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SRRM: Ranking-based Route Mutation Scheme for Software-Defined WSNs

2021· article· en· W4210362763 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.

Bibliographic record

Venue2021 IEEE Global Communications Conference (GLOBECOM) · 2021
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceComputer networkNetwork packetNode (physics)Energy consumptionReliability (semiconductor)Routing (electronic design automation)Distributed computingPacket forwardingWireless sensor networkScheme (mathematics)Software deploymentSoftwareObfuscationComputer securityEngineering

Abstract

fetched live from OpenAlex

In WSNs, packets are delivered through mostly static shortest paths to their destination. However, static packet delivery makes WSNs highly vulnerable to traffic analysis attacks due to open area deployment. Existing defence proposals fail to achieve a balance between the protection level and the resource constraints. In this paper, we present a proactive SDN-based Route Mutation (SRRM) scheme that enables changing the routes of the multiple flows in WSNs simultaneously to defend against passive and stealthy reconnaissance and sniffer attacks while preserving reliable and energy-aware routing. Multiple routes are ranked for packet flow based on node reliability, energy consumption, link cost, and route overlapping. Our extensive simulation results show that these techniques can effectively provide route obfuscation for software-defined WSNs.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.771
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0040.001
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 head0.055
GPT teacher head0.305
Teacher spread0.250 · 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