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Record W3202150882 · doi:10.18280/isi.260408

Internet of Things and Heterogeneous Networks Technologies: Concepts, Challenges and Perspectives

2021· article· en· W3202150882 on OpenAlexvenueno aff
Chirihane Gherbi

Bibliographic record

VenueIngénierie des systèmes d information · 2021
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceBluetoothThe InternetComputer networkKey (lock)Process (computing)Internet of ThingsWireless sensor networkField (mathematics)Radio-frequency identificationRouting (electronic design automation)IPv6Smart objectsTelecommunicationsObject (grammar)Resource (disambiguation)Computer securityWirelessWorld Wide Web

Abstract

fetched live from OpenAlex

The IoT network is used to provide an object with connectivity to the Internet to allow the feedback of information. The IoT has the ambition to make objects interact with each other and with people. It promises to be the engine of great transformations in the lives of individuals by democratizing new uses and services in the mobility sector. This is why it is compared to the internet of the future. The global size of the IoT, the huge number of Things in the IoT, the changing topology of the IoT, and the resource constraints make routing in the IoT difficult. Routing is a necessary process in the Internet of Things because it enables the interchange of data between Things by quickly guiding and reliably delivering data through the network from its origins to its destinations. The Internet of Things is enabled by key pillars: radio frequency identification (RFID) and wireless sensor networks (WSNs) (IoT), NFC (Near Field Communication), BLE (Bluetooth Low-Energy technology), LoRa and Sigfox. This paper examines these different technologies as well as the barriers and challenges that must be solved.

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 score0.893
Threshold uncertainty score0.424

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 communication0.0000.002
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 head0.022
GPT teacher head0.227
Teacher spread0.205 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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