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  1. CS 554 Introduction to Real-Time and Embedded SystemsOverview of Sensor NetworksProfessor Kyoung Don KangLecture 16October 24, 2006

  2. Overview • What is a sensor network? • Sensing • Micro-sensors • Constraints, Problems, and Design Goals • Overview of Research Issues and Challenges • I have borrowed liberally from other presentations

  3. Sensing Sensing Remote In-situ Networked Other…

  4. Hardware • Processor + Wireless Communication + Sensors (and Actuators) • Mass production of miniature hardware

  5. MICA2 Motes

  6. Typical Node Hardware 1Kbps - 1Mbps, 3-100 Meters, Lossy Transmissions 128KB-1MB Limited Storage Radio Transceiver Memory Low Power Embedded Processor 8-bit, 10 MHz Slow Computations Sensors Expensive -- Requires Supervision Battery Limited Lifetime

  7. A more detailed view

  8. Enablers – Micro-Sensors • Small (coin->matchbox->PDA range) • Limited resources • Battery operated • Embedded processor (8-bit to PDA-class processor) • Memory: Kbytes—Mbytes range • Radio: (Kbps – Mbps; often small range) • Storage (none to a few Mbits)

  9. General Characteristics • Large-scale fine-grained heterogeneous sensing • 100s to 1000s of nodes providing high resolution • Spaced a few feet to 10s of meters apart • Collaborative • Each sensor has a limited view • Spatially • In terms of sensed data type • Distributed • Communication is expensive • Localized decisions and data fusion necessary

  10. Wireless, Distributed Sensing • Why Distributed Sensing? • Closer to phenomena • Improved opportunity for LOS • 1/r4 • Why Wireless? • Ad hoc deployment • Remote locations • Why Collaborative? • Battery operated • Communication much more expensive than compute (will this always be true?) • In network processing to reduce data size closer to source

  11. Applications

  12. Applications • Many applications need real-time sensing (and control)! • Interface between Physical and Digital Worlds • A great many applications • Military • Target tracking/Reconnaissance • Weather prediction for operational planning • Battlefield monitoring • Industry: industrial monitoring, fault-detection… • Civilian: traffic, homeland security, medical… • Scientific: eco-monitoring, seismic sensors, plume tracking…

  13. Applications

  14. Habitat Monitoring: Great Duck Island

  15. Habitat Monitoring: Redwood Trees Sourece: David Culler’s Mobihoc 2005 Keynote

  16. Structural Monitoring: Golden Gate Bridge

  17. Medical Care: CodeBlue at Harvard

  18. Cargo Tracking

  19. Caveat… • Not all sensor networks are this way • Unclear whether really deeply embedded custom devices are the way to go (vs. something like a PDA) • Costly to design • Difficult to program and control • Cannot leverage the economy of scale advantage of COTS related technology such as PDAs and flash memories • Other scales possible: e.g., have a sensor network made of wired devices without so much of the power issues

  20. Basic Terminology and Concepts • Phenomenon: the physical entity being monitored • Observer (aka sink, or base station): a collection point to which the sensor data is disseminated • Usually a relatively resource rich node • Sensors observer data relay sometimes called reachback • Sensor network provides discrete sampling of the phenomena in space and time • Observer asks questions in terms of the phenomena – does not care about the infrastructure of the sensor network

  21. Typical Scenario Deploy Wake/Diagnosis Disseminate Self-Organize

  22. But others possible • Sensors mobile or not? • Phenomena discrete or continuous? • Monitoring in real-time or for replay analysis? • Dynamic queries vs. long term queries

  23. Sensor Nets vs. Ad Hoc Nets • Greater number of nodes • Densely deployed • More failure-prone • Mobility? • Many-to-one, not point-to-point • Sensor node limitations: power, computational capabilities, memory • Data driven: possibly no global identification for sensor nodes

  24. Challenges in Networked Sensing • Energy is a design constraint for battery operated sensors • Network lifetime is a performance metric • Communication a major cost (1000:1 ratio to computation) • Application objectives vs. available resources • Control redundancy • Load balance • Aggregate data • Local situational awareness

  25. Challenges (cont’d) • Data centric operation • Challenges traditional network design and QoS • Self-configuration • Resilience to node failure and attacks • Multidisciplinary • Effective network design requires application understanding • Physical world messier than what we’re used to

  26. Protocol Stack

  27. Alternative, more data-centric model

  28. Protocol Stack: Physical Layer • Frequency selection • Carrier frequency generation • Signal detection • Modulation Responsible for:

  29. Protocol Stack: Physical Layer • Hardware cost • How do we get down to $1/node? • Radio • Ultrawideband? • Very low powered, short pulse radio spread over several GHz • 40Mbps ~ 600Mbps Issues:

  30. Protocol Stack: Physical Layer • Radio (Cont.) • Zigbee/IEEE 802.15.4 • 2.4GHz radio band (= 802.11.b & Bluetooth) • 250Kbps • Up to 30 meters • Pico radio • 100Kbps • Limit power consumption to 100 uW • Other? (infrared, passive elements …)

  31. Protocol Stack: Data Link Layer • The multiplexing of data streams • Data frame detection • Medium access • Error control • Encryption Responsible for:

  32. Data Link Layer: Medium Access Control • Goals: • Creation of the network infrastructure • Fair and efficient sharing of communication resources between sensor nodes • Existing solutions? • Cellular - single hop network is impractical for sensor networks • Ad hoc MACs (e.g., 802.11 or Bluetooth): Power conservation still not emphasized • Scale • Data centric operation • Security is not considered! • WEP for 802.11 is broken • Do we care about link layer security?

  33. Data Link Layer: Medium Access Control • Basic strategy: turn off radio transmitter when idle • This can be ineffective due to startup costs • Dynamic power management schemes may provide an answer • Error handling • Existing MAC protocls: S-MAC, B-MAC, Z-MAC,… Power Savings:

  34. Protocol Stack: Network Layer • Power efficiency • Data-centric nodes • Data aggregation when desired/possible • Attribute-based addressing and location awareness Design principles:

  35. Minimum Energy Routing • Maximum PA route • Minimum energy route • Minimum hop (MH) route • Maximum minimum PA node route

  36. Directed Diffusion • Route based on attributes and interests

  37. Protocol Stack: Network Layer Schemes: • Data-centric routing • Directed Diffusion • Data Aggregation • Flooding • Gossiping/non-uniform dissemination • Sensor protocols for information via negotiation (SPIN) • Sequential assignment routing (SAR) • Low-Energy Adaptive Clustering Hierarchy (LEACH)

  38. Protocol Stack: Transport Layer • End-to-end Reliability • Multi-hop retransmission • Congestion • End-to-end security • Like SSL: authentication, encryption, data integrity • Good? What about data aggregation?

  39. Protocol Stack: Application Layer • Sensor network management • Database queries

  40. Other Issues • Operating system • TinyOS • MANTIS OS • Smart Card OS • Localization, Synchronization and Calibration • Aggregation/Data Fusion • Security • Encryption • Authentication • Data Integrity • Availability – DOS attacks • Also, Non-repudiation and Authorization

  41. Time and Space Problems • Timing synchronization • Node Localization • Sensor Coverage

  42. Time Synchronization • Time sync is critical at many layers in sensor nets • Aggregation, localization, power control, distributed DSP Ref: based on slides by J. Elson

  43. Sources of time synchronization error • Send time • Kernel processing • Context switches • Transfer from host to NIC • Access time • Specific to MAC protocol • E.g. in Ethernet, sender must wait for clear channel • Propagation time • Dominant factor in WANs • Router-induced delays • Very small in LANs • Receive time • Common denominator: non-determinism

  44. Conventional Approaches • GPS at every node (around 10ns accuracy) • But • doesn’t work everywhere • cost, size, and energy issues • NTP • some “primary time servers” are synchronized via GPS, atomic clock etc. • pre-defined server hierarchy (stratums) • nodes synchronize with one of a pre-specified list of time servers • Problems: • potentially long and varying paths to time-servers • delay and jitter due to MAC and store-and-forward relaying • discovery of time servers • Perfectly acceptable for most cases • E.g. Internet (coarse grain synchronization) • Inefficient when fine-grain sync is required • e.g. sensor net applications: localization, beamforming, TDMA etc

  45. Limitations of What Exists • Existing work is a critical building block BUT… • Energy • e.g., we can’t always be listening or using CPU! • Wide range of requirements within a single app; no method optimal on all axes • Cost and form factor: can disposable motes have GPS receivers, expensive oscillators? Completely changes the economics… • Needs to be fully decentralized, infrastructure-free Ref: based on slides by J. Elson

  46. Localization • Each node finding its position – why? • Data meaningless without context • Localization of targets and events • Geographical forwarding/addressing • Why not just GPS at every node? • Large size and expensive • High power consumption • Works only outdoors with LOS to satellites • Overkill – often only relative position is needed • Works only on earth :-)

  47. What is Location? • Absolute position on geoid • e.g. GPS • Location relative to fixed beacons • e.g. LORAN • Location relative to a starting point • e.g. inertial platforms • Most applications: • location relative to other people or objects, whether moving or stationary, or the location within a building or an area • Range and resolution of the position location needs to be proportionate to the scale of the objects being located

  48. Localization Techniques • Measure proximity to “landmarks” • e.g. near a basestation in a room • example systems: • Olivetti’s Active Badge for indoor localization • infrared basestations in every room • localizes to a room as room walls act as barriers • Most commercial RF ID Tag systems • strategically located tag readers • improved localization if near more than one landmark • Estrin’s system for outdoor sensor networks • grid of outdoor beaconing nodes with know position • position = centroid of nodes that can be heard • # of periodic beacon packets received in a time interval exceeds a threshold • a problem: not really location sensing • it really is proximity sensing • accuracy of location is a function of the density of landmarks • Location accuracy = O(distance between landmarks)

  49. BS 2 BS 1 MS 3 BS Techniques for Location Sensing • Measure direction of landmarks • Simple geometric relationships can be used to determine the location by finding the intersections of the lines-of-position • e.g. Radiolocation based on angle of arrival (AoA) measurements of beacon nodes (e.g. basestations) • can be done using directive antennas or antenna arrays • need at least two measurements

  50. Techniques for Location Sensing • Measure distance to landmarks, or Ranging • e.g. Radiolocation using signal-strength or time-of-flight • also done with optical and acoustic signals • Distance via received signal strength • mathematical model that describes the path loss attenuation with distance • each measurement gives a circle on which the MS must lie • use pre-measured signal strength contours around fixed basestation (beacon) nodes • can combat shadowing • location obtained by overlaying contours for each BS • Distance via Time-of-arrival (ToA) • distance measured by the propagation time • distance = time * c • each measurement gives a circle on which the MS must lie • active vs. passive • active: receiver sends a signal that is bounced back so that the receiver know the round-trip time • passive: receiver and transmitter are separate • time of signal transmission needs to be known • N+1 BSs give N+1 distance measurements to locate in N dimensions