Intelligent Offloading in Blockchain-Based Mobile Crowdsensing Using Deep Reinforcement Learning

Chen, Zheyi and Yu, Zhengxin (2023) Intelligent Offloading in Blockchain-Based Mobile Crowdsensing Using Deep Reinforcement Learning. IEEE Communications Magazine, 61 (6): 6. pp. 118-123. ISSN 0163-6804

[thumbnail of ComMag]
Text (ComMag)
ComMag.pdf - Accepted Version
Available under License Creative Commons Attribution.

Download (1MB)

Abstract

Mobile Crowdsensing (MCS) utilizes sensing data collected from users' mobile devices (MDs) to provide high-quality and personalized services, such as traffic monitoring, weather prediction, and service recommendation. In return, users who participate in crowdsensing (i.e., MCS participants) get payment from cloud service providers (CSPs) according to the quality of their shared data. Therefore, it is vital to guarantee the security of payment transactions between MCS participants and CSPs. As a distributed ledger, the blockchain technology is effective in providing secure transactions among users without a trusted third party, which has found many promising applications such as virtual currency and smart contract. In a blockchain, the proof-of-work (PoW) executed by users plays an essential role in solving consensus issues. However, the complexity of PoW severely obstructs the application of blockchain in MCS due to the limited computational capacity of MDs. To solve this issue, we propose a new framework based on Deep Reinforcement Learning (DRL) for offloading computation-intensive tasks of PoW to edge servers in a blockchain-based MCS system. The proposed framework can be used to obtain the optimal offloading policy for PoW tasks under the complex and dynamic MCS environment. Simulation results demonstrate that our method can achieve a lower weighted cost of latency and power consumption compared to benchmark methods.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Communications Magazine
Uncontrolled Keywords:
Research Output Funding/yes_externally_funded
Subjects:
?? electrical and electronic engineeringcomputer networks and communicationscomputer science applicationsyes - externally fundednocomputer networks and communicationscomputer science applicationselectrical and electronic engineering ??
ID Code:
202330
Deposited By:
Deposited On:
24 Aug 2023 12:50
Refereed?:
Yes
Published?:
Published
Last Modified:
15 Jul 2024 23:57