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Record W1555094252 · doi:10.1049/iet-com.2010.0952

Collaborative routing and camera selection for visual wireless sensor networks

2011· article· en· W1555094252 on OpenAlex
Shahram Amiri, Panos Nasiopoulos, Victor C. M. Leung

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

VenueIET Communications · 2011
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceComputer networkWireless sensor networkRouting protocolRouting (electronic design automation)Wireless networkGeographic routingNode (physics)Distributed computingWirelessStatic routingTelecommunications

Abstract

fetched live from OpenAlex

The authors propose a new approach for network lifetime maximisation in visual wireless sensor networks (VWSN). The existence of redundant multiple sensing for some parts of the field results in additional computational complexity and requires more bandwidth and battery power in the nodes, hence reducing the network lifetime. In a VWSN, efficient node collaboration for data sensing and gathering is a key factor to determine network lifetime. The proposed approach provides a jointly optimised traffic routing and camera selection strategy to enhance the lifetime of the whole network. In this approach, different nodes can collaborate with each other to prevent unnecessary multiple sensing of different areas in the network, and at the same time collaborate in the routing of the generated traffics to the network sink.

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: Methods · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.274
Teacher spread0.249 · 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