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Network lifetime and stealth time of wireless video sensor intrusion detection systems under risk-based scheduling. Prof . Congduc Pham ~cpham Université de Pau, France. ISWPC, 2011 Hong-Kong Wednesday, February 23 rd. Wireless Video Sensors. Imote2.

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Prof . Congduc Pham ~cpham Université de Pau, France

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Prof congduc pham http www univ pau fr cpham universit de pau france

Network lifetime and stealth time of wirelessvideosensor intrusion detectionsystemsunderrisk-basedscheduling

Prof. Congduc Pham

Université de Pau, France

ISWPC, 2011


Wednesday, February 23rd

Wireless video sensors

Wireless VideoSensors


Multimedia board

Surveillance scenario 1

Surveillance scenario (1)

  • Randomlydeployedvideosensors

  • Not onlybarriercoverage but general intrusion detection

  • Most of the time, network in so-calledhibernate mode

  • Most of active sensornodes in idle modewithlow capture speed

  • Sentrynodeswithhigher capture speed to quicklydetect intrusions

Surveillance scenario 2

Surveillance scenario (2)

  • Nodesdetecting intrusion must alert the rest of the network

  • 1-hop to k-hopalert

  • Network in so-calledalerted mode

  • Capture speed must beincreased

  • Ressources shouldbefocused on makingtracking of intruderseasier

Surveillance scenario 3

Surveillance scenario (3)

  • Network should go back to hibernate mode

  • Nodes on the intrusion path must keep a high capture speed

  • Sentrynodeswithhigher capture speed to quicklydetect intrusions

Don t miss important events

Wholeunderstanding of the sceneiswrong!!!

Don’t miss important events!

Real scene


How to meet surveillance app s criticality

How to meet surveillance app’s criticality

  • Capture speed canbe a « quality » parameter

  • Capture speed for node v shoulddepend on the app’scriticality and on the level of redundancy for node v

  • Note thatcapturing an image does not meantransmittingit

  • V’scapture speed canincreasewhen as V has more nodescoveringitsownFoV - coverset

Redundancy node s cover set

RedundancyNode’scover set

  • Each node v has a Field of View, FoVv

  • Coi(v) = set of nodes v’ such as v’Coi(v)FoVv’ covers FoVv

  • Co(v)= set of Coi(v)







Criticality model 1

Criticality model (1)

  • Link the capture rate to the size of the coverset

  • High criticality

    • Convexshape

    • Most projections of x are close to the max capture speed

  • Lowcriticality

    • Concave shape

    • Most projections of x are close to the min capture speed

  • Concave and convexshapesautomaticallydefinesentrynodes in the network

Criticality model 2

Criticality model (2)

  • r0canvary in [0,1]

  • BehaViorfunctions (BV) defines the capture speed according to r0

  • r0 < 0.5

    • Concave shape BV

  • r0 > 0.5

    • Convexshape BV

  • We propose to use Beziercurves to model BV functions

Some typical capture speed

Some typical capture speed

  • Set maximum capture speed: 6fps or 12fps for instance

  • Nodeswithcoverset size greaterthan N capture at the maximum speed





How to build an intrusion detection system

How to build an intrusion detection system

  • Static

    • Prior to deployment, define r° in [0,1] according to the application’scriticality

  • Risk-based

    • R0is set initiallylow : R°min

    • Somenodes serve as sentrynodes

    • On intrusion, increase R° to R°maxduring an givenalertperiod (Ta)

    • After Ta, go back to R°min

    • 2 variants

      • R°moves fromR°minto R°max in one step

      • R°moves fromR°minto R°maxby reinforcementbehavior

Risk based scheduling in images 1

Risk-basedscheduling in images (1)

  • R°=R°min=0.1, R°max=0.9, no alert









Risk based scheduling in images 2

Risk-basedscheduling in images (2)

  • R°R°=R°max=0.9









Simulation settings

Simulation settings

  • OMNET++ simulation model

  • Videonodes have communication range of 30m and depth of view of 25m, AoVis 36°. 150 sensors in an 75m.75m area.

  • Battery has 100 units, 1 image = 1 unit of batteryconsumed.

  • Max capture rate is 3fps. 12 levels of cover set.

  • Full coverageisdefined as the regioninitiallycoveredwhen all nodes are active

Mean stealth time mst

meanstealthtime (MST)

t1-t0is the intruder’sstealth time

velocityis set to 5m/s



  • intrusions startsat t=10s

  • when an intruderisseen, computes the stealth time, and starts a new intrusion until end of simulation

Mean stealth time static scheduling


Mean stealth time risk based scheduling

meanstealth timerisk-basedscheduling



  • Sensornodesstartat 0.1 thenincrease to 0.9 if alerted (by intruders or neighbors) and stayalerted for Ta seconds

Mean stealth time w wo reinforcement

meanstealth timew/woreinforcement


  • On alert 0.1Ir, then

  • 2 alertmsg IrIr+1

  • Until Ir=R°max

  • Reinforcementalwaysincreases the network lifetime

  • Meanstealth time is close to the no-reinforcement case, especiallywhen Ta>20s

With reinforcement various initial threshold

Withreinforcementvarious initial threshold

  • Ir=0.4

  • Ir=0.5

  • Ir=0.6

  • Reduce Iralwaysincreases the network lifetime

  • For small value of Ta, MST increaseisnoticeable

  • It isbetter to increase Irthanincrease Ta.

Sentry nodes











Sentry nodes

# of cover sets

# intrusion detected



  • Models the application’scriticality as beziercurves and schedules the videonode capture rate according to the redundancylevel

  • Withthis model, a risk-basedschedulingcanincrease the network lifetimewhilemaintaining a highlevel of service (meanstealth time)

  • Reinforcementbehaviorisbeneficial and itisbetter to keep the alertperiodlow <=20s for instance

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