15-744: Computer NetworkingSensor NetworksOutlineSmart-Dust/MotesSlide 5Berkeley MotesBerkeley Motes (Levis & Culler, ASPLOS 02)Sensor Net Sample AppsMetric: CommunicationCommunication In Sensor NetsSlide 11The long term goalMotivationDirected DiffusionMotivating ExampleInterest and Event NamingDiffusion (High Level)Illustrating Directed DiffusionSummarySlide 20TAG IntroductionBasic AggregationIllustration: AggregationSlide 24Slide 25Slide 26Slide 27Types of AggregatesTaxonomy of AggregatesBenefit of In-Network ProcessingOptimization: Channel Sharing (“Snooping”)Optimization: Hypothesis TestingOptimization: Use Multiple ParentsMultiple Parents ResultsSlide 35Aggregation in Wireless SensorsTraditional ApproachExploiting Broadcast MediumA Naïve ODI AlgorithmSynopsis Diffusion (SenSys’04)Synopsis Diffusion over RingsEvaluation15-744: Computer NetworkingL-13 Sensor NetworksSensor Networks•Directed Diffusion•Aggregation•Assigned reading•TAG: a Tiny AGgregation Service for Ad-Hoc Sensor Networks•Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks2Outline•Sensor Networks•Directed Diffusion•TAG•Synopsis Diffusion34Smart-Dust/Motes•First introduced in late 90’s by groups at UCB/UCLA/USC•Published at Mobicom/SOSP conferences•Small, resource limited devices•CPU, disk, power, bandwidth, etc.•Simple scalar sensors – temperature, motion•Single domain of deployment (e.g. farm, battlefield, etc.) for a targeted task (find the tanks)•Ad-hoc wireless network5Smart-Dust/Motes•Hardware•UCB motes•Programming•TinyOS•Query processing•TinyDB•Directed diffusion•Geographic hash tables•Power management•MAC protocols•Adaptive topologies•Devices that incorporate communications, processing, sensors, and batteries into a small package •Atmel microcontroller with sensors and a communication unit •RF transceiver, laser module, or a corner cube reflector •Temperature, light, humidity, pressure, 3 axis magnetometers, 3 axis accelerometers Berkeley Motes67Berkeley Motes (Levis & Culler, ASPLOS 02)Sensor Net Sample Apps8Traditional monitoring apparatus.Earthquake monitoring in shake-test sites.Vehicle detection: sensors along a road, collect data about passing vehicles.Habitat Monitoring: Storm petrels on great duck island, microclimates on James Reserve.9Metric: Communication•Lifetime from one pair of AA batteries •2-3 days at full power•6 months at 2% duty cycle•Communication dominates cost•< few mS to compute•30mS to send messageTime v. Current Draw During Query Processing051015200 0.5 1 1.5 2 2.5 3Time (s)Current (mA)SnoozingProcessingProcessingand ListeningTransmitting10Communication In Sensor Nets•Radio communication has high link-level losses•typically about 20% @ 5m•Ad-hoc neighbor discovery•Tree-based routingABCDFEOutline•Sensor Networks•Directed Diffusion•TAG•Synopsis Diffusion11The long term goal12Disaster ResponseCirculatory NetEmbedEmbed numerous distributed devices to monitor and interact with physical world: in work-spaces, hospitals, homes, vehicles, and “the environment” (water, soil, air…)Network these devices so that they can coordinate to perform higher-level tasks.Requires robust distributed systems of tens of thousands of devices.Motivation•Properties of Sensor Networks•Data centric, but not node centric•Have no notion of central authority•Are often resource constrained•Nodes are tied to physical locations, but:•They may not know the topology•They may fail or move arbitrarily•Problem: How can we get data from the sensors?13Directed Diffusion•Data centric – nodes are unimportant•Request driven:•Sinks place requests as interests•Sources are eventually found and satisfy interests•Intermediate nodes route data toward sinks•Localized repair and reinforcement•Multi-path delivery for multiple sources, sinks, and queries14Motivating Example•Sensor nodes are monitoring a flat space for animals•We are interested in receiving data for all 4-legged creatures seen in a rectangle•We want to specify the data rate15Interest and Event Naming•Query/interest:1. Type=four-legged animal2. Interval=20ms (event data rate)3. Duration=10 seconds (time to cache)4. Rect=[-100, 100, 200, 400]•Reply:1. Type=four-legged animal2. Instance = elephant3. Location = [125, 220]4. Intensity = 0.65. Confidence = 0.856. Timestamp = 01:20:40•Attribute-Value pairs, no advanced naming scheme16Diffusion (High Level)•Sinks broadcast interest to neighbors•Interests are cached by neighbors•Gradients are set up pointing back to where interests came from at low data rate•Once a sensor receives an interest, it routes measurements along gradients1718Illustrating Directed DiffusionSinkSourceSetting up gradientsSinkSourceSending dataSinkSourceRecoveringfrom node failureSinkSourceReinforcingstable pathSummary•Data Centric• Sensors net is queried for specific data• Source of data is irrelevant• No sensor-specific query •Application Specific• In-sensor processing to reduce data transmitted• In-sensor caching•Localized Algorithms• Maintain minimum local connectivity – save energy• Achieve global objective through local coordination•Its gains due to aggregation and duplicate suppression may make it more viable than ad-hoc routing in sensor networks19Outline•Sensor Networks•Directed Diffusion•TAG•Synopsis Diffusion20TAG Introduction•Programming sensor nets is hard!•Declarative queries are easy•Tiny Aggregation (TAG): In-network processing via declarative queries•In-network processing of aggregates•Common data analysis operation•Communication reducing•Operator dependent benefit•Across nodes during same epoch•Exploit semantics improve efficiency!•Example: •Vehicle tracking application: 2 weeks for 2 students•Vehicle tracking query: took 2 minutes to write, worked just as well!21SELECT MAX(mag) FROM sensors WHERE mag > threshEPOCH DURATION 64msBasic Aggregation•In each epoch:•Each node samples local sensors once•Generates partial state record (PSR)•local readings •readings from children •Outputs PSR during its comm. slot.•At end of epoch, PSR for whole network output at root•(In paper: pipelining, grouping)2212 34523Illustration: Aggregation1 2 3 4 51 12341123451Sensor #Slot #Slot 1SELECT COUNT(*) FROM sensors24Illustration: Aggregation1 2 3 4 51 12 2341123452Sensor #Slot #Slot 2SELECT
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