MétaCan
Menu
Back to cohort
Record W2889188116 · doi:10.1109/iwcmc.2018.8450314

An Overview of the Internet of Things Closed Source Operating Systems

2018· article· en· W2889188116 on OpenAlexaff
Aya Al‐Sakran, Mahmoud H. Qutqut, Fadi Almasalha, Hossam S. Hassanein, Mohammad Hijjawi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsQueen's University
FundersApplied Science Private University
KeywordsComputer scienceInteroperabilityScalabilityInternet of ThingsArchitectureScheduling (production processes)World Wide WebOperating systemEngineering

Abstract

fetched live from OpenAlex

The Internet of Things (IoT) attract a great deal of research and industry attention recently and are envisaged to support diverse emerging domains including intelligent transportation, smart cities, and health informatics. Operating system support for IoT plays a pivotal role in developing interoperable and scalable applications that are efficient and reliable. IoT is implemented by both high-end and low-end devices that require an operating system to run. Recently, we have witnessed a diversity of OSs emerging into IoT environment to facilitate IoT deployments and developments. In this paper, we present an overview of the common and existing closed source OSs for IoT. This paper is written in a tutorial style where each OS is described in details based on a set of designing and development aspects that we established. These aspects include architecture and kernel, memory management, scheduling, power consumption, networking protocols support, security, programming model, and multimedia support. The objective of this survey is to provide a well-structured guide to developers and researchers to determine the most appropriate OS for each specific IoT applications/devices based on their functional and non-functional requirements.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.218

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 communication0.0000.000
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 head0.044
GPT teacher head0.286
Teacher spread0.241 · 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
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

Citations6
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicIoT and Edge/Fog ComputingFrench-language works237,207