1. Robert Parker USC INFORMATION SCIENCES INSTITUTE Distributed Sensors Group Goals, Metrics, and Challenges -Work in Progress- PAC/C PI Meeting November 1 – 3, 2000 Annapolis, Maryland Robert Parker USC/ISI East

2. Overview Who We Are Challenge/Approach Energy Scavenging Hardware Power Baseline New Ideas Simulation Problems and Problem Owners Robert Parker USC INFORMATION SCIENCES INSTITUTE

3. PAC/C Sensor Group ChandrakasanPower Aware Wireless Microsensor Networks Prasanna PacMan Rabaey Ultra-Low Energy Wireless Sensor and Monitor Networks SchottDistributed Sensor Networks Robert Parker USC INFORMATION SCIENCES INSTITUTE

4. Sensor Group Top Level Goal GOAL: Create a tactically significant distributed sensor system capable of operating indefinitely on energy scavenged from the environment. APPROACH: Create a wide-dynamic-range component base controlled by a system-wide, hierarchical power management system. Robert Parker USC INFORMATION SCIENCES INSTITUTE

5. Distributed Sensor Assumptions Infrequent Events Complex Task Involves Multiple Sensors/Modes (Distributed) System is Taskable Events are Automatically Exfiltrated Robert Parker USC INFORMATION SCIENCES INSTITUTE

6. Energy Scavenging Energy Sources SOURCE: P. Wright & S. Randy UC ME Dept. 1 mW Average Power

7. Energy Scavenging [ISSCC00] MEMS Generator PicoJoule DSP Power Controller Scavenge energy from mechanical vibrations to power micro-power sensor systems Power delivered ~ 10mW Hardwired Fabrics enable No Power Signal Processing Robert Parker USC INFORMATION SCIENCES INSTITUTE

8. Hardware Baseline Rockwell WINS is a modular stack consisting of: Power Board StrongARM Board Radio Board Sensor Board This architecture is fairly representative of other sensor nodes in the community. We plan to adapt this node to allow module-level power instrumentation and logging both in the lab and in the field. Note: The processor has idle and sleep modes, but they are currently not implemented. Robert Parker USC INFORMATION SCIENCES INSTITUTE

9. Motorola StarTac Cellular Battery (3.6V) Pico Radio Test Bed Casing Cover Serial Port Window PicoNode I Connectors for sensor boards Flexible platform for experimentation on networking and protocol strategies Size: 3”x4”x2” Power dissipation < 1 W (peak) Multiple radio modules: Bluetooth, Proxim, … Collection of sensor and monitor cards

10. PAC/C Power Roadmap Robert Parker USC INFORMATION SCIENCES INSTITUTE 200020022005 10,000 1,000 100 10 1.1 Average Power (mW) Deployed (5W) PAC/C Baseline (.5W) (50 mW)  (1mW) Rehosting (10x) -Simple Power Management -Algorithm Optimization (10x) -System-On-Chip -Adv Power Management -Algorithms (50x)

11. Power Management Trade-offs in Sensor Networks Lifetime (power) Rapidity (latency -1 ) Quality (coverage, fidelity)

12. Code Rate Computation Energy Code Rate Total Energy Lowest energy for a given BER Communication Energy Sense Compute Communicate Highly Structured Highly Adaptive PAC/C

13. Approach – Distributed Microcontroller Model w/ Local Power Control

14. Benchmark Roadmap ARL: Remote Netted Acoustic Detection System DSP board – 2 Motorola 96002 chips MIT  AMPS system Each node has one SA-1100 E = 3.28mJ Ported FFT/BF C code directly on SA-1100 Optimized Code (Floating to Fixed point, etc.) Network Computation Partitioning and DVS E=119.3mJX20E=6.01mJX2 > X1000 Future MIT Power Aware Processor Variable precision arithmetic Multiple/Adaptive voltages Hierarchical Interconnect Leakage control techniques … MEM PE MEM PE MEM PE MEM PE EmbeddedFPGA

15. PicoNode II (two-chip) ADC DAC Chip 2 Chip 1 Custom analog circuitry Mixed analog/ digital Digital Baseband processing Fixed logic Program- mable logic Software running on processor Analog RF Protocol Direct down-conversion front-end (Yee et al)

16. Reconfigurable DataPath Reconfigurable State Machines Embedded uP FPGA Dedicated DSP Envisioned PicoNode Platform Small footprint direct- down conversion R/F front end Digital base band processing implemented on combination of fixed and configurable data path structures Protocol stack implemented on combination FPGA/reconfigurable state machines Embedded microprocessor running at absolute minimal rates

17. SensorSim Hybrid Simulator Motivation: study sensor network deployment, protocols, applications, and power- quality trade-offs at scale in a controlled setting Three key capabilities –Sensor and target modeling Target, sensor channel, and sensor transducer characteristics –Power modeling Power characterization via data from instrumented platforms Energy consumer models: radio, CPU, sensors Energy source models: batteries Power-quality trade-off analysis and visualization –Hybrid simulation selected nodes in a simulation can be “real” nodes –currently supports only higher layers in “real” nodes “real” applications can run on nodes in a simulation Current implementation based on ns simulator

18. SensorSim Architecture monitor and control hybrid network (local or remote) Simulation Machine Gateway Machine ns modified event scheduler V R V V V GUI app R real sensor apps on virtual sensor nodes gateway socket comm serial comm HS Interface Ethernet RS232 Proxies for real sensor nodes GUI Interface app

19. SensIT Program Challenges SURVEILLANCE: Detection, classification and tracking of multiple simultaneous events TWO SCENARIOS: 1.Precision distributed tracking of multiple moving targets, migrate track tables and exfiltrate reports in one second. Cue image from acoustic. 2.Fixed/Mobile mbits of data to a UAV (i.e. an image) Robert Parker USC INFORMATION SCIENCES INSTITUTE

20. Army Applications Surveillance and monitoring –360 o field of view coverage –Excellent “wake-up” and cueing sensor –Tactical decision aid Detection, tracking and classification –Ground vehicles –Troop movements –Fixed and rotary wing aircraft's Others –Detection and localization of gun fire (e.g., sniper), artillery / mortar fire, rocket launch, etc. –Physiological monitoring of soldiers Nino Srour Robert Parker USC INFORMATION SCIENCES INSTITUTE

21. Localization and Tracking M1 Tank T72 Tank 4 Acoustic Sensor Location Line of bearing from sensor 4 1 Sensor Array Sensor Array Sensor Array Sensor 3 2 Acoustic sensor arrays (blue) detect bearing angle of targets(yellow), estimate location in real time and tracks their path as a function of time (green and red) A test bed exists to evaluate performance of detection, tracking, identification and localization algorithms in real time against real targets. Field experiments are conducted at least once a year Nino Srour Robert Parker USC INFORMATION SCIENCES INSTITUTE

22. Benchmark : ARL RNADS Sensor database provided by the Army Research Laboratory Microphone arrays are typically 4 ft – 8 ft in diameter, not restricted to a specific geometry Acoustic Sensor Array - RNADS All processing is done locally at the sensor arrays Target tracking occurs in real time Courtesy of N. Srour, Army Research Lab

23. What’s Next? Refine Challenges Create Umbrella Research Roadmap What’s Available? What do we Co-Develop? Robert Parker USC INFORMATION SCIENCES INSTITUTE

24. Sensor Node Model in SensorSim Node Function Model Network Layer Micro Sensor Node Applications Power Model (Energy Consumers and Providers) Battery Model Radio Model CPU Model Sensor #1 Model Sensor #2 Model MAC Layer Physical Layer Sensor Layer Wireless Channel Sensor Channel Network Protocol Stack Sensor Protocol Stack Middleware Physical Layer State Change Status Check