Oveview of Wireless Sensor Networks KD Kang

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Oveview of Wireless Sensor Networks KD Kang. Overview. What is a sensor network? Sensing Microsensors Constraints, Problems, and Design Goals Overview of Research Issues and Challenges. Applications. Applications. Interface between Physical and Digital Worlds Many applications - PowerPoint PPT Presentation

Text of Oveview of Wireless Sensor Networks KD Kang

  • Oveview of Wireless Sensor Networks

    KD Kang

  • OverviewWhat is a sensor network?SensingMicrosensorsConstraints, Problems, and Design Goals

    Overview of Research Issues and Challenges

  • Applications

  • ApplicationsInterface between Physical and Digital Worlds Many applicationsMilitaryTarget tracking/ReconnaissanceWeather prediction for operational planningBattlefield monitoringIndustry: industrial monitoring, fault-detectionCivilian: traffic, medicalScientific: eco-monitoring, seismic sensors, plume tracking

  • Microsensors for in-situ sensingSmallLimited resourcesBattery poweredEmbedded processor, e.g., 8bit processorMemory: KBMB rangeRadio: Kbps Mbps, tens of metersStorage (none to a few Mbits)

  • Redwood trees: An application

  • Mica2 Mote128KB Instruction EEPROM4KB Data RAMAtmega 128Lmicroprocessor7.3827MHzChipcornCC1000Radio TranscieverMax 38Kbps- Lossy transmissionFlashMemory128KB 512KBUART51 pin expansionconnectorUART, ADC

  • ObjectiveLarge-scale, fine-grained, heterogeneous sensing100s to 1000s of nodes providing high resolutionSpaced a few feet to 10s of meters apartIn-situ sensingHetegerogeneous sensors

  • PropertiesWirelessEasy to deploy: ad hoc deploymentMost power-consuming: transmiting 1 bit executing 1000 instructionsDistributed, multi-hopCloser to phenomenaImproved opportunity for LOSradio signal is proportional to 1/r4Centralized apporach do not scaleSpatial multiplexingCollaborativeEach sensor has a limited view in terms of location and sensor typeSensors are battery poweredIn-network processing to reduce power consumption and data redundancy

  • Basic Terminology and ConceptsPhenomenon: Physical entity being monitored

    Sink or base station: A collection point to which the sensor data is disseminatedRelatively resource rich node

    Sensor network periodically samples phenomena in space and time

    Sink floods a query

  • Typical ScenarioDeployWake/DiagnosisSelf-OrganizeDisseminate

  • Other variationsSensors mobile or not?Phenomena discrete or continuous?Monitoring in real-time or for replay analysis?Ad hoc queries vs. long-running queries

  • Protocol Stack

  • Alternative, more data-centric model

  • Protocol Stack: Physical LayerFrequency selectionCarrier frequency generationSignal detectionModulation

    Not the focus of this classWe will focus on the link layer and above

  • Protocol Stack: Physical LayerIssuesHardware costHow do we get down to $1/node?RadioZigbee/IEEE 802.15.42.4GHz radio band (= 802.11.b & Bluetooth)250KbpsUp to 30 metersDe facto standard

  • Protocol Stack: Data Link LayerMedium access controlMultiplexing of data streamsData frame detectionError control

  • Data Link LayerGoals:Creation of the network infrastructureFair and efficient sharing of of communication resources between sensor nodesExisting solutions?Cellular: Single hop network is impractical for sensor networks802.11: Power consumingScaleData centric operation

  • Data Link Layer: Medium Access ControlBasic strategy: Turn off radio transceiver as much as possible, while receiving and transmitting data

    Techniques: Application-layer transmission scheduling, TDMA, SMAC, ZMAC, BMAC, ...

  • Protocol Stack: Network LayerDesign principles Power efficiencyData-centricData aggregation when desired and possibleAttribute-based addressing and location awareness: no IP address

  • Minimum Energy RoutingMaximum power available routeMinimum energy routeMinimum hop (MH) route

  • Directed DiffusionRoute based on attributes and interestsHow it works:Sink floods interestSensors send data toward the sinkSink reinforces gradients

    One of the first data-centric routing protocolsFlooding is expensive

  • Network LayerData-centric routingDirected DiffusionData Aggregation and In-Network Data ProcessingFloodingGossiping/non-uniform disseminationGeographic routing

  • Transport LayerEnd-to-end ReliabilityMulti-hop retransmission: worth it?Congestion: relatively little related workEnd-to-end securityLike SSL: authentication, encryption, data integrityGood? What about data aggregation?

  • Protocol Stack: Application LayerActual WSN applcationsSensor databaseTinyDBCougarVirtual machines/middleware

  • Other Important IssuesOperating system TinyOS: Event-drivenMANTIS OS, LiteOS, etc: MultithreadedLocalization, Time Synchronization, and CalibrationAggregation/Data FusionSecurityEncryptionAuthenticationData integrityAvailability: DOS & jamming attacksPrivacy

  • Time and Space ProblemsTiming synchronization Node LocalizationSensor Coverage

  • Time SynchronizationTime sync is critical at many layers in sensor netsAggregation, localization, power controlRef: based on slides by J. Elson

  • Sources of time synchronization errorSend timeKernel processingContext switchesTransfer from host to NICAccess timeSpecific to MAC protocolE.g. in 802.11, sender must wait for CTS (Clear To Send)Propagation timeDominant factor in WANsRouter-induced delaysVery small in LANsReceive time

    Common denominator: non-determinism

  • Conventional ApproachesGPS at every node (around 10ns accuracy)Doesnt work indoorCost, size, and energy issuesNTPPrimary time servers are synchronized via atomic clockPre-defined server hierarchyNodes synchronize with one of a pre-specified time serversCan support coarse-grain time synchronizationInefficient when fine-grain sync is requiredSensor net applications, e.g., localization, TDMA Discovery of time serversPotentially long and varying paths to time-servers Delay and jitter due to MAC and store-and-forward relaying

  • LocalizationWhy each node should find its location? Data meaningless without contextGeographical forwarding/addressingWhy not just GPS at every node?Large size and expensiveHigh power consumptionWorks only outdoors with LOS to satellitesOverkill: Often only relative position is needed

  • What is Location?Absolute position on geoidLocation relative to fixed beaconsLocation relative to a starting pointe.g. inertial platformsMost applications:location relative to other people or objects, whether moving or stationary, or the location within a building or an area

  • Techniques for LocalizationMeasure proximity to beaconsNear a base station in a roomActive badge for indoor localizationInfrared base stations in every roomLocalizes to a room as room walls act as barriersMost commercial RF ID Tag systemsStrategically located tag readersBeacon grid for outdoor localizationEstrins system for outdoor sensor networksGrid of outdoor beaconing nodes with know positionPosition = centroid of nodes that can be heardProblemNot location sensing but proximity sensingAccuracy of location is a function of the density of beacons

  • LocalizationMeasure direction of landmarksSimple geometric relationships can be used to determine the location by finding the intersections of the lines-of-positione.g. Radiolocation based on angle of arrival (AoA)can be done using directional antennas or antenna arraysneed at least two measurements

  • Localization: Range-basedMeasure distance to beaconsMeasure signal-strength or time-of-flightEstimate distance via received signal strengthMathematical model that describes the path loss attenuation with distanceUse pre-measured signal strength contours around fixed beacon nodesDistance via Time-of-arrival (ToA)Distance measured by the propagation delayDistance = time * cN+1 BSs give N+1 distance measurements to locate in N dimensions

  • Radiolocation via ToA and RSSIx1x2x3d1d3d2SENSORBSBSBS

  • Many other issuesWhat about errors? Collisions? No LOS?

    If sensors are mobile, when should we localize?

    Multi-hop localization?

  • Sensor Network CoverageThe Problem:Given:Ad hoc sensor field with some number of nodes with known locationStart and end positions of an agentHow well can the field be observed?

    Example usageSoldier in the battlefieldStrongest path: what path to take for maximum coverage by my command?Weakest path: how to walk through enemy sensor net or through minefield?Ref: based on slides by Seapahn Megerian

  • Exposure Model of SensorsLikelihood of detection by sensors is a function of time interval and distance from sensors.

    Ref: based on slides by Seapahn Megerian

  • Data Management ProblemsObserver interested in phenomena with certain toleranceAccuracy, freshness, delaySensors sample the phenomenaSensor Data ManagementDetermining spatio-temporal sampling scheduleDifficult to determine locallyData aggregation and fusionInteraction with routingNetwork/Resource limitationsCongestion managementLoad balancingQoS/Real-time schedulingphenomenasensorsobserver

  • Summary: Key Design ChallengesEnergy efficiencySensor nodes should run for several years without battery replacementEnergy efficient protocols are requiredMore efficient batteriesBut, efficient battery development is always slower than processor/memory development Energy harvesting

  • Key Design ChallengesResponsivenessPeriodic sleep & wake-up can reduce the responsiveness of sensorsImportant events could be missedIn real-time applications, the latency induced by sleep schedules should be kept within bounds even when the network is congested

  • Key Design ChallengesRobustnessInexpensive sensors deployed in a harsh physical environment could be unreliableSome sensor could be faulty or brokenGlobal performance should not be sensitive to individual sensor failuresGraceful performance degradation when there are faulty sensors

  • Key Design ChallengesSynergyMoores law app