Using Deep Reinforcement Learning to Improve Sensor Selection in the Internet of Things
Bibliographic record
Abstract
We study the problem of handling timeliness and criticality trade-off when gathering data from multiple resources in complex environments. In IoT environments, where several sensors transmitting data packets - with various criticality and timeliness, the rate of data collection could be limited due to associated costs (e.g., bandwidth limitations and energy considerations). Besides, environment complexity regarding data generation could impose additional challenges to balance criticality and timeliness when gathering data. For instance, when data packets (either regarding criticality or timeliness) of two or more sensors are correlated, or there exists temporal dependency among sensors, incorporating such patterns can expose challenges to trivial policies for data gathering. Motivated by the success of the Asynchronous Advantage Actor-Critic (A3C) approach, we first mapped vanilla A3C into our problem to compare its performance in terms of criticality-weighted deadline miss ratio to the considered baselines in multiple scenarios. We observed degradation of the A3C performance in complex scenarios. Therefore, we modified the A3C network by embedding long short term memory (LSTM) to improve performance in cases that vanilla A3C could not capture repeating patterns in data streams. Simulation results show that the modified A3C reduces the criticality-weighted deadline miss ratio from 0.3 to 0.19.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".