Mobicom tutorial-1-de
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  • Over the past seven years, nodes have gone both in the directions of smaller size and increased capabilities. Some of these are now to the point that they can be conveniently programmed by groups other than the developers, and thus may be used in support of scientific experiments. All the nodes illustrated are descendents in various senses of the UCLA LWIM project.
  • Sensor nodes combine signal processing, communications and sensors in one package, together with an energy supply. A combination of advances in IC technology, customization, and most importantly system optimization methods will lead to node energy consumption decreasing steadily over time.
  • There are many means for powering nodes, although the reality is that various electrical sources are by far the most convenient. Technology trends indicate that within the lifetime of CENS, nodes will likely be available that could live off ambient light. However, this cannot be accomplished without aggressive energy management at many levels; continuous communications alone would exceed the typical energy budgets.
  • This fact together with scalability concerns provides strong incentive for processing data at source, rather than sending it to some central collection point.

Mobicom tutorial-1-de Mobicom tutorial-1-de Presentation Transcript

  • Part I: Introduction Deborah Estrin I-1
  • Outline• Introduction – Motivating applications – Enabling technologies – Unique constraints – Application and architecture taxonomy I-2
  • Embedded Networked Sensing Potential • Micro-sensors, on- board processing, and wireless interfaces all feasible at very small scale – can monitorSeismic Structure phenomena “up Contaminant response close” Transport • Will enable spatially and temporally dense Ecosystems, Marine environmental BiocomplexityMicroorganisms monitoring • Embedded Networked Sensing will reveal previously unobservable phenomena I-3
  • App#1: Seismic • Interaction between ground motions and structure/foundation response not well understood. – Current seismic networks not spatially dense enough to monitor structure deformation in response to ground motion, to sample wavefield without spatial aliasing. • Science – Understand response of buildings and underlying soil to ground shaking – Develop models to predict structure response for earthquake scenarios. • Technology/Applications – Identification of seismic events that cause significant structure shaking. – Local, at-node processing of waveforms. – Dense structure monitoring systems. ENS will provide field data at sufficient densities to develop predictive models of structure, foundation, soil response. I-4
  • Field Experiment• 38 strong-motion seismometers in 17-story steel-frame Factor Building.• 100 free-field seismometers in UCLA campus ground at 100-m spacing  1 km  I-5
  • Research challenges• Real-time analysis for rapid response.• Massive amount of data → Smart, efficient, innovative data management and analysis tools.• Poor signal-to-noise ratio due to traffic, construction, explosions, ….• Insufficient data for large earthquakes → Structure response must be extrapolated from small and moderate-size earthquakes, and force- vibration testing.• First steps – Monitor building motion – Develop algorithm for network to recognize significant seismic events using real-time monitoring. – Develop theoretical model of building motion and soil structure by numerical simulation and inversion. – Apply dense sensing of building and infrastructure (plumbing, ducts) with experimental nodes. I-6
  • App#2: Contaminant Transport • ScienceAir – Understand intermedia contaminant Emissio ns transport and fate in real systems. Water Well – Identify risky situations before they become exposures. Subterranean Soil Zone deployment. Spill • Multiple modalities (e.g., pH, redox Path conditions, etc.) Volatization • Micro sizes for some applications (e.g., pesticide transport in plant roots). • Tracking contaminant “fronts”. Dissolution • At-node interpretation of potential Groundwater for risk (in field deployment). I-7
  • ENS Research Implications • Environmental Micro-Sensors – Sensors capable of recognizing phases in air/water/soil mixtures. – Sensors that withstand physically and chemically harsh conditions.Contaminant – Microsensors.plume • Signal Processing – Nodes capable of real-time analysis of signals. – Collaborative signal processing to expend energy only where there is risk. I-8
  • App#3: Ecosystem MonitoringScience• Understand response of wild populations (plants and animals) to habitats over time.• Develop in situ observation of species and ecosystem dynamics.Techniques• Data acquisition of physical and chemical properties, at various spatial and temporal scales, appropriate to the ecosystem, species and habitat.• Automatic identification of organisms (current techniques involve close-range human observation).• Measurements over long period of time, taken in-situ.• Harsh environments with extremes in temperature, moisture, obstructions, ... I-9
  • Field Experiments• Monitoring ecosystem processes – Imaging, ecophysiology, and environmental sensors – Study vegetation response to climatic trends and diseases.• Species Monitoring – Visual identification, tracking, and population measurement of birds and other vertebrates QuickTime™ and a Photo - JPEG decompressor are needed to see this picture. – Acoustical sensing for identification, spatial position, population estimation. Vegetation change detection• Education outreach – Bird studies by High School Science classes (New Roads and Buckley Schools). Avian monitoring Virtual field observations I-10
  • ENS Requirements for Habitat/Ecophysiology Applications• Diverse sensor sizes (1-10 cm), spatial sampling intervals (1 cm - 100 m), and temporal sampling intervals (1 µs - days), depending on habitats and organisms.• Naive approach → Too many sensors →Too many data. – In-network, distributed signal processing.• Wireless communication due to climate, terrain, thick vegetation.• Adaptive Self-Organization to achieve reliable, long-lived, operation in dynamic, resource-limited, harsh environment.• Mobility for deploying scarce resources (e.g., high resolution sensors). I-11
  • Transportation and Urban MonitoringDisaster Response I-12
  • Intelligent Transportation Project (Muntz et al.)
  • Smart Kindergarten Project: Sensor-based Wireless Networks of Toysfor Smart Developmental Problem-solving Environments (Srivastava et al) Middleware Framework Network Sensor Sensor Speech Database Management Management Fusion Recognizer & Data Miner WLAN Access Wired Network Point High-speed Wireless LAN (WLAN) WLAN-Piconet WLAN-Piconet Bridge Bridge Piconet Sensors Piconet Modules Sensor Badge Networked Toys
  • Enabling TechnologiesEmbed numerous distributed Network devicesdevices to monitor and interact to coordinate and performwith physical world higher-level tasks Embedded Networked Control system w/ Exploit Small form factor collaborative Untethered nodes Sensing, action Sensing Tightly coupled to physical world Exploit spatially and temporally dense, in situ, sensing and actuation I-15
  • Sensors• Passive elements: seismic, acoustic, infrared, strain, salinity, humidity, temperature, etc.• Passive Arrays: imagers (visible, IR), biochemical• Active sensors: radar, sonar – High energy, in contrast to passive elements• Technology trend: use of IC technology for increased robustness, lower cost, smaller size – COTS adequate in many of these domains; work remains to be done in biochemical I-16
  • Some Networked Sensor Node DevelopmentsLWIM III AWAIRS IUCLA, 1996 UCLA/RSC 1998Geophone, RFM Geophone, DS/SSradio, PIC, star Radio, strongARM,network Multi-hop networks WINS NG 2.0UCB Mote, 2000 Sensoria, 20014 Mhz, 4K Ram Node development512K EEProm, platform; multi-128K code, sensor, dual radio,CSMA Linux on SH4,half-duplex RFM radio Preprocessor, GPS Processor I-17
  • Source: ISI & DARPA PAC/C Program Sensor Node Energy Roadmap 10,000 • Deployed (5W) Rehosting to Low Power COTS Average Power (mW) 1,000 (10x) • PAC/C Baseline (.5W) 100 • (50 mW) -System-On-Chip 10 -Adv Power Management Algorithms (50x) 1  (1mW) .1 2000 2002 2004 I-18
  • Source: UC Berkeley Comparison of Energy Sources Power (Energy) Density Source of Estimates 3 Batteries (Zinc-Air) 1050 -1560 mWh/cm (1.4 V) Published data from manufacturers 3 Batteries(Lithium ion) 300 mWh/cm (3 - 4 V) Published data from manufacturers 2 15 mW/cm - direct sun 2 Solar (Outdoors) 0.15mW/cm - cloudy day. Published data and testing. 2 .006 mW/cm - my desk 2 Solar (Indoor) 0.57 mW/cm - 12 in. under a 60W bulb Testing 3 Vibrations 0.001 - 0.1 mW/cm Simulations and Testing 2 3E-6 mW/cm at 75 Db sound level 2 Acoustic Noise 9.6E-4 mW/cm at 100 Db sound level Direct Calculations from Acoustic Theory Passive Human 2 Powered 1.8 mW (Shoe inserts >> 1 cm ) Published Study. Thermal Conversion 0.0018 mW - 10 deg. C gradient Published Study. 3 80 mW/cm 3 Nuclear Reaction 1E6 mWh/cm Published Data. 3 300 - 500 mW/cm 3 Fuel Cells ~4000 mWh/cm Published Data. With aggressive energy management, ENS might live off the environment. I-19
  • Source: ISI & DARPA PAC/C Program Communication/Computation Technology Projection 1999 (Bluetooth 2004 Technology) (150nJ/bit) (5nJ/bit) Communication 1.5mW* 50uW ~ 190 MOPS Computation (5pJ/OP) Assume: 10kbit/sec. Radio, 10 m range. Large cost of communications relative to computation continues I-20
  • “The network is the sensor” (Oakridge National Labs) Requires robust distributed systems of thousands ofphysically-embedded, unattended, and often untethered, devices. I-21
  • New Design Themes• Long-lived systems that can be untethered and unattended – Low-duty cycle operation with bounded latency – Exploit redundancy and heterogeneous tiered systems• Leverage data processing inside the network – Thousands or millions of operations per second can be done using energy of sending a bit over 10 or 100 meters (Pottie00) – Exploit computation near data to reduce communication• Self configuring systems that can be deployed ad hoc – Un-modeled physical world dynamics makes systems appear ad hoc – Measure and adapt to unpredictable environment – Exploit spatial diversity and density of sensor/actuator nodes• Achieve desired global behavior with adaptive localized algorithms – Cant afford to extract dynamic state information needed for centralized control I-22
  • From Embedded Sensing to Embedded Control• Embedded in unattended “control systems” – Different from traditional Internet, PDA, Mobility applications – More than control of the sensor network itself• Critical applications extend beyond sensing to control and actuation – Transportation, Precision Agriculture, Medical monitoring and drug delivery, Battlefied applications – Concerns extend beyond traditional networked systems • Usability, Reliability, Safety• Need systems architecture to manage interactions – Current system development: one-off, incrementally tuned, stove- piped – Serious repercussions for piecemeal uncoordinated design: insufficient longevity, interoperability, safety, robustness, scalability... I-23
  • Sample Layered Architecture User Queries, External DatabaseResourceconstraints call In-network: Application processing,for more tightly Data aggregation, Query processingintegrated layers Data dissemination, storage, cachingOpen Question:Can we define an Adaptive topology, Geo-RoutingInternet-likearchitecture forsuch application-specific MAC, Time, Locationsystems?? Phy: comm, sensing, actuation, SP I-24
  • Systems Taxonomy Load/Event Metrics Models• Spatial and Temporal • Frequency • Efficiency Scale – spatial and – System – Extent temporal density of lifetime/System – Spatial Density (of events resources sensors relative to • Locality • Resolution/Fidelity stimulus) – Data rate of stimulii – spatial, temporal – Detection,• Variability correlation Identification – Ad hoc vs. engineered • Mobility • Latency system structure – Rate and pattern – Response time – System task variability • Robustness – Mobility (variability in – Vulnerability to space) node failure and• Autonomy environmental – Multiple sensor dynamics modalities • Scalability – Computational model – Over space and complexity time• Resource constraints – Energy, BW – Storage, Computation I-25