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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Efficient node deployment for enhancing coverage and connectivity in Wireless Sensor Networks.

Rahul Priyadarshi1

  • 1Faculty of Engineering and Technology, ITER, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, 751030, India. rahulpriyadarshi@soa.ac.in.

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Summary
This summary is machine-generated.

This study introduces a Particle Swarm Optimization (PSO) deployment framework for heterogeneous Wireless Sensor Networks (WSNs). The PSO approach significantly enhances network coverage, energy efficiency, and operational lifetime compared to traditional methods.

Keywords:
Adaptive deploymentConnectivityCoverageDeployment optimizationEnergy efficiencyFault toleranceHeterogeneous networksMetaheuristicsParticle swarm optimization (PSO)Wireless sensor networks (WSNs)

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Wireless Sensor Networks (WSNs) require efficient deployment for optimal performance.
  • Traditional deployment methods struggle with heterogeneous nodes and dynamic environments.
  • Key performance metrics include coverage, connectivity, energy efficiency, and network longevity.

Purpose of the Study:

  • To propose a novel Particle Swarm Optimization (PSO)-based deployment framework for heterogeneous WSNs.
  • To improve coverage quality, minimize energy consumption, and enhance fault tolerance.
  • To adapt to dynamic environmental conditions and heterogeneous node capabilities.

Main Methods:

  • Development of a PSO-based deployment framework.
  • Incorporation of intelligent role assignment and metaheuristic optimization.
  • Inclusion of adaptive maintenance phases for network resilience.
  • Simulation-based evaluation against nine traditional deployment strategies.

Main Results:

  • Achieved superior performance with 91.4% ± 1.8% average coverage.
  • Extended operational lifetime beyond 3,400 rounds.
  • Demonstrated improved connectivity robustness, scalability, and resilience to node failures.

Conclusions:

  • The proposed PSO-based deployment framework is a viable solution for heterogeneous WSNs.
  • It offers significant improvements in performance metrics for real-world applications.
  • The framework is suitable for large-scale and energy-constrained WSN deployments.