Efficient Allocation of Resource-Intensive Mobile Cyber–Physical Social System Applications on a Heterogeneous Mobile Ad Hoc Cloud
Bibliographic record
Abstract
Mobile ad hoc cloud (MAC) is one of the key enabling technologies for realizing mobile cyber-physical–social systems (MCPSSs). A MAC is a distributed computing infrastructure that enables mobile devices to share computing resources in an ad hoc environment. Resource allocation is one of the key components of MAC and plays a vital role in system and application performance. Existing resource allocation schemes are designed to utilize single wireless communication technology (WCT) or rely on an eclectic system that exclusively selects single communication technology. Moreover, these schemes do not consider link lifetime, which significantly affects application performance. Consequently, these schemes cannot satisfy low latency and high data rate requirements of emerging resource-intensive MCPSS applications, such as merged reality-based multiplayer games. Thus, this work proposes a new resource allocation scheme that simultaneously uses multiple WCTs and considers link lifetime during the resource allocation process. This study also proposes a Markov chain-based link lifetime prediction mechanism. In comparison with existing mechanisms, the proposed link lifetime prediction mechanism considers the history of a user's visited locations and time spent at each location. The performance of the proposed scheme is evaluated in a wide range of network and application scenarios using Network Simulator 3.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".