Differential Privacy and IRS Empowered Intelligent Energy Harvesting for 6G Internet of Things

Qianqian Pan, Jun Wu, Xi Zheng, Wu Yang, Jianhua Li

研究成果: Article査読

抄録

In the era of the sixth generation (6G), the deployment of massive Internet of Things (IoT) generates and processes large amounts of data, resulting in high energy demand and huge challenges to the energy-limited IoT devices. To achieve green and sustainable communication, energy harvesting is a feasible technology to prolong the lifetime of IoT. However, the existing energy harvesting architecture cannot guarantee the privacy of energy users while improving the intelligence and effectiveness of energy transmission. To solve these issues, we propose a differential privacy and intelligent reflecting surface empowered privacy-preserving energy harvesting framework for 6G-enabled IoT. First, a secure and intelligent energy harvesting framework is designed, which includes an intelligent reflecting surface-aided radio frequency power transmission mechanism and a differential privacy-based energy harvesting mechanism. Secondly, an exponential mechanism-based privacy-preserving energy harvesting scheme is established, where we analyze the adversary mode, propose the differential privacy-enabled location-preserving algorithm, and provide security analysis and proof. Third, we quantify the user satisfaction for energy harvesting and propose a deep reinforcement learning empowered resource allocation scheme to maximize the weighted satisfaction of all system users. Finally, simulation results show the effectiveness of the proposed secure and intelligent energy harvesting architecture for 6G IoT.

本文言語English
ジャーナルIEEE Internet of Things Journal
DOI
出版ステータスAccepted/In press - 2021
外部発表はい

ASJC Scopus subject areas

  • 信号処理
  • 情報システム
  • ハードウェアとアーキテクチャ
  • コンピュータ サイエンスの応用
  • コンピュータ ネットワークおよび通信

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