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Multi-channel Interference Measurement and Modeling in Low-Power Wireless Networks

Multi-channel Interference Measurement and Modeling in Low-Power Wireless Networks. Guoliang Xing 1 ,  Mo Sha 2 ,  Jun Huang 1 Gang Zhou 3 , Xiaorui Wang 4 , Shucheng Liu 5 1 Michigan State University,  2 Washington University in St. Louis,  3 College of William and Mary, 

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Multi-channel Interference Measurement and Modeling in Low-Power Wireless Networks

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  1. Multi-channel Interference Measurement and Modelingin Low-Power Wireless Networks Guoliang Xing1,  Mo Sha2,  Jun Huang1 Gang Zhou3, Xiaorui Wang4, Shucheng Liu5 1Michigan State University,  2Washington University in St. Louis,  3College of William and Mary,  4University of Tennessee, Knoxville 5City University of Hong Kong

  2. Low-power Wireless Networks (LWNs) • Low communication power (10~100 mw) • Personal area networks • ZigBee remote controls and game consoles, Bluetooth headsets…. • Wireless sensor networks • Environmental monitoring, structural monitoring, Industrial/home automation ZigBee thermostat (HAI ) industrial automation (Intel fabrication plant)

  3. Challenges • LWNs are increasingly used for critical apps • Stringent requirements on throughput & delay • Interference is often inevitable • Low throughput & unpredictable comm. delay • Worse for LWNs due to limited radio bandwidth

  4. Mitigating Interference • Avoid interference by assigning links different channels • 802.15.4: 16 channels in 2.4-2.483 GHz, 5MHz separation s2 s1 channel X channel Y r2 collisions r1 signal power frequency 4

  5. Channels Are Overlapping! • Power leakage causes inter-channel Interference • Only 3 or 4 channels of ZigBee are orthogonal theoretical channel bandwidth Interference on adjacent channel 0 signal power(dbm) -20 -40 -60 -80 -100 Channel X Channel X+1 Channel X-1 1 MHz

  6. Outline Motivation Measurement-based interference modeling Lightweight interference measurement algorithm Extensions to channel assignment protocols Experimental results 6

  7. Strongly Overlapping Channels • When two channels are close • Received Signal Strength (RSS) grows nearly linearly with transmit power channel 19, power level [0~31] s1 r1 channel Y, received signal strength (RSS)

  8. Weakly Overlapping Channels • When two channels are not close • RSS donotstrongly correlate with transmit power Sender periodically changes transmit power on channel 19

  9. Modeling Inter-Channel RSS • Sender u on channel x and receiver v on channel y • Strongly correlated channels, sender transmit power P RSS ( ux, vy, P ) = Au,x,v,y × P + Bu,x, v,y • Weakly correlated channels, for given quantile α∊ [0,1] RSS ( ux, vy, α ) = X |Prob(RSS<X) = α determined by measurements

  10. Outline Motivation Measurement-based interference modeling Lightweight interference measurement algorithm Extensions to channel assignment protocols Experimental results 10

  11. Measurement Complexity • RSS models need to be measured for each combination (sender ch. X, receiver ch. Y) • Complexity is O(M2) for M overlapping channels • Complexity of measuring node S O(1) • Our algorithm reduces the complexity to O(M)

  12. Lightweight Measurement Algorithm • For any receiver R on channel Y • RSS (SX, RY, P) = P – ZX,Y – BY,R • ZX,Y -- sender Inter-channel signal power decay between ch. X and ch. Y • BY,R-- intra-channel signal decay • No channel switches for receiver if ZX,Y and BY,R are known! channel Y channel X ZX,Y (dB) S S BY,R (dB) R RSS (SX,RY,P)

  13. Measuring Spectral Power Density signal power (dbm) • SPD is receiver-independent! • Randomly use M neighbors on M different channels • Measure inter-channel RSS models simultaneously • Derive inter-channel decay ZX,Y for all channels {Y} ZX,Y = P – RSS (SX, RY, P) – BY,R • Other nodes derive RSS models w/o channel switching

  14. Outline • Motivation • Measurement-based interference modeling • Lightweight interference measurement algorithm • Extensions to channel assignment protocols • Tree-based Multi-Channel Protocol [Wu et al., Infocom 08] • Control based multi-channel MAC [Le et al., IPSN 07] • Experimental results 14

  15. Tree-based Multi-Channel Protocol (TMCP) [Wu et al. 2008] • Main idea • Partition the whole network into multiple vertex-disjoint subtrees • Allocate different channels to different subtrees • Problems • Distance-based interference model • Minimization of “interference value” rather than throughput BS Channel X Channel Y

  16. Extending TMCP • Apply our RSS models for interference assessment • Assign channel c to maximize the current PRRs Ti – subtree assigned channel i PRR(v, pv) – packet reception ratio from v to its parent, and is obtain by our RSS model and PRR-SINR model • PRR considers both intra- and inter-tree interference

  17. Experimental Setup • Implemented on TelosB with TinyOS-2.0.2 • 30 TelosB motes deployed in a 29×28 ft office • Two different network topologies • Five 3-node chains • Five 3-node clusters

  18. Accuracy of the SPD Algorithm

  19. Improvement of TMCP

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