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Record W2793000811 · doi:10.4108/eai.15-1-2018.153566

Secure ID-Based Routing for Data Communication in IoT

2017· article· en· W2793000811 on OpenAlexfundno aff
Madhusudan Singh

Bibliographic record

VenueEAI Endorsed Transactions on Internet of Things · 2017
Typearticle
Languageen
FieldComputer Science
TopicCaching and Content Delivery
Canadian institutionsnot available
FundersInstitute for Information and Communications Technology PromotionFonds de Recherche du Québec - SantéMinistry of Science, ICT and Future Planning
KeywordsInternet of ThingsComputer scienceAuthentication (law)Scope (computer science)Computer securityComputer networkAuthorizationRouting (electronic design automation)WirelessSecure communicationThe InternetWorld Wide WebTelecommunicationsEncryption

Abstract

fetched live from OpenAlex

Internet of Things is rising technology that could inspire the way wireless network access is provided. In IoT, secure data communication has lot of research scope. Especially message authentication, authorization cure path for IoT devices still remains as an open research problem. Some researcher proposed security mechanism without certification, MIC and token but still we didn’t find standard security mechanism for data communication between sensor nodes. In this article author has proposed an Identity based security (IBS) mechanism for IoT devices during data communication in IoT networks. IBS mechanism uses identity-based cryptography (IBC) to avoid certificates, MIC and tokens to minimize the computational overhead. IBS Mechanism is resistant to most common security attacks such as modification, fabrication, replay attacks and it can also protect hop count. It does provide secure data communication between sensor nodes. The results, based on NS-3 simulation, reveal that proposed mechanism is effectively able to protect the black hole attacks. In results, we have shown the packet delivery ratio, quality of services (QoS), and throughput between IBS mechanism, AODV path and AODV with black hole.

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 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)0.000
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.000
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 head0.051
GPT teacher head0.296
Teacher spread0.245 · 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 teacher head, not a consensus.

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

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

Citations2
Published2017
Admission routes1
Has abstractyes

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