Publicly Verifiable Distributed Computation for MEC Setting

With the rapid expansion of the Internet of Things (IoT), the shift from cloud computing to Mobile Edge Computing (MEC) has become necessary to address the low-latency requirements of real-time applications. Verifiable computation (VC) enables resource-limited clients to outsource their computation-intensive tasks to a powerful cloud while ensuring the correctness of the computation result. However, traditional VC schemes, originally designed for cloud computing, face challenges when applied to MEC environments, such as scalability issues, robustness, and efficiency concerns. To this end, we propose a verifiable distributed computation scheme for MEC, where computation tasks are distributed between a cloud server cluster (consisting of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> servers) and an edge server. The cloud handles most of the computation through parallel sub-tasks, while the edge server verifies intermediate results and performs minimal computation to recover the final outcome. Our scheme guarantees that the result can be recovered if at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula> servers, out of a total of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> servers in the cloud server cluster, perform their computations honestly. By leveraging batch verification and matrix-optimized polynomial evaluations, our scheme significantly enhances scalability, fault tolerance, and efficiency. The extensive analysis and simulations demonstrate that our proposed scheme is more feasible than existing solutions.

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Publication Details

Journal
IEEE Transactions on Parallel and Distributed Systems
Published
2025-05-02
DOI
https://doi.org/10.1109/tpds.2025.3566080
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Publicly Verifiable Distributed Computation for MEC Setting

Jian Xu, Qiang Wang, Changsheng Zhang, Fucai Zhou et al.
IEEE Transactions on Parallel and Distributed Systems
Scientific Computing and Data Management
article

Publicly Verifiable Distributed Computation for MEC Setting

Jian Xu, Qiang Wang, Changsheng Zhang, Fucai Zhou, Zhicheng Li
article en

Abstract

With the rapid expansion of the Internet of Things (IoT), the shift from cloud computing to Mobile Edge Computing (MEC) has become necessary to address the low-latency requirements of real-time applications. Verifiable computation (VC) enables resource-limited clients to outsource their computation-intensive tasks to a powerful cloud while ensuring the correctness of the computation result. However, traditional VC schemes, originally designed for cloud computing, face challenges when applied to MEC environments, such as scalability issues, robustness, and efficiency concerns. To this end, we propose a verifiable distributed computation scheme for MEC, where computation tasks are distributed between a cloud server cluster (consisting of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> servers) and an edge server. The cloud handles most of the computation through parallel sub-tasks, while the edge server verifies intermediate results and performs minimal computation to recover the final outcome. Our scheme guarantees that the result can be recovered if at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula> servers, out of a total of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> servers in the cloud server cluster, perform their computations honestly. By leveraging batch verification and matrix-optimized polynomial evaluations, our scheme significantly enhances scalability, fault tolerance, and efficiency. The extensive analysis and simulations demonstrate that our proposed scheme is more feasible than existing solutions.

IEEE Transactions on Parallel and Distributed SystemsVol. 36(7)
Northeastern University (CN)
Openalex Percentile: Top 90%
Scientific Computing and Data Management
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