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

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 Avoid interference by assigning links different channels – : 16 channels in GHz, 5MHz separation s2s2 r2r2 s1s1 r1r1 collisions Mitigating Interference signal power frequency 4 channel X channel Y

5 Channels Are Overlapping! signal power ( dbm) 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

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

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)

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

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

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

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)

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!

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)

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

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 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

17 Experimental Setup Implemented on TelosB with TinyOS 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