A receive signal strength (RSS) fingerprinting-based indoor wireless localization system (I-WLS) uses a localization machine learning (ML) algorithm to estimate the location of an indoor user using RSS measurements as the position-dependent signal parameter (PDSP). There are two stages in the system's localization process: the offline phase and the online phase. The offline phase starts with the collection and generation of RSS measurement vectors from radio frequency (RF) signals received at fixed reference locations, followed by the construction of an RSS radio map. In the online phase, the instantaneous location of an indoor user is found by searching the RSS-based radio map for a reference location whose RSS measurement vector corresponds to the user's instantaneously acquired RSS measurements. The performance of the system depends on a number of factors that are present in both the online and offline stages of the localization process. This survey identifies these factors and examines how they impact the overall performance of the 2-dimensional (2-D) RSS fingerprinting-based I-WLS. The effects of these factors are discussed, as well as previous researchers' suggestions for minimizing or mitigating them and future research trends in RSS fingerprinting-based I-WLS.

Department of Electronics and Telecommunications Engineering Ahmadu Bello University Zaria 810106 Nigeria

Department of Informatics and Quantitative Methods Faculty of Informatics and Management University of Hradec Kralove 500 03 Hradec Kralove Czech Republic

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So H.C. Source Localization: Algorithms and Analysis. In: Michael B.R., editor. Handbook of Position Location: Theory, Practice, and Advances. John Wiley & Sons, Inc.; Hoboken, NJ, USA: 2012. pp. 25–66.

Yaro A.S., Sha’ameri A.Z. Development of an Association Technique for a 3-Dimensional Minimum Configuration Multilateration System. Int. J. Integr. Eng. 2020;12:59–71. doi: 10.30880/ijie.2020.12.01.006. DOI

Xiaohua T., Xinyu T., Xinbing W. Wireless Localization Techniques. 1st ed. Volume 1 Springer International Publishing; Cham, Switzerland: 2023.

Shang S., Wang L. Overview of WiFi Fingerprinting-based Indoor Positioning. IET Commun. 2022;16:725–733. doi: 10.1049/cmu2.12386. DOI

Obeidat H., Shuaieb W., Obeidat O., Abd-Alhameed R. A Review of Indoor Localization Techniques and Wireless Technologies. Wirel. Pers. Commun. 2021;119:289–327. doi: 10.1007/s11277-021-08209-5. DOI

Ji T., Li W., Zhu X., Liu M. Survey on Indoor Fingerprint Localization for BLE; Proceedings of the 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC); Chongqing, China. 4–6 March 2022; pp. 129–134.

Fronckova K., Prazak P. Possibilities of Using Kalman Filters in Indoor Localization. Mathematics. 2020;8:1564. doi: 10.3390/math8091564. DOI

Roy P., Chowdhury C. A Survey on Ubiquitous WiFi-Based Indoor Localization System for Smartphone Users from Implementation Perspectives. CCF Trans. Pervasive Comput. Interact. 2022;4:298–318. doi: 10.1007/s42486-022-00089-3. DOI

Liu W., Cheng Q., Deng Z., Chen H., Fu X., Zheng X., Zheng S., Chen C., Wang S. Survey on CSI-Based Indoor Positioning Systems and Recent Advances; Proceedings of the 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN); Beijing, China. 5–8 September 2019; pp. 1–8.

Tian X., Tong X., Wang X. Wireless Localization Techniques. Springer International Publishing; Cham, Switzerland: 2023. RSS Localization for Large-Scale Deployment; pp. 155–267.

Alitaleshi A., Jazayeriy H., Kazemitabar J. EA-CNN: A Smart Indoor 3D Positioning Scheme Based on Wi-Fi Fingerprinting and Deep Learning. Eng. Appl. Artif. Intell. 2023;117:105509. doi: 10.1016/j.engappai.2022.105509. DOI

Martin-Escalona I., Zola E. Improving Fingerprint-Based Positioning by Using IEEE 802.11mc FTM/RTT Observables. Sensors. 2022;23:267. doi: 10.3390/s23010267. PubMed DOI PMC

Kriz P., Maly F., Kozel T. Improving Indoor Localization Using Bluetooth Low Energy Beacons. Mob. Inf. Syst. 2016;2016:2083094. doi: 10.1155/2016/2083094. DOI

Kunhoth J., Karkar A., Al-Maadeed S., Al-Ali A. Indoor Positioning and Wayfinding Systems: A Survey. Hum. Cent. Comput. Inf. Sci. 2020;10:18. doi: 10.1186/s13673-020-00222-0. DOI

Khalajmehrabadi A., Gatsis N., Akopian D. Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges. IEEE Commun. Surv. Tutor. 2017;19:1974–2002. doi: 10.1109/COMST.2017.2671454. DOI

Alhomayani F., Mahoor M.H. Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review. J. Locat. Based Serv. 2020;14:129–200. doi: 10.1080/17489725.2020.1817582. DOI

Isaia C., Michaelides M.P. A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G. Signals. 2023;4:90–136. doi: 10.3390/signals4010006. DOI

Subedi S., Pyun J.-Y. A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies. Sensors. 2020;20:7230. doi: 10.3390/s20247230. PubMed DOI PMC

Farid Z., Nordin R., Ismail M. Recent Advances in Wireless Indoor Localization Techniques and System. J. Comput. Netw. Commun. 2013;2013:185138. doi: 10.1155/2013/185138. DOI

Simon G., Sujbert L. Special Issue on “Recent Advances in Indoor Localization Systems and Technologies”. Appl. Sci. 2021;11:4191. doi: 10.3390/app11094191. DOI

Tiglao N.M., Alipio M., dela Cruz R., Bokhari F., Rauf S., Khan S.A. Smartphone-Based Indoor Localization Techniques: State-of-the-Art and Classification. Measurement. 2021;179:109349. doi: 10.1016/j.measurement.2021.109349. DOI

Zafari F., Gkelias A., Leung K.K. A Survey of Indoor Localization Systems and Technologies. IEEE Commun. Surv. Tutor. 2019;21:2568–2599. doi: 10.1109/COMST.2019.2911558. DOI

Roy P., Chowdhury C. A Survey of Machine Learning Techniques for Indoor Localization and Navigation Systems. J. Intell. Robot. Syst. 2021;101:63. doi: 10.1007/s10846-021-01327-z. DOI

Hossain A.K.M.M., Soh W.-S. A Survey of Calibration-Free Indoor Positioning Systems. Comput. Commun. 2015;66:1–13. doi: 10.1016/j.comcom.2015.03.001. DOI

Liu F., Liu J., Yin Y., Wang W., Hu D., Chen P., Niu Q. Survey on WiFi-based Indoor Positioning Techniques. IET Commun. 2020;14:1372–1383. doi: 10.1049/iet-com.2019.1059. DOI

BASRI C., el Khadimi A. Survey on Indoor Localization System and Recent Advances of WIFI Fingerprinting Technique; Proceedings of the 2016 5th International Conference on Multimedia Computing and Systems (ICMCS); Marrakech, Morocco. 29 September–1 October 2016; pp. 253–259.

Lie M.M.K., Kusuma G.P. A Fingerprint-Based Coarse-to-Fine Algorithm for Indoor Positioning System Using Bluetooth Low Energy. Neural Comput. Appl. 2021;33:2735–2751. doi: 10.1007/s00521-020-05159-0. DOI

Cao X., Chen G., Zhuang Y., Wang X., Yang X. The Deployment of a Wi-Fi Positioning System via Crowdsourcing. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2022;XLVI-3/W1-2022:7–14. doi: 10.5194/isprs-archives-XLVI-3-W1-2022-7-2022. DOI

Ji W., Zhao K., Zheng Z., Yu C., Huang S. Multivariable Fingerprints with Random Forest Variable Selection for Indoor Positioning System. IEEE Sens. J. 2022;22:5398–5406. doi: 10.1109/JSEN.2021.3103863. DOI

Alfakih M., Keche M., Benoudnine H. Gaussian Mixture Modeling for Indoor Positioning WIFI Systems; Proceedings of the 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT); Tlemcen, Algeria. 25–27 May 2015; pp. 1–5.

Wang X., Cong S. An Advanced Algorithm for Fingerprint Localization Based on Kalman Filter; Proceedings of the 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS); Wuhan, China. 22–23 March 2018; pp. 1–5.

Singh N., Choe S., Punmiya R. Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview. IEEE Access. 2021;9:127150–127174. doi: 10.1109/ACCESS.2021.3111083. DOI

Feng X., Nguyen K.A., Luo Z. A Survey of Deep Learning Approaches for WiFi-Based Indoor Positioning. J. Inf. Telecommun. 2022;6:163–216. doi: 10.1080/24751839.2021.1975425. DOI

Chen R., Chen L. Smartphone-Based Indoor Positioning Technologies. In: Wenzhong S., Michael F.G., Michael B., Mei-Po K., Anshu Z., editors. Urban Informatics. Springer; Singapore: 2021. pp. 467–490.

Eadicicco L. Apple and Samsung Newest Phones Use a Little-Known Technology That Lets Your Phone Understand Exactly Where It Is—And Could Mean You Never Misplace Anything Again. [(accessed on 17 February 2023)];Bus. Insider Afr. 2020 Available online: https://www.businessinsider.com/uwb-explained-samsung-galaxy-note-ultra-apple-iphone-features-airdrop-2020-8.

Uradzinski M., Guo H., Liu X., Yu M. Advanced Indoor Positioning Using Zigbee Wireless Technology. Wirel. Pers. Commun. 2017;97:6509–6518. doi: 10.1007/s11277-017-4852-5. DOI

Hayward S.J., van Lopik K., Hinde C., West A.A. A Survey of Indoor Location Technologies, Techniques and Applications in Industry. Internet Things. 2022;20:100608. doi: 10.1016/j.iot.2022.100608. DOI

Yang H., Wang Y., Seow C.K., Sun M., Si M., Huang L. UWB Sensor-Based Indoor LOS/NLOS Localization With Support Vector Machine Learning. IEEE Sens. J. 2023;23:2988–3004. doi: 10.1109/JSEN.2022.3232479. DOI

Zhang H., Wang Q., Yan C., Xu J., Zhang B. Research on UWB Indoor Positioning Algorithm under the Influence of Human Occlusion and Spatial NLOS. Remote Sens. 2022;14:6338. doi: 10.3390/rs14246338. DOI

Flueratoru L., Shubina V., Niculescu D., Lohan E.S. On the High Fluctuations of Received Signal Strength Measurements With BLE Signals for Contact Tracing and Proximity Detection. IEEE Sens. J. 2022;22:5086–5100. doi: 10.1109/JSEN.2021.3095710. DOI

Karanja H.S., Atayero A. Cellular Received Signal Strength Indicator Dataset. IEEE Dataport 2020. [(accessed on 17 February 2023)]. Available online: https://data.mendeley.com/datasets/648sy7skrh.

Wei Y., Zheng R. Handling Device Heterogeneity in Wi-Fi Based Indoor Positioning Systems; Proceedings of the IEEE INFOCOM 2020—IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS); Toronto, ON, Canada. 6–9 July 2020; pp. 556–561.

Zhang S., Xiao W., Zhang B., Soong B.H. Wireless Indoor Localization for Heterogeneous Mobile Devices; Proceedings of the 2012 International Conference on Computational Problem-Solving (ICCP); Leshan, China. 19–21 October 2012; pp. 96–100.

Park J., Curtis D., Teller S., Ledlie J. Implications of Device Diversity for Organic Localization; Proceedings of the 2011 Proceedings IEEE INFOCOM; Shanghai, China. 10–15 April 2011; pp. 3182–3190.

Tsui A.W., Chuang Y.-H., Chu H.-H. Unsupervised Learning for Solving RSS Hardware Variance Problem in WiFi Localization. Mob. Netw. Appl. 2009;14:677–691. doi: 10.1007/s11036-008-0139-0. DOI

Mahtab Hossain A.K.M., Jin Y., Soh W.-S., Van H.N. SSD: A Robust RF Location Fingerprint Addressing Mobile Devices’ Heterogeneity. IEEE Trans. Mob. Comput. 2013;12:65–77. doi: 10.1109/TMC.2011.243. DOI

Gentner C., Gunther D., Kindt P.H. Identifying the BLE Advertising Channel for Reliable Distance Estimation on Smartphones. IEEE Access. 2022;10:9563–9575. doi: 10.1109/ACCESS.2022.3140803. DOI

Nabati M., Ghorashi S.A. A Real-Time Fingerprint-Based Indoor Positioning Using Deep Learning and Preceding States. Expert Syst. Appl. 2023;213:118889. doi: 10.1016/j.eswa.2022.118889. DOI

Mohsin N., Payandeh S., Ho D., Gelinas J.P. Study of Activity Tracking through Bluetooth Low Energy-Based Network. J. Sens. 2019;2019:6876925. doi: 10.1155/2019/6876925. DOI

Neburka J., Tlamsa Z., Benes V., Polak L., Kaller O., Bolecek L., Sebesta J., Kratochvil T. Study of the Performance of RSSI Based Bluetooth Smart Indoor Positioning; Proceedings of the 2016 26th International Conference Radioelektronika (RADIOELEKTRONIKA); Kosice, Slovakia. 19–20 April 2016; pp. 121–125.

Paek J., Ko J., Shin H. A Measurement Study of BLE IBeacon and Geometric Adjustment Scheme for Indoor Location-Based Mobile Applications. Mob. Inf. Syst. 2016;2016:8367638. doi: 10.1155/2016/8367638. DOI

Ghaboosi K., Xiao Y., Latva-Aho M., Khalaj B.H. Emerging Wireless LANs, Wireless PANs, and Wireless MANs. John Wiley & Sons, Inc.; Hoboken, NJ, USA: 2009. Overview of IEEE 802.15.2: Coexistence of Wireless Personal Area Networks with Other Unlicensed Frequency Bands Operating Wireless Devices; pp. 135–150.

Golmie N., Chevrollier N., Rebala O. Bluetooth and WLAN Coexistence: Challenges and Solutions. IEEE Wirel. Commun. 2003;10:22–29. doi: 10.1109/MWC.2003.1265849. DOI

Koubaa A., ben Jamaa M., AlHaqbani A. An Empirical Analysis of the Impact of RSS to Distance Mapping on Localization in WSNs; Proceedings of the Third International Conference on Communications and Networking; Hammamet, Tunisia. 29 March–1 April 2012; pp. 1–7.

Ibrahim M., Torki M., ElNainay M. CNN Based Indoor Localization Using RSS Time-Series; Proceedings of the 2018 IEEE Symposium on Computers and Communications (ISCC); Natal, Brazil. 25–28 June 2018; pp. 01044–01049.

Cheng W., Tan K., Omwando V., Zhu J., Mohapatra P. RSS-Ratio for Enhancing Performance of RSS-Based Applications; Proceedings of the 2013 Proceedings IEEE INFOCOM; Turin, Italy. 14–19 April 2013; pp. 3075–3083.

Huang B., Liu J., Sun W., Yang F. A Robust Indoor Positioning Method Based on Bluetooth Low Energy with Separate Channel Information. Sensors. 2019;19:3487. doi: 10.3390/s19163487. PubMed DOI PMC

Polak L., Rozum S., Slanina M., Bravenec T., Fryza T., Pikrakis A. Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning. Sensors. 2021;21:4605. doi: 10.3390/s21134605. PubMed DOI PMC

Zhou R., Meng F., Zhou J., Teng J. A Wi-Fi Indoor Positioning Method Based on an Integration of EMDT and WKNN. Sensors. 2022;22:5411. doi: 10.3390/s22145411. PubMed DOI PMC

Ezhumalai B., Song M., Park K. An Efficient Indoor Positioning Method Based on Wi-Fi RSS Fingerprint and Classification Algorithm. Sensors. 2021;21:3418. doi: 10.3390/s21103418. PubMed DOI PMC

Zhou R., Yang Y., Chen P. An RSS Transform—Based WKNN for Indoor Positioning. Sensors. 2021;21:5685. doi: 10.3390/s21175685. PubMed DOI PMC

Zou H., Jin M., Jiang H., Xie L., Spanos C.J. WinIPS: WiFi-Based Non-Intrusive Indoor Positioning System With Online Radio Map Construction and Adaptation. IEEE Trans. Wirel. Commun. 2017;16:8118–8130. doi: 10.1109/TWC.2017.2757472. DOI

al Mamun M.A., Anaya D.V., Yuce M.R. FaStER: Fast, Stable, Expendable and Reliable Radio Map for Indoor Localization; Proceedings of the 2021 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL); Virtual. 22–25 March 2021; pp. 1–4.

Kim T., Lee J.H., Shin B., Yu C., Kyung H., Lee T. Very Fast Fingerprinting DB Construction for Precise Indoor Localization; Proceedings of the 2022 International Conference on Electronics, Information, and Communication (ICEIC); Jeju, Republic of Korea. 6–9 February 2022; pp. 1–4.

Khoo H.W., Ng Y.H., Tan C.K. Enhanced Radio Map Interpolation Methods Based on Dimensionality Reduction and Clustering. Electronics. 2022;11:2581. doi: 10.3390/electronics11162581. DOI

Kolakowski M. Automated Calibration of RSS Fingerprinting Based Systems Using a Mobile Robot and Machine Learning. Sensors. 2021;21:6270. doi: 10.3390/s21186270. PubMed DOI PMC

Kolakowski M. Automatic Radio Map Creation in a Fingerprinting-based BLE/UWB Localisation System. IET Microw. Antennas Propag. 2020;14:1758–1765. doi: 10.1049/iet-map.2019.0953. DOI

Kawecki R., Hausman S., Korbel P. Performance of Fingerprinting-Based Indoor Positioning with Measured and Simulated RSSI Reference Maps. Remote Sens. 2022;14:1992. doi: 10.3390/rs14091992. DOI

Nguyen T.L.N., Shin Y. An Efficient RSS Localization for Underwater Wireless Sensor Networks. Sensors. 2019;19:3105. doi: 10.3390/s19143105. PubMed DOI PMC

Ji Y. Fingerprint Map Construction Based on Multi-Chain Interpolation. In: Xiao M., Yu L., editors. Proceedings of the International Conference on Signal Processing and Communication Security (ICSPCS 2022), Dalian, China, 2 November 2022. SPIE; Paris, France: 2022. p. 11.

Wang Y., Hua G., Tao W., Zhang L. Improved RSS Data Generation Method Based on Kriging Interpolation Algorithm. Wirel. Pers. Commun. 2020;115:2457–2469. doi: 10.1007/s11277-020-07690-8. DOI

Alsadik B., Karam S. The Simultaneous Localization and Mapping (SLAM)-An Overview. J. Appl. Sci. Technol. Trends. 2021;2:120–131. doi: 10.38094/jastt204117. DOI

Ji Y., Zhao X., Wei Y., Wang C. Generating Indoor Wi-Fi Fingerprint Map Based on Crowdsourcing. Wirel. Netw. 2022;28:1053–1065. doi: 10.1007/s11276-022-02898-x. DOI

Poulose A., Kim J., Han D.S. A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements. Appl. Sci. 2019;9:4379. doi: 10.3390/app9204379. DOI

Li W., Xu X., Wang Y., Li D. A Survey of Crowdsourcing-Based Indoor Map Learning Methods Using Smartphones. Results Control. Optim. 2023;10:100186. doi: 10.1016/j.rico.2022.100186. DOI

Assayag Y., Oliveira H., Souto E., Barreto R., Pazzi R. Indoor Positioning System Using Synthetic Training and Data Fusion. IEEE Access. 2021;9:115687–115699. doi: 10.1109/ACCESS.2021.3105188. DOI

Wilson C., Lang H.-D., Li Y., Sarris C.D., Zhang X. Deterministic Wireless Propagation Model Assisted Indoor Positioning; Proceedings of the 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI); Singapore. 4–10 December 2021; pp. 453–454.

Nessa A., Adhikari B., Hussain F., Fernando X.N. A Survey of Machine Learning for Indoor Positioning. IEEE Access. 2020;8:214945–214965. doi: 10.1109/ACCESS.2020.3039271. DOI

Behlül N.Ö., Ayhan C. Effect of Calibration Point Density on Indoor Positioning Accuracy: A Study Based on Wi-Fi Fingerprinting Method. Adv. Geomat. 2021;1:21–26.

Aranda F.J., Parralejo F., Álvarez F.J., Paredes J.A. Performance Analysis of Fingerprinting Indoor Positioning Methods with BLE. Expert Syst. Appl. 2022;202:117095. doi: 10.1016/j.eswa.2022.117095. DOI

Liu S., de Lacerda R., Fiorina J. Performance Analysis of Adaptive K for Weighted K-Nearest Neighbor Based Indoor Positioning; Proceedings of the 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring); Helsinki, Finland. 19–22 June 2022; pp. 1–5.

Rezgui Y., Pei L., Chen X., Wen F., Han C. An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices. Mob. Inf. Syst. 2017;2017:6268797. doi: 10.1155/2017/6268797. DOI

Wang Y., Shang Y., Tao W., Yu Y. Target Positioning Algorithm Based on RSS Fingerprints of SVM of Fuzzy Kernel Clustering. Wirel. Pers. Commun. 2021;119:2893–2911. doi: 10.1007/s11277-021-08377-4. DOI

Javadi S., Moosaei H., Ciuonzo D. Learning Wireless Sensor Networks for Source Localization. Sensors. 2019;19:635. doi: 10.3390/s19030635. PubMed DOI PMC

Alfakih M., Keche M., Benoudnine H., Meche A. Improved Gaussian Mixture Modeling for Accurate Wi-Fi Based Indoor Localization Systems. Phys. Commun. 2020;43:101218. doi: 10.1016/j.phycom.2020.101218. DOI

Maung Maung N.A., Lwi B.Y., Thida S. An Enhanced RSS Fingerprinting-Based Wireless Indoor Positioning Using Random Forest Classifier; Proceedings of the 2020 International Conference on Advanced Information Technologies (ICAIT); Yangon, Myanmar. 4–5 November 2020; pp. 59–63.

Lee S., Kim J., Moon N. Random Forest and WiFi Fingerprint-Based Indoor Location Recognition System Using Smart Watch. Hum. Cent. Comput. Inf. Sci. 2019;9:6. doi: 10.1186/s13673-019-0168-7. DOI

Ye Q., Fan X., Fang G., Bie H. Exploiting Temporal Dependency of RSS Data with Deep for IoT-Oriented Wireless Indoor Localization. Internet Technol. Lett. 2022:1. doi: 10.1002/itl2.366. DOI

Yang T., Cabani A., Chafouk H. A Survey of Recent Indoor Localization Scenarios and Methodologies. Sensors. 2021;21:8086. doi: 10.3390/s21238086. PubMed DOI PMC

Arora S., Barak B. Computational Complexity. Cambridge University Press; Cambridge, UK: 2009.

Torres-Sospedra J., Montoliu R., Martinez-Uso A., Avariento J.P., Arnau T.J., Benedito-Bordonau M., Huerta J. UJIIndoorLoc: A New Multi-Building and Multi-Floor Database for WLAN Fingerprint-Based Indoor Localization Problems; Proceedings of the 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN); Busan, Republic of Korea. 27–30 October 2014; pp. 261–270.

Bi J., Wang Y., Yu B., Cao H., Shi T., Huang L. Supplementary Open Dataset for WiFi Indoor Localization Based on Received Signal Strength. Satell. Navig. 2022;3:25. doi: 10.1186/s43020-022-00086-y. DOI

Baronti P., Barsocchi P., Chessa S., Mavilia F., Palumbo F. Indoor Bluetooth Low Energy Dataset for Localization, Tracking, Occupancy, and Social Interaction. Sensors. 2018;18:4462. doi: 10.3390/s18124462. PubMed DOI PMC

Byrne D., Kozlowski M., Santos-Rodriguez R., Piechocki R., Craddock I. Residential Wearable RSSI and Accelerometer Measurements with Detailed Location Annotations. Sci. Data. 2018;5:180168. doi: 10.1038/sdata.2018.168. PubMed DOI PMC

Mendoza-Silva G., Richter P., Torres-Sospedra J., Lohan E., Huerta J. Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning. Data. 2018;3:3. doi: 10.3390/data3010003. DOI

Potorti F., Park S., Crivello A., Palumbo F., Girolami M., Barsocchi P., Lee S., Torres-Sospedra J., Ruiz A.R.J., Perez-Navarro A., et al. The IPIN 2019 Indoor Localisation Competition—Description and Results. IEEE Access. 2020;8:206674–206718. doi: 10.1109/ACCESS.2020.3037221. DOI

Abubakarsidiq M.R., Bang W. Wi-Fi Fingerprinting Radio Map Database for Indoor Localization. IEEE Dataport 2023. [(accessed on 16 February 2023)]. Available online: https://ieee-dataport.org/documents/wifi-fingerprinting-radio-map-database-indoor-localization.