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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Optimal Energy Resources Allocation Method of Wireless Sensor Networks for Intelligent Railway Systems.

Sheng Bin1, Gengxin Sun1

  • 1School of Data Science and Software Engineering, Qingdao University, Qingdao 266071, China.

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|January 19, 2020
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Summary

This study introduces an optimal energy allocation method for wireless sensor networks in intelligent railway systems. The proposed method enhances network lifetime by minimizing and balancing node energy consumption through clustering and partial coverage models.

Keywords:
clustering optimizationenergy resources allocationintelligent railway systempartial coveragewireless sensor network

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Area of Science:

  • Railway Engineering
  • Wireless Sensor Networks
  • Intelligent Transportation Systems

Background:

  • High-speed trains necessitate real-time monitoring of trains, infrastructure, and environment for safety and reliability.
  • Wired monitoring systems face challenges in complex railway environments due to high costs and limited coverage, making wireless sensor networks (WSNs) a suitable alternative.
  • The sustainability of WSNs is critically dependent on node energy resources, posing a significant challenge for long-term operation.

Purpose of the Study:

  • To propose a construction method for specialized WSNs tailored for railway status monitoring.
  • To develop an optimal energy resource allocation method for WSNs in intelligent railway systems to enhance network sustainability.
  • To minimize and balance energy consumption across WSN nodes for extended operational lifecycles.

Main Methods:

  • A novel construction method for railway-specific WSNs.
  • An optimal energy resource allocation strategy employing cluster head selection and a rotating probability model.
  • Clustering generation and optimization models combined with a partial coverage model to manage node energy consumption.

Main Results:

  • The proposed methods effectively minimize and balance energy consumption among WSN nodes.
  • Simulation experiments demonstrate a significant maximization of WSN lifetime.
  • The optimal energy allocation method, based on clustering optimization and partial coverage, proves effective.

Conclusions:

  • The developed optimal energy resource allocation method significantly extends the operational lifetime of WSNs in intelligent railway systems.
  • The proposed approach addresses the critical challenge of energy sustainability in WSNs for railway monitoring.
  • This research provides a viable solution for reliable and long-term status monitoring in complex railway environments.