The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Recent Advances in Wireless Indoor Localization Techniques and System
Open accessAbstract
The advances in localization based technologies and the increasing importance of ubiquitous computing and context-dependent information have led to a growing business interest in location-based applications and services. Today, most application requirements are locating or real-time tracking of physical belongings inside buildings accurately; thus, the demand for indoor localization services has become a key prerequisite in some markets. Moreover, indoor localization technologies address the inadequacy of global positioning system inside a closed environment, like buildings. Based on this, though, this paper aims to provide the reader with a review of the recent advances in wireless indoor localization techniques and system to deliver a better understanding of state-of-the-art technologies and motivate new research efforts in this promising field. For this purpose, existing wireless localization position system and location estimation schemes are reviewed, as we also compare the related techniques and systems along with a conclusion and future trends.
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“RFID tags have some benefits: their signals can penetrate walls and obstacles, which cover a wide area, and reuse existing infrastructures, resulting in cost reduction [90].”
From Making Shopping Easy for People with Visual Impairment Using Mobile Assistive Technologies (2019) · cites this paper as [90]
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Cites
RADAR: an in-building RF-based user location and tracking system
Bahl, P., & Padmanabhan, V. N. (2002) · Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064) · doi:10.1109/infcom.2000.832252 · Cited by 8,380
Survey of Wireless Indoor Positioning Techniques and Systems
Liu, H., Darabi, H., Banerjee, P. P., & Liu, J. (2007) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2007.905750 · Cited by 4,184
A survey of indoor positioning systems for wireless personal networks
Gu, Y., Lo, A., & Niemegeers, I. G. M. M. (2009) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2009.090103 · Cited by 1,564
WLAN location determination via clustering and probability distributions
Youssef, M. A., Agrawala, A. K., & Shankar, A. U. (2004) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom.2003.1192736 · Cited by 925
Yang, Z., Wu, C., & Liu, Y. (2012) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/2348543.2348578 · Cited by 741
A Survey on Wireless Position Estimation
Gezici, S. (2007) · Wireless Personal Communications · doi:10.1007/s11277-007-9375-z · Cited by 555
Location, Localization, and Localizability
Liu, Y., Yang, Z., Wang, X., & Jian, L. (2010) · Journal of Computer Science and Technology · doi:10.1007/s11390-010-9324-2 · Cited by 313
FIFS: Fine-Grained Indoor Fingerprinting System
Xiao, J., Wu, K., Yi, Y., & Ni, L. M.-S. (2012) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn.2012.6289200 · Cited by 298
Localization Technologies for Indoor Human Tracking
Zhang, D., Xia, F., Yang, Z., Yao, L., & Zhao, W. (2010) · International Conference on Future Information Technology · doi:10.1109/futuretech.2010.5482731 · Cited by 272
A survey of mathematical methods for indoor localization
Seco, F. A., Jiménez, A. R., Prieto, C., Roa, J. O., & Koutsou, K. (2009) · IEEE International Symposium on Intelligent Signal Processing, · doi:10.1109/wisp.2009.5286582 · Cited by 209
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Low-Cost Indoor Localization Using RSSI and IoT: A Machine Learning Approach
Gupta, A., Agrawal, A., Shrivastava, S., Bhatt, U. R., Sarsodia, T., & Bhat, V. (2025) · IEEE International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET) · doi:10.1109/acroset66531.2025.11281051
Yadav, R. K., Kumar, D., Saraswat, R., Gite, S., & Pradhan, B. (2026) · Smart sensors, measurement and instrumentation · doi:10.1007/978-3-032-05507-1_4
Improvement of Geolocation Mobile Application Development for Emergency Situations
Shetty, K. S., & Manoj, R. K. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-2901-8_12
Paetow, T., Wichmann, J., & Leyer, M. (2026) · PLoS ONE · doi:10.1371/journal.pone.0344133
Contributions to RSSI-based geolocation
Elgui, K. (2020) · theses.fr (ABES) · doi:10.70675/759bab01z1f65z4d61z8facze326ea3b29a5
SANJAYA – Campus Tracking Intelligence
Kothuri Shashaank, B. S., & Lavanya, H. N. S. G. (2026) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-33210
A Deep Learning Lora-Based Indoor Localization Technique
Aqqad, K., Aqqad, K., Khdeir, N., Zabade, R., Ghanem, W. T., Lwas, A. K., Al-Rimawi, A., Habaebi, M. H., & Zyoud, A. (2025) · International Conference on Computer and Communication Engineering · doi:10.1109/iccce66530.2025.11473992
Protsak, D. V., & Pavlenko, V. V. (2026) · Komp'ûternì sistemi ta merežì · doi:10.23939/csn2026.01.203
Vătăjelu, D. O., & Beu, D. (2026) · Junior Conference on Lighting (Lighting) · doi:10.1109/lighting69141.2026.11598319
A RSSI Attenuation Model Considering Incident Angle and Wall Thickness in Indoor Environments
Gyu, H. J., Kim, S.-W., & Park, J. G. (2026) · International Conference on Ubiquitous and Future Networks · doi:10.1109/icufn69619.2026.11628604
Patel, B. U., & Patel, K. D. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-31523-6_36
Edge-AI Powered Autonomous Nano-Drones for Suspicious Activity Detection
Yadlapaty, D. S., Latha, S. A., Vijetha, T., Loganathan., A. S., Bhagyalakshmi, L., & Suman, S. K. (2026) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon69332.2026.11629727
Metadata
- Authors
- Farid, Z., Nordin, R., & Ismail, M.
- Affiliations
- National University of Malaysia (MY)
- Corresponding author
- Farid, Zahid
- Venue
- Journal of Computer Networks and Communications
- Publisher
- Wiley
- Published
- 2013
- Volume
- 2013
- Pages
- 1-12
- ISSN
- 2090-7141 (ISSN-L)
- 2090-715X
- OpenAlex
- W1963996689
- Cited by
- 514
- References
- 40
- Citations by year
- 2026: 11 · 2025: 17 · 2024: 15 · 2023: 18 · 2022: 30 · 2021: 44 · 2020: 59 · 2019: 79 · 2018: 67 · 2017: 79 · 2016: 44 · 2015: 39 · 2014: 12
- FWCI
- 19.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal of Computer Networks and Communications (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- Ministério da Ciência, Tecnologia e Inovação
- Universiti Kebangsaan Malaysia
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Wireless Communication Technologies
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- indoor positioning, wireless positioning, location-based services, ubiquitous computing, location estimation
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