The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A Survey of Indoor Localization Systems and Technologies
Open accessAbstract
Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques, such as angle of arrival (AoA), time of flight (ToF), return time of flight (RTOF), and received signal strength (RSS); based on technologies, such as WiFi, radio frequency identification device (RFID), ultra wideband (UWB), Bluetooth, and systems that have been proposed in the literature. This paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability, and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.
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Cites
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Internet of Things in industries: A survey
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Cited by
A Survey on Indoor Positioning Systems for IoT-Based Applications
Farahsari, P. S., Farahzadi, A., Rezazadeh, J., & Bagheri, A. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3149048 · Cited by 546
A Survey of Machine Learning for Indoor Positioning
Nessa, A., Adhikari, B., Hussain, F., & Fernando, X. N. (2020) · IEEE Access · doi:10.1109/access.2020.3039271 · Cited by 269
Federated Learning for RSS Fingerprint-based Localization: A Privacy-Preserving Crowdsourcing Method
Ciftler, B. S., Albaseer, A., Lasla, N., & Abdallah, M. (2020) · 2020 International Wireless Communications and Mobile Computing (IWCMC) · doi:10.1109/iwcmc48107.2020.9148111 · Cited by 76
Li, W., Zhang, C., & Tanaka, Y. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2998116 · Cited by 73
Wang, C., Zhang, D., Yue, J., Zhang, X., Lin, H., Sun, X., Cui, A., Zhang, T., Chen, C., & Fei, T. (2023) · Nature Communications · doi:10.1038/s41467-023-40341-7 · Cited by 35
“Especially with the advent of smartphones and WIFI, people canaccess the Internet to obtainimages and informationanytimeand anywhere 9 .”
Metadata
- Authors
- Zafari, F., Gkelias, A., & Leung, K. K.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2019
- Volume
- 21
- Issue
- 3
- Pages
- 2568-2599
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W2964029185
- Cited by
- 2,530
- References
- 6
- FWCI
- 103.19
- Type
- journal article
- Language
- en
- Open access
- green
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