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Extended Lifetime Sensor Networks

Hong Huang, Eric Johnson

Klipsch School of Electrical and Computer Engineering New Mexico State University

December 2008

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Contents

• Introduction and motivation• Research issues and tasks• Schedule and deliverables• Leveraged research and facility

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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.

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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

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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.

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Solar Energy Resource in US Southwest

• Solar power output under direct sunlight @18% efficiency: 18mW/cm2

• Available technology: Panasonic Sunceram’s 37x82mm solar panel ($15 retail)– providing ~0.546 W for 7hrs

most days – 102 larger than sensor daily

consumption (6 mAh)– no need for additional power!

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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

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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?

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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?

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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?

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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

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Deliverables and Schedule

2009 (4th quarter) Mathematical models relating sensor net effectiveness and lifetime, simulation results validating the developed mathematical models relating sensor net effectiveness and lifetime (Task 1)

2010 Simulating results showing how the cross-layer energy conserving methods interact (Task 2 )

2011 Test bed measurements results showing how the cross-layer energy conserving methods interact (Task 2)

2012 Design of adaptive methods to integrate solar cell into sensor networks (Task 3)

2013 Simulation results validating our new cross-layer optimization methods which incorporates solar cells. (Task 2 and 3)

2014 Test 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)

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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.

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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

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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.

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Previous Work on Sleep Scheduling