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Intelligent Wireless Charging Path Optimization for Critical Nodes in Internet of Things-Integrated Renewable Sensor

Nelofar Aslam1, Hongyu Wang1, Muhammad Farhan Aslam2

  • 1School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China.

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Wireless sensor networks face battery limitations. A new Ant Colony Optimization for Wireless Portable Charging Devices (WPCD-ACO) method optimizes charging paths, significantly reducing energy consumption and extending network lifespan.

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Internet of Thingsant colony optimizationrenewable wireless sensor networkswireless power transferwireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless sensor networks (WSNs) are vital for the Internet of Things (IoT), enabling widespread data acquisition.
  • Limited battery life in WSN nodes hinders long-term scalability and sustainability.
  • Wireless power transfer offers a solution for recharging nodes, but creates 'bottleneck nodes' with critical energy levels.

Purpose of the Study:

  • To address the bottleneck node issue in WSNs by optimizing the charging path of a Wireless Portable Charging Device (WPCD).
  • To enhance network stability and minimize energy consumption in WSNs utilizing wireless power transfer.

Main Methods:

  • Formulation of an optimization problem for the WPCD's traveling path using Ant Colony Optimization (WPCD-ACO).
  • Integration of a time-varying 'z' phase managed by linear programming into the objective function to tackle bottleneck nodes.
  • Continuous energy level monitoring and relaying to the IoT cloud via a gateway node.

Main Results:

  • WPCD-ACO achieved an outage-optimal distance of 6092 m, outperforming shortest path (7225 m) and Dijkstra's algorithm (6142 m).
  • WPCD-ACO minimized energy consumption to 1.543 KJ, significantly better than single-hop (4.8643 KJ), GR-Protocol (3.165 KJ), grid clustering (2.4839 KJ), and C-SARSA (2.5869 KJ).
  • Monte Carlo simulations confirmed WPCD-ACO's superiority in network lifetime, stability, sensor node survival rate, and energy efficiency.

Conclusions:

  • The proposed WPCD-ACO method effectively resolves bottleneck node challenges in WSNs.
  • WPCD-ACO demonstrates significant improvements in energy efficiency and network longevity compared to existing approaches.
  • This optimization strategy enhances the overall performance and sustainability of IoT-enabled WSNs.