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Record W2771112465 · doi:10.1109/pacrim.2017.8121884

An energy efficient real-time vehicle tracking system

2017· article· en· W2771112465 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 and GPS-based Vehicle Safety Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceReal-time computingUploadGSMCloud computingAndroid (operating system)Vehicle tracking systemInternet of ThingsEmbedded systemEnergy consumptionTracking systemReduction (mathematics)Computer networkEngineeringArtificial intelligenceElectrical engineeringOperating systemKalman filter

Abstract

fetched live from OpenAlex

Internet of Things (IoT) is a growing technology that combines and connects a variety of devices to generate more beneficial information. In this paper, an example of IoT portable system has been built and tested, which is called Smart Vehicle System (SVS). SVS includes three main parts: the Tracking Unit, Cloud, and Android application. The Tracking Unit is positioned inside a vehicle to sense the vehicle's temperature, speed, and location then uploads them to the cloud via a GSM network. Components and communication of the SVS are described in detail. constraints are included in the system to notify the administrator and the driver of certain events. SVS is a portable system, whose operation depends on batteries; therefore, a power reduction algorithm is proposed and examined. We have performed 19 different experiments, before and after applying the proposed algorithm, four of them are dynamic and 15 are at a fixed location. With the power reduction algorithm, we are able to reduce the energy consumption of the tracking unit by up to 17%.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.638

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 head0.217
Teacher spread0.209 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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