WiCop: Engineering WiFi Temporal White-Spaces for Safe Operations of Wireless Personal Area Networks in Medical Applications

ZigBee and other wireless technologies operating in the (2.4GHz) ISM band are being applied in Wireless Personal Area Networks (WPAN) for many medical applications. However, these low duty cycle, low power, and low data rate medical WPANs suffer from WiFi co-channel interferences. WiFi interference can lead to longer latency and higher packet losses in WPANs, which can be particularly harmful to safety-critical applications with stringent temporal requirements, such as ElectroCardioGraphy (ECG). This paper exploits the Clear Channel Assessment (CCA) mechanism in WiFi devices and proposes a novel policing framework, WiCop, that can effectively control the temporal white-spaces between WiFi transmissions. Such temporal white-spaces can be utilized for delivering low duty cycle WPAN traffic. We have implemented and validated WiCop on SORA, a software-defined radio platform. Experimental results show that with the assistance of the proposed WiCop policing schemes, the packet reception rate of a ZigBee-based WPAN can increase by up to 116% in the presence of a heavy WiFi interferer. A case study on the medical application of WPAN ECG monitoring demonstrates that WiCop can bound ECG signal distortion within 2% even under heavy WiFi interference. An analytical framework is devised to model the CCA behavior of WiFi interferers and the performance of WPANs under WiFi interference with or without WiCop protection. The analytical results are corroborated by experiments.

[1]  M. Sabarimalai Manikandan,et al.  Wavelet energy based diagnostic distortion measure for ECG , 2007, Biomed. Signal Process. Control..

[2]  JeongGil Ko,et al.  MEDiSN: medical emergency detection in sensor networks , 2008, SenSys '08.

[3]  Jianfeng Wang,et al.  Applications, challenges, and prospective in emerging body area networking technologies , 2010, IEEE Wireless Communications.

[4]  Kang G. Shin,et al.  Enabling coexistence of heterogeneous wireless systems: case for ZigBee and WiFi , 2011, MobiHoc '11.

[5]  Nada Golmie,et al.  Performance analysis of low rate wireless technologies for medical applications , 2005, Comput. Commun..

[6]  Guido Dolmans,et al.  Coexistence of WBAN and WLAN in Medical Environments , 2009, 2009 IEEE 70th Vehicular Technology Conference Fall.

[7]  Julien Penders,et al.  Artifacts mitigation in ambulatory ECG telemetry , 2010, The 12th IEEE International Conference on e-Health Networking, Applications and Services.

[8]  R. J. Kennelly.  The IEEE 1073 Standard for Medical Device Communications , 1998, 1998 IEEE AUTOTESTCON Proceedings. IEEE Systems Readiness Technology Conference. Test Technology for the 21st Century (Cat. No.98CH36179).

[9]  Chenyang Lu,et al.  Reliable clinical monitoring using wireless sensor networks: experiences in a step-down hospital unit , 2010, SenSys '10.

[10]  Andrea Goldsmith,et al.  Wireless Communications: Path Loss and Shadowing , 2005 .

[11]  Rong Zheng,et al.  WiCop: Engineering WiFi Temporal White-Spaces for Safe Operations of Wireless Personal Area Networks in Medical Applications , 2011, IEEE Transactions on Mobile Computing.

[12]  Srinivasan Seshan,et al.  Understanding and mitigating the impact of RF interference on 802.11 networks , 2007, SIGCOMM 2007.

[13]  Sofie Pollin,et al.  Harmful Coexistence Between 802.15.4 and 802.11: A Measurement-based Study , 2008, 2008 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008).

[14]  Amjed S. Al-Fahoum,et al.  Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure , 2006, IEEE Transactions on Information Technology in Biomedicine.

[15]  M. Looi,et al.  A trivial denial of service attack on IEEE 802.11 direct sequence spread spectrum wireless LANs , 2004, 2004 Symposium on Wireless Telecommunications.

[16]  Mario Gerla,et al.  Minimizing 802.11 interference on ZigBee medical sensors , 2009, BODYNETS.

[17]  Nada Golmie,et al.  Interference Evaluation of Bluetooth and IEEE 802.11b Systems , 2003, Wirel. Networks.

[18]  Wook Hyun Kwon,et al.  Lecture Notes in Computer Science: Packet Error Rate Analysis of IEEE 802.15.4 Under IEEE 802.11b Interference , 2005, WWIC.

[19]  Haitao Wu,et al.  Sora: High Performance Software Radio Using General Purpose Multi-core Processors , 2009, NSDI.

[20]  Vlado Handziski,et al.  Experimental Study of the Impact of WLAN Interference on IEEE 802.15.4 Body Area Networks , 2009, EWSN.

[21]  Stefan Savage,et al.  802.11 Denial-of-Service Attacks: Real Vulnerabilities and Practical Solutions , 2003, USENIX Security Symposium.

[22]  Guoliang Xing,et al.  Beyond co-existence: Exploiting WiFi white space for Zigbee performance assurance , 2010, The 18th IEEE International Conference on Network Protocols.

[23]  David J. Thuente,et al.  Intelligent jamming in wireless networks with applications to 802.11b and other networks , 2006 .

[24]  Nada Golmie,et al.  On the Use of Wireless Network Technologies in Healthcare Environments | NIST , 2005 .

[25]  Biswanath Mukherjee,et al.  Wireless sensor network survey , 2008, Comput. Networks.

[26]  Qixin Wang,et al.  Evaluating the IEEE 802.15.6 2.4GHz WBAN Proposal on Medical Multi-Parameter Monitoring under WiFi/Bluetooth Interference , 2011, Int. J. E Health Medical Commun..

[27]  Nikolas Mitrou,et al.  Cognitive radio-aided wireless sensor networks for emergency response , 2010 .

[28]  Andreas Terzis,et al.  Surviving wi-fi interference in low power ZigBee networks , 2010, SenSys '10.

[29]  JeongGil Ko,et al.  MEDiSN: Medical emergency detection in sensor networks , 2010, TECS.