Blockchain-based Resource Trading in Multi-UAV-assisted Industrial IoT Networks: A Multi-agent DRL Approach

Authors

Abegaz Mohammed Seid, College of Science and Engineering, Division of Information and Computing Technology, Hamad Bin Khalifa University, Doha, Qatar
Hayla Nahom Abishu, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaFollow
Yasin Habtamu Yacob, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
Tewodros Alemu Ayall, Department of computer science, Zhejiang Normal University, Jinhua, China
Aiman Erbad, College of Science and Engineering, Division of Information and Computing Technology, Hamad Bin Khalifa University, Doha, Qatar
Mohsen Guizani, Mohamed bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

IEEE Transactions on Network and Service Management

Abstract

With the Industrial Internet of Things (IIoT), mobile devices (MDs) and their demands for low-latency data communication are increasing. Due to the limited resources of MDs, such as energy, computation, storage, and bandwidth, IIoT systems cannot meet MDs’ quality of service (QoS) and security requirements. Recently, UAVs have been deployed as aerial base stations in the IIoT network to provide connectivity and share resources with MDs. We consider a resource trading environment where multiple resource providers compete to sell their resources to MDs and maximize their profit by continually adjusting their pricing strategies. Multiple MDs, on the other hand, interact with the environment to make purchasing decisions based on the prices set by resource providers to reduce costs and improve QoS. We propose a novel intelligent resource trading framework that integrates multi-agent deep reinforcement Learning (MADRL), blockchain, and game theory to manage dynamic resource trading environments. A consortium blockchain with a smart contract is deployed to ensure the security and privacy of the resource transactions. We formulated the optimization problem using a Stackelberg game. However, the formulated optimization problem in the multi-agent IIoT environment is complex and dynamic, making it difficult to solve directly. Thus, we transform it into a stochastic game to solve the dynamics of the optimization problem. We propose a dynamic pricing algorithm that combines the Stackelberg game with the MADRL algorithm to solve the formulated stochastic game. The simulation results show that our proposed scheme outperforms others to improve resource trading in UAV-assisted IIoT networks. IEEE

First Page

1

Last Page

1

DOI

10.1109/TNSM.2022.3197309

Publication Date

8-9-2022

Keywords

Blockchain, Blockchains, DRL, Games, Heuristic algorithms, Industrial Internet of Things, Industrial IoT, Optimization, Quality of service, Resource management, Resource trading, Unmanned Aerial Vehicles

Comments

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OA version (pathway a) : Accepted version

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Publisher copyright and source must be acknowledged

Recommended Citation

A. M. Seid, H. N. Abishu, Y. H. Yacob, T. A. Ayall, A. Erbad and M. Guizan, "Blockchain-based Resource Trading in Multi-UAV-assisted Industrial IoT Networks: A Multi-agent DRL Approach," in IEEE Transactions on Network and Service Management, 2022, doi: 10.1109/TNSM.2022.3197309.