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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Motion-related resource allocation in dynamic wireless visual sensor network environments.

Angeliki V Katsenou, Lisimachos P Kondi, Konstantinos E Parsopoulos

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |October 23, 2013
    PubMed
    Summary
    This summary is machine-generated.

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    This study optimizes resource allocation for wireless visual sensor networks by jointly managing power and coding rates. It introduces dynamic, quality-driven methods for efficient video transmission based on motion levels.

    Area of Science:

    • Wireless Sensor Networks
    • Resource Allocation
    • Cross-Layer Optimization

    Background:

    • Direct Sequence Code Division Multiple Access (DS-CDMA) wireless visual sensor networks (WVSNs) face challenges in resource allocation.
    • Optimizing transmission power and source-channel coding rates is crucial for quality-driven WVSNs.

    Purpose of the Study:

    • To investigate quality-driven cross-layer optimization for resource allocation in DS-CDMA WVSNs.
    • To enable joint allocation of transmission power and source-channel coding rates based on video motion characteristics.
    • To develop dynamic resource re-allocation mechanisms for WVSNs.

    Main Methods:

    • Considered a single-hop network topology with a centralized control unit (CCU).
    • Evaluated two approaches: individual sensor allocation and motion-based clustering.

    Related Experiment Videos

    Last Updated: May 6, 2026

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    1.3K
  • Employed particle swarm optimization with restarting schemes for dynamic resource management.

    • Main Results:

    • Demonstrated the efficiency of quality-driven resource allocation strategies.
    • Showcased the effectiveness of motion-adaptive resource management in WVSNs.
    • Validated the proposed particle swarm optimization approach for dynamic optimization.

    Conclusions:

    • The proposed quality-driven cross-layer optimization effectively manages resources in WVSNs.
    • Dynamic resource allocation based on motion characteristics improves network performance.
    • The particle swarm optimization approach provides an efficient solution for real-time WVSN resource management.