Collaboration & Mobile Cloud-Computing: Using CoAP to Enable Resource-Sharing between Clouds of Mobile Devices
Bibliographic record
Abstract
As the number of mobile devices per user increases, the need to connect/combine them grows. Current approaches focus on the use of cloud-hosted backend services which allow file and app-state synchronization but fail in providing true resource sharing among mobile devices. To enable true resource/service sharing, the mobile devices of a single user should be combined into a cloud of cooperating mobile devices. Instead of accessing the resources/services of an individual device, a user should be able to seamlessly access the combined resources/services of his/her device cloud. Enabling seamless access to the resources/services hosted on different mobile devices is therefore a key challenge. Exposing the resources/services of each mobile devices within the user's device cloud via Restful micro-services, is one possible approach. This paper focusses on the use of the IoT protocol CoAP as an application layer protocol. To minimize the energy costs of communication, it was necessary to replace CoAP's standard transport protocol (UDP) with BLE 4.1. This paper presents the performance of the CoAP protocol using BLE 4.1 on Android Lollipop.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".