communication theory as applied to wireless sensor networks
DESCRIPTION
Communication Theory as Applied to Wireless Sensor Networks. muse. Objectives. Understand the constraints of WSN and how communication theory choices are influenced by them Understand the choice of digital over analog schemes - PowerPoint PPT PresentationTRANSCRIPT
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Communication Theory as Applied to Wireless Sensor
Networks
muse
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Objectives
• Understand the constraints of WSN and how communication theory choices are influenced by them
• Understand the choice of digital over analog schemes
• Understand the choice of digital phase modulation methods over frequency or amplitude schemes
muse
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Objectives (cont.)
• Understand the cost/benefits of implementing source and channel coding for sensor networks
• Understand fundamental MAC concepts• Grasp the importance of node synchronization• Synthesize through examples these concepts
to understand impact on energy and bandwidth requirements
muse
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Outline
• Sensor network constraints• Digital modulation• Source coding and Channel coding• MAC• Synchronization• Synthesis: Energy and bandwidth
requirements
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WSN Communication Constraints
• Energy!
Communication constraints
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Data Collection Costs
• Sensors
• Activation
• Conditioning
• A/D
Communication constraints
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Computation Costs
• Node life support
• Simple data processing
• Censoring and Aggregation
• Source/Channel coding
Communication constraints
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Communication Costs
Communication constraints
current ~= 1.0313 * rf_power + 20.618
R2 = 0.9572
15
20
25
30
35
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RF Transmit Power (dBm)
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Putting it all together
Communication constraints
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Outline
• Sensor network constraints• Digital modulation• Source coding and Channel coding• MAC• Synchronization• Synthesis: Energy and bandwidth
requirements
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Modulation
• Review
• Motivation for Digital
Modulation
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The Carrier
Modulation
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Amplitude Modulation (AM)
DSB-SC (double sideband – suppressed carrier)
Modulation
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Frequency representation for DSB-SC (the math)
Modulation
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Frequency representation for DSB-SC (the cartoon)
Modulation
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Demodulation – coherent receiver
Modulation
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DSB-LC (or AM as we know it)
Modulation
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Frequency representationof DSB-LC
Modulation
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Amplitude Modulation
Modulation
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Frequency Modulation (FM)
Modulation
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Frequency Modulation
Modulation
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Phase Modulation
Modulation
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SNR Performance
ModulationFig. Lathi
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Digital Methods
Digital Modulation
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Quadrature Modulation
Digital Modulation
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BPSK
Digital Modulation
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QPSK
Digital Modulation
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Constellation Plots
Digital Modulation
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BER Performance vs. Modulation Method
Digital ModulationFig. Lathi
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BER Performance vs. Number of Symbols
Digital ModulationFig. Lathi
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Outline
• Sensor network constraints• Digital modulation• Source coding and Channel coding• MAC• Synchronization• Synthesis: Energy and bandwidth
requirements
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Source Coding
• Motivation
• Lossless
• Lossy
Source Coding
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Lossless Compression
• Zip files
• Entropy coding (e.g., Huffman code)
Source Coding
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Lossless Compression Approaches for Sensor Networks
• Constraints
• Run length coding
• Sending only changes in data
Source Coding
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Lossy Compression
• Rate distortion theory (general principles)
• JPEG
Source Coding
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Example of Lossy Compression - JPEG
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Another comparison
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Lossy Compression Approachesfor Sensor Networks
• Constraints
• Transformations / Mathematical Operations
• Predictive coding / Modeling
Source Coding
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Example Actions by Nodes
• Adaptive Sampling
• Censoring
Source Coding
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In-Network Processing
• Data Aggregation
Source Coding
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Outline
• Sensor network contraints• Digital modulation• Source coding and Channel coding• MAC• Synchronization• Synthesis: Energy and bandwidth
requirements
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Channel Coding (FEC)
• Motivation
• Block codes
• Convolution codes
Channel Coding
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Channel Coding Approachesfor Sensor Networks
• Coding constraints
• Block coding
Channel Coding
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Example: Systematic Block Code
Channel Coding
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Alternative: Error Detection
• Motivation
• CRC
Channel Coding
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Performance
• Benefits
• Costs
Channel CodingFig. Lathi
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Outline
• Sensor network contraints• Digital modulation• Source coding and Channel coding• MAC• Synchronization• Synthesis: Energy and bandwidth
requirements
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Sharing Spectrum
MACFig. Frolik (2007)
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MAC
• Motivation
• Contention-based
• Contention-free
MAC
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ALOHA (ultimate in contention)
• Method
• Advantages
• Disadvantages
MAC
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CSMA (contentious but polite)
• Method
• Advantages
• Disadvantages
MAC
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Throughput comparison
MAC
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Contention Free Approaches
• RTS/CTS
• Reservations
MAC
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MAC for Sensor Networks: 802.15.4
• Beacon enabled mode for star networks
MAC
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Bandwidth details: 802.15.4
• 2.4 GHz band (2.40-2.48 GHz)• Sixteen channels spaced at 5 MHz (CH 11 – 26)• Data rate – 250 kbps• Direct sequence spread spectrum (DSSS) • 4 bits → symbol → 32 chip sequence• Chip rate of 2 Mcps• Modulation – O-QPSK• Total bandwidth requirement: ~3 MHz
MAC
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DSSS
• Motivation
• Operation
MAC
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Outline
• Sensor network contraints• Digital modulation• Source coding and Channel coding• MAC• Synchronization• Synthesis: Energy and bandwidth
requirements
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Synchronization
• Motivation
• Categories
Synchronization
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Node Scheduling
• Sleep
• Listening
• Transmitting
Synchronization
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Sleep Scheduling for Sensor Networks: S-MAC
Synchronization
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Synchronizing for Effective Communications
• Carrier
• Bit/Symbol
• Frame
Synchronization
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Outline
• Sensor network contraints• Digital modulation• Source coding and Channel coding• MAC• Synchronization• Synthesis: Energy and bandwidth
requirements
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Putting the Pieces Together
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Synthesis: Energy and Bandwidth
• M-ary Signaling
• Channel Coding
Energy & Bandwidth
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Sensor Network Example 1: Single vs. Multihop
• Multihop
• Single hop
Energy & Bandwidth
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Sensor Network Example 2: Polling vs. Pushing
• Polling
• Pushing
Energy & Bandwidth
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Conclusions
• A digital communications approach to WSN has advantages in robustness, energy, and bandwidth performance
• Source coding reduces overall system level energy requirements
• Simple channel coding schemes improve data reliability minimizing the need for retransmissions
muse
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Conclusions - 2
• MAC and routing strategies should be chosen with an eye towards network architecture – cross-layer design
• Node synchronization must occur regularly due to clock drift between nodes
• Simple digital communication techniques enable low-energy, low-bandwidth WSN system requirements
muse
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What to know more?
• B. Lathi, Modern Analog and Digital Communication Systems, 3rd ed., Oxford, 1998.
• B. Krishnamachari, Networking Wireless Sensors, Cambridge Press, 2005.
• J. Frolik, “Implementation Handheld, RF Test Equipment in the Classroom and the Field,” IEEE Trans. Education, Vol. 50, No. 3, August 2007.