Collaborative Computing for Advanced Tactile Internet Human-to-Robot (H2R) Communications in Integrated FiWi Multirobot Infrastructures
Bibliographic record
Abstract
With the emergence of the Tactile Internet and advent of remote-controlled robots, proper task allocation among robots has attracted significant attention to enable robotic applications and services based on the human-to-robot communications paradigm. However, limited computing, energy, and storage resources of robots may hinder the successful launch of such applications. Task offloading to collaborative nodes is a promising approach to improve the task execution time and energy efficiency of robots. In this paper, we investigate a proper task allocation strategy by combining suitable host robot selection and computation task offloading onto collaborative nodes. We exploit conventional cloud, decentralized cloudlets, and neighboring robots as collaborative nodes for computation offloading in support of a host robot’s task execution. More specifically, our proposed task allocation policy selects a suitable robot based on several key parameters, including robot availability, remaining energy, and task execution time. Furthermore, our proposed computation offloading strategy examines the suitability of collaborative nodes in terms of task response time and energy consumption and then chooses an appropriate collaborative node to conduct the requested computation. We introduce an adaptive resource allocation model and develop an analytical framework to evaluate the task allocation delay, energy consumption, and task response time for noncollaborative and collaborative task execution scheme across integrated fiber-wireless multirobot networks. The results show that the proposed collaborative task execution scheme outperforms the noncollaborative scheme in terms of task response time and energy consumption efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".