Unsupervised WiFi-Enabled IoT Device-User Association for Personalized Location-Based Service
Abstract
A fundamental building block toward personalized location-based service and context-aware service in smart buildings is the knowledge about the identity and mobility of users in indoor environments. Conventional user identification systems require the deployment of dedicated infrastructure or the active user involvement. Motivated by the widespread usage of the WiFi-enabled mobile device (MD), e.g., people usually carry at least one MD in their daily lives, in this paper, we propose WinDUA, a WiFi-enabled nonintrusive device and user association scheme to infer user identity and mobility via a novel unsupervised association learning algorithm. First, we utilize our WiFi-based indoor positioning system to obtain the historical location data of each MD using only existing WiFi infrastructure in a nonintrusive manner. Then, we classify all the MDs into two categories: 1) static device (SD) and 2) mobile phone (MP), according to their location variations and overnight presences. Subsequently, we estimate the correct mapping between each SD and its user through hierarchical clustering and location similarity matching between its location and user's personal space. Finally, we make possible pairs of MP and SD according to their duration of coexistence as well as the historical location similarity to associate the owner of each MP. Real-world experiments are conducted in an office, verifying that WinDUA is able to associate the MD to the correct users in a nonintrusive and unsupervised manner.
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Occupancy detection represents an opportunity for ML algorithms, ranging from decision trees to deep neural networks using various inputs.
“Moreover, occupancy detection itself represents an opportunity for ML algorithms, ranging from decision trees [193,882] to deep neural networks [896] that take input from occupancy sensors [193], WiFi signals [896,897], or appliance power consumption data [882].”
From Tackling climate change with machine learning (2023) · cites this paper as [897]
Cites
Sampling the Dirichlet Mixture Model with Slices
Walker, S. G. (2007) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610910601096262 · Cited by 437 · see citing works
Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization
Chen, Z., Zou, H., Jiang, H., Zhu, Q., Soh, Y. C., & Xie, L. (2015) · Sensors · doi:10.3390/s150100715 · Cited by 382 · see citing works
Self-citationA realistic evaluation and comparison of indoor location technologies
Lymberopoulos, D., Liu, J., Yang, X., et al. (2015) · International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1145/2737095.2737726 · Cited by 331 · see citing works
An Indoor Location-Aware System for an IoT-Based Smart Museum
Alletto, S., Cucchiara, R., Fiore, G. D., Mainetti, L., Mighali, V., Patrono, L., & Serra, G. (2015) · IEEE Internet of Things Journal · doi:10.1109/jiot.2015.2506258 · Cited by 263 · see citing works
Biometrics Systems Under Spoofing Attack: An evaluation methodology and lessons learned
Hadid, A., Evans, N., Marcel, S., & Fiérrez, J. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2015.2437652 · Cited by 218 · see citing works
Hu, P.-F., Ning, H., Qiu, T., Song, H., Wang, Y., & Yao, X. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2659783 · Cited by 216 · see citing works
A Multi-Task Deep Network for Person Re-Identification
Chen, W., Chen, X., Zhang, J., & Huang, K. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11201 · Cited by 205 · see citing works
Zou, H., Jin, M., Jiang, H., et al. (2017) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2017.2757472 · Cited by 196 · see citing works
Self-citationZou, H., Huang, B., Lu, C. X., Jiang, H., & Xie, L. (2015) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2015.2487963 · Cited by 191 · see citing works
Self-citationWinLight: A WiFi-based occupancy-driven lighting control system for smart building
Zou, H., Zhou, Y., Jiang, H., et al. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.09.001 · Cited by 174 · see citing works
Self-citationA Survey of Biometric Gait Recognition: Approaches, Security and Challenges
Gafurov, D. (2007) · Cited by 163 · see citing works
SpeakerSense: Energy Efficient Unobtrusive Speaker Identification on Mobile Phones
Lu, H., Brush, A. J. B., Priyantha, N. B., Karlson, A. K., & Liu, J. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21726-5_12 · Cited by 155 · see citing works
A Feature-Scaling-Based $k$ -Nearest Neighbor Algorithm for Indoor Positioning Systems
Li, D., Zhang, B., & Li, C. (2015) · IEEE Internet of Things Journal · doi:10.1109/jiot.2015.2495229 · Cited by 148 · see citing works
Accurate indoor localization and tracking using mobile phone inertial sensors, WiFi and iBeacon
Zou, H., Chen, Z., Jiang, H., et al. (2017) · IEEE International Symposium on Inertial Sensors and Systems (INERTIAL) · doi:10.1109/isiss.2017.7935650 · Cited by 146 · see citing works
Self-citationZou, H., Lu, X., Jiang, H., et al. (2015) · Sensors · doi:10.3390/s150101804 · Cited by 143 · see citing works
Self-citationZou, H., Jiang, H., Luo, Y., et al. (2016) · Sensors · doi:10.3390/s16020268 · Cited by 115 · see citing works
Self-citationNon-intrusive occupancy sensing in commercial buildings
Zou, H., Jiang, H., Yang, J., et al. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.08.045 · Cited by 108 · see citing works
Self-citationHolz, C., Buthpitiya, S., & Knaust, M. (2015) · doi:10.1145/2702123.2702518 · Cited by 106 · see citing works
Robust Cooperative Wi-Fi Fingerprint-Based Indoor Localization
Chen, L., Yang, K., & Wang, X. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2609405 · Cited by 80 · see citing works
Adaptive Localization in Dynamic Indoor Environments by Transfer Kernel Learning
Zou, H., Zhou, Y., Jiang, H., et al. (2017) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc.2017.7925444 · Cited by 78 · see citing works
Self-citationExploiting cyclic features of walking for pedestrian dead reckoning with unconstrained smartphones
Huang, B., Qi, G., Yang, X., et al. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971742 · Cited by 75 · see citing works
Tasking networked CCTV cameras and mobile phones to identify and localize multiple people
Teixeira, T., Jung, D., & Savvides, A. (2010) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/1864349.1864367 · Cited by 69 · see citing works
Indoor occupancy tracking in smart buildings using passive sniffing of probe requests
Vattapparamban, E., Çiftler, B. S., Güvenç, İ., et al. (2016) · IEEE International Conference on Communications workshops · doi:10.1109/iccw.2016.7503761 · Cited by 63 · see citing works
Indoor localization using smartphone sensors and iBeacons
Chen, Z., Zhu, Q., Jiang, H., et al. (2015) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2015.7334389 · Cited by 56 · see citing works
Platform and Algorithm Development for a RFID-Based Indoor Positioning System
Zou, H., Xie, L., Jia, Q., et al. (2014) · Unmanned Systems · doi:10.1142/s2301385014400068 · Cited by 51 · see citing works
Self-citationZhang, H., Du, W., Zhou, P., Li, M., & Mohapatra, P. (2016) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/2973750.2973775 · Cited by 50 · see citing works
A mutual information based online access point selection strategy for WiFi indoor localization
Zou, H., Luo, Y., Lu, X., et al. (2015) · 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2015.7294059 · Cited by 47 · see citing works
Self-citationCausal meets Submodular: Subset Selection with Directed Information
Zhou, Y., & Spanos, C. J. (2016) · neural information processing systems · Cited by 45 · see citing works
Self-citationStandardizing location fingerprints across heterogeneous mobile devices for indoor localization
Zou, H., Huang, B., Lu, X., et al. (2016) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc.2016.7564800 · Cited by 40 · see citing works
Self-citationChipless Radio Frequency Identification Tag for IoT Applications
Attaran, A., & Rashidzadeh, R. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2589928 · Cited by 39 · see citing works
Robust occupancy inference with commodity WiFi
Lu, X., Wen, H., Zou, H., et al. (2016) · IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) · doi:10.1109/wimob.2016.7763228 · Cited by 36 · see citing works
Self-citationLiang, D., Zhang, Z., & Peng, M. (2015) · IEEE Internet of Things Journal · doi:10.1109/jiot.2015.2453419 · Cited by 35 · see citing works
SMARTPHONE-BASED USER ACTIVITY RECOGNITION METHOD FOR HEALTH REMOTE MONITORING APPLICATIONS
Bisio, I., Lavagetto, F., Marchese, M., et al. (2012) · International Conference on Pervasive and Embedded Computing and Communication Systems · doi:10.5220/0003905502000205 · Cited by 34 · see citing works
Veto-Consensus Multiple Kernel Learning
Zhou, Y., Hu, N., & Spanos, C. J. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10251 · Cited by 30 · see citing works
Self-citationBisio, I., Lavagetto, F., Marchese, M., et al. (2013) · CINECA IRIS Institutial Research Information System (University of Genoa) · Cited by 28 · see citing works
Towards Cooperative Localization of Wearable Sensors using Accelerometers and Cameras
Jung, D., Teixeira, T., & Savvides, A. (2010) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2010.5462059 · Cited by 28 · see citing works
Zou, H., Xie, L., Jia, Q., et al. (2013) · International Conference on Indoor Positioning and Indoor Navigation · doi:10.1109/ipin.2013.6817858 · Cited by 26 · see citing works
Self-citationIndoor Occupant Positioning System Using Active RFID Deployment and Particle Filters
Weekly, K., Zou, H., Xie, L., et al. (2014) · IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS) · doi:10.1109/dcoss.2014.53 · Cited by 25 · see citing works
Self-citationLocal business ambience characterization through mobile audio sensing
Wang, H., Lymberopoulos, D., & Liu, J. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2566486.2568027 · Cited by 22 · see citing works
Consensus-Based Parallel Extreme Learning Machine for Indoor Localization
Qiu, Z., Zou, H., Jiang, H., et al. (2016) · Globecom · doi:10.1109/glocom.2016.7841684 · Cited by 19 · see citing works
Self-citationWinIPS: WiFi-based non-intrusive IPS for online radio map construction
Zou, H., Jin, M., Jiang, H., et al. (2016) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infcomw.2016.7562263 · Cited by 15 · see citing works
Self-citationLu, C. X., Wen, H., Wang, S., et al. (2017) · ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1145/3055031.3055073 · Cited by 14 · see citing works
A novel fusion methodology for indoor positioning in IoT-based mobile applications
Ijaz, R., Pasha, M. A., Hassan, N. U., et al. (2018) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot.2018.8355168 · Cited by 13 · see citing works
Accelerometer-based Activity Recognition on Smartphone
Su, X. Y., Tong, H., & Ji, P. (2014) · doi:10.1145/2661829.2661836 · Cited by 11 · see citing works
IGMM-Based Co-Localization of Mobile Users With Ambient Radio Signals
Varela, P. M., Hong, J., Ohtsuki, T. O., et al. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2568258 · Cited by 10 · see citing works
Passive Classification of Wi-Fi Enabled Devices
Redondi, A. E. C., Sanvito, D., & Cesana, M. (2016) · ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM) · doi:10.1145/2988287.2989161 · Cited by 7 · see citing works
Bisio, I., Lavagetto, F., Garibotto, C., et al. (2017) · Mobile Networks and Applications · doi:10.1007/s11036-017-0876-z · Cited by 7 · see citing works
Towards Self-supervised Face Labeling via Cross-modality Association
Lu, C. X., Kan, X., Rosa, S., et al. (2017) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3131672.3136991 · Cited by 2 · see citing works
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Tackling climate change with machine learning
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- Authors
- Zou, H., Zhou, Y., Yang, J., & Spanos, C. J.
- Affiliations
- University of California, Berkeley (US)
- Nanyang Technological University (SG)
- Venue
- IEEE Internet of Things Journal
- Published
- 2019-02
- Volume
- 6
- Issue
- 1
- Pages
- 1238-1245
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W2891205429
- Cited by
- 38
- References
- 48
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 4 · 2023: 4 · 2022: 11 · 2021: 5 · 2020: 6 · 2019: 6
- FWCI
- 1.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Funders
- 2018 Seed Fund Award from CITRIS
- University of California
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IPv6, Mobility, Handover, Networks, Security
- Keywords
- user identification, hierarchical clustering, smart buildings
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