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Record W4225716271 · doi:10.1109/tvt.2022.3167861

Non-Orthogonal Multiple Access Assisted Secure Computation Offloading via Cooperative Jamming

2022· article· en· W4225716271 on OpenAlexafffund
Yuan Wu, Guangyuan Ji, Tianshun Wang, Liping Qian, Bin Lin, Xuemin Shen

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversidade de MacauNational Natural Science Foundation of China
KeywordsJammingComputer scienceComputer networkComputation offloadingComputationEmbedded systemAlgorithmPhysicsEdge computing

Abstract

fetched live from OpenAlex

In this paper, we investigate non-orthogonal multiple access (NOMA) assisted secure computation offloading under the eavesdropping-attack, in which a malicious node overhears the edge-computing user's (EU’s) offloading transmission to the edge-computing server (ES), and NOMA is used for the EU's offloading and meanwhile for providing artificial jamming to the eavesdropper. Since multi-user simultaneous transmission over a same frequency channel can be enabled by NOMA, a wireless user (WU) can form a NOMA pair with the EU to provide cooperative jamming to the eavesdropper while also gaining an opportunity of sending its data. Focusing on the EU-WU pair with the fixed WU's energy-provisioning, we exploit the physical layer security to quantify the EU's offloading throughput with the help of WU's jamming. We then study the joint optimization of the EU's computation offloading and the EU-WU's NOMA transmission for minimizing the EU's total energy consumption subject to its latency-requirement in completing the computation-task. By utilizing the feature of analytical solution of the WU's transmission, we then investigate the WU's optimal energy-provisioning for the EU-WU pair, such that both the EU and WU can benefit from the cooperative jamming in a fairness manner. Specifically, we formulate the EU-WU's cooperation as a Nash bargaining game. By identifying the monotonic feature of Nash bargaining problem, we propose a polyblock approximation based algorithm for determining the WU's optimal energy-provisioning to achieve the win-win solution for the paired EU and WU. Finally, we investigate the scenario of multiple EUs and WUs, and aim at finding the stable pairing between the EUs and WUs, such that no individual EU (or WU) would like to change its partner. An efficient algorithm, which is based on the Gale-Shapley theory while exploiting the quantitative feature of EUs’ and WUs’ net-rewards, is proposed to achieve the stable EU-WU pairings. Numerical results are provided to validate our proposed algorithms and demonstrate the advantage of our proposed NOMA assisted computation offloading via cooperative jamming.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.255
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations46
Published2022
Admission routes2
Has abstractyes

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