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Record W2106055052 · doi:10.1504/ijsnet.2015.067585

Lifetime analysis for wireless sensor networks

2015· article· en· W2106055052 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

VenueInternational Journal of Sensor Networks · 2015
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsConcordia University
Fundersnot available
KeywordsWireless sensor networkComputer scienceComputer networkSink (geography)Key distribution in wireless sensor networksTopology (electrical circuits)Wireless networkWirelessTelecommunicationsMathematicsGeography

Abstract

fetched live from OpenAlex

The sensor nodes of wireless sensor networks (WSNs) have the dual function of gathering information and relaying it to a central collection point, the sink. In this paper, we derive connectivity, as expressed by the probability that a node lies on a path to the sink, as a function of the probability that adjacent cells in a grid are connected. The nodes are assumed to have two modes of operation, the active and sleep states and the energy is consumed only in the active state. For battery powered nodes, lifetime is an important factor. The effect of the demise of nodes on the nodal density and, in turn, on network connectivity is quantified. The novelty of the results is that they determine the degree of connectivity in networks with finite number of nodes, unlike to many results in the literature which are asymptotic and for full connectivity.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.884
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0030.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.267
Teacher spread0.246 · 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