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

Technologies and solutions for location-based services in smart cities: past, present, and future

Location-based services (LBS) in smart cities have drastically altered the way cities operate, giving a new dimension to the life of citizens. LBS rely on location of a device, where proximity estimation remains at its core. The applications of LBS range from social networking and marketing to vehicle-toeverything communications. In many of these applications, there is an increasing need and trend to learn the physical distance between nearby devices. This paper elaborates upon the current needs of proximity estimation in LBS and compares them against the available Localization and Proximity (LP) finding technologies (LP technologies in short). These technologies are compared for their accuracies and performance based on various different parameters, including latency, energy consumption, security, complexity, and throughput. Hereafter, a classification of these technologies, based on various different smart city applications, is presented. Finally, we discuss some emerging LP technologies that enable proximity estimation in LBS and present some future research areas

A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives

Efficient localization plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would contribute to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities for enhancing localization of UAVs and UGVs. In this paper, we review the radio frequency (RF) based approaches for localization. We review the RF features that can be utilized for localization and investigate the current methods suitable for Unmanned vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localization for both UAVs and UGVs is examined, and the envisioned 5G NR for localization enhancement, and the future research direction are explored

Localization in Wireless Networks: The Potential of Triangulation Techniques

User localization is one of the key service-enablers in broadband mobile communications. Moreover, from a different point of view, next steps towards automatic network optimization also depend upon the capability of the system to perform real-time user localization, in order to obtain the traffic distribution. The aim of this paper is to get deeper into the feasibility and accuracy of different localization mechanisms ranging from triangulation to database correlation. Call tracing data extracted from a real operating mobile network have been used to assess these algorithms after the execution of an extensive measurements campaign. Results show that enhanced triangulation offers the best performance even outperforming other more sophisticated mechanisms like fingerprinting, without introducing any change in the network and without requiring any special characteristic of the user equipment. Indeed, the lack of precision of channel estimates, which for the same position could differ up to 10 dB, introduces a large uncertainty that harms localization mechanisms based on database correlation. Finally, this paper identifies the areas for improvement in triangulation to reach its maximum potential, provides details for its implementation and analyzes the performance of the different proposed enhancements. © 2012 Springer Science+Business Media, LLC.The authors would like to thank the funding received from the Ministerio de Industria, Turismo y Comercio within the Project number TSI-020100-2010-183 and from the Generalitat Valenciana IMIDTA/2010/800 funds.Osa Ginés, V.; Matamales Casañ, J.; Monserrat Del Río, JF.; López Bayo, J. (2013). Localization in Wireless Networks: The Potential of Triangulation Techniques. Wireless Personal Communications. 68(4):1525-1538. https://doi.org/10.1007/s11277-012-0537-2S15251538684Laiho J., Wacker A., Novosad T. (2006) Radio Network Planning and Optimisation for UMTS 2nd Edn. Wiley, AmsterdamOsa V., Matamales J., Monserrat J. et al (2010) Expert systems for the automatic optimisation of 3G networks. WAVES 2: 97–105Gustafsson F., Gunnarsson F. (2005) Mobile positioning using wireless networks: Possibilites and fundamental limitations based on available wireless network measurements. IEEE Signal Processing Magazine 22(4): 41–53. doi: 10.1109/MSP.2005.1458284Gezici S. (2008) A survey on wireless position estimation. Springer Wireless Personal Communications 44(3): 263–282. doi: 10.1007/s11277-007-9375-zBahillo, A., Mazuelas, S., & Lorenzo, R.M., et al. (2010). Accurate and integrated localization system for indoor environments based on IEEE 802.11 round-trip time measurements.EURASIP Journal on Wireless Communications and Networking, 2010, Article ID 102095, p. 13. doi: 10.1155/2010/102095 .Yang Z., Liu Y. (2010) Quality of trilateration: Confidence-based iterative localization. IEEE Transactions on Parallel and Distributed Systems 21(5): 631–640. doi: 10.1109/TPDS.2009.90Zimmermann, D., et al. (2004). Database correlation for positioning of mobile terminals in cellular networks using wave propagation models. In IEEE 60th Vehicular Technology Conference (Vol. 7, pp. 4682–4686) doi: 10.1109/VETECF.2004.1404980 .Zhao Y. (2002) Standardization of mobile phone positioning for 3G systems. IEEE Communications Magazine 40(7): 108–116. doi: 10.1109/MCOM.2002.1018015Caffery J.J., Stuber G.L. (1998) Overview of radiolocation in CDMA cellular systems. IEEE Communications Magazine 36(4): 38–45. doi: 10.1109/35.667411Kaaranen H., Ahtiainen A., Laitinen L., Naghian S., Niemi V. (2005) UMTS networks: Architecture, mobility and services. Wiley, Amsterdam3GPP. (2010). TS 25.215 Physical layer; Measurements (FDD). http://www.3gpp.org/ftp/Specs/archive/25_series/25.215/25215-920.zip .3GPP. (2010). TS 25.133 Requirements for support of radio resource management. http://www.3gpp.org/ftp/Specs/archive/25_series/25.133/25133-950.zip .3GPP. (2009). TS 45.010 Radio subsystem synchronization. http://www.3gpp.org/ftp/Specs/archive/45_series/45.010/45010-900.zip .Kos, T., Grgic, M., & Sisul, G. (2006). Mobile user positioning in GSM/UMTS cellular networks. In 48th International Symposium ELMAR-2006 focused on multimedia signal processing and communications (pp. 185–188). doi: 10.1109/ELMAR.2006.329545 .Kirkpatrick S., Gelatt C. D. Jr., Vecchi M. P. (1983) Optimization by simulated annealing. Science 220(4598): 671–680. doi: 10.1126/science.220.4598.671Hepsaydir, E. (1999). Analysis of mobile positioning measurements in CDMA cellular networks. In Radio and Wireless Conference, RAWCON 99 (pp. 73–76). doi: 10.1109/RAWCON.1999.810933 .Villebrun, E., Ben Hadj Alaya, A., Boursier, Y., & Noisette, N. (2006). Indoor Outdoor user discrimination in mobile wireless networks. In Vehicular Technology Conference 2006 Fall (pp. 1–5, 25–28). doi: 10.1109/VTCF.2006.500 .Farr, T.G., et al. (2007). The shuttle radar topography mission. Reviews of geophysics, Vol. 45, RG2004, 33 pp. doi: 10.1029/2005RG000183

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D