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Energy Aware Data Compression in WSN by Signal Processing

Izadian, Roshanak | 2012

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 42964 (52)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Manzuri Shalmani, Mohammad Taghi
  7. Abstract:
  8. Wireless sensor networks (WSNs) consume energy for their sensing, computation, and communication. To extend the lifetime of the network, sensor nodes are equipped with energy storage devices. Recharging their batteries is impossible in most applications. Therefore, energy consumption needs to be monitored and be limited to extend the high performance operation of the network. In this network, the communication module consumes the highest amount of energy. While several methods are proposed to reduce the energy consumption, data compression is one of the most effective ways for energy management by reducing the number of bits to be broadcast. To determine the energy efficiency of the communication module, the energy consumption of the broadcasting data as text and image in original and compressed forms has been measured. It is shown that JPEG and TIFF for images and Adaptive Huffman algorithm for text, consume less energy for both mesh and random node networks.

  9. Keywords:
  10. Data Compression ; Wireless Sensor Network ; Energy Consumption

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  • AUTHOR'S DECLARATION
  • Abstract
  • Acknowledgements
  • Dedication:
  • Chapter 1 Introduction
  • Chapter 2 Wireless Sensor Networks
    • 2.1 Background
    • 2.2 Sensor Networks vs. Traditional Wireless Networks
    • 2.3 WSN Architecture
      • 2.3.1 Sensor Platforms
      • 2.3.2 Operating Systems
      • 2.3.3 Communication Modules
    • 2.4 WSN Applications
    • 2.5 Constraints in Sensor Networks
      • 2.5.1 Node Constraints
      • 2.5.2 Network Constraints
    • 2.6 Design Issues & Challenges in WSNs
    • 2.7 Performance Evaluation Metrics
  • Chapter 3
  • Related Works
    • 3.1 Overview of Data Compression
    • 3.2 Data Compression
    • 3.3 Methodology for Energy Aware
    • 3.4 Data Fusion
    • 3.5 Data Aggregation
  • Chapter 4 Technical Background
    • 4.1 Introduction of NS2
      • 4.1.1 Structure of NS2
    • 4.2 Data Compression for text data
      • 4.2.1 Run Length Encoding Algorithm
      • 4.2.2 Huffman Encoding
      • 4.2.3 Shannon Fano Algorithm
      • 4.2.4 Arithmetic Encoding
      • 4.2.5 The Lempel Zev Welch Algorithm
    • 4.3 Data compression for image data
      • 4.3.1 Bitmap
      • 4.3.2 Portable Network Graphics
      • 4.3.3 Tagged Image File Format
      • 4.3.4 Joint Photographic Experts Group
  • Chapter 5 Simulations and Results
    • 5.1 Text Data
      • 5.1.1 Comparison of Compression Algorithms
    • 5.2 Measuring Compression Performances
    • 5.3 Comparison of compression algorithms
    • Compression Ratio
    • 5.4 Wireless Network and data Compression Energy Calculations
    • Mesh Network Analysis
    • Random Network Analysis
    • 5.5 Image data
  • Chapter 6 Conclusion
  • References:
  • Appendix A
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