C4.5: programs for machine learning
C4.5: programs for machine learning
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Computer Networks: The International Journal of Computer and Telecommunications Networking
Understanding and mitigating the impact of RF interference on 802.11 networks
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
A Peak-Trough Detection Algorithm Based on Momentum
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
RFDump: an architecture for monitoring the wireless ether
Proceedings of the 5th international conference on Emerging networking experiments and technologies
Surviving wi-fi interference in low power ZigBee networks
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Characterization of an unintentional Wi-Fi interference device-the residential microwave oven
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
PIE in the sky: online passive interference estimation for enterprise WLANs
Proceedings of the 8th USENIX conference on Networked systems design and implementation
Bluetooth and WLAN coexistence: challenges and solutions
IEEE Wireless Communications
Packet Error Rate Analysis of ZigBee Under WLAN and Bluetooth Interferences
IEEE Transactions on Wireless Communications
MobiCom 2011 poster: AirTrack: locating non-WiFi interferers using commodity WiFi hardware
ACM SIGMOBILE Mobile Computing and Communications Review
Catching whales and minnows using WiFiNet: deconstructing non-WiFi interference using WiFi hardware
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
Estimation of expectable network quality in wireless mesh networks
IFIP'12 Proceedings of the 2012 international conference on Networking
ACM SIGBED Review - Special Issue on the 3rd International Workshop on Networks of Cooperating Objects (CONET 2012)
MagnoTricorder: what you need to do before leaving home
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Measuring channel occupancy for 802.11 wireless LAN in the 2.4 GHz ISM band
Proceedings of the 15th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
ZIMO: building cross-technology MIMO to harmonize zigbee smog with WiFi flash without intervention
Proceedings of the 19th annual international conference on Mobile computing & networking
Observing home wireless experience through WiFi APs
Proceedings of the 19th annual international conference on Mobile computing & networking
Push the limit of wireless network capacity: a tale of cognitive and coexistence
Proceedings of the 1st ACM workshop on Cognitive radio architectures for broadband
Spatially Resolved Monitoring of Radio-Frequency Electromagnetic Fields
Proceedings of First International Workshop on Sensing and Big Data Mining
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In this paper, we propose Airshark -- a system that detects multiple non-WiFi RF devices in real-time and using only commodity WiFi hardware. To motivate the need for systems like Airshark, we start with measurement study that characterizes the usage and prevalence of non-WiFi devices across many locations. We then present the design and implementation of Airshark. Airshark extracts unique features using the functionality provided by a WiFi card to detect multiple non-WiFi devices including fixed frequency devices (e.g., ZigBee, analog cordless phone), frequency hoppers (e.g., Bluetooth, game controllers like Xbox), and broadband interferers (e.g., microwave ovens). Airshark has an average detection accuracy of 91-96%, even in the presence of multiple simultaneously active RF devices operating at a wide range of signal strengths (-80 to -30 dBm), while maintaining a low false positive rate. Through a deployment in two production WLANs, we show that Airshark can be a useful tool to the WLAN administrators in understanding non-WiFi interference.