When Mobile Blockchain Meets Edge Computing
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Abstract (arXiv)
Blockchain, as the backbone technology of the current popular Bitcoin digital currency, has become a promising decentralized data management framework. Although blockchain has been widely adopted in many applications, e.g., finance, healthcare, and logistics, its application in mobile services is still limited. This is due to the fact that blockchain users need to solve preset proof-of-work puzzles to add new data, i.e., a block, to the blockchain. Solving the proof-of-work, however, consumes substantial resources in terms of CPU time and energy, which is not suitable for resource-limited mobile devices. To facilitate blockchain applications in future mobile Internet of Things systems, multiple access mobile edge computing appears to be an auspicious solution to solve the proof-of-work puzzles for mobile users. We first introduce a novel concept of edge computing for mobile blockchain. Then, we introduce an economic approach for edge computing resource management. Moreover, a prototype of mobile edge computing enabled blockchain systems is presented with experimental results to justify the proposed concept.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Nov 2017.
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Citing papers per year: 2021: 3; 2022: 2; 2023: 3; 2024: 2; 2025: 0; 2026: 1.
11 papers.
- 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 55
- 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Datacited by 39
- 2022Trustworthy Federated Learning via Blockchaincited by 15
- 2021Blockchain-based Recommender Systems: Applications, Challenges and Future Opportunitiescited by 11
- 2024Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learningcited by 8
- 2024Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Datacited by 4
- 2022Digital Twins for Dynamic Management of Blockchain Systemscited by 2
- 2023Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networkscited by 2
- 2023A Survey of Blockchain, Artificial Intelligence, and Edge Computing for Web 3.0cited by 1
- 2023Edge AI for Internet of Energy: Challenges and Perspectivescited by 1
- 2026Entropy-Aware Task Offloading in Mobile Edge Computingcited by 0
- 2026Entropy-Aware Task Offloading in Mobile Edge Computing16 Mar 2026
- 2024Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data10 Apr 2024
- 2024Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning28 Mar 2024
- 2023Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networks21 Dec 2023
- 2023Edge AI for Internet of Energy: Challenges and Perspectives28 Nov 2023
- 2023A Survey of Blockchain, Artificial Intelligence, and Edge Computing for Web 3.022 Nov 2023
- 2022Trustworthy Federated Learning via Blockchain13 Aug 2022
- 2022Digital Twins for Dynamic Management of Blockchain Systems26 Apr 2022
- 2021Blockchain-based Recommender Systems: Applications, Challenges and Future Opportunities22 Nov 2021
- 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data14 May 2021
- 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation18 Jan 2021