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Self Organizing Wireless Sensor Network Middleware CleanPoint PowerPoint Presentation
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Self Organizing Wireless Sensor Network Middleware CleanPoint

Self Organizing Wireless Sensor Network Middleware CleanPoint

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Self Organizing Wireless Sensor Network Middleware CleanPoint

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  1. Self Organizing Wireless Sensor Network MiddlewareCleanPoint University of Virginia PI: John A. Stankovic December 2004

  2. Outline • Operational Scenario • Goals • Overview and Status of Middleware • Middleware Services • Key Services • Power Management/Sentry/Tripwire Service • Group Management Service • 3-Tier Classification • Self-healing • Other Services • Lessons Learned • Remaining Work FY ‘05

  3. Energy Efficient Surveillance System 1. An unmanned plane (UAV) deploys motes Zzz... Sentry 3. Sensor network detectsvehicles and wakes up the sensor nodes 2. Motes establish an sensor network with power management

  4. Goals • Develop an operational self-organizing sensor network of size 1000 • Cover an area of 1000m x 100m • Stealthy • Lifetime 3-6 months • Timely detection, track and classification • Large or small vehicle • Person, person with weapon • Wakeup other devices when necessary • Extend the lifetime of those devices as well • Exhibit self-healing capabilities

  5. Project Milestones FY04 March 3rd May 28th Aug. 6 Oct.29 Dec 6/13 V1.0 V1.1 V1.2 V1.3 Final

  6. Summary: Deliverables • ANSCD V1.3 middleware code delivered • About 40,000 lines of code and 600 files • About 30 Middleware services provided • Tested with a network with hundred(s) of nodes • ANSCD Data Packages V1.3 Delivered • System Architecture designed/documented • Mote-Relay Interface designed/documented • Relay requirements defined/documented • Requirements analysis/documented • Demo Test scenario design/documented • ANSCD & Mission GUI Manual documented • Wireless Download Manual documented • About 20 related papers published

  7. Summary: Objectives Achieved

  8. ANSCD Architecture V1.3

  9. Time-Driven System Operation

  10. Key Software Components (1) • 2-way software interface to RSCC and Avalanche (see ICD) • Flexible Tripwire based power management with sentry and wakeup services • Group-Based Entity Tracking (EnviroTrack) • Hierarchical Multi-tier Detection and Classification via heterogeneous sensors (4 PIRs (motion), acoustic, magnetometers) • Frequency-Filter and continuous threshold adaptations for robust sensing

  11. Key Software Components (2) • Flow control with Aggregate display/health/Tracking message • Localization (walking GPS) • Radio-based network wakeup • Asymmetry detection for robust routing establishment • Robust velocity calculation with least squares estimation • Wakeup service for relay to conserve energy

  12. Key Software Components (3) • Stripped-down version of Vanderbilt clock sync • Multi-hop Dynamic reconfiguration • Multi-hop wireless download (Berkeley’s Deluge) • Golden image support • Modified B-MAC to avoid communication-sensing interference

  13. System Scenario Supported (1) • Flexibility to define various system architectures • Independent deployment with Tripwires • ANSCD Middleware V1.3 • ANSCD GUI

  14. System Scenario Supported (2) • ANSCD Middleware 1.3 • Single RSCC • Mission GUI

  15. Additional Additional RSCC and Sensor RSCC and Sensor Networks Networks Long Haul (LH) Long Haul (LH) C2PC Client C2PC Client C2PC Client Comms Comms Link Link RELAY RELAY Comms Comms Antenna Antenna Ground Station Ground Station Element Element C2PC C2PC RF RF Long Haul Long Haul Gateway Gateway TACTICAL TACTICAL SENSOR SENSOR RF Radio Radio (& Client) (& Client) FIELD FIELD SEIWG SEIWG SENSOR DISPLAY DISPLAY Antenna Antenna FIELD Mission GUI Mission GUI Socket Socket Socket Socket RS232 RS232 IR/EO IR/EO MOC MOC Interface Interface CStat CStat CAMERA(s) CAMERA(s) Server Server RS232 IR/EO Interface Interface Interface FCD FCD CAMERA(s) TCP/IP TCP/IP Socket Socket Portal Portal LH Socket LH Socket Mission Mission MOTE MOTE LH LH Converter Converter Hardwired Sensors GUI GUI FIELD FIELD LH Socket Server Server Mission Interface Interface Converter GUI MOTE MOTE - - FIELD FIELD SOPHISTICATED SOPHISTICATED RSCC RSCC (SENSOR (SENSOR RSCC RSCC MOC/P MOC/P SENSORS (SSU) SENSORS (SS) SENSOR SENSOR MOC/P MOC/P NETWORK) NETWORK) Courtesy of Northrop Grumman System Scenario Supported (3) • ANSCD Middleware V1.3 with Tripwires • RSCC • Relay • C2PC • SISA • …

  16. ANSCD GUI – Vehicle & Person

  17. ANSCD GUI – Person w/ Weapon

  18. Mission GUI

  19. Mote - Relay Interface V1.5 Format of notification and command messages Notification Data Records Command Data Records • Tracking Request • Node status Reset • Network configuration Tracking record Aggregate status record Request record

  20. 3 4 2 1 Power Management • Sentry Service • Tripwire • Rotation Sentry 10mA@3v Base node Non-Sentry

  21. Dormant Active Dormant Dormant Dormant Active Dormant Dormant Active Active Tripwire-based Surveillance • Partition sensor network into multiple sections. • Turn off all the nodes in dormant sections. • Apply sentry-based power management in tripwire sections • Periodically, sections rotate to balance energy. Road

  22. Estimation of Network Lifetime • Lifetime is determined by • Individual Mica 2 mote consumption • Energy plot for a sentry node • Energy plot for a sleep node

  23. Tripwire + Sentry One tripwire section out of every 4 sections with 10% sentry  expected 142 days (20x) lifetime.

  24. Lifetime Analysis

  25. Group Management IR Camera Leader Follower Member Node

  26. Group Management IR Camera Leader Follower Member Node

  27. Detection Delay

  28. Base mote Report Group Performing base level classification Report Group Group leader, performing group level classification Group Normal mote, performing sensor (mote) level classification 3-Tier Classification

  29. First Tier: Robust Sensing • PIR Sensing • Magnetic Sensing • Acoustic Sensing • Commonality: • Initial Threshold Calibration • Continuous Threshold Calibration with changing environment • Power & Frequency Filtering

  30. PIR Sensing Module (1) • The current PIR detection algorithm using XSM sensors can distinguish walking persons in a range of 12-20 ft in hot environments • About 19 ft/person running • About 12 ft/person walking • 30-40 ft in cool environments. • Almost all false alarms are reliably removed. • Radio interference has been also removed.

  31. PIR Sensing Module (2) • Environmental factors • Grass and Trees. • Temperature. • Wind and Sunshine. • Frequency Analysis • Uses high/low-pass filters to filter out noise, so that no false alarms are generated due to environmental effects. • Self-adaptive • Continuous filtering and calibration to adapt to environment. • Data sampling is turned off for 60 ms when there is radio transmission.

  32. PIR Sensing Module (3): Data This figure displays the raw data, the dynamic threshold, and the confidence of the detection. The detection report is based on frequency analysis of the signals and compared with an adaptively adjusted threshold.

  33. Magnetometer Sensing (1) • Requirement • Detect vehicles and persons with a weapon • Challenges • ADC reading may saturate • Response latency • Magnetic and electric noise from environment and mote circuitry • Thermal reading drift • Radio/Mag interference • Short range • XSM-2 has greater noise than XSM-1

  34. Magnetometer Sensing (2) • Raw ADC reading can saturate • Translate the pair of POT/ADC values to a single scaled mag point • Moving average of recent scaled ADC readings. • Compare to difference between slow and fast moving average

  35. Magnetometer Sensing (3) Response time • Mag sensor chain needs about 40ms to settle. • ADC readings need about 50ms to settle after a potentiometer change. • The averaging algorithm needs at least 3 initial readings to perform computation. • A fast-detect logic speeds up detection of obvious signals

  36. Magnetometer Sensing (4) • Signal/noise ratio • Signals (Scaled ADC readings) are hard to distinguish for small targets or targets at far distances • Signals for iron bar moving at 5 ft. • Use a moving average of recent readings (Mag Points) to filter out noise. • Mag Points show signals whose amplitude is often lower than that of noise • Mag Points for iron bar moving at 5 ft.

  37. Acoustic Sensing (1) • Properties: • Power based approach. • Automatic and continuous calibration due to temperature fluctuations, noisy environments and individual sensor characteristics. • Differentiates between vehicles, humans, background noise and wind (collaboration with PIR sensors necessary). • Limitations: • No differentiation between small-big vehicles currently available.

  38. Acoustic Sensing (2) Three Cars Initial Calibration No Detection Detection when Energy Crosses Standard Deviation

  39. Acoustic Sensing (3) • Moving average curve plus 3 times the standard deviation curve = THR curve (called standard deviation on previous slide) • Count number of crossings of THR out of the last N readings and if percentage is greater than x% then this is a target • X is about 60%

  40. DetectionRange Second Tier: Group Aggregation DOA controls minimal aggregation degree to reduce false alarms Node Member Follower Leader Awareness Range

  41. System Issues: False alarms Impact of DOA on False Alarms • Probability of false positives • reduces as DOA increases • Probability of false negatives • increases as DOA increases • With DOA = 3 we had zero false • alarms • The DOA parameter can be tuned • based on sensing range and the • density with which motes are • deployed Spatial-temporal correlated data aggregation can effectively reduce false alarms

  42. Third Tier: Base Mote (1) • The base mote keeps received tracking messages in FLASH. • It then makes use of the spatio-temporal correlation to decide which target a tracking message belongs to. (e.g., 30 m and 5 sec) • When a specific target gets enough (according to a adjustable parameter) messages for one target, a “detection” report is sent from the base mote to the RSCC.

  43. Third Tier: Base Mote (2) • After the “detection” report is sent and enough information is gathered for classification, a “classification” report is sent from the base mote to RSCC. (2 additional reports beyond detection) • The base mote also uses a least square calculation to calculate the velocity of the target. A “velocity” report is sent to RSCC. (5 additional reports beyond classification) • Afterwards, send reports according to an adjustableflow rate parameter.

  44. Classification Scheme

  45. Detection/Classification/Velocity Delay

  46. Self-Healing (1) • Wide spectrum of capabilities • Not binary • In Routing • Multiple parents in backbone tree • No cost for periodic probing • Stealthiness is maintained • Local decision on choosing alternative parent is fast • Re-create n-parent tree on system rotation • In MAC • For unicast – retransmission of lost packet

  47. Self-Healing (2) • At Application Level • Critical messages are transmitted multiple times to better ensure delivery • In Sensing • Fail-stop – use of many sensors as targets move avoids problems here • Byzantine failure – detect node continuously reporting and shut it down • In Localization • If node fails to obtain location during walking GPS, it gets info from neighbors and uses tri-lateration

  48. Self-Healing (3) • In System Initialization • Each phase is coordinated and sequential • If a node is not in-step it becomes silent until next system rotation • In Tracking • If group leader fails, info is still with the members and is passed to next leader • In Wakeup • Decentralized and if some nodes fail to wake-up it is not a problem because many others will be awake

  49. Self-Healing (4) • Limited Effect • Clock sync, neighbor discovery, etc. are highly decentralized and local. Single node failures only affect that node and does not propagate to the rest of the network. • System Rotation • Can correct many issues • Currently, only executed based on time • Could be extended to re-run when many failures are detected BUT this means extra messages which affects lifetime and stealthiness!

  50. Other Middleware Services • System Initialization • List of system parameters • MAC • Routing • Asymmetric Detection • Localization – Walking GPS • Clock Sync • Velocity Calculation