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1 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, 4 University of Tennessee, Knoxville 5 City University of Hong Kong
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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)
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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
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4 Avoid interference by assigning links different channels – 802.15.4: 16 channels in 2.4-2.483 GHz, 5MHz separation s2s2 r2r2 s1s1 r1r1 collisions Mitigating Interference signal power frequency 4 channel X channel Y
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5 Channels Are Overlapping! signal power ( dbm) 0 -20 -40 -60 -80 -100 Channel X 1 MHz Channel X+1 Channel X-1 Power leakage causes inter-channel Interference Only 3 or 4 channels of ZigBee are orthogonal theoretical channel bandwidth Interference on adjacent channel
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6 Outline Motivation Measurement-based interference modeling Lightweight interference measurement algorithm Extensions to channel assignment protocols Experimental results
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7 Strongly Overlapping Channels When two channels are close Received Signal Strength (RSS) grows nearly linearly with transmit power s1s1 r1r1 channel 19, power level [0~31] channel Y, received signal strength (RSS)
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8 Weakly Overlapping Channels When two channels are not close RSS do not strongly correlate with transmit power Sender periodically changes transmit power on channel 19
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9 Modeling Inter-Channel RSS Sender u on channel x and receiver v on channel y – Strongly correlated channels, sender transmit power P RSS ( u x, v y, P ) = A u,x,v,y × P + B u,x, v,y – Weakly correlated channels, for given quantile α ∊ [0,1] RSS ( u x, v y, α ) = X | Prob(RSS<X) = α determined by measurements
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10 Outline Motivation Measurement-based interference modeling Lightweight interference measurement algorithm Extensions to channel assignment protocols Experimental results
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11 Measurement Complexity RSS models need to be measured for each combination (sender ch. X, receiver ch. Y) Complexity is O(M 2 ) for M overlapping channels – Complexity of measuring node S O(1) Our algorithm reduces the complexity to O(M)
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12 Lightweight Measurement Algorithm SS R channel X channel Y Z X,Y (dB) B Y,R (dB) RSS (S X,R Y,P) For any receiver R on channel Y RSS (S X, R Y, P) = P – Z X,Y – B Y,R Z X,Y -- sender Inter-channel signal power decay between ch. X and ch. Y B Y,R -- intra-channel signal decay No channel switches for receiver if Z X,Y and B Y,R are known!
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13 Measuring Spectral Power Density SPD is receiver-independent! – Randomly use M neighbors on M different channels – Measure inter-channel RSS models simultaneously Derive inter-channel decay Z X,Y for all channels {Y} Z X,Y = P – RSS (S X, R Y, P) – B Y,R Other nodes derive RSS models w/o channel switching signal power (dbm)
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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
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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
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16 Extending TMCP Apply our RSS models for interference assessment Assign channel c to maximize the current PRRs T i – subtree assigned channel i PRR(v, p v ) – 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
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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
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18 Accuracy of the SPD Algorithm
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19 Improvement of TMCP
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