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Phone of Things (PoT) : Empowering IoT Systems Through the Ubiquitous Telephone Network Infrastructure and Voice Commands

2021· article· en· W3214547726 on OpenAlexaff
Haytham Khalil, Khalid Elgazzar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsComputer scienceCloud computingTelecommunicationsPhoneVoice over IPComputer securityModular designTelephone networkInternet of ThingsComputer networkWorld Wide WebThe InternetOperating system

Abstract

fetched live from OpenAlex

This paper introduces the concept of Phone of Things (PoT), a novel approach to extend the connectivity options for IoT systems through the pervasive telephone network infrastructure. This is a paradigm shift that integrates traditional telephones to monitor and control IoT-enabled devices via VoIP protocols and public switched telephone network (PSTN). This integration democratizes IoT through the promotion of a Do-It-Yourself (DIY) IoT system that takes advantage of the freedom the comes with open-source technologies and leverages the emerging cloud-hosted services to build customized IoT solutions while accentuating affordability, accessibility, performance, ease of use, and security. Nevertheless, the proposed system inhibits a modular design philosophy that allows the system to be tailored and scaled according to the application needs with minimum hardware and software modification requirements. The proposed paradigm provides an arbitrarily shared data space between IoT systems which would coalesce into a social network of things (SNT) and support assistive services in public sensing.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.633
Threshold uncertainty score0.529

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 communication0.0000.000
Open science0.0010.001
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 head0.009
GPT teacher head0.227
Teacher spread0.218 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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