Ben Gaudette CSE 535: Mobile Computing 12/1/2010 Energy Management in WSN’s with Multiple...
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Transcript of Ben Gaudette CSE 535: Mobile Computing 12/1/2010 Energy Management in WSN’s with Multiple...
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Ben GaudetteCSE 535: Mobile Computing
12/1/2010
Energy Management in WSN’s with Multiple Modalities
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Node Characterization MSP430
Flash Read – 2.3 mA Flash Write – 13.3mA Computation – 6.33mA Frequency – 16 MHz Operation voltage – 3 V
CC2500 Receive – 13.3 mA - 2.6 ms Transmit – 0.840 ms
Sensing - PhotoElectric Base Range – 2 meters Base Energy draw – 35 mA
– 0.5 ns – 5 second refresh rate
Relationship – Quadratic E = 3.6*10^-6*R2
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Data StructuresMatrix
BDD
S1,R1
S1,R2
… S1,RP
… SN,RP
T1 1 1 … 1 … 0
T2 0 0 … 1 … 1
… … … … … … ....
TM 0 0 … 0 … 1
BDDComputation – O(N*P)Space – O(22N*P-1)*
MatrixComputation –
O((M*N*P))Space – O((M*N*P))
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Centralized Algorithms M3C updated
M3C (XR,Xon, S)
(1)XM3C XR;(2)S SORT_BY_WEIGHT(S);(3)if Xon = NULL(4) i 0;(5)else (6) i
MIN_WEIGHT_NODE_INDEX(Xon)
(7) XM3C ∀(XM3C, s1…si);
(8)if XM3C is ø
(9) return Xon;(10) foreach j (i+1 to length
of array S)(11) result ∀(XM3C,
sj,r, ~Si,rk)(12) if result ∉ ø(13) XM3C
result;(14) return XM3C;
Centralized Switch New
M3C_Sequential_Schedule(XR, S)
(1)T 0; Xon NULL;
(2)while XR ∉ ø
(3) Xon M3C(XR, Xon, S);
(4) Δk t;
(5) T T + Δk;
(6) UPDATE_WEIGHTS(S, Xon, Δk, T);
(7) Sd ø;(8) foreach s ∈ S(9) foreach range ∈ s(10) if ws,r(T) < t
(11) Sd Sd ⋃ sr
(12) if Sd = ø
(13) XR A(XR, Sd);
(14) return T;
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Distributed Algorithm– Neighbor Discovery Phase• Share information with neighbors.• Predicted lifetime, each available
range and the targets the ranges can cover
– Arbitration Phase• Starting with the weakest node.• Check to see if any points
exclusive to the largest range can be covered by stronger nodes.
• If no turn on to that range, else repeat for lower ranges.
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Results - SetupTests
Simulations – Based off of some of the power numbers
TargetsVaried between 3 and 5Grid points are targetsBase Station in the middle
Ranges: 1-3{1.5, 3, 4.5}, {1.5, 3}, {1.5}
Sensors 20 and 50.Random LocationsSame starting Energy
Performed each test 10 times.
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Results – Number of Targets
20 Sensors
50 Sensors
Results – Number of Ranges
3 x 3 Grid
5 x 5 Grid
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Results – Number of Sensors
4x4 Grid of Targets, 3 Ranges
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ConclusionResults
Data Structure – Speed vs. MemoryAlgorithms – Multi-range is beneficial in sparse
environments.Outperforms AR-SC by an average factor of 1.8.When many sensors are close to one another (densely
placed), the algorithms show no benefit of having multiple ranges.
Future WorkDistributed Algorithms implementation and data
structure analysis?Improve the simulator
Battery modelMemory access power predictionVoltage scaling
Expand the scope of the problemEnergy Harvesting
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QUESTIONS?
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ReferencesS. Dasika, S. Vrudhula, K. Chopra, S.
Ramasubramanian, A framework for battery-aware sensor management, in: Proc. of the Conf. on Design, Automation, and Test in Europe (DATE), 2004, pp. 1–6.
M. Cardei, J. Wu, M. Lu, and M. O. Pervaiz, Maximum networklifetime in wireless sensor networks with adjustablesensing ranges. Proc. of IEEE International Conference onWireless and Mobile Computing, Networking and Communications(WiMob), 2005.
http://focus.ti.com/docs/toolsw/folders/print/ez430-rf2500.html