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A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks.

Jidian Yang1, Shiwen He2, Yang Xu3

  • 1School of Software, Central South University, Changsha 410083, China. gideonyang@csu.edu.cn.

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Summary

This study introduces a novel trusted routing scheme for wireless sensor networks (WSNs) using blockchain and reinforcement learning. The approach enhances routing security and efficiency, even with a high percentage of malicious nodes.

Keywords:
blockchaindelay performanceefficiencyreinforcement learningrouting schemetrustwireless sensor networks

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Area of Science:

  • Computer Science
  • Network Security
  • Artificial Intelligence

Background:

  • Wireless Sensor Networks (WSNs) require secure and efficient routing.
  • Existing schemes struggle with dynamic identification of untrusted nodes and preventing malicious attacks.
  • Current methods often lack practical implementability in real-world scenarios.

Purpose of the Study:

  • To propose a novel trusted routing scheme for WSNs.
  • To enhance routing security and efficiency by addressing limitations of current approaches.
  • To improve the dynamic identification of untrusted nodes and prevent malicious attacks.

Main Methods:

  • Utilizing blockchain technology for traceable and tamper-proof routing information.
  • Implementing a reinforcement learning model for dynamic selection of trusted and efficient routing links.
  • Developing a feasible routing scheme for obtaining and verifying routing data on the blockchain.

Main Results:

  • The proposed scheme demonstrates good delay performance, even in environments with up to 50% malicious nodes.
  • Experimental results show improved energy consumption and throughput compared to other routing algorithms.
  • The scheme proves feasible and effective in enhancing WSN routing security and efficiency.

Conclusions:

  • The integration of blockchain and reinforcement learning offers a robust solution for trusted routing in WSNs.
  • The proposed method effectively mitigates the impact of malicious nodes on network performance.
  • This approach provides a traceable, tamper-proof, and dynamically adaptive routing mechanism for WSNs.