Energy Consumption Issues in Sensor Networks

Cintia B. MargiCMPE259 – March 09th, 2005

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Outline• Energy Model for Communications

[MASCOTS04 paper]• Energy consumption for Processing

Tasks• Power TOSSIM [SenSys04]• Prediction-based energy map [Ad-hoc

Journal 05]• Energy Harvesting [ISLPED'03]

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Energy model for communication

• MASCOTS 04 paper by Cintia & Katia

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Energy model for communication

• Power-awareness in sensor networks:– MAC protocols: S-MAC [Ye02], TRAMA

[Rajendran03], T-MAC [vanDam03]– Directed Diffusion [Intanagonwiwat00],

aggregation [Solis04]

• QualNet, GloMoSim and ns-2:– Either do not model all the radio states– Or do not take proper accounting– Accounting done on different layers

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Energy model for communication

Related Work

• Measurements of energy consumed by NICs:– NICs in hand-helds [Stemm97]– WaveLAN laptops [Feeney01]

• Models– LEACH [Heinzelman00]– Sensor network lifetime [Bharwaj02]– Measure battery discharge to model

communications [Lochin03]

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Energy model for communication

Features

• Explicitly accounts for low-power radio modes.

• Considers the different energy costs associated with each one of the possible radio states.

• For example:State TR1000 WaveLAN

Transmitting 24.75 mW 1400 mWRx/overhearing/sensing 13.5 mW 900 mWIdle 13.5 mW 900 mWSleeping 15 uW

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Energy model for communication

Model

• Energy spent while in a given radio state y is:– Ey = Py * Ty

• Py = V * iy

• tx: Ty = PacketSize/TransmissionRate

• Otherwise, use a timer

• Implemented in GloMoSim and QualNet.

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Energy model for communication

Validation

• Sanity check: compare with original GloMoSim

• Testbed in S-MAC paper• More on MASCOTS04 paper

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Energy model for communication

Validation IEEE 802.11• Original vs. Instrumented GloMoSim• Simulation parameters:

– No mobility– CBR traffic node 0 to 2, data size is 200

bytes.– Duration is 250 seconds.– Energy parameters for radio: original

GloMoSim. Original (mJ) Instrumented (mJ)Node 0 224999.46 224999.46Node 1 224998.74 224998.74Node 2 224999.28 224999.28

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Energy model for communication

Validation S-MAC• Qualitative comparison:

– Simulation vs. testbed

• S-MAC protocol [Ye02]• 5-node 2-hop topology• App.: 10 x 380 bytes• Low power radio (TR1000)• Simulation/measurements

lasts enough time for all packets to be transmitted.

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Energy model for communication

Validation S-MAC

• Same behavior as results in [Ye02].

• Source: average nodes 0 & 1.

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Case Studies

• Protocol comparison:– 802.11 vs. S-MAC [MASCOTS 2004]

• Analytical Model Validation– Single-hop saturated IEEE 802.11 wireless

network [ICCCN 2004]

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Energy model for communication

802.11 vs. S-MAC• Parameters:

– 50 nodes– low power

radio (TR1000)– CBR with 10

sources, 380 bytes

– routing: AODV– Duration: 150s

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Energy model for communication

802.11 vs. S-MAC

TX RX Overhear Sensing Idle Sleep1

10

100

1000

Average Time per state (IAT = 1s)

802.11

S-MAC

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Energy model for communication

Summary

• Simple energy model for communication.

• Implemented at GloMoSim & QualNet.• Instrumentation provides complete

energy and time accounting per radio state.

• Useful tool to evaluate and understand power-aware protocols.

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Processing/sensing energy model

• ongoing work

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Processing/sensing energy model

• For simple sensors (e.g., temperature), energy consumed by communication subsystem dominates.

• However, for more sophisticated sensors, (e.g., accelerometers & magnetometers) this is not true [Doherty01].

• How about camera as sensors?

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Processing/sensing energy model

Related Work

• Energy savings due data compression [Barr03].

• Power management architecture for laptops [Balakrishnan01].

• Power Management in Wireless Networks [Zheng03].

• Energy budget (Great Duck Island deployment) [Mainwaring02].

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Processing/sensing energy model

Approach

• Energy cost based on tasks.• Energy measurements

– Current– Discharge rate

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Processing/sensing energy model

Testbeds• Dell laptops• Stargates• Motes

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Processing/sensing energy model

Methodology• Macroscopic view• Set of experiments:

– baseline system– processing (FFT)– disk access (dbench for laptops)– network transmission (Iperf for laptops)– Network reception (Iperf for laptops)

• Well-known benchmarks whenever possible.

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Processing/sensing energy model

Methodology - Laptops• Power Management: off• Use ACPI to obtain voltage & discharge rate.– Standard for power management– Define methods to read the parameters– Under Linux: /proc/acpi/– Everytime a “file” in /proc/acpi/ is read,

corresponding ACPI method is executed.

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Processing/sensing energy model

Methodology – Stargates & Motes• Stargates:

– measure current using power suply– use battery monitor chip– Vladi's project

• Motes:– measure current using power suply– Samit's project with motes

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Processing/sensing energy model

Results

Laptops:Task Av. Discharge RateBaseline 10.200 WFFT 25.047 WDisk 13.430 WTX 22.389 WRX 16.101 W

Stargates:Task CurrentIdle 475 mAFFT 735mATX 740mARX 700mAsleep 67mA

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Then what?

• From a complete energy consumption characterization, we can:– derive energy consumption prediction

model• application dependent• hardware dependent

– resource manager

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Smart usage of energy in sensor nodes

• Define a methodology for sensor nodes to make decisions that allow energy savings.

• Interesting application: Visual Sensor Nodes

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Power TOSSIM [SenSys04]

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Power TOSSIM [SenSys04]• extension to TOSSIM (TinyOS Simulator)

to include energy consumption;• add a module that keeps track of power

state;• modifications to other modules to report

transitions;• CPU energy usage -> estimate number

of cycles in AVR;• generate traces that will processed later.

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Power TOSSIM

Mica2 Power Model

Mode Current Mode CurrentCPU   Radio  Active 8.0 mA Rx 7.03 mAIdle 3.2 mA Tx (power = 00) 3.72 mAADC Noise Reduction 1.0 mA Tx (power = 01) 5.21 mAPower-down 103 µA Tx (power = 03) 5.37 mAPower-save 110 µA Tx (power = 06) 6.47 mAStandby 216 µA Tx (power = 09) 7.05 mAExtended Standby 223 µA Tx (power = 0F) 8.47 mAInternal Oscillator 0.93 mA Tx (power = 60) 11.57 mALeds 2.2 mA/led Tx (power = 80) 13.77 mAMica2 sensorboard 0.7 mA Tx (power = C0) 17.37 mAEEPROM   Tx (power = FF) 21.48 mARead 6.2 mARead time 565 µsWrite 18.4 mAWrite time 12.9 ms

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Power TOSSIM

Benchmarks

Graph from slides used at SenSys04 by authors.

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Prediction-based energy map [Ad-hoc Journal 05]

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Prediction-based energy map [Ad-hoc Journal 05]

• Goal: – construct an energy map of a wireless

sensor network using prediction-based approach.

• Naive approach: nodes send periodically updates with its available energy to monitoring node.– Problem?

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Prediction-based energy map

Approach• Nodes send a message with current

energy available and parameters of energy dissipation model.

• Nodes send updates if prediction is off by a pre-determine threshold (e.g. 3%).

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Prediction-based energy map

Energy dissipation model• Probabilistic model based on Markov

chains;• node operation modes are the states;• transition probability matrix is

constructed based on the node past history;

• then can calculate energy dissipated based on time spent on each state.

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Prediction-based energy map

Diagram

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Energy Harvesting [ISLPED'03]

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Energy Harvesting [ISLPED'03]• Harvesting problem: problem of

extracting the maximum work out of a given energy environment.

• Goal: – learn about energy environment (energy

available and recharging capabilities);– use this info for task sharing among

nodes.

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Energy Harvesting

Challenges• workload X recharging cycles;• residual energy is not enough info, so

need to know how recharging occurs:– needs to predict recharging opportunities,

otherwise consider only residual energy.

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Energy Harvesting

EEHF algorithms

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Energy sources

Paper: PicoRadio Supports Ad Hoc Ultra-Low Power Wireless Networking

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Energy sources

Microbial Fuel Cells • EcoBot II (http://www.ias.uwe.ac.uk/)

• Anode: bacteria found in sludge, act as catalysts to generate energy from the given substrate (flies or rotten apple);

• Cathode: O2 from free air acts as the oxidising agent to take up the electrons and protons to produce H2O.

• EcoBot I:• Anode: a freshly grown culture of E. coli fed

with refined sugar;• Catholyte: ferricyanide.

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Energy sources

Microbial Fuel Cells• MFC X Alkaline battery:

– single MFC: output voltage is 0.8V, capacity is 163mAh and energy is 37mWh. It weighs 100g and costs ~ £3.00.

– AA alkaline cell: output voltage of 1.5V, capacity of 2.8Ah and an energy is 4.2Wh. It weighs 25g and costs ~ £0.30.

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• Questions?