CS294-1 Deeply Embedded Networks Platforms Sept 2, 2003Review of last timeReading MaterialsOutlineEarly HistoryClassic Wireless ArchitectureEmergences of WINSWINS case for distributed sensor netsRadio propagationPassive VigilanceWINS node archPicoRadio Concept ArchPicoRadio I implementation (2002)Smart DustEnergy PictureCommunicationCOTS Mote (Seth Hollar)System Characteristics of WSNsRene: Second Generation ‘Mote’Rene @ SensitMote Platform and CommunityRene Power BreakdownSystem tradeoffs: simple radio, fast on/off, application behaviorMote Expansion Connector (v1)Rene Mote Connector SchematicsPower LinesAnalog Lines and AD ConversionI2C BusSPIUARTWhat is the sensor-interface of the future?AVR Processor CoreClocks and Timers a key resourceProgramming the ProcessorCoprocessor CircuitRadio CircuitRadio Signal StrengthRadio Transmission Strength ControlLEDsNew Architectures?Mica ArchitectureBasic Packet ProcessingCross Layer OptimizationsExample ApplicationOptimization evaluationUC Berkeley Family of MotesMica2 and Mica2DotBasic Sensor BoardMica Sensor BoardCOTS-BOTS (UCB) Commercial Off-The-Shelf roBOTSMotor/Servo BoardRobomote (USC)MICAbot (Notre Dame)Mica Interface BoardPNI Magnetometer/CompassUltrasonic TransceiverMica Weather BoardConnexus InterfaceDot Weather BoardsTinyOS-driven architectureReturn of BluetoothCS294-1 Deeply Embedded NetworksPlatformsSept 2, 2003David CullerFall 2003University of California, Berkeley01/14/19CS294-1 F032Review of last time•3 different network architectures for habitat or environmental monitoring–Multi-levels–Extent, duration, energy sources•Role of mobility and adaptation•Where was distributed control involved?•Insight from description vs empirical evaluation?01/14/19CS294-1 F033Reading Materials•G. J. Pottie and W. J. Kaiser, Wireless Integrated Network Sensors , CACM, 43(5), May 2000.•Mica: A Wireless Platform for Deeply Embedded Networks, Jason Hill and David Culler, IEEE Micro., vol 22(6), Nov/Dec 2002.•L. Doherty, B.A. Warneke, B.E. Boser, and K.S.J. Pister, "Energy and Performance Considerations for Smart Dust." International Journal of Parallel Distributed Systems and Networks, Volume 4, Number 3, 2001.•The PicoRadio Test Bed, Burghardt, Mellors, Rabaey•PicoRadio Supports Ad Hoc Ultra-Low Power Wireless Networking, Rabey, Ammer, da Silva, Patel, and Roundy01/14/19CS294-1 F034Outline•Touch of history•3 Platform Perspectives–WINS – estimation theory –SmartDust – analog design, chip technology–Mica - systems•WINS vision–bi-part architecture for passive vigilance•PicoRadio–Incorporate programmable logic for protocol processing, as well as signal processing•Smart Dust–Really serious about energy limits–Simple, un-partitioned architecture•Berkeley TinyOS motes take off•Mica –accelerate primitives not solutions–Rich interfaces and flexible composition for cross-layer optimization01/14/19CS294-1 F035Early History•1966 Igloo White–http://home.att.net/~c.jeppeson/igloo_white.html•1986 DARPA packet Radio programName Model Variant Type Length Weight ADSID I (N) - - - - - - normal seismic 31.00 ins 26.0 lbs ADSID I (S) MA-36 short seismic 20.10 ins 13.7 lbs ACOUSID II TC-415 seismic-acoustic 53.14 ins 38.8 lbs ACOUSID III MA-31 seismic- acoustic 47.63 ins . 37.2 lbs ADSID III (N) MA-33 normal seismic 37.66 ins . 37.2 lbs ADSID III (S) MA-37 short acoustic 20.10 ins . 13.7 lbs MODS 81 mm mortar seismic 33.00 ins . 9.6 lbs01/14/19CS294-1 F036Classic Wireless Architecture•Partitioned system with narrow, std interfaces•Similar picture to 802.11 NIC•uP are poor at driving radios directly–Over sampling, start symbol, …•Partitioning driven by many factors–Local optimization–Standardization–IP protection•Works best in well-established areas•Encumbers deep innovation–Ex: ppp for data on cell01/14/19CS294-1 F037Emergences of WINS•1994 Pottie and Kaiser propose Low Power Wireless Integrated Microsensor–LWIM nodes built around 1996•DARPA Sensit Program•Late 97-98 handhelds emerge–palm–ITSY, BWRC PicoRadio, Srivastava UCLA, Chandrakasan MIT, …–Matchbox PCs–Bluetooth promised•Berkeley SmartDust–1999 WeC mote offshoot•SCADDS (USC/UCLA) pc104s & tags•00 Mote / TinyOS platforms•WINS ng finally appears in Linux for Sensit•02 Mica NEST OEP creates de facto platform•03 Bluetooth revival01/14/19CS294-1 F038WINS case for distributed sensor nets•must distribute to detect reliably regardless of $–All signals decay with distance (r^2) + absorption, scattering, dispersion, …even with line of sight–Often need to track multiple objects–Obstructions, clustering•Detection and estimation theory–observables {Xj} – sample outputs of sensors»target signal plus background noise & interference–features {fk} – reduced representation of observations»Fourier, LPC, wavelet coefficients–hypotheses {hi} – presence/absence based on estimates of feature set–Choose hi if P(hi | {fk} ) > P(hj | {fk} ) for j != I–Reliability: number of independent observations and SNR–Complexity: dimensions of feature space, # hypotheses=> More observations, rather than more processing per observation=> Short range means better SNR=> Fewer targets (hypoth’s) in range of set of sensors=> Nearly homogeneous over small regions01/14/19CS294-1 F039Radio propagation•Energy required to transmit distance d–Et = dn–n is about 2 in freespace, about 4 near ground–Indoor has lots of other complications•Small energy => short range+ Allows spatial multiplexing–Multihop routing required to achieve distance»Energy per hop is more+ routes around obstacles–Requires discovery, topology formation, maintenance»may dominate cost of communication–Requires media access control»Time, space, frequency, …•Energy to receive ~ Et at short range–Dominated by listening time (potential receive)–Radio must be OFF most of the time!•WINS asserts diversity through spreading & coding01/14/19CS294-1 F0310Passive VigilanceParts of the system must be sampling environment all the time–Reliable detection costs too much energyuse low-cost, low resolution techniques to detect potential eventBring in more powerful, more costly options (infrequently) when importantExample: seismic sensor triggering cameraProcessing hierarchy=> Introduces processing, storage, and
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