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Record W1586265031 · doi:10.1109/icc.2015.7249431

QoS and energy-aware dynamic routing in Wireless Multimedia Sensor Networks

2015· article· en· W1586265031 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
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceQuality of serviceComputer networkWireless sensor networkRouting (electronic design automation)WirelessEfficient energy useTransmission (telecommunications)Energy (signal processing)Wireless networkTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

The increasing availability of low-cost hardware along with the rapid growth of wireless devices has enabled the development of Wireless Multimedia Sensor Networks (WMSNs). Multimedia content such as video and audio streaming is transmitted over a WMSN which can easily be deployed with low cost. However, enabling real-time data applications in those networks demands not only Quality of Service (QoS) awareness, but also efficient energy management. Sensor network devices have limited energy resources. The limited energy poses significant threats on the QoS of WMSNs. In this paper, to improve the efficiency of QoS-aware routing, we examine an angle-based QoS and energy-aware dynamic routing scheme designed for WMSNs. The proposed approach uses the inclination angle and the transmission distance between nodes to optimize the selection of the forwarding candidate set and extend network lifetime. Simulation results indicate that considerable lifetime values can be achieved.

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 score0.863
Threshold uncertainty score0.997

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 studies0.0000.000
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 head0.012
GPT teacher head0.222
Teacher spread0.210 · 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

Citations32
Published2015
Admission routes1
Has abstractyes

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