Secure Task Offloading in Blockchain-Enabled MEC Networks With Improved PBFT Consensus
Bibliographic record
Abstract
In this paper, we investigate the secure task offloading and computation resource allocation issues in a consortium blockchain-enabled multi-access edge computing (MEC) system. Specifically, edge servers and a cloud center provides user equipments (UEs) with augmented computing power for task processing, while consortium blockchain can provide trust and secure guarantee to UEs in task offloading. Within the MEC system, we intend to minimize the task processing cost of all UEs by jointly optimizing the binary task offloading decision and the computation resource block allocation. Meanwhile, in the blockchain system, we first enhance the consensus procedure by proposing an improved practical Byzantine fault tolerance (IPBFT) consensus algorithm, and then conduct consensus committee selection, thus to minimize consensus delay and fail ratio. The two systems are jointly optimized, subjecting to the computation power of edge nodes, the node number limitation of IPBFT, the task processing and blockchain consensus delay, etc. To address the problem effectively, we reform it into a Markov decision process (MDP) and use proximal policy optimization (PPO) to dynamically learn the optimal joint solution. Simulation results demonstrate that our proposed algorithm converges fast, and performs well in total reward maximization, and UEs’ cost, consensus delay and fail ratio minimization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".