Device interaction at the Physical layer What the RF-channel can tell us Stephan Sigg Milton Keynes, 05.07.2012
Introduction Recognition Calculation Security Conclusion Introduction Stephan Sigg | Physical layer device interaction | 2
Introduction Recognition Calculation Security Conclusion Introduction IoT environments feature communication, sensing and actuating These devices contain a great diversity of sensing hardware and differ in their design, power consumption, size or purpose. Stephan Sigg | Physical layer device interaction | 2
Introduction Recognition Calculation Security Conclusion Introduction IoT environments feature communication, sensing and actuating These devices contain a great diversity of sensing hardware and differ in their design, power consumption, size or purpose. They will all have a single interface in common:The RF-interface Stephan Sigg | Physical layer device interaction | 2
Introduction Recognition Calculation Security Conclusion Aspects of the mobile radio channel Stephan Sigg | Physical layer device interaction | 3
Introduction Recognition Calculation Security Conclusion Aspects of the mobile radio channel Stephan Sigg | Physical layer device interaction | 4
Introduction Recognition Calculation Security Conclusion Introduction How to utilise this common interface for Ubiquitous applications? Environmental sensing Calculation of mathematical functions Secure communication based on RF Stephan Sigg | Physical layer device interaction | 5
Introduction Recognition Calculation Security Conclusion Outline Introduction RF-based activity recognition Calculation during transmission on the wireless channel Security from environmental stimuli Conclusion Stephan Sigg | Physical layer device interaction | 6
Introduction Recognition Calculation Security Conclusion RF-based activity recognition In the IoT wireless interfaces will be virtually everywhere Can we re-use this hardware to gain additional value? The RF-channel is a ubiquitous source of environmental information Multi-path propagation Reflection Scattering Blocking of signal paths Stephan Sigg | Physical layer device interaction | 7
Introduction Recognition Calculation Security Conclusion RF-based activity recognition Device free localisation (DFL) a a M. Youssef et al., Challenges: Device-free passive localisation for wireless environments, MobiCom2007 Localisation from RF-data RSSI-based passive localisation Monitoring of breathing a a N. Patwari et al., Spatial models for human motion-induced signal strength variance on static links, IEEE Transactions on Information Forensics and Security, vol. 6, no. 3, pp. 2011 Two-way RSSI measurements Accuracy: 0.1 to 0.4 beats Stephan Sigg | Physical layer device interaction | 8
Introduction Recognition Calculation Security Conclusion RF-based activity recognition Sensewaves Video Stephan Sigg | Physical layer device interaction | 9
Introduction Recognition Calculation Security Conclusion RF-based activity recognition Can we use RF-channel information for activity detection? 1 1 USRP transmitter 2 individuals 1 USRP receiver 5 activities 900 Mhz constant signal 15 locations 1Stephan Sigg, Markus Scholz, Yusheng Ji, Michael Beigl, Active and Passive sensing of activities from amplitude-based RF-features in device-free recognition systems, (Submitted to IEEE Transaction on Mobile Computing, 01.2012) Stephan Sigg | Physical layer device interaction | 10
Introduction Recognition Calculation Security Conclusion RF-based activity recognition Classified Truth crawling walking empty lying standing crA .986 .013 .001 waA .019 .975 .006 emA .911 .031 .059 Activities and location lyA .01 .2 .704 .086 stA .215 .005 .78 detected with high crB .002 .998 waB .016 .004 .002 .979 accuracy emB .004 .058 .937 lyB .003 .191 .086 .719 Location-accuracy stB .007 .001 .252 .005 .736 crC .998 .002 about 1-2 meters waC .009 .986 .005 emC .002 .933 .005 .061 Lying detected worst lyC .001 .156 .646 .197 stC .001 .008 .17 .012 .808 crD .933 .065 .001 .001 Transmitter and waD .034 .964 .001 .001 emD .021 .889 .004 .085 receiver under lyD .191 .725 .084 complete control stD .015 .143 .012 .829 crE .991 .001 .008 waE .997 .003 .001 emE .937 .005 .059 lyE .001 .238 .676 .086 stE .001 .005 .157 .016 .82 Stephan Sigg | Physical layer device interaction | 11
Introduction Recognition Calculation Security Conclusion RF-based activity recognition Potential and challenges The advancing IoT adds to the penetration by RF-capable devices This fosters the evolution of smart environments to support activity recognition and awareness. Stephan Sigg | Physical layer device interaction | 12
Introduction Recognition Calculation Security Conclusion RF-based activity recognition Potential and challenges The advancing IoT adds to the penetration by RF-capable devices This fosters the evolution of smart environments to support activity recognition and awareness. Sustainable: Transceiver might suffice for common classification tasks; Re-use of RF-interface (already part of the design) No additional cost for further sensors Stephan Sigg | Physical layer device interaction | 12
Introduction Recognition Calculation Security Conclusion RF-based activity recognition Potential and challenges The advancing IoT adds to the penetration by RF-capable devices This fosters the evolution of smart environments to support activity recognition and awareness. Sustainable: Transceiver might suffice for common classification tasks; Re-use of RF-interface (already part of the design) No additional cost for further sensors Potential: Improve features to cover less static scenarios Identify features that do not require training Tolerate static environmental changes Can we detect at the time the device is carried/moved Stephan Sigg | Physical layer device interaction | 12
Introduction Recognition Calculation Security Conclusion Outline Introduction RF-based activity recognition Calculation during transmission on the wireless channel Security from environmental stimuli Conclusion Stephan Sigg | Physical layer device interaction | 13
Introduction Recognition Calculation Security Conclusion Calculation during transmission on the wireless channel IoT devices will frequently draw energy from the environment Devices will be sharply restricted in their computational resources This contradicts traditional WSN paradigms Stephan Sigg | Physical layer device interaction | 14
Introduction Recognition Calculation Security Conclusion Calculation during transmission on the wireless channel Motivation: Computation during transmission a a A. Giridhar and P. Kumar, Toward a theory of in-network computation in wireless sensor networks, IEEE Comm. Mag., vol. 44, no 4, pp. 98-107, april 2006 Max. rate to compute & communicate functions Mention: Collisions might contain information Calculation of by means of post- and pre-processing a a M. Goldenbaum, S. Stanczak, and M. Kaliszan, On function computation via wireless sensor multiple-access channels, IEEE Wireless Communications and Networking Conf., 2009 Requires accurate channel state information Requires identical absolute transmit power Stephan Sigg | Physical layer device interaction | 15
Introduction Recognition Calculation Security Conclusion Calculation during transmission on the wireless channel Utilising Poisson-distributed burst-sequences transmit burst sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . burst . . . . . . . . . . . . . . . . . . time superimposed received burst sequence . . . . . . . . . . . . t K Addition, subtraction, division and multiplication at the time of wireless data transmission via Poisson-distributed burst-sequences Adding Poisson processes i with mean µ i will result in a Poisson process with mean � n i =1 µ i . Stephan Sigg | Physical layer device interaction | 16
Introduction Recognition Calculation Security Conclusion Calculation during transmission on the wireless channel Errors for calculating during transmission on the wireless channel t = 10 6 ; κ = 10 3 10 nodes 20 nodes 30 nodes 40 nodes 50 nodes mean err .0322 .0466 .0609 .051 .0719 std-dev. .0232 .0368 .0536 .0336 .0438 max N i 9 14 18.5 26 31 median T 2653.5 5161.5 7393 101816 124179 t = 10 7 ; κ = 10 3 10 nodes 20 nodes 30 nodes 40 nodes 50 nodes mean err .0049 .0176 .0402 .0475 .0781 std-dev. .0062 .0127 .0233 .0292 .0405 max N i 12 18 23 27 31 median T 25708.5 52617.5 78502 101381 114348 t = 10 7 ; κ = 10 2 10 nodes 20 nodes 30 nodes 40 nodes 50 nodes mean err .0190 .1337 .2619 .4903 .6597 std-dev. .0107 .0358 .0591 .0708 .1129 max N i 9.5 16 19 24 27 median T 24165 50037 71686.5 96829 114383 Stephan Sigg | Physical layer device interaction | 17
Introduction Recognition Calculation Security Conclusion Calculation during transmission on the wireless channel Case study to compare the calculation accuracy Utilise data from the Intel Berkeley laboratory network (here: temperature) 2 Transmission of data by simple sensor nodes 2http://db.csail.mit.edu/labdata/labdata.html Stephan Sigg | Physical layer device interaction | 18
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