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Energy-efficient sensing in wireless sensor networks using compressed sensing.

Mohammad Abdur Razzaque1, Simon Dobson2

  • 1Faculty of Computing, Universiti Teknologi Malaysia, Skudai, JB 81310, Malaysia. marazzaque@utm.my.

Sensors (Basel, Switzerland)
|February 15, 2014
PubMed
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This summary is machine-generated.

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Wireless sensor networks can save energy by using compressed sensing (CS) and distributed compressed sensing (DCS). These methods are more efficient than traditional techniques, especially for power-hungry sensors.

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Wireless sensor networks (WSNs) rely on sensing for environmental monitoring.
  • Current energy management often overlooks significant sensing energy costs.
  • Sensing energy can rival or exceed radio transmission/reception energy in WSNs.

Purpose of the Study:

  • Quantitatively analyze energy costs of WSN components.
  • Investigate compressed sensing (CS) and distributed compressed sensing (DCS) for energy-efficient sensing.
  • Evaluate CS and DCS effectiveness against traditional methods.

Main Methods:

  • Quantitative analysis of sensor, radio, and sensor mote energy consumption.
  • Implementation and evaluation of CS and DCS algorithms.

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  • Comparative analysis with transform coding and model-based adaptive sensing.

    • Main Results:

    • Sensing energy consumption is significant in many WSN applications.
    • CS and DCS demonstrate potential for energy savings in WSNs.
    • CS and DCS outperform transform coding and model-based adaptive sensing in specific scenarios.

    Conclusions:

    • Sensing energy costs are critical for WSNs, particularly with power-hungry sensors.
    • Compressed sensing and distributed compressed sensing offer promising energy-efficient solutions.
    • CS and DCS can enhance overall energy efficiency in wireless sensor networks.