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An efficient algorithm for energy harvesting in IIoT based on machine learning and swarm intelligence.

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This study introduces an efficient federated learning algorithm to extend the lifespan of Internet of Things (IoT) devices by optimizing resource management. The new method improves performance compared to existing solutions, addressing battery limitations in large-scale IoT networks.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • The Internet of Things (IoT) and Industrial IoT (IIoT) are expanding rapidly, becoming integral to various applications.
  • Limited battery life and high maintenance costs pose significant challenges for large-scale IoT deployments.
  • Efficient resource management is crucial for sustaining the performance and longevity of IoT devices.

Purpose of the Study:

  • To propose an efficient federated learning-based algorithm for resource management in IIoT networks.
  • To address the challenge of prolonging IoT device lifespan while maintaining optimal performance.
  • To overcome the limitations imposed by battery constraints in massive IoT systems.

Main Methods:

  • The proposed algorithm decomposes the optimization problem into sub-problems.
  • Particle Swarm Optimization (PSO) is utilized to manage the energy budget.
  • An iterative matching algorithm is employed for communication resource optimization.

Main Results:

  • The developed algorithm demonstrates superior performance compared to existing methods.
  • Effective energy budget management was achieved through PSO.
  • Optimized communication resource allocation was successfully implemented.

Conclusions:

  • The proposed federated learning algorithm offers an efficient solution for resource management in IIoT.
  • This approach effectively extends the lifespan of IoT devices and enhances overall network performance.
  • The findings contribute to overcoming battery limitations in large-scale IoT networks.