A Multi Task Allocation Based Time Optimization Framework Using Social Networks in Mobile Crowd Sensing
Bibliographic record
Abstract
The quality of data and its sensing cost is the important concern for task allocation in crowd sensing. The sensing capabilities of a device to send the collection of sensor data to a cloud requires crowd sensing in order to receive reliable data. Crowd sensing is used in many areas such as traffic monitoring, smart cities, health care, transportations, environmental monitoring and many more. Most of existing works are only based on assumptions in task scheduling about the number of candidate users and are mainly performed optimization of single task allocation. If the candidate users are few, then the completion of task with in the schedule can be difficult for many sensor applications. In this work, we proposed a social network-based task allocation scheme for the optimization of multi task allocation. The main idea of the proposed work is to maximize the task completion within the allocated schedule. It is evident that the task scheduling algorithms are NP-hard and we introduced a decreasing threshold task allocation (DTT) and fast greedy selections (FGS) algorithms along with Crow COOT Foraging Optimization (CCFO) algorithm to allocate the tasks parallelly with maximum efficiency. The proposed algorithms such as C-DTT (CCFO-DTT) and C-FGS (CCFO-FGS_ are used for the efficient allocation of tasks. The combination of these algorithms can be helpful in selecting the candidate users who will perform the completion of maximum tasks. Due to the selection of proper users in each round, the time consumption of the tasks to be completed is greatly reduced. The experimental results also indicates that the proposed work performs well in the optimization of multi task allocation than the other state of the art models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".