Communication, Computation, and Caching Resource Sharing for the Internet of Things
Bibliographic record
Abstract
The Internet of Things connects a large number of smart mobile devices with the Internet, where these devices are embedded with often limited communication, computation, and caching resources. To address the heterogeneity of these devices and achieve efficient overall system resource utilization, researchers have proposed various device-to-device resource sharing models, enabling mobile devices to form device-todevice connections and to share their resources for cooperative task execution. Most of these existing works, however, considered scenarios where mobile devices can share one or two types of resources, and hence inadequately explore the potential of resource sharing among mobile devices. In this article, we introduce a general framework where mobile devices can share any combination of the three types of resources, and it can generalize many existing deviceto- device resource sharing models. In addition, it can achieve more efficient resource allocation by offering mobile devices more flexibility in terms of resource sharing. Based on the proposed framework, we focus on discussing two issues: the optimization issue, regarding how to schedule resources among devices; and the economic issue, regarding how to motivate the device owners to share their resources. We introduce the challenges and potential solutions to these two issues. We further outline several open issues and future directions for the proposed general resource sharing framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".