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Carlo FischioneReducing Energy Consumption
in Wireless Sensor Networks
Reducing Energy Consumptionin Wireless Sensor Networks
CHESS seminarDecember 11, 2007
Carlo Fischione
University of California at Berkeleye-mail: fischion@eecs.berkeley.edu
Carlo FischioneReducing Energy Consumption
in Wireless Sensor Networks
Introductory Overview
Minimum Energy Coding
Radio Power Control
Bit Error Rate
Energy Consumption
Numerical Results
Conclusions and Future Work
UCB December 11, 2007
Carlo Fischione
Definition of WSNsDefinition of WSNs
“Wireless Sensor Networks (WSNs) are networks forcommunication, control, sensing and actuation by smallnodes communicating through wireless links”
Sensor Networks
Gateway
Network Infrastructure
Application
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
ApplicationsApplications
Tremendous space of applications:
¬ Monitoring space: ocean water, pollution, ...¬ Monitoring things: robots, human body,…
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
• WSNs are an area of active research from electronic to computer sciencesince few years.
• Many industries are now investing in this new technology:¬ ABB
¬ Fiat
¬ Pirelli
¬ Siemens
¬ United Technology
¬ …
UCB December 11, 2007
DARPA DSN node (~1960) Mica node (~2000)
Tmote-sky node (~2003)
History of WSNsHistory of WSNs
Reducing Energy Consumptionin Wireless Sensor Networks
Carlo Fischione
Design Principles of WSNsDesign Principles of WSNs
Reducing Energy Consumptionin Wireless Sensor Networks
• Coding • Radio Power• Routing• Modulation•…
Communication
• Optimization• Hybrid Systems• Networked Control• System Identification•…
Control• Embedded Software• Middleware• Operating Systems•…
Computer Science
WSN
• Formal Verification• Logic Synthesis• Microprocessors•…
CAD
UCB December 11, 2007
Carlo Fischione
Some Challenges...Some Challenges...
♣ How to schedule the link each node should select to forward packets?
♣ How to quantize the measurements to transmit the minimum amount ofinformation in the presence of uncertainties of the network?
♣ How to do radio power and rate control, when neither accurate channelmodels and channel state information are available?
♣ How to access to the channel?
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
COSI project: Synthesis of Embedded Networks for Building Automation and Control
A. Pinto & A. Sangivanni-Vincentelli, UC Berkeley
Carlo Fischione
Design Principles of WSNsDesign Principles of WSNs……
♣ Design of WSNs includes several techniques (distributed computation,distributed source coding, distributed control,...).
♣ Cross-layer approaches are necessary to take into account severalinteracting techniques and protocols.
PlatformDesign-Space
Export
PlatformMapping
Architectural Space
Application Space
Application Instance
Platform Instance
System (Software + Hardware)Platform
CognitiveRadio
Cross Layer Design
Platform BasedDesign
A. Sangiovanni-Vincentelli et al.
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo FischioneReducing Energy Consumption
in Wireless Sensor Networks
Introductory Overview
Minimum Energy Coding
Radio Power Control
Bit Error Rate
Energy Consumption
Numerical Results
Conclusions and Future Work
UCB December 11, 2007
Carlo Fischione
Minimum Energy CodingMinimum Energy Coding
♣ Performance analysis of minimum energy coding schemes in CodedDivision Multiple Access (CDMA) wireless sensor networks:
1. Minimum Energy coding (ME);
2. Modified Minimum Energy coding (MME).
♣ Detailed models of the energy consumption and bit error rate:1. coding schemes;2. wireless channel;3. power control;4. hardware characteristics of the transceivers.
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
System DescriptionSystem Description
♣ Data sensed by a node are coded with a Minimum Energy coding scheme.
♣ The bits of the ME coded data are OOK modulated: only bits having value 1are transmitted after a DS-CDMA spreading operation.
Source
Tx 1
Rx 1 1
Tx 2
Rx 2 2
Tx K
Rx K K
Transmitter-receive pairs
Tx i
Rx i i
Reducing Energy Consumptionin Wireless Sensor Networks
100111 1000010000Source Codeword ME Codeword
UCB December 11, 2007
Carlo Fischione
ME CodingME Coding
♣ In ME Coding (Erin and Asada: “Energy Optimal Codes for WirelessCommunications” in 38th IEEE CDC 99) a source codeword is mappedinto a new codeword having larger length but less number of 1 (or high)bits.
♣ Source codewords having large probability of occurrence are associatedto ME codewords with less high bits.
♣ Only high bits are transmitted with OOK modulation.
Source codeword
ME codeword
Reducing Energy Consumptionin Wireless Sensor Networks
Tx i
Rx i i
UCB December 11, 2007
Carlo Fischione
♣ Only bits having value 1 are transmitted
♣ is the average transmit time per codeword.
OOK ModulationOOK Modulation
• Lower Activity• Lower interference• Higher bit error rate
Tx i
Rx i i
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
ME Coding (2)ME Coding (2)
♣ Energy consumption per ME codeword
♣ ME coding increases the value of three systemparameters:1. the codeword length;
2. the Transmitter active time (it is negligible with respect to the radiopower consumption);
3. the Receiver active time: it is not negligible, the power spent toreceive is approximately the same as that used to transmit.
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
♣ MME coding exploits a structure of the codeword that allows thereceiver to go in a sleep state: Kim and Andrews: “An EnergyEfficient Source Coding and Modulation Scheme for Wireless SensorNetworks”. In IEEE WSPAWC, 2005.
♣ In MME coding, energy is saved not only at the transmitter as MEcoding, but also at the receiver.
MME CodingMME Coding
Reducing Energy Consumptionin Wireless Sensor Networks
Tx i
Rx i i
UCB December 11, 2007
Carlo Fischione
MME coding: the ME codewords are partitioned into sub-frames starting with anindicator bit.
Indicator bit = 1 => there are not high bits in the sub-frame, there is no need fordecoding, and the receiver goes to sleep.
Indicator bit = 0 => there are high bits in the sub-frame, so the decodingoperation must be performed, and the receiver cannot go to sleep.
MME Coding (2)MME Coding (2)
Tx i
Rx i i
MME codeword
Source codeword
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
MME Coding (3)MME Coding (3)
♣ MEE energy consumption per codeword:
♣ = average receiver activity (it is a function of the numberof sub-frames, and bit error rate)
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
Energy of ME and MMEEnergy of ME and MME
Reducing Energy Consumptionin Wireless Sensor Networks
Tx i
Rx i i
Tx i
Rx i i
UCB December 11, 2007
Carlo FischioneReducing Energy Consumption
in Wireless Sensor Networks
Introductory Overview
Minimum Energy Coding
Radio Power Control
Bit Error Rate
Energy Consumption
Numerical Results
Conclusions and Future Work
UCB December 11, 2007
Carlo Fischione
Radio Power ControlRadio Power Control
♣ Efficient power control algorithms for wireless sensor networks(WSNs) are crucial to reduce energy consumption.
¬ Lifetime: in scenario where recharging is not possible, radio powercontrol is very important to increase the network lifetime.
¬ Collisions: power control is beneficial to reduce packet collisions(more retransmitted packets means wasting energy), and increasethroughput.
♣ Energy for radio power is responsible from 37% up to 85% ofthe total Energy consumption for off-the-shelf sensor nodes.
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
Radio Power Control (2)Radio Power Control (2)
Node K
Node 1TX
SINRi(t)
Node i
Multiple Access Interference
Power-Rate updating
SINR Estimation
+
Delay
xSINR(t)
+
Node i
-~SINRSINR
th
Quantization
Channel attenuation
Tx 1Rx 1
1
Tx 2Rx 2
2
Tx KRx K
K
Transmitter-receive pairs
Tx i
Rx i i
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
Radio Power Control (3)Radio Power Control (3)
♣ Received signal
Desired Signal
MAI
Thermal Noise
Transmitter Activity (ME or MME)
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Tx 1Rx 1
1
Tx 2Rx 2
2
Tx KRx K
K
Transmitter-receive pairs
Tx i
Rx i i
Delay
SINR Estimation
+x
+ Node i
Controllingnode
-+Quantization
Power updating
Power to transmit data packets.
Carlo Fischione
♣ The SINR of the signal of a node is expressed as
The SINRThe SINR
Vector of Binary on/off Processes:ME and MME coding
t
SINR(t)
SINR threshold
Outages
The Signal to Interference + Noise Ratio
UCB December 11, 2007Reducing Energy Consumption
in Wireless Sensor Networks
Carlo Fischione
The Optimization ProblemThe Optimization Problem
♣Mixed integer-real optimization problem
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
SINR
Outages
• K-L. Hsiung, S-J. Kim, S. Boyd, “Power Control in Lognormal Fading Wireless Channel with Uptime Probability Specifications via Robust Geometric Programming”, ACC 2005.• J. Papandriopoulos, J. Evans, S. Dey, “Outage-Based Power Control for Generalized Multiuser Fading Channels”, IEEE Trans. Comm. 2006.
Delay
SINR Estimation
+x
+ Node i
Controllingnode
-+Quantization
Power updating
Power to transmit data packets.
Carlo Fischione
The Optimization Problem (2)The Optimization Problem (2)
♣ How to express the constraints on the outage probability?
♣ How to solve efficiently the problem?¬ good precision of the solution (otherwise waste of
resources);¬ computationally affordable (WSNs have limited
processing capabilities).
UCB December 11, 2007Reducing Energy Consumption
in Wireless Sensor Networks
Delay
SINR Estimation
+x
+ Node i
Controllingnode
-+Quantization
Power updating
Power to transmit data packets.
t
SINR(t)
SINR threshold
Outages
The Signal to Interference + Noise Ratio
Carlo Fischione
♣ The SINR is a combination of log-normal processes weighted with on/offbinary random processes, it is approximated with an overall Log-normal:
Outage Probability ConstraintOutage Probability Constraint
Interference Function
Fischione, Graziosi, Santucci,IEEE Trans. Comm., October 2007
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
SINR
Outages
Carlo Fischione
♣ Main Problem The Relaxation Problem
♣ Any pair of vectors and that solves the relaxation problem, with a costfunction less then , provides a feasible solution of the original problem.
♣ Proposition 1: if then for all i and .
♣ Theorem: If the pair is a solution of the relaxation problem, then it verifiesthe following equations:
Relaxation ProblemRelaxation Problem
UCB December 11, 2007Reducing Energy Consumption
in Wireless Sensor Networks
Carlo Fischione
♣ Proposition 2: if is feasible, the pair can be a solution of theoriginal problem only if
The proposition says that, given a feasible vector , the search for thefeasible solutions can be restricted to the set
♣ Proposition 3: if is unfeasible, then any is unfeasible aswell.
The proposition says that, if is unfeasible, the set of unfeasible solutionscan be reduced as follows:
Branch-and-bound SolutionBranch-and-bound Solution
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
Radio Power Control (4)Radio Power Control (4)
♣ T Relaxation Problem in solved by contraction mappings (Fischione,Butussi, IEEE ICC 2007)
♣ A branch-and-bound algorithm builds the set of feasible andunfeasible rates by solving the Relaxation Problem, collecting feasiblesolutions, and reducing the candidate solutions via Proposition 2 and3.
♣ It can be proved that the algorithm converges within a random timeshorter then an exhaustive search over the initial set of possible rates.
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Relaxation Problem
Carlo Fischione
An exhaustive search over all rate vectors would have required solutions of the relaxation problem.
Numerical Results for PowerNumerical Results for PowerControlControl
Convergence of the Relaxation Problem
Number of rate vectors explored (solutions of the Relaxation Problem) to get the optimal solution with our proposed method:
UCB December 11, 2007Reducing Energy Consumption
in Wireless Sensor Networks
Carlo FischioneReducing Energy Consumption
in Wireless Sensor Networks
Introductory Overview
Minimum Energy Coding
Radio Power Control
Bit Error Rate
Energy Consumption
Numerical Results
Conclusions and Future Work
UCB December 11, 2007
Carlo Fischione
Bit Error RateBit Error Rate
♣ Decision variable at the receiver
♣ The decision variable is erroneously decoded:
¬ as a high bit, when a low bit was transmitted:
¬ as a low bit when a high bit was transmitted:
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Tx 1Rx 1
1
Tx 2Rx 2
2
Tx KRx K
K
Transmitter-receive pairs
Tx i
Rx i i
Carlo Fischione
Bit Error Rate (2)Bit Error Rate (2)
♣ The probabilities are computed adopting the usual standard Gaussianapproximation, where is modelled as a Gaussian random variableconditioned to the distribution of the channel coefficients and coding.
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
Bit Error Rate (4)Bit Error Rate (4)
♣ The Bit error probability is computed using the Log NormalSINR expression, and the Stirling approximation for thestatistical Expectation:
♣ It can be proved that the BER is a convex function.¬ The gradient method is used to derive the optimal decision
threshold.Reducing Energy Consumption
in Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
Bit Error Rate of ME and MMEBit Error Rate of ME and MME
The characterization of can be used tocompute the BER of
1. ME:
2. MME: the coding structure,
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
♣ Introductory Overview
♣ Main Contribution
♣ System Description
♣ Radio Power Control
♣ Bit Error Rate
♣ Energy Consumption
♣ Numerical Results
♣ Conclusions and Future Work
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
Average ME Energy ConsumptionAverage ME Energy Consumption
Tx 1Rx 1
1
Tx 2Rx 2
2
Tx KRx K
K
Transmitter-receive pairs
Tx i
Rx i i
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
Average Energy MMEAverage Energy MMEConsumptionConsumption
♣The energy is function of the BER
MME Energy Gain
Receiver activity per MME codeword
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
♣ Introductory Overview
♣ Main Contribution
♣ System Description
♣ Radio Power Control
♣ Bit Error Rate
♣ Energy Consumption
♣ Numerical Results
♣ Conclusions and Future Work
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
Numerical ResultsNumerical Results
CC2420 was taken as reference for the energyparameters Number of transmitter receiver pairs
Path loss at reference distance
Spreading gain
Noise Variance
SINR Threshold
Bit rate
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
Radio Power OptimizationRadio Power Optimization
♣ Convergence of the power minimization algorithm. On thex-axis is reported the number of iterations.
Tx 1
Rx 1 1
Tx 2
Rx 2 2
Tx K
Rx K K
Transmitter-receive pairs
Tx i
Rx i i
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Relaxation Problem
Carlo Fischione
BERBER
♣ Bit error probability for the ME and MME cases (note thatthey overlap).
Tx i
Rx i i
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
Energy ConsumptionEnergy Consumption
♣ MME Energy Gain as function of the sub-frame length, fordifferent values of the transmitter activity
Tx i
Rx i i
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
Conclusions and Future WorkConclusions and Future Work
♣ A general framework for accurate analysis of the Energy Consumptionof ME and MME coding has been proposed.
¬ Accurate Energy model and wireless propagation scenario.
¬ Distributed power minimization strategy.
¬ Optimal decision thresholds.
♣ Future studies¬ delay characterization.¬ distributed estimation.¬ simpler power control (geometric programming...)
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
AcknowledgementsAcknowledgements
♣ M. Butussi
♣ K. H. Johannson
♣ F. Graziosi
♣ A. Sangiovanni-Vincentelli
♣ F. Santucci
♣ B. Zurita Ares
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
Carlo Fischione
QuestionsQuestions
Thank you for your attention!
Any questions?
Reducing Energy Consumptionin Wireless Sensor NetworksUCB December 11, 2007
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