Clustering spatio-temporal bi-partite graphs for finding crowdsourcing communities in IoMT networks
Bibliographic record
Abstract
The Internet of Moving Things is rapidly becoming a reality where intelligent devices and infrastructures are fostering real-time data sustainability in smart cities and advancing crowdsourced tasks to improve energy consumption, waste management, and traffic operations. These intelligent devices create a complex network scenario in which they often move together or in conjunction with one another to complete crowdsourced tasks. Our research premise is that mobility relationships matter when performing these tasks, and therefore, a graph model based on representing the changes in mobility relationships is needed to help identify the neighbour devices that are moving close to one another in our physical world but also seamlessly connected in their virtual world. We propose a bi-partite community mobility graph model for linking intelligent devices in both virtual and physical worlds, as well as reaching a trade-off between crowdsourced tasks designed with explicit and implicit citizen participation. This paper aims to explore a bi-partite graph as a promising spatio-temporal representation of IoMT networks since changes in mobility relationships over time can indicate volunteer organisation at the device and community levels. The Louvain community detection method is proposed to find communities of intelligent devices to reveal a value conscious participation of citizens. The proposed bi-partite graph model is evaluated using a real-world scenario in transportation, confirming the main role of evolving communities in developing crowdsourcing IoMT networks.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".