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IoT Gateway Middleware for SDN Managed IoT

2018· article· en· W2948048952 on OpenAlexaff
Jyoti Budakoti, Amit Singh Gaur, Chung–Horng Lung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceMiddleware (distributed applications)InteroperabilityCloud computingMessage oriented middlewareMQTTTestbedComputer networkEdge computingGateway (web page)Plug and playEdge deviceSoftware-defined networkingEnhanced Data Rates for GSM EvolutionSoftwareInternet of ThingsEmbedded systemDistributed computingOperating systemWorld Wide WebSoftware architectureTelecommunications

Abstract

fetched live from OpenAlex

Internet of Things (IoT) refers to interconnection of a significant number of “things” which include objects, services and living beings. The realization of IoT systems will fundamentally change how we interact with the world; a key technology in that direction is Middleware. Middleware is an intermediary software system between IoT devices and application services. The objective of this paper is to propose and evaluate a lightweight Middleware solution which can be deployed either on the Cloud (remote data centers) for deep analytics or on the Edge Network (nearby IoT Gateways) for local analytics to support near real-time applications. Middleware supports interoperability between heterogeneous devices and applications, which is one of the most important system requirements, by providing multiple protocol bindings as plug and play services. Experiments have been conducted on a SDN (Software-defined Networking) managed IoT network testbed and the results show that the proposed Middleware solution is suitable for both Cloud (resourceful) and Edge network devices (IoT Gateway, designed on a resource constrained single board computer such as Raspberry Pi 3) and provides interoperability between IoT devices and applications.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.420
Threshold uncertainty score0.495

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 science0.0010.000
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 head0.028
GPT teacher head0.257
Teacher spread0.230 · 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 designNot applicable
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

Citations8
Published2018
Admission routes1
Has abstractyes

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