research-article

Secure indoor positioning against signal strength attacks via optimized multi-voting

Authors:

Terri HedgpethAuthors Info & Claims

IWQoS '19: Proceedings of the International Symposium on Quality of Service

Article No.: 33, Pages 1 - 10

https://doi.org/10.1145/3326285.3329068

Published: 24 June 2019 Publication History

Abstract

Indoor positioning systems (IPSes) can enable many location-based services in large indoor venues where GPS signals are unavailable or unreliable. Among the most viable types of IPSes, RSS-IPSes rely on ubiquitous smartphones and indoor WiFi infrastructures and explore distinguishable received signal strength (RSS) measurements at different indoor locations as their location fingerprints. RSS-IPSes are unfortunately vulnerable to physical-layer RSS attacks that cannot be thwarted by conventional cryptographic techniques. Existing defenses against RSS attacks are all subject to an inherent tradeoff between indoor positioning accuracy and attack resilience. This paper presents the design and evaluation of MV-IPS, a novel RSS-IPS based on weighted multi-voting, which does not suffer from this tradeoff. In MV-IPS, every WiFi access point (AP) that receives a user's RSS measurement gives a weighted vote for every reference location, and the reference location that receives the highest accumulative votes from all APs is output as the user's most likely position. Trace-driven simulation studies based on real RSS measurements demonstrate that MV-IPS can achieve much higher positioning accuracy than prior solutions no matter whether RSS attacks are present.

References

[1]

{n.d.}. Wi-Fi Indoor Location in Retail Worth $2.5 Billion by 2020. https://www.abiresearch.com/press/wi-fi-indoor-location-retail-worth-25-billion-2020

[2]

Rony M Adelsman and Andrew B Whinston. 1977. Sophisticated voting with information for two voting functions. Journal of Economic Theory 15, 1 (1977), 145--159.

[3]

Larry Armijo. 1966. Minimization of functions having Lipschitz continuous first partial derivatives. Pacific J. Math. 16, 1 (November. 1966), 1--4.

[4]

Martin Azizyan, Ionut Constandache, and Romit Roy Choudhury. 2009. SurroundSense: Mobile Phone Localization via Ambience Fingerprinting. In Annual International Conference on Mobile Computing and Networking (Mobicom'09). Beijing, China, 261--272.

Digital Library

[5]

Paramvir Bahl and Venkata N. Padmanabhan. 2000. RADAR: an in-building RF-based user location and tracking system. In IEEE International Conference on Computer Communications (INFOCOM'00), Vol. 2. Tel Aviv, Israel, 775--784.

[6]

Paramvir Bahl, Venkata N Padmanabhan, and Anand Balachandran. 2000. Enhancements to the RADAR user location and tracking system. Microsoft Research 2, MSR-TR-2000-12 (Feb. 2000), 775--784.

[7]

Paul H. Calamai and Jorge J. Moré. 1987. Projected gradient methods for linearly constrained problems. Mathematical Programming 39, 1 (01 Sepember 1987), 93--116.

Digital Library

[8]

Yingying Chen, Konstantinos Kleisouris, Xiaoyan Li, Wade Trappe, and Richard P. Martin. 2009. A Security and Robustness Performance Analysis of Localization Algorithms to Signal Strength Attacks. ACM Trans. Sen. Netw. 5, 1 (Feb. 2009), 2:1--2:37.

Digital Library

[9]

Shihhau Fang, Chungchih Chuang, and Chiapin Wang. 2012. Attack-Resistant Wireless Localization Using an Inclusive Disjunction Model. IEEE Transactions on Communications 60, 5 (May 2012), 1209--1214.

[10]

Brian Ferris, Dieter Fox, and Neil Lawrence. 2007. WiFi-SLAM Using Gaussian Process Latent Variable Models. In International Joint Conference on Artifical Intelligence (IJCAI'07). Hyderabad, India, 2480--2485.

Digital Library

[11]

Abhishek Goswami, Luis E. Ortiz, and Samir R. Das. 2011. WiGEM: A Learning-based Approach for Indoor Localization. In International Conference on emerging Networking Experiments and Technologies (CoNEXT'11). 3:1--3:12.

Digital Library

[12]

Suining He and S.-H. Gary Chan. 2014. Sect junction: Wi-Fi indoor localization based on junction of signal sectors. In IEEE International Conference on Communications (ICC'14). Sydney, NSW, 2605--2610.

[13]

Yifei Jiang, Xin Pan, Kun Li, Qin Lv, Robert P. Dick, Michael Hannigan, and Li Shang. 2012. ARIEL: automatic wi-fi based room fingerprinting for indoor localization. In ACM Conference on Ubiquitous Computing (UbiComp'12). Pittsburgh, PA, 441--450.

Digital Library

[14]

Azadeh Kushki, Konstantinos N. Plataniotis, and Anastasios N. Venetsanopoulos. 2008. Sensor selection for mitigation of RSS-based attacks in wireless local area network positioning. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'08). Las Vegas, NV, 2065--2068.

[15]

Hong Li, Limin Sun, Haojin Zhu, Xiang Lu, and Xiuzhen Cheng. 2014. Achieving privacy preservation in WiFi fingerprint-based localization. In IEEE International Conference on Computer Communications (INFOCOM'14). Toronto, Canada, 2337--2345.

[16]

Tao Li, Yimin Chen, Rui Zhang, Yanchao Zhang, and Terri Hedgpeth. 2018. Secure crowdsourced indoor positioning systems. In IEEE International Conference on Computer Communications (INFOCOM'18). Honolulu, HI, 1034--1042.

[17]

Zang Li, Wade Trappe, Yanyong Zhang, and Badri Nath. 2005. Robust Statistical Methods for Securing Wireless Localization in Sensor Networks. In International Symposium on Information Processing in Sensor Networks (IPSN'05). Los Angeles, CA, 1--12.

Digital Library

[18]

David Madigan, Eiman Elnahrawy, Richard P. Martin, Wenhua Ju, Prajindra Sankar A/l Krishnanm, and A. S. Krishnakumar. 2005. Bayesian indoor positioning systems. In IEEE International Conference on Computer Communications (INFOCOM'05), Vol. 2. Miami, FL, 1217--1227 vol. 2.

[19]

Galo Nuno and Jose Paez Borrallo. 2006. A New Location Estimation System for Wireless Networks Based on Linear Discriminant Functions and Hidden Markov Models. EURASIP Journal on Applied Signal Processing 2006 (01 2006), 159--159.

Digital Library

[20]

Souvik Sen, Božidar Radunovic, Romit Roy Choudhury, and Tom Minka. 2012. You Are Facing the Mona Lisa: Spot Localization Using PHY Layer Information. In International Conference on Mobile Systems, Applications, and Services (MobiSys'12). Low Wood Bay, Lake District, UK, 183--196.

Digital Library

[21]

Xuyu Wang, Lingjun Gao, Shiwen Mao, and Santosh Pandey. 2015. DeepFi: Deep learning for indoor fingerprinting using channel state information. In IEEE Wireless Communications and Networking Conference (WCNC'15). New Orleans, LA, 1666--1671.

[22]

Xuyu Wang, Lingjun Gao, Shiwen Mao, and Santosh Pandey. 2017. CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach. IEEE Transactions on Vehicular Technology 66, 1 (January 2017), 763--776.

[23]

Chaolin Wu, Lichen Fu, and Fengli Lian. 2004. WLAN location determination in e-home via support vector classification. In IEEE International Conference on Networking, Sensing and Control (ICNSC'04), Vol. 2. Taipei, Taiwan, 1026--1031, Vol.2.

[24]

Kaishun Wu, Jiang Xiao, Youwen Yi, Dihu Chen, Xiaonan Luo, and Lionel M. Ni. 2013. CSI-Based Indoor Localization. IEEE Transactions on Parallel and Distributed Systems 24, 7 (July 2013), 1300--1309.

Digital Library

[25]

Chao Yang, Yimin Song, and Guofei Gu. 2012. Active User-Side Evil Twin Access Point Detection Using Statistical Techniques. IEEE Transactions on Information Forensics and Security 7, 5 (October 2012), 1638--1651.

Digital Library

[26]

Jie Yang, Yingying Chen, Victor B. Lawrence, and Venkataraman Swaminathan. 2009. Robust wireless localization to attacks on access points. In IEEE Sarnoff Symposium. Princeton, NJ, 1--5.

Digital Library

[27]

Zheng Yang and Kimmo Javinen. 2018. The Death and Rebirth of Privacy-Preserving WiFi Fingerprint Localization with Paillier Encryption. In IEEE International Conference on Computer Communications (INFOCOM'18). Honolulu, HI, 1223--1231.

[28]

Moustafa Youssef and Ashok Agrawala. 2005. The Horus WLAN Location Determination System. In International Conference on Mobile Systems, Applications, and Services (MobiSys'05). Seattle, WA, 205--218.

Digital Library

[29]

Lizhou Yuan, Yidan Hu, Yunzhi Li, Rui Zhang, Yanchao Zhang, and Terri Hedgpeth. 2018. Secure RSS-Fingerprint-Based Indoor Positioning: Attacks and Countermeasures. In IEEE Conference on Communications and Network Security (CNS'18). Beijing, 1--9.

Cited By

Namee KSrikamta NKaewkajone TThierchot TPolpinij JKaewsaeng-On R(2023)Enhancing Indoor Positioning Accuracy: A Comprehensive Study on Euclidean Distance, Trilateration, Wi-Fi RTT and FTM Protocol IntegrationProceedings of the 2023 6th International Conference on Computational Intelligence and Intelligent Systems10.1145/3638209.3638235(173-180)Online publication date: 25-Nov-2023
https://dl.acm.org/doi/10.1145/3638209.3638235
Ye QFan XBie HPuthal DWu TSong XFang G(2023)SE-Loc: Security-Enhanced Indoor Localization With Semi-Supervised Deep LearningIEEE Transactions on Network Science and Engineering10.1109/TNSE.2022.317467410:5(2964-2977)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TNSE.2022.3174674
Sartayeva YChan H(2023)A survey on indoor positioning security and privacyComputers and Security10.1016/j.cose.2023.103293131:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.cose.2023.103293
Show More Cited By

Index Terms

Secure indoor positioning against signal strength attacks via optimized multi-voting

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IWQoS '19: Proceedings of the International Symposium on Quality of Service

June 2019

420 pages

ISBN:9781450367783

DOI:10.1145/3326285

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 24 June 2019

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IWQoS '19

IWQoS '19: IEEE/ACM International Symposium on Quality of Service

June 24 - 25, 2019

Arizona, Phoenix

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Cited By

Namee KSrikamta NKaewkajone TThierchot TPolpinij JKaewsaeng-On R(2023)Enhancing Indoor Positioning Accuracy: A Comprehensive Study on Euclidean Distance, Trilateration, Wi-Fi RTT and FTM Protocol IntegrationProceedings of the 2023 6th International Conference on Computational Intelligence and Intelligent Systems10.1145/3638209.3638235(173-180)Online publication date: 25-Nov-2023
https://dl.acm.org/doi/10.1145/3638209.3638235
Ye QFan XBie HPuthal DWu TSong XFang G(2023)SE-Loc: Security-Enhanced Indoor Localization With Semi-Supervised Deep LearningIEEE Transactions on Network Science and Engineering10.1109/TNSE.2022.317467410:5(2964-2977)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TNSE.2022.3174674
Sartayeva YChan H(2023)A survey on indoor positioning security and privacyComputers and Security10.1016/j.cose.2023.103293131:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.cose.2023.103293
Li YHu YAseeri ADong YZhang R(2022)Continuous Indoor Tracking via Differential RSS Fingerprinting2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)10.1109/MASS56207.2022.00072(467-475)Online publication date: Oct-2022
https://doi.org/10.1109/MASS56207.2022.00072
Hu ZLi YJiang GZhang RXie M(2022)PriHorus: Privacy-Preserving RSS-Based Indoor PositioningICC 2022 - IEEE International Conference on Communications10.1109/ICC45855.2022.9839103(5627-5632)Online publication date: 16-May-2022
https://doi.org/10.1109/ICC45855.2022.9839103
Elsagheer Mohamed SParvez MAlShalfan KAlaidy MAl-Hagery MBen Othman M(2021)Autonomous Real-Time Speed-Limit Violation Detection and Reporting Systems Based on the Internet of Vehicles (IoV)Journal of Advanced Transportation10.1155/2021/98887892021(1-15)Online publication date: 24-Sep-2021
https://doi.org/10.1155/2021/9888789
Na XGuo XHe YXi R(2021)Wi-attack: Cross-technology Impersonation Attack against iBeacon Services2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)10.1109/SECON52354.2021.9491605(1-9)Online publication date: 6-Jul-2021
https://doi.org/10.1109/SECON52354.2021.9491605

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