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Record W2148876221 · doi:10.1109/jsen.2007.894913

SENORA: A P2P Service-Oriented Framework for Collaborative Multirobot Sensor Networks

2007· article· en· W2148876221 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

VenueIEEE Sensors Journal · 2007
Typearticle
Languageen
FieldEngineering
TopicRobotics and Automated Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsScalabilityMobile robotRobotDistributed computingComputer scienceFlexibility (engineering)Service (business)Fault toleranceArchitectureEmbedded systemService-oriented architectureEngineeringComputer architectureComputer networkArtificial intelligenceOperating systemWeb service

Abstract

fetched live from OpenAlex

SENORA is an open hardware and software architecture for the cooperative coordination of multiple heterogeneous mobile robots operating in a common environment. It is designed to meet the stringent requirements of modern loosely coupled multirobot architectures, such as flexibility, reliability, and fault tolerance. As such, the proposed architecture enables the robots to cope with the ubiquitous presence of various types of uncertainties in their operating environments. SENORA is a fully autonomous and scalable sensory-based peer-to-peer (P2P) framework. It also offers a real-time inter-robot communication protocol and it is based on the state-of-the-art P2P technology, which is specifically designed to satisfy the requirements of physical sensory data publishing and fusion. This architecture is implemented and evaluated on a team of indoor mobile robots. The test results manifest the architecture's distinguished features and capabilities

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: Empirical · Consensus signal: none
Teacher disagreement score0.708
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.258
Teacher spread0.242 · 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