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