Blockchain-Based Resource Trading in Multi-UAV Edge Computing System

Abstract
Unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) systems have emerged as a promising technology with the capability to expand terrestrial networks. UAVs, working as edge computing nodes and mobile base stations, can be deployed closer to user equipment (UEs). However, with the rapid increase of UEs, the scarcity of spectrum resources and computing resources has become a critical challenge for future mobile communication systems. Additionally, the inherent characteristics of wireless transmission and untrusted broadcasting pose significant security and privacy concerns for multi-UAV networks. To address these issues, this paper presents a blockchain-based resource trading mechanism (BRTM) and a double auction-based resource trading algorithm (DARA) for multi-UAV edge computing systems. It combines blockchain technology with double auction theory to ensure the security and fairness of resource trading. The relations between UEs and UAVs as a two-stage Stackelberg game is formulated and a pricing-based incentive strategy is proposed. The proposed scheme encourages active participation from both UEs and UAVs while maximizing the sum of their utilities. The security assessment and numerical outcomes show that the proposed method is effective and outperforms other benchmark schemes.
Main Authors
Format
Articles Research article
Published
2024
Series
Subjects
Publication in research information system
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202404162923Käytä tätä linkitykseen.
Review status
Peer reviewed
ISSN
2372-2541
DOI
https://doi.org/10.1109/jiot.2024.3375918
Language
English
Published in
IEEE Internet of Things Journal
Citation
  • Xu, R., Chang, Z., Zhang, X., & Hämäläinen, T. (2024). Blockchain-Based Resource Trading in Multi-UAV Edge Computing System. IEEE Internet of Things Journal, Early Access. https://doi.org/10.1109/jiot.2024.3375918
License
In CopyrightOpen Access
Additional information about funding
This work is partly supported by the National Natural Science Foundation of China (NSFC) under Grant 62071105.
Copyright© 2024 IEEE.

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