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Record W2949133450 · doi:10.32370/ia_2019_02_8

The Practical Side of IoT Implementation in Smart Cities

2019· article· en· W2949133450 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIntellectual Archive · 2019
Typearticle
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsInternet of ThingsComputer scienceBusinessComputer securityEnvironmental planningInternet privacyData scienceEnvironmental science

Abstract

fetched live from OpenAlex

Smart cities are an innovative concept of sustainable urban design that gains popularity across the globe. Its core premise is the provision of smart educational, utility, traffic, waste and resource management, and health services with an advanced level of connectivity. The Internet of Things (IoT) is employed as the technology powering that connectivity and ensuring that the smart services operate based on intelligent data collection, sharing, and analysis. However, while IoT is actively explored today and exhibits numerous tech achievements, there are still pressing challenges for its practical implementation in the smart city architectures. This article explores the unresolved challenges such as impaired device interoperability, vulnerability of IoT grids to hacks, data security and privacy issues, as well as the slow transition of governments to smart services. The study also offers insights into the future directions of IoT research and development to empower smart city creation.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.195

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 science0.0000.000
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 head0.014
GPT teacher head0.261
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