1. 1 Extended Lifetime Sensor Networks Hong Huang, Eric Johnson Klipsch School of Electrical and Computer Engineering New Mexico State University December 2008

2. 2 Contents Introduction and motivation Research issues and tasks Schedule and deliverables Leveraged research and facility

3. 3 Introduction: Sensor Networks Composed of a large number of inexpensive sensors. Application relevant to DHS: providing unattended, continuous, extended monitoring of remote, inaccessible regions. Limitation: battery powered, limited lifetime.

4. 4 A Typical Sensor: TelosB from Crossbox Technology Features –TI MSP430 (16 bit RISC) Microcontroller with 10kB RAM Integrated onboard antenna Integrated sensors, e.g., temperature, light, vibration, video camera –High Data Rate Radio 250 kbps IEEE 802.15.4 (ZigBee) compliant Outdoor range: 75 m to 100 m

5. 5 Sensor: Power Consumption and Lifetime Device current draw –25 mA active mode –20 μA sleep mode Power source: 2X AA batteries –3000mAh capacity Theoretical lifetime@1% duty cycle: 500 days –In practice, much less, due to packet collision/retransmission and additional tasks such as distributed localization, route maintenance, etc.

6. 6 Solar Energy Resource in US Southwest Solar power output under direct sunlight @18% efficiency: 18mW/cm 2 Available technology: Panasonic Sunceram’s 37x82mm solar panel ($15 retail) –providing ~0.546 W for 7hrs most days –10 2 larger than sensor daily consumption (6 mAh) –no need for additional power!

7. 7 Objectives Develop extended life-time technologies for sensor networks operating in the US Southwest border environments. –motivation: abundant solar power, vast inaccessible border area suitable for monitoring by sensor networks

8. 8 Research Issue I There is a tussle between application performance measures and sensor network lifetime –Higher application performance measures require higher sensor density/redundancy, duty cycle, which reduces sensor network life time. How to make optimal trade off on parameters?

9. 9 Research Issue II Energy conserving can be performed on individual network layers: deployment density/topology, sleeping scheduling, media access control, data routing, etc. How do the energy conserving methods at different network layers interact and how to optimize across layers?

10. 10 Research Issue III Solar energy is abundant but highly dynamic –Energy recovered depends on time of day, panel alignment, cloud condition, shading, etc. –Need highly adaptive cross-layer algorithms to take full advantage of solar energy. How to design adaptive methods to utilize solar energy efficiently in the Southwest border environments?

11. 11 Task Breakdown 1.Build mathematical models and perform simulations to understand the relationship between the sensor net effectiveness, network parameters and lifetime 2.Perform simulation studies and test bed measurements to understand how the energy conserving methods at different layers interact and develop cross-layer extended lifetime technologies 3.Perform simulation and experiments to investigate how to efficiently utilize solar energy in sensor network using adaptive algorithms

12. 12 Deliverables and Schedule 2009 (4 th quarter)Mathematical models relating sensor net effectiveness and lifetime, simulation results validating the developed mathematical models relating sensor net effectiveness and lifetime (Task 1) 2010Simulating results showing how the cross-layer energy conserving methods interact (Task 2 ) 2011Test bed measurements results showing how the cross-layer energy conserving methods interact (Task 2) 2012Design of adaptive methods to integrate solar cell into sensor networks (Task 3) 2013Simulation results validating our new cross-layer optimization methods which incorporates solar cells. (Task 2 and 3) 2014Test bed measurement results showing the effectiveness of adaptive methods to utilize solar energy in sensor networks for extended lifetime operation in the Southwest border environments. (Task 3)

13. 13 Leveraged Research An on-going project (2007-2012) funded by US Army to investigate secure sensor data dissemination and aggregation. A 2-year project (2006-2008) funded by Los Alamos National Laboratory to investigate low-power methods to collect data in sensor nets. A 1-year project (2004) funded by Sandia National Laboratories to investigate target tracking in sensor nets.

14. 14 NMSU Sensor Network Lab 2 faculties: Eric Johnson, Hong Huang 2 Ph.D. students, ~8 MS students >100 Telos-B motes Workstations for software development Network simulation software include ns-2, opnet, qualnet, etc. RF instrumentation available at PSL

15. 15 Costs and Equipment Project cost: ~$200K NMSU can provide an existing sensor network test bed funded by Los Alamos National Laboratory, consisting of 100 nodes.

16. 16 Previous Work on Sleep Scheduling