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SIS Project 2011. Energy-aware strategies for environmental monitoring with the MICAz. Cordier David & Fäh Isabelle – SIE Bachelor 6. TA : Adrian Arfire. Outline. 1 - Goal of the Project 2 - Material & Installation 3 - Method & Strategy 4 – Implementation 5 - Experiments & Results

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Energy-aware strategies for environmental monitoring with the MICAz


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slide1

SIS Project 2011

Energy-aware strategies

for environmental monitoring

with the MICAz

Cordier David & Fäh Isabelle – SIE Bachelor 6

TA : Adrian Arfire

slide2

Outline

1 - Goal of the Project

2 - Material & Installation

3 - Method & Strategy

4 – Implementation

5 - Experiments & Results

6 – Conclusion

7 – References

slide3

1 - Goal of the project

Implementing an adaptive method for light measurement

Saving energy

Keep enough data to be consistent

Test different scenarios

slide4

2 – Material & Installation

2.1 – Material

  • 2 MICAz mote;
  • 4 AA batteries ;
  • 2 USB serial cable;
  • 2 MIB510 interface board ;
  • 2 MTS300CA sensor board ;
  • A camera to take these beautiful pictures ;
slide5

2 – Material & Installation

2.2 – Installation

And all together :

The two MICAz

Ready to be used !

slide6

3 - Method & Strategy

3.1 – Method

Discrete event method

Threshold method

Intermittent method

Illustration of intermittent method

3.2 – Strategy

  • Control test
  • Step scenario
  • Smooth scenario
  • Fast smooth scenario
slide7

4 - Implementation

4.1 – C code

// Event called when a new reading is done

event void Read.readDone(error_t result, uint16_t data)

{

message_t new_msg;

DataMsg *data_msg;

// YOU CAN DECLARE LOCAL VARIABLES HERE

if (result == SUCCESS){

call Sounder.beep(10);

// build message payload

data_msg = (DataMsg*)(call Packet.getPayload(&new_msg, sizeof(DataMsg)));

data_msg->value = data;

data_msg->time = tstamp;

call Packet.setPayloadLength(&new_msg, sizeof(DataMsg));

call AMPacket.setType(&new_msg, AM_DATAMSG);

call MessageQueue.enqueue(new_msg);

post send_next_message();

// YOUR CODE STARTS HERE

// you need to implement a method for deciding on the sampling interval dt

if (data<300) {

dt=1000;

}

else {

dt=100;

}

// YOUR CODE ENDS HERE

call Timer.startPeriodic(dt);

}

}

The threshold value is changed here

The time step can be modified here

slide8

4 - Implementation

4.2 – Matlab code

function [outu, outa, Ainterp, M] = readLog(logFile, logFile2)

log = load(logFile);

out = log(:,2:3);

log2 = load(logFile2);

out2 = log2(:,2:3);

plot(out(:,1)/1000,out(:,2),'.r');

hold on;

plot(out2(:,1)/1000,out2(:,2),'o');

xlabel('Time [s]');

ylabel('Light sensor value');

title('Logged Signal');

xlim([0 30]);

x=([0:0.1:30]);

plot(x,300,'k');

Ainterp=interp1(out2(:,1), out2(:,2), out(:,1));

plot(out(:,1)/1000, Ainterp, 'k')

legend('uniform method', 'adaptive method','location','best')

plot(out(:,1)/1000,out(:,2),'r');

for i=1:length(out)

if out(i,1)<=30000

outu(i,:)=out(i,:);

end

end

for j=1:length(out2)

if out2(j,1)<=30000

outa(j,:)=out2(j,:);

end

end

long_u=length(outu);

long_a=length(outa);

th=300;

alpha=0.5;

beta=0.5;

t=30;

Edet=0;

Etot=0;

for k=1:long_a

if outa(k,2)>th

Edet=Edet+1;

end

end

for l=1:long_u

if outu(l,2)>th

Etot=Etot+1;

end

end

M=alpha*0.5*((Edet/Etot)+1)+beta*(1-long_a/(long_u*t));

return

Extracting the data from the logFile containing the measurements

Plotting the data for both curves and the threshold in the given range of time

This value is the threshold (changes depending on the scenario)

At this point, we keep only the values comprised in the time limit (30 seconds)

Here, we count the number of event detected by the two methods

Finally, we can calculate the performance metrics

slide9

5 – Experiments & Results

5.1 – Step scenario

  • dt = 1000 ms
  • Threshold = 100
  • dt = 200 ms
  • Threshold = 300
slide10

5 – Experiments & Results

5.2 – Smooth scenario

  • dt = 500 ms
  • Threshold = 300
  • dt = 500 ms
  • Threshold = 100
slide11

5 – Experiments & Results

5.3 – Fast smooth scenario

  • dt = 1000 ms
  • Threshold = 100
  • dt = 1000 ms
  • Threshold = 300
slide12

5 – Experiments & Results

5.4 – Performance metrics

dt

Threshold

α, β : coefficients that set the importance of each term (their sum needs to be equal to 1)

S : the number of measurements taken by the node whose performance we evaluate

T : the duration of the experiment

Fmax : the maximum sampling frequency (this should be equal to the fixed sampling frequency of the uniform strategy in your case)

: the number of events reported

: the total number of events

: the number of false positives reported

Mean of the performance metrics for each case

In red, the step scenario ; In orange, the smooth scenario ; In yellow, the fast smooth scenario.

slide13

6 - Conclusion

  • Project with real implication
  • Small implementation but significant results
  • Best parameters depends on the task
  • Some errors due to the MICAz
  • Further tests to be done for more accurate results

Note : no arrow was mistreated during the preparation of this presentation

slide14

7 - References

  • C. M. Cianci, Distributed intelligent algorithms for robotic sensor

networks monitoring discontinuous anisotropic environmental fields,

Lausanne, 2009, pp.51-52,73-94.

  • A Djafari Marbini, L. E. Sacks, Adaptive Sampling Mechanisms in

Sensor Networks, University College London, 2003.