research-article

Privacy-Preserved Task Offloading in Mobile Blockchain With Deep Reinforcement Learning

Authors:

Dinh C. Nguyen,

Pubudu N. Pathirana,

Aruna SeneviratneAuthors Info & Claims

IEEE Transactions on Network and Service Management, Volume 17, Issue 4

Pages 2536 - 2549

https://doi.org/10.1109/TNSM.2020.3010967

Published: 01 December 2020 Publication History

Abstract

Blockchain technology with its secure, transparent and decentralized nature has been recently employed in many mobile applications. However, the process of executing extensive tasks such as computation-intensive data applications and blockchain mining requires high computational and storage capability of mobile devices, which would hinder blockchain applications in mobile systems. To meet this challenge, we propose a mobile edge computing (MEC) based blockchain network where multi-mobile users (MUs) act as miners to offload their data processing tasks and mining tasks to a nearby MEC server via wireless channels. Specially, we formulate task offloading, user privacy preservation and mining profit as a joint optimization problem which is modelled as a Markov decision process, where our objective is to minimize the long-term system offloading utility and maximize the privacy levels for all blockchain users. We first propose a reinforcement learning (RL)-based offloading scheme which enables MUs to make optimal offloading decisions based on blockchain transaction states, wireless channel qualities between MUs and MEC server and user’s power hash states. To further improve the offloading performances for larger-scale blockchain scenarios, we then develop a deep RL algorithm by using deep Q-network which can efficiently solve large state space without any prior knowledge of the system dynamics. Experiment and simulation results show that the proposed RL-based offloading schemes significantly enhance user privacy, and reduce the energy consumption as well as computation latency with minimum offloading costs in comparison with the benchmark offloading schemes.

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cover image IEEE Transactions on Network and Service Management

IEEE Transactions on Network and Service Management Volume 17, Issue 4

Dec. 2020

759 pages

ISSN:1932-4537

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1932-4537 © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 December 2020

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Wang ZZhang SJin XChen YLu C(2025)Joint resource allocation and privacy protection for MEC task offloading in industrial InternetCluster Computing10.1007/s10586-024-04826-428:2Online publication date: 1-Apr-2025
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Li CJiang KZhang YJiang LLuo YWan S(2024)Deep Reinforcement Learning-based Mining Task Offloading Scheme for Intelligent Connected Vehicles in UAV-aided MECACM Transactions on Design Automation of Electronic Systems10.1145/365345129:3(1-29)Online publication date: 3-May-2024
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