passive interference measurement in wireless sensor networks

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Passive Interference Measurement in Wireless Sensor Networks. Shucheng Liu 1,2 , Guoliang Xing 3 , Hongwei Zhang 4 , Jianping Wang 2 , Jun Huang 3 , Mo Sha 5 , Liusheng Huang 1 1 University of Science and Technology of China, - PowerPoint PPT Presentation

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Passive Interference Measurement in Wireless Sensor Networks

1/24Passive Interference Measurement in Wireless Sensor NetworksShucheng Liu1,2, Guoliang Xing3, Hongwei Zhang4,

Jianping Wang2, Jun Huang3, Mo Sha5, Liusheng Huang1

1University of Science and Technology of China,2City University of Hong Kong, 3Michigan State University,4Wayne State University, 5Washington University in St. Louis

1Passive Interference Measurement in Wireless Sensor NetworksShucheng Liu1,2; Guoliang Xing3; Hongwei Zhang4; Jianping Wang2;JunHuang3;MoSha5; Liusheng Huang11University of Science and Technology of China and USTC-CityU Joint Advanced Research Centre;2City University of Hong Kong;3Michigan State University, USA;4Wayne State University, USA;5Washington University in St. Louis, USA12/24OutlineMotivationUnderstanding the PRR-SINR interference modelPassive Interference Measurement (PIM) protocolTestbed evaluation2Design of C-MACPerformance evaluation

3/24Data-intensive Sensing ApplicationsReal-time target detection & tracking, earthquake monitoring, structural monitoring etc.Ex: accelerometers must sample a structure at 100 Hz

100 seismometers in UCLA campus [Estrin 02]

acoustic sensors detecting AAV http://www.ece.wisc.edu/~sensit/ 3real-time sports performancehttp://research.ict.csiro.au/research/labs/autonomous-systems/sensor-networks/health-and-sportsThe new Berlin main station: Long-term structural health monitoring by BAMhttp://www.bam.de/en/kompetenzen/fachabteilungen/abteilung_8/fg81/fg81_ag2.htm

4/24ChallengesWireless sensors have limited bandwidth

Excessive packet collisions in high-rate appsEnergy waste and poor communication quality

Interference mitigation schemesTDMA, link scheduling, channel assignmentsRely on accurate interference models5/24Interference ModelsProtocol modelPerfect comm. rangeBinary packet receptionPRR-SINR modelPacket reception ratio vs. signal to interference plus noise ratioPRR=100%

Ganesan 20025Benefit of measuring PRR-SINR6/24Empirical Study on PRR-SINR Model

Measurement in different timesMeasurement at different locationsSignificant spatial and temporal variation Real-time interference model measurement is necessary6Necessity of measurement7/24A State-of-the-Art Measurement MethodTimeSend eventReceive/measure eventSenderReceiverInterferer SynchronizationNoise Level MeasurementSINR measurementReceived?Measuring multiple (PRR,SINR) pairs for many nodes Prohibitively high overhead!7Defects of existing method8/24OutlineMotivationUnderstanding PRR-SINR modelPassive Interference Measurement (PIM) protocolPerformance evaluation8Design of C-MACPerformance evaluation

9/24Key ObservationsData traffic generates many packet collisions

Spatial diversity leads to different SINRsSINR=1dB

SINR=2dB

SINR=5dB9Design of C-MACPerformance evaluation

10/24Overview of PIMbase stationM-nodeMeasure M-nodes PRR-SINR modelR-node selectionInformation collectionInterference detectionModel generationR-node 1R-node 2Interference linkData link10In chronological order10Information Collectionnode idpkt idtimestampRSSAggregatorM-nodeR1p1t1TXR2p2t2TXRSS measurements of collision-free packetsp1p1p2p2Mp1t1RSS(p1)Mp2t2RSS(p2)R-node 1R-node 2Received Signal Strength11Use examples to illustrate the main components11Information Collectionnode idpkt idtimestampRSSAggregatorm-nodeR1r11t1R2p2t2TXTX/RX statistics of colliding packets p3p4R1p1t1TXR1p3t3TXMp1t1RSS(p1)Mp2t2RSS(p2)Mp4t3RSS(p3+p4)R2p4t3TXReceive with collisionr-node 1r-node 212Use examples to illustrate the main components12Information Collectionnode idpkt idtimestampRSSAggregatorm-nodeR1r11t1R2p2t2TXColliding packets for TX/RX statisticsp5p6R1r11t1R1p1t1TXR1p3t3TXMp1t1RSS(p1)Mp2t2RSS(p2)Mp4t3RSS(p3+p4)R2p4t3TXLost due to collisionR1p5t4TXR2p6t4TXr-node 1r-node 213Use examples to illustrate the main components13Interference DetectionDetect interferer with collected timestampsRemove fake collisionsPackets may overlap without interference!Remove using measured RSS informationnode idpkt idtimestampRSSR1r11t1R1p1t1TXMp1t1RSS(p1)R2p2t2TXMp2t2RSS(p2)R1r11t1R1p3t3TXR2p4t3TXMp4t3RSS(p3+p4)R1p5t4TXR2p6t4TXp4 collides with p3, but received by Mp6 collides with p5, lost at M14Use examples to illustrate the main components14Model GenerationDerive SINR for collision of p3, p4SINR(p3+p4) = RSS(p4) RSS(p3) Noise = RSS(p2) RSS(p1) Noise

node idpkt idtimestampRSSR1r11t1R1p3t3TXR2p4t3TXMp4t3RSS(p3+p4)R1p5t4TXR2p6t4TXp4 collides with p3, but received by Mp6 collides with p5, lost at MCompute PRR PRR = 50%Mp1t1RSS(p1)Mp2t2RSS(p2)15Use examples to illustrate the main components1516/24R-Node SelectionMinimize the number of r-nodes used to measure the (PRR,SINR) pairs of all M-nodesProved to be NP-hardDesigned a efficient greedy algorithmM-nodeSINRInterfering R-node setM11 dB{R1, R2}, {R4, R5}M12 dB{R1, R3}, {R2, R3}, {R3, R4, R5}M21 dB{R2, R3}, {R3, R4}, {R1, R4, R5}M22 dB{R1, R3}, {R1, R5}, {R3, R5}R-Nodes Set {R1, R2, R3}16In chronological order1617/24Experimental SetupImplemented on TelosB with TinyOS-2.0.2Both a 13-node portable testbed and a 40-node static testbedCompared with the ACTIVE method

18/24

Accuracy of PIMCreate a model using 5 min statisticsPredict the throughput of from another sender Baseline methodsActive method w/ 256 and 1024 control packetsAnalytical model in Tinyos2.119/24Overhead of PIM

19We put slides for error with time and duty cycle after the end20/24ConclusionsEmpirical study of PRR-SINR interference model

Passive interference measurementSignificantly lower overheadHigh accuracy of PRR-SINR modelingReal time interference modeling

Performance evaluation on real testbeds

2021/24

Accuracy of PIM22/24Thanks!2223/24Remove Fake Interfering PacketsRule 1: If a interfering packet set of node v maintains the same SINR when removing packet w, then the forwarder/sender of w is a fake r-node of node v.

Rule 2: If node u is a fake r-node of node v, then any packet sent by u does not interfere with any packet received by v.232324/24ExampleFake r-node of N4:N7N5m-nodeSINRInterfering r-node setN41{N1, N2}, {N1, N2, N5}, {N1, N2, N5, N7} N42242425/24Average Errors Over Time

26/24Average Errors with Duty Cycles

27/24

OverviewThe system architecture of PIMrecords the time when an r-node forwards each packetrecords the time when an m-node receives each packetchosen to help measure the PRR-SINR model of the m-nodewhose PRR-SINR models are to be measuredrecords the RSS values of the received packets. collects information and generates the PRR-SINR models of m-nodes27Arch. In chronological order2728/24

OverviewThe system architecture of PIMdetects interferer using collected informationgenerates PRR-SINR models of m-nodesdecreases overhead by identifies interferers of m-nodes28In chronological order2829/24Information CollectionTimestampingRecord the time of forwarding/sending and receiving packet

RSS measurementRecord the RSS value of received packet

All the recorded informations are then transmitted to the aggregator29Use examples to illustrate the main components2930/24Why PRR-SINR Model?Packet-level physical interference model Easy to estimated based on packet statistics Directly describes the impact of dynamicsEnvironmental noise Concurrent transmissions

average power ofambient noiseprobability of receiving packet

received signal power of packet

received signal power of interfering transmissions collisions

s1rs230Benefit of measuring PRR-SINR

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