ABSTRACT
Easily establishing pairing between Internet-of-Things (IoT) devices is important for fast deployment in many smart home scenarios. Traditional pairing methods, including passkey, QR code, and RFID, often require specific user interfaces, surface's shape/material, or additional tags/readers. The growing number of low-resource IoT devices without an interface may not meet these requirements, which makes their pairing a challenge. On the other hand, these devices often already have sensors embedded for sensing tasks, such as inertial sensors. These sensors can be used for limited user interaction with the devices, but are not suitable for pairing on their own.
In this paper, we present UniverSense, an alternative pairing method between low-resource IoT devices with an inertial sensor and a more powerful networked device equipped with a camera. To establish pairing between them, the user moves the low-resource IoT device in front of the camera. Both the camera and the on-device sensors capture the physical motion of the low-resource device. UniverSense converts these signals into a common state-space to generate fingerprints for pairing. We conduct real-world experiments to evaluate UniverSense and it achieves an F1 score of 99.9% in experiments carried out by five participants.
- J. G. Allen, R. Y. Xu, and J. S. Jin. Object tracking using camshift algorithm and multiple quantized feature spaces. In Proceedings of the Pan-Sydney area workshop on Visual information processing, pages 3--7. Australian Computer Society, Inc., 2004. Google ScholarDigital Library
- M. Baldauf, M. Salo, S. Suette, and P. Fröhlich. The screen is yours-comparing handheld pairing techniques for public displays. In International Joint Conference on Ambient Intelligence. Springer, 2013.Google ScholarCross Ref
- A. Bannis and J. A. Burke. Creating a secure, integrated home network of things with named data networking, 2015.Google Scholar
- Bitcraze, AB. Crazyflie 2.0, 2016.Google Scholar
- Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh. Realtime multi-person 2d pose estimation using part affinity fields. In CVPR, 2017.Google ScholarCross Ref
- C. Chen, K. Liu, R. Jafari, and N. Kehtarnavaz. Home-based senior fitness test measurement system using collaborative inertial and depth sensors. In Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pages 4135--4138. IEEE, 2014.Google ScholarCross Ref
- J. Farrell and M. Barth. The global positioning system and inertial navigation, volume 61. McGraw-Hill New York, NY, USA:, 1999.Google Scholar
- R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 580--587, 2014. Google ScholarDigital Library
- Gruman, Galen. IoT silliness: 'Headless' devices without a UI., 2015. https://www.infoworld.com/article/2867356/internet-of-things/beware-this-iot-fallacy-the-headless-device.html.Google Scholar
- J. Han, M. Harishankar, X. Wang, A. J. Chung, and P. Tague. Convoy: Physical context verification for vehicle platoon admission. In Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications, pages 73--78. ACM, 2017. Google ScholarDigital Library
- J. F. Henriques, R. Caseiro, P. Martins, and J. Batista. High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3):583--596, 2015.Google ScholarDigital Library
- Joseph Palenchar. Security Cameras Lead Smart-Home Adoption. http://www.twice.com/news/statistics/security-cameras-lead-smart-home-adoption/61081.Google Scholar
- L. Kriara, M. Alsup, G. Corbellini, M. Trotter, J. D. Griffin, and S. Mangold. Rfid shakables: Pairing radio-frequency identification tags with the help of gesture recognition. In Proceedings of the ninth ACM conference on Emerging networking experiments and technologies, pages 327--332. ACM, 2013. Google ScholarDigital Library
- S. Madgwick. An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Report x-io and University of Bristol (UK).Google Scholar
- J. Martin, T. Mayberry, C. Donahue, L. Foppe, L. Brown, C. Riggins, E. C. Rye, and D. Brown. A study of mac address randomization in mobile devices and when it fails. arXiv preprint arXiv:1703.02874, 2017.Google Scholar
- MetaSensor Inc. Meet Sensor-1, The security system that fits in the palm of your hand., 2017. https://www.metasensor.com/.Google Scholar
- M. Miettinen, N. Asokan, T. D. Nguyen, A.-R. Sadeghi, and M. Sobhani. Context-based zero-interaction pairing and key evolution for advanced personal devices. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pages 880--891. ACM, 2014. Google ScholarDigital Library
- P. Neto, J. N. Pires, and A. P. Moreira. 3-d position estimation from inertial sensing: minimizing the error from the process of double integration of accelerations. In Industrial Electronics Society, IECON 2013--39th Annual Conference of the IEEE, pages 4026--4031. IEEE, 2013.Google Scholar
- Networking, Cisco Visual. Cisco global cloud index: forecast and methodology, 2015--2020. white paper, 2017.Google Scholar
- L. T. Nguyen, Y. S. Kim, P. Tague, and J. Zhang. Identitylink: user-device linking through visual and rf-signal cues. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pages 529--539. ACM, 2014. Google ScholarDigital Library
- Notion Inc. Home awareness, simplified. Monitor your home with a single sensor, wherever you are., 2017. http://getnotion.com/.Google Scholar
- T. J. Pierson, X. Liang, R. Peterson, and D. Kotz. Wanda: securely introducing mobile devices. In The 35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016, pages 1--9. IEEE, 2016.Google ScholarCross Ref
- S. Ren, K. He, R. Girshick, and J. Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems, pages 91--99, 2015. Google ScholarDigital Library
- J. Riekki, T. Salminen, and I. Alakarppa. Requesting pervasive services by touching rfid tags. IEEE Pervasive computing, 5(1):40--46, 2006. Google ScholarDigital Library
- Samsung Inc. Use gesture control with the latest Smart Interaction., 2017. http://www.samsung.com/uk/tv-accessories/tv-camera-stc5000/.Google Scholar
- Samsung Inc. The easiest way to turn your home into a smart home., 2018. https://www.samsung.com/us/smart-home/smartthings/.Google Scholar
- A. Savitzky and M. J. Golay. Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry, 36(8), 1964.Google Scholar
- A. Studer, T. Passaro, and L. Bauer. Don't bump, shake on it: The exploitation of a popular accelerometer-based smart phone exchange and its secure replacement. In Proceedings of the 27th Annual Computer Security Applications Conference, pages 333--342. ACM, 2011. Google ScholarDigital Library
- C. T. Zenger, M. Pietersz, J. Zimmer, J.-F. Posielek, T. Lenze, and C. Paar. Authenticated key establishment for low-resource devices exploiting correlated random channels. Computer Networks, 109:105--123, 2016.Google ScholarCross Ref
- Z. Zhang. A flexible new technique for camera calibration. IEEE Transactions on pattern analysis and machine intelligence, 22(11), 2000. Google ScholarDigital Library
- C. Zhao, S. Yang, X. Yang, and J. A. McCann. Rapid, user-transparent, and trustworthy device pairing for d2d-enabled mobile crowdsourcing. IEEE Transactions on Mobile Computing, 16(7):2008--2022, 2017.Google ScholarCross Ref
Index Terms
- UniverSense: IoT Device Pairing through Heterogeneous Sensing Signals
Recommendations
T2Pair: Secure and Usable Pairing for Heterogeneous IoT Devices
CCS '20: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications SecuritySecure pairing is key to trustworthy deployment and application of Internet of Things (IoT) devices. However, IoT devices lack conventional user interfaces, such as keyboards and displays, which makes many traditional pairing approaches inapplicable. ...
WhereWear: Calibration-free Wearable Device Identification through Ambient Sensing
WearSys '19: The 5th ACM Workshop on Wearable Systems and ApplicationsWith the growth of wearable devices, numerous health and smart building applications are enabled. As a result, many people wear multiple devices for different applications, such as fitness tracking. Being able to match devices' physical identity (e.g., ...
MagicPairing: Apple's take on securing bluetooth peripherals
WiSec '20: Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile NetworksDevice pairing in large Internet of Things (IoT) deployments is a challenge for device manufacturers and users. Bluetooth offers a comparably smooth trust on first use pairing experience. Bluetooth, though, is well-known for security flaws in the ...
Comments