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Record W2976715550 · doi:10.1109/infcomw.2019.8845189

LoRa-based Localization System for Emergency Services in GPS-less Environments

2019· article· en· W2976715550 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIoT Networks and Protocols
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsGlobal Positioning SystemHybrid positioning systemComputer scienceSoftware deploymentReal-time computingLocation-based servicePrecision Lightweight GPS ReceiverWirelessAssisted GPSPositioning systemTime to first fixGPS signalsEmbedded systemTelecommunicationsEngineeringGps receiverNode (physics)

Abstract

fetched live from OpenAlex

The introduction of Global Positioning Systems (GPS) to the public has provided millions of people with navigation and positioning services around the globe. However, it is known that no matter the improvements to accuracy, fundamentally, GPS cannot provide location information in extreme environments, such as underwater and underground. Hence, there is a growing need for alternative technologies and methodologies of providing localization in such extreme or GPS-less environments. This paper introduces a low-power and low-cost substitute founded on Long Range (LoRa) to realize similar localization capabilities, based on the Received Signal Strength Indicator (RSSI) techniques. LoRa transceivers support swift deployment in GPS-less emergency scenarios, providing emergency teams with critical location and sensing data. According to outdoor experimental results, LoRa is a promising solution for wireless localization systems at GPS-less environments.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score0.305

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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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

Quick stats

Citations37
Published2019
Admission routes1
Has abstractyes

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