MétaCan
Menu
← Back to cohort
Record W7092178304 · doi:10.18280/isi.300803

S-MQTT: A Secure MQTT Protocol with Merkle Tree Authentication and AES Encryption for IoT Communication Systems

2025· article· W7092178304 on OpenAlexvenueno aff

Bibliographic record

VenueIngénierie des systèmes d information · 2025
Typearticle
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEncryptionMQTTMerkle treeAuthentication (law)Protocol (science)Authentication protocolInternet of ThingsTree (set theory)

Abstract

fetched live from OpenAlex

As the number of Internet of Things (IoT) devices grows, there is a greater need for secure and efficient communication protocols.A growing number of people are using the Message Queuing Telemetry Transport (MQTT) protocol because of its real-time and lightweight data sharing capabilities.However, security concerns, particularly in scenarios involving the transmission of sensitive information, necessitate the development of augmented security measures.This research introduces a pioneering protocol, Secured MQTT (S-MQTT), designed to address vulnerabilities inherent in the traditional MQTT protocol.To protect the confidentiality, integrity, and authenticity of transmitted data, S-MQTT combines sophisticated encryption methods with access control and authentication protocols.The proposed system S-MQTT in this research employs the MQTT protocol for data transfer within a communication system, comprising three key components: Publisher, Broker, and Subscriber.The study focuses on optimizing time-consuming procedures within the system and fortifying data security in communication systems.Using a Watchdog timer and AES data security, the investigation seeks to assess the broker's dependability in terms of activity level.Comparative analysis of the proposed system against the current system demonstrates superior performance.The results shows that the proposed protocol achieved an overall mitigation efficiency of 97.78%, completely blocking man-in-themiddle attacks and reducing malware intrusions by 96.61%.Encryption and authentication added only minimal latency and moderate resource overhead while significantly enhancing confidentiality, integrity, and availability.Including these metrics in the abstract will provide a balanced view of both the security effectiveness and performance trade-offs of S-MQTT.Additionally, the study presents an assessment of the time and space complexity of the suggested system design.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.007
GPT teacher head0.254
Teacher spread0.247 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueIngénierie des systèmes d information→Same topicEvolution and Genetic Dynamics→French-language works237,207→