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Record W2127562956 · doi:10.1109/cnsr.2009.32

Applications of Wireless Sensor Networks and RFID in a Smart Home Environment

2009· article· en· W2127562956 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
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsAcadia University
Fundersnot available
KeywordsRadio-frequency identificationWireless sensor networkComputer scienceWirelessFocus (optics)Home automationIdentification (biology)ArchitectureTelecommunicationsWork (physics)Wireless networkPopulationUbiquitous computingSmart environmentComputer securityComputer networkHuman–computer interactionInternet of ThingsEngineeringMedicineGeography

Abstract

fetched live from OpenAlex

With the aging population and increased need to care for the elderly there are fewer of the younger generation to administer the necessary care and supervision. This condition is one of the reasons many researchers devote their time in evolving smart homes. These homes offer the occupant(s) a level of convenience not seen in traditional homes by using technology to create an environment that is aware of the activities taking place within it. The focus of this paper is on the integration of radio frequency identification (RFID) and wireless sensor network (WSN) in smart homes and applications of this system such as identifying a caregiver who enters the home. In the following work we present an architecture consisting of RFID, a WSN to identify motion within an environment and who is moving as well as several useful applications which take advantage of this information.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.271

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 head0.010
GPT teacher head0.212
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