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Record W2172604031 · doi:10.1504/ijwmc.2015.070947

Mobile services provisioning and sensor data sharing

2015· article· en· W2172604031 on OpenAlex
Richard K. Lomotey, Ralph Deters

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 Wireless and Mobile Computing · 2015
Typearticle
Languageen
FieldComputer Science
TopicPeer-to-Peer Network Technologies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceComputer networkProvisioningLatency (audio)Mobile computingMobile deviceDistributed computingComputer securityTelecommunicationsWorld Wide Web

Abstract

fetched live from OpenAlex

The interest in the Internet of Things (IoT) and cyber-physical devices by consumers creates the need to study the prospects of mobile service hosting to enable integration of sensor devices with mobile technologies. This new ecosystem can boost the dissemination of timely information in a mobile P2P environment. The challenge however is that, in group sharing scenarios, a mobile node can become burdened during peak-loads which can cause intolerable message response latency, unresponsiveness, and failures. This paper proposes a mobile hosting architecture to enable mobile-to-mobile communication following the edge-based technique. Hence, instead of a single mobile serving as a centralised provider hub, the communication is distributed across nearby and available provider nodes. The request distribution is developed following the sequential, parallelism, loop, and choice services flow patterns. Preliminary testing of the flow patterns focused on latency reduction by tracing (a) the optimal path, (b) request re-assignment, and (c) fault-tolerance.

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: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.502

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 communication0.0010.001
Open science0.0020.004
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 head0.039
GPT teacher head0.317
Teacher spread0.278 · 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