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In the past, researchers designed, deployed, and evaluated Wi-Fi based localization techniques in order to locate users and devices without adding extra or costly infrastructure. However, as infrastructure deployments change, one must reexamine the role of Wi-Fi localization. Today, cameras are becoming increasingly deployed, and therefore this work examines how contextual and vision data obtained from cameras can be integrated with Wi-Fi localization techniques. We present an approach called CALM that works on commodity APs and cameras. Our approach contains several contributions: a camera line fitting technique to restrict the search space of candidate locations, single AP and camera localization via a deprojection scheme inspired from 3D cameras, simple and robust AP weighting that analyzes the context of users via the camera, and a new virtual camera methodology to scale analysis. We motivate our scheme by analyzing real camera and AP topologies from a major vendor. Our evaluation over 9 rooms and 102,300 wireless readings shows CALM can obtain decimeter-level accuracy, improving performance over previous Wi-Fi techniques like FTM by 2.7× and SpotFi by 2.3×.
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MotionCompass: pinpointing wireless camera via motion-activated traffic
Wireless security cameras are integral components of security systems used by military installations, corporations, and, due to their increased affordability, many private homes. These cameras commonly employ motion sensors to identify that something is occurring in their fields of vision before starting to record and notifying the property owner of the activity. In this paper, we discover that the motion sensing action can disclose the location of the camera through a novel wireless camera localization technique we call MotionCompass. In short, a user who aims to avoid surveillance can find a hidden camera by creating motion stimuli and sniffing wireless traffic for a response to that stimuli. With the motion trajectories within the motion detection zone, the exact location of the camera can be then computed. We develop an Android app to implement MotionCompass. Our extensive experiments using the developed app and 18 popular wireless security cameras demonstrate that for cameras with one motion sensor, MotionCompass can attain a mean localization error of around 5 cm with less than 140 seconds. This localization technique builds upon existing work that detects the existence of hidden cameras, to pinpoint their exact location and area of surveillance.
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- Award ID(s):
- 1948547
- PAR ID:
- 10289674
- Date Published:
- Journal Name:
- Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services (ACM MobiSys)
- Page Range / eLocation ID:
- 215 to 227
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3458864.3467683
