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Wireless Sensor Networks

Wireless Sensor Networks. Outline. Introduction Sensor Node Platforms & Energy Issues Time & Space Problems in Sensor Networks Sensor Network Protocols. Part I: Introduction. Outline. Introduction Motivating applications Enabling technologies Unique constraints

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Wireless Sensor Networks

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  1. Wireless Sensor Networks Outline • Introduction • Sensor Node Platforms & Energy Issues • Time & Space Problems in Sensor Networks • Sensor Network Protocols

  2. Part I: Introduction

  3. Outline • Introduction • Motivating applications • Enabling technologies • Unique constraints • Application and architecture taxonomy

  4. Embedded Networked Sensing Potential • Micro-sensors, on-board processing, and wireless interfaces all feasible at very small scale • can monitor phenomena “up close” • Will enable spatially and temporally dense environmental monitoring • Embedded Networked Sensing will reveal previously unobservable phenomena Seismic Structure response Contaminant Transport Ecosystems, Biocomplexity Marine Microorganisms

  5. 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.

  6. 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 ¾¾¾¾¾¾ 1 km ¾¾¾¾¾¾

  7. 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.

  8. App#2: Contaminant Transport Air Emissions Water Well • Science • Understand intermedia contaminant transport and fate in real systems. • Identify risky situations before they become exposures. Subterranean deployment. • Multiple modalities (e.g., pH, redox conditions, etc.) • Micro sizes for some applications (e.g., pesticide transport in plant roots). • Tracking contaminant “fronts”. • At-node interpretation of potential for risk (in field deployment). Soil Zone Spill Path Volatization Dissolution Groundwater

  9. ENS Research Implications Contaminant plume • Environmental Micro-Sensors • Sensors capable of recognizing phases in air/water/soil mixtures. • Sensors that withstand physically and chemically harsh conditions. • Microsensors. • Signal Processing • Nodes capable of real-time analysis of signals. • Collaborative signal processing to expend energy only where there is risk.

  10. App#3: Ecosystem Monitoring Science • 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, ...

  11. 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 • Acoustical sensing for identification, spatial position, population estimation. • Education outreach • Bird studies by High School Science classes (New Roads and Buckley Schools). Vegetation change detection Avian monitoring Virtual field observations

  12. ENS Requirements for Habitat/Ecophysiology Applications • Diverse sensor sizes (1-10 cm), spatial sampling intervals (1 cm - 100 m), and temporal sampling intervals (1 ms - 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).

  13. Transportation and Urban Monitoring Disaster Response

  14. Intelligent Transportation Project (Muntz et al.)

  15. Smart Kindergarten Project: Sensor-based Wireless Networks of Toysfor Smart Developmental Problem-solving Environments(Srivastava et al) Middleware Framework NetworkManagement SensorManagement SensorFusion Speech Recognizer Database & Data Miner Wired Network WLAN Access Point High-speed Wireless LAN (WLAN) WLAN-Piconet Bridge WLAN-Piconet Bridge Piconet Piconet SensorsModules Sensor Badge Networked Toys

  16. Enabling Technologies Embednumerous distributed devices to monitor and interact with physical world Networkdevices tocoordinate and perform higher-level tasks Embedded Networked Exploitcollaborative Sensing, action Control system w/ Small form factor Untethered nodes Sensing Tightly coupled to physical world Exploit spatially and temporally dense, in situ, sensing and actuation

  17. 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

  18. Some Networked Sensor NodeDevelopments LWIM III UCLA, 1996 Geophone, RFM radio, PIC, star network AWAIRS I UCLA/RSC 1998 Geophone, DS/SS Radio, strongARM, Multi-hop networks WINS NG 2.0 Sensoria, 2001 Node development platform; multi- sensor, dual radio, Linux on SH4, Preprocessor, GPS UCB Mote, 2000 4 Mhz, 4K Ram 512K EEProm, 128K code, CSMA half-duplex RFM radio Processor

  19. Sensor Node Energy Roadmap Source: ISI & DARPA PAC/C Program 10,000 1,000 100 10 1 .1 Rehosting to Low Power COTS (10x) • Deployed (5W) • PAC/C Baseline (.5W) Average Power (mW) • (50 mW) -System-On-Chip -Adv Power Management Algorithms (50x) (1mW) 2000 2002 2004

  20. Comparison of Energy Sources Source: UC Berkeley With aggressive energy management, ENS might live off the environment.

  21. Communication/Computation Technology Projection Source: ISI & DARPA PAC/C Program Assume: 10kbit/sec. Radio, 10 m range. Large cost of communications relative to computation continues

  22. “The network is the sensor” (Oakridge National Labs) Requires robust distributed systems of thousands of physically-embedded, unattended, and often untethered, devices.

  23. New Design Themes • Long-lived systems that can be untetheredand 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

  24. 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...

  25. Sample Layered Architecture User Queries, External Database Resource constraints call for more tightly integrated layers Open Question: Can we define anInternet-like architecture for such application-specific systems?? In-network: Application processing, Data aggregation, Query processing Data dissemination, storage, caching Adaptive topology, Geo-Routing MAC, Time, Location Phy: comm, sensing, actuation, SP

  26. Systems Taxonomy Metrics Load/Event Models • Spatial and Temporal Scale • Extent • Spatial Density (of sensors relative to stimulus) • Data rate of stimulii • Variability • Ad hoc vs. engineered system structure • System task variability • Mobility (variability in space) • Autonomy • Multiple sensor modalities • Computational model complexity • Resource constraints • Energy, BW • Storage, Computation • Frequency • spatial and temporal density of events • Locality • spatial, temporal correlation • Mobility • Rate and pattern • Efficiency • System lifetime/System resources • Resolution/Fidelity • Detection, Identification • Latency • Response time • Robustness • Vulnerability to node failure and environmental dynamics • Scalability • Over space and time

  27. Part II: Sensor Node Platforms & Energy Issues

  28. Sensor Node H/W-S/W Platforms In-node processing Wireless communication with neighboring nodes Event detection Acoustic, seismic, image, magnetic, etc. interface Electro-magnetic interface sensors radio CPU Limited battery supply battery Energy efficiency is the crucial h/w and s/w design criterion

  29. Overview of this Section • Survey of sensor node platforms • Sources of energy consumption • Energy management techniques

  30. Variety of Real-life Sensor Node Platforms • RSC WINS & Hidra • Sensoria WINS • UCLA’s iBadge • UCLA’s Medusa MK-II • Berkeley’s Motes • Berkeley Piconodes • MIT’s AMPs • And many more… • Different points in (cost, power, functionality, form factor) space

  31. Rockwell WINS & Hidra Nodes • Consists of 2”x2” boards in a 3.5”x3.5”x3” enclosure • StrongARM 1100 processor @ 133 MHz • 4MB Flash, 1MB SRAM • Various sensors • Seismic (geophone) • Acoustic • magnetometer, • accelerometer, temperature, pressure • RF communications • Connexant’s RDSSS9M Radio @ 100 kbps, 1-100 mW, 40 channels • eCos RTOS • Commercial version: Hidra • C/OS-II • TDMA MACwith multihop routing • http://wins.rsc.rockwell.com/

  32. Sensoria WINS NG 2.0, sGate, and WINS Tactical Sensor • WINS NG 2.0 • Development platform used in DARPA SensIT • SH-4 processor @ 167 MHz • DSP with 4-channel 16-bit ADC • GPS • imaging • dual 2.4 GHz FH radios • Linux 2.4 + Sensoria APIs • Commercial version: sGate • WINS Tactical Sensor Node • geo-location by acoustic ranging and angle • time synchronization to 5 s • cooperative distributed event processing Ref: based on material from Sensoria slides

  33. Sensoria Node Hardware Architecture Flash RAM Processor 10/100 Ethernet Address/Data Bus Preprocessor Interface Modular Wireless and Digital Interfaces Imager Module Imager Interface RF Modem 1 RF Modem 2 Digital I/O GPS Analog Front End Multi- Channel Sensor Interface DSP Preprocessor Ref: based on material from Sensoria slides

  34. Sensoria Node Software Architecture General Purpose App 1 General Purpose App 2 General Purpose App 3 General Purpose App 4 WINS Node API RF Modem Control Discovery/ Self-Assembly Routing Messaging Geolocation Sensing Signal Processing Inter-Node Ranging Process Management Platform Management Linux 2.4 Kernel RF Modem 1 Interface RF Modem 2 Interface DSP Interface Sensor Interface GPS Interface Acoustic Ranging Interface Platform Interface GPIO Serial RF 1 RF 2 DSP/ ADC GPS Dual Codec uC Ref: based on material from Sensoria slides

  35. Berkeley Motes • 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 • TinyOS light, temperature, 10 kbps @ 20m

  36. The Mote Family Ref: from Levis & Culler, ASPLOS 2002

  37. TinyOS Messaging Component Internal State Internal Tasks Commands Events • System composed of concurrent FSM modules • Single execution context • Component model • Frame (storage) • Commands & event handlers • Tasks (computation) • Command & Event interface • Easy migration across h/w -s/w boundary • Two level scheduling structure • Preemptive scheduling of event handlers • Non-preemptive FIFO scheduling of tasks • Compile time memory allocation • NestC • http://webs.cs.berkeley.edu Bit_Arrival_Event_Handler State: {bit_cnt} Start Yes Send Byte Eventbit_cnt = 0 bit_cnt==8 bit_cnt++ Done No Ref: from Hill, Szewczyk et. al., ASPLOS 2000

  38. Complete TinyOS Application Ref: from Hill, Szewczyk et. al., ASPLOS 2000

  39. UCLA iBadge • Wearable Sensor Badge • acoustic in/out + DSP • temperature, pressure, humidity, magnetometer, accelerometer • ultrasound localization • orientation via magnetometer and accelerometer • bluetooth radio • Sylph Middleware

  40. Sylph Middleware Speech Recogn. Service Bayesian Fusion Service Storage Service Sensor Apps Sensor Configuration Manager Browsers

  41. UCLA Medusa MK-II Localizer Nodes • 40MHz ARM THUMB • 1MB FLASH, 136KB RAM • 0.9MIPS/MHz 480MIPS/W (ATMega 242MIPS/W) • RS-485 bus • Out of band data collection, formation of arrays • 3 current monitors (Radio, Thumb, rest of the system) • 540mAh Rechargeable Li-Ion battery

  42. BWRC’s PicoNode TripWire Sensor Node Chip encapsulation (1.5 mm) Solar Cell (0.5 mm) PCB (1 mm) Battery (3.6 mm) 7.6 mm Version 1: Light Powered Components and batterymounted on back 5 cm 3 cm Size determined by power dissipation (1 mW avg) Version 2: Vibration Powered Ref: from Jan Rabaey, PAC/C Slides

  43. BWRC PicoNode (contd.) Sensor/ actuators 16kB CODE Sensor/actuator interface App/UI User interface 4kB XDATA Transport Aggregation/ forwarding 256 DATA DW8051 Chip Supervisor Network Serial Interconnect network Chip Supervisor Locationing DLL (MAC) Energy train FlashIF MAC SIF Serial Baseband ADC GPIO Reactive radio SIF RF (TX/RX) LocalHW PHY ADC Antenna • Reactive inter- and intra-chip signaling • Modules in power-down (low-leakage) mode by default • Events at interface cause wake-up • Hw Modules selected to meet flexibility needs while optimizing energy efficiency (e.g. 8051 microcontroller) 1 mW on < 10 mW sleep Size: 6 mm2 Ref: from Jan Rabaey, PAC/C Slides

  44. Quick-and-dirty iPaq-based Sensor Node! HM2300 Magnetic Sensor - uC Based with RS232 • Range of +/- 2Gausus • Adjustable Sampling Rate • X, Y, Z output • Device ID Management WaveLan Card • IEEE 802.11b Compliant • 11 Mbit/s Data Rate Familiar v0.5 • Linux Based OS for iPAQ H3600s • JFFS2, read/write iPAQ’s flush • Tcl ported iPAQ 3670 • Intel StrongARM • Power Management (normal, idle & sleep mode) • Programmable System Clock • IR, USB, Serial (RS232) Transmission Acoustic Sensor & Actuator • Built-in microphone • Built-in speaker

  45. Sensor Node Energy Roadmap(DARPA PAC/C) 10,000 1,000 100 10 1 .1 • Deployed (5W) Rehosting to Low Power COTS (10x) • PAC/C Baseline (.5W) -Simple Power Management -Algorithm Optimization (10x) Average Power (mW) • (50 mW) -System-On-Chip -Adv Power Management -Algorithms (50x) (1mW) 2000 2002 2004 • Low-power design • Energy-aware design

  46. Where does the energy go? • Processing • excluding low-level processing for radio, sensors, actuators • Radio • Sensors • Actuators • Power supply

  47. Processing • Common sensor node processors: • Atmel AVR, Intel 8051, StrongARM, XScale, ARM Thumb, SH Risc • Power consumption all over the map, e.g. • 16.5 mW for ATMega128L @ 4MHz • 75 mW for ARM Thumb @ 40 MHz • But, don’t confuse low-power and energy-efficiency! • Example • 242 MIPS/W for ATMega128L @ 4MHz (4nJ/Instruction) • 480 MIPS/W for ARM Thumb @ 40 MHz (2.1 nJ/Instruction) • Other examples: • 0.2 nJ/Instruction for Cygnal C8051F300 @ 32KHz, 3.3V • 0.35 nJ/Instruction for IBM 405LP @ 152 MHz, 1.0V • 0.5 nJ/Instruction for Cygnal C8051F300 @ 25MHz, 3.3V • 0.8 nJ/Instruction for TMS320VC5510 @ 200 MHz, 1.5V • 1.1 nJ/Instruction for Xscale PXA250 @ 400 MHz, 1.3V • 1.3 nJ/Instruction for IBM 405LP @ 380 MHz, 1.8V • 1.9 nJ/Instruction for Xscale PXA250 @ 130 MHz, .85V (leakage!) • And, the above don’t even factor in operand size differences! • However, need power management to actually exploit energy efficiency • Idle and sleep modes, variable voltage and frequency

  48. Radio • Energy per bit in radios is a strong function of desired communication performance and choice of modulation • Range and BER for given channel condition (noise, multipath and Doppler fading) • Watch out: different people count energy differently • E.g. • Mote’s RFM radio is only a transceiver, and a lot of low-level processing takes place in the main CPU • While, typical 802.11b radios do everything up to MAC and link level encryption in the “radio” • Transmit, receive, idle, and sleep modes • Variable modulation, coding • Currently around 150 nJ/bit for short ranges • More later…

  49. Computation & Communication Energy breakdown for MPEG Energy breakdown for voice Decode Decode Transmit Encode Encode • Radios benefit less from technology improvements than processors • The relative impact of the communication subsystem on the system energy consumption will grow Receive Receive Transmit Radio: Lucent WaveLAN at 2 Mbps Processor: StrongARM SA-1100 at 150 MIPS

  50. Sensing • Several energy consumption sources • transducer • front-end processing and signal conditioning • analog, digital • ADC conversion • Diversity of sensors: no general conclusions can be drawn • Low-power modalities • Temperature, light, accelerometer • Medium-power modalities • Acoustic, magnetic • High-power modalities • Image, video, beamforming

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