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Adaptive Node Clustering for Underwater Sensor Networks.

Muhammad Fahad Khan1, Muqaddas Bibi1, Farhan Aadil1

  • 1Department of Computer Science, Attock Campus, COMSATS University Islamabad, Attock 43600, Pakistan.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive node clustering technique (ANC-UWSNs) for underwater wireless sensor networks (UWSNs). The dragonfly optimization algorithm significantly improves routing efficiency and network lifespan.

Keywords:
ANC-UWSNsadaptive node clustering techniquedragonfly optimizationnodes clusteringoptimized routingtransmission rangeunderwater sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Marine Technology

Background:

  • Underwater wireless sensor networks (UWSNs) are crucial for marine applications like habitat monitoring and resource exploration.
  • Challenges in UWSNs include harsh environmental conditions (current, pressure) and signal propagation issues (low bandwidth, delay, errors).
  • Effective routing protocols are vital for optimizing energy consumption and extending network operational life.

Purpose of the Study:

  • To propose a novel adaptive node clustering technique (ANC-UWSNs) for enhancing communication in UWSNs.
  • To leverage the dragonfly optimization (DFO) algorithm for determining optimal cluster configurations.
  • To improve network performance metrics such as routing efficiency and overall network lifespan.

Main Methods:

  • Development of the adaptive node clustering technique (ANC-UWSNs).
  • Application of the dragonfly optimization (DFO) algorithm, inspired by natural swarm behavior, for cluster head selection and routing.
  • Performance evaluation using a simulation matrix considering grid size, transmission range, and node density.
  • Comparative analysis against other optimization algorithms like Ant Colony Optimizer (ACO), CLPSO, GWO, and Moth Flame Optimizer (MFO).

Main Results:

  • The proposed ANC-UWSNs technique with DFO demonstrated superior performance compared to other tested algorithms.
  • DFO achieved a higher number of optimized clusters, leading to more efficient routing.
  • The optimized clustering resulted in a significant increase in the overall lifespan of the UWSN.

Conclusions:

  • The dragonfly optimization algorithm is highly effective for adaptive node clustering in UWSNs.
  • ANC-UWSNs offers a promising solution for overcoming communication challenges in underwater environments.
  • This approach enhances network efficiency and longevity, supporting critical marine monitoring and exploration tasks.