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

Nguyen, Dinh C., Pathirana, Pubudu N., Ding, Ming and Seneviratne, Aruna 2020, Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning, IEEE Transactions on Network and Service Management, pp. 1-14, doi: 10.1109/TNSM.2020.3010967.

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Title Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning
Author(s) Nguyen, Dinh C.
Pathirana, Pubudu N.ORCID iD for Pathirana, Pubudu N. orcid.org/0000-0001-8014-7798
Ding, Ming
Seneviratne, Aruna
Journal name IEEE Transactions on Network and Service Management
Start page 1
End page 14
Total pages 14
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2020
ISSN 1932-4537
1932-4537
Keyword(s) Blockchain
mobile edge computing
mining
task offloading
privacy
deep reinforcement learning.
Summary 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.
Notes Early Access Article
Language eng
DOI 10.1109/TNSM.2020.3010967
Indigenous content off
Field of Research 0805 Distributed Computing
0906 Electrical and Electronic Engineering
1005 Communications Technologies
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30141100

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