An Overlapping Coalition Game Approach for Collaborative Block Mining and Edge Task Offloading in MEC-assisted Blockchain Networks
Licheng Ye, Zehui Xiong, Lin Gao, Dusit Niyato

TL;DR
This paper introduces a novel overlapping coalition game model for collaborative block mining and edge task offloading in MEC-assisted blockchain networks, enhancing efficiency and scalability through multi-coalition cooperation.
Contribution
It develops a comprehensive two-stage Stackelberg game model for multi-coalition collaboration, including a closed-form Nash equilibrium and a stable coalition formation algorithm.
Findings
Derived a closed-form Nash equilibrium for the ERC game.
Proposed an OCF-based algorithm for stable coalition formation.
Developed a near-optimal pricing strategy for the ECP.
Abstract
Mobile edge computing (MEC) is a promising technology that enhances the efficiency of mobile blockchain networks, by enabling miners, often acted by mobile users (MUs) with limited computing resources, to offload resource-intensive mining tasks to nearby edge computing servers. Collaborative block mining can further boost mining efficiency by allowing multiple miners to form coalitions, pooling their computing resources and transaction data together to mine new blocks collaboratively. Therefore, an MEC-assisted collaborative blockchain network can leverage the strengths of both technologies, offering improved efficiency, security, and scalability for blockchain systems. While existing research in this area has mainly focused on the single-coalition collaboration mode, where each miner can only join one coalition, this work explores a more comprehensive multi-coalition collaboration…
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Taxonomy
TopicsBlockchain Technology Applications and Security · IoT and Edge/Fog Computing
