Confining Wi-Fi Coverage: A Crowdsourced Method Using Physical Layer Information

Many small businesses and public areas offer free Wi-Fi access, but may wish to restrict network access only to their customers or patrons inside the physical property. Unfortunately, due to the nature of wireless networks, this is difficult to accomplish. We develop and implement CLAC, a Crowdsourced Location aware Access Control scheme using physical layer information to address this challenge. It crowdsources both channel state information (CSI) and received signal strength (RSS) of already validated users to classify future users. We propose and use two CSI metrics in CLAC: CSI Cross-Antenna Stability Metric and CSI Cross-Frame Stability Metric, which summarize well the spatial and temporal CSI characteristics respectively. CLAC is evaluated in an office and a classroom. Evaluation results show that CLAC performs well in both environments, allowing most valid users inside the area to access the network, while the chance that invalid users outside the boundary may access the network is small.

[1]  K. J. Ray Liu,et al.  Extrinsic Channel-Like Fingerprint Embedding for Authenticating MIMO Systems , 2011, IEEE Transactions on Wireless Communications.

[2]  Xiang-Yang Li,et al.  Rejecting the attack: Source authentication for Wi-Fi management frames using CSI Information , 2012, 2013 Proceedings IEEE INFOCOM.

[3]  David Wetherall,et al.  Tool release: gathering 802.11n traces with channel state information , 2011, CCRV.

[4]  Peng Ning,et al.  Enhanced wireless channel authentication using time-synched link signature , 2012, 2012 Proceedings IEEE INFOCOM.

[5]  Larry J. Greenstein,et al.  Channel-based spoofing detection in frequency-selective rayleigh channels , 2009, IEEE Transactions on Wireless Communications.

[6]  Mun Choon Chan,et al.  PiLoc: A self-calibrating participatory indoor localization system , 2014, IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks.

[7]  Jitendra K. Tugnait,et al.  A channel-based hypothesis testing approach to enhance user authentication in wireless networks , 2010, 2010 Second International Conference on COMmunication Systems and NETworks (COMSNETS 2010).

[8]  Kyu-Han Kim,et al.  SAIL: single access point-based indoor localization , 2014, MobiSys.

[9]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[10]  Yan Dong,et al.  PHY-CRAM: Physical Layer Challenge-Response Authentication Mechanism for Wireless Networks , 2013, IEEE Journal on Selected Areas in Communications.

[11]  Yunhao Liu,et al.  Locating in fingerprint space: wireless indoor localization with little human intervention , 2012, Mobicom '12.

[12]  Larry J. Greenstein,et al.  Using the physical layer for wireless authentication in time-variant channels , 2008, IEEE Transactions on Wireless Communications.

[13]  李向阳,et al.  Communicating Is Crowdsourcing: Wi-Fi Indoor Localization with CSI-Based Speed Estimation , 2014 .

[14]  Jitendra K. Tugnait,et al.  Wireless User Authentication via Comparison of Power Spectral Densities , 2013, IEEE Journal on Selected Areas in Communications.

[15]  Moustafa Youssef,et al.  No need to war-drive: unsupervised indoor localization , 2012, MobiSys '12.

[16]  Srinivasan Seshan,et al.  Geo-fencing: Confining Wi-Fi Coverage to Physical Boundaries , 2009, Pervasive.

[17]  Mario Gerla,et al.  FreeLoc: Calibration-free crowdsourced indoor localization , 2013, 2013 Proceedings IEEE INFOCOM.

[18]  Srihari Nelakuditi,et al.  SpinLoc: spin once to know your location , 2012, HotMobile '12.

[19]  Anshul Rai,et al.  Zee: zero-effort crowdsourcing for indoor localization , 2012, Mobicom '12.