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Record W2054937374 · doi:10.5555/1460047.1460080

Design of sensor networks with guaranteed connectivity and lifetime

2007· article· en· W2054937374 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 Wireless Internet Conference · 2007
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsRelayWireless sensor networkComputer networkComputer scienceNode (physics)Routing (electronic design automation)Key distribution in wireless sensor networksDistributed computingWirelessWireless networkTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Nodes in sensor networks are often prone to failure, particularly when deployed in hostile territories, where chances of damage/destruction are significantly higher. In many applications it is necessary to have some guarantees on the coverage, connectivity and lifetime of the sensor network. The network should also be able to adapt to single and/or multiple node failures as well as disruptions due to the inherent limitations of the wireless communication medium. In hierarchical sensor networks using relay nodes, sensor nodes are arranged in clusters and higher-powered relay nodes can be used as cluster heads. In this paper, we propose an integer linear program (ILP) for determining the minimum number of relay nodes, along with their locations and a suitable communication strategy such that the network is able to meet specified performance guarantees with respect to coverage, connectivity and lifetime. To the best of our knowledge, this is the first formulation that jointly optimizes energy-aware placement and routing of relay nodes in two-tiered sensor networks.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.726
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.016
GPT teacher head0.234
Teacher spread0.219 · 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