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Record W2913840322 · doi:10.3390/info10020043

Efficient Security Scheme for Disaster Surveillance UAV Communication Networks

2019· article· en· W2913840322 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInformation · 2019
Typearticle
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRedundancy (engineering)Computer scienceScheme (mathematics)Computer securityOverhead (engineering)Computer networkReliability (semiconductor)Network packetConfidentialityExploit

Abstract

fetched live from OpenAlex

The Unmanned Aerial Vehicles (UAVs) play a significant role to alleviate the negative impacts of disasters by providing essential assistance to the rescue and evacuation operations in the affected areas. Then, the reliability of UAV connections and the accuracy of exchanged information are critical parameters. In this paper, we propose networking and security architecture for disaster surveillance UAV system. The networking scheme involves a two-tier cluster network based on IEEE 802.11ah, which can provide traffic isolation between the tiers. The security scheme guarantees the accuracy and availability of the collected information from the disaster area applying fingerprint features and data redundancy techniques; the proposed scheme also utilizes the lightweight Ring-Learning with Errors (Ring-LWE) crypto-system to assure the confidentiality of the transmitted data with low overhead.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.715
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.003
GPT teacher head0.180
Teacher spread0.178 · 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