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Record W3013064459 · doi:10.1109/access.2020.2983438

Centralized Routing Protocol for Detecting Wormhole Attacks in Wireless Sensor Networks

2020· article· en· W3013064459 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

VenueIEEE Access · 2020
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsLakehead University
Fundersnot available
KeywordsComputer scienceComputer networkWireless Routing ProtocolRouting protocolZone Routing ProtocolWireless sensor networkDynamic Source RoutingProtocol (science)WormholeKey distribution in wireless sensor networksEnhanced Interior Gateway Routing ProtocolRouting (electronic design automation)WirelessWireless networkComputer securityTelecommunications

Abstract

fetched live from OpenAlex

Nodes in wireless sensor networks (WSN) are resource and energy-constrained because they are generally batteries powered and therefore have limited computational capability. Due to the less secure environment in WSN, some malicious nodes at one point can tunnel packets to another location to damage the network in terms of packets dropping and eavesdropping and this is a so-called wormhole attack. Many of the current protocols solve the wormhole attack problem in isolation from the node energy consumption. However, some other proposed solutions consider reducing the energy consumption to detect such attacks but still it is needed to probe better performance. In this paper, we present a lightweight multi-hop routing protocol for 802.15.4 WSN that aims to minimize the energy consumption and also to detect the wormhole attacks. Simulation results prove that our MAC Centralized Routing Protocol (MCRP) outperforms other existing similar protocols.

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 categoriesMeta-epidemiology (narrow)
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 score0.902
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.063
GPT teacher head0.336
Teacher spread0.273 · 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