Joint Sparse Observation and Coding Design for Multiple Phenomena Monitoring
Bibliographic record
Abstract
Energy-efficient designs play an important role in the Internet of Things (IoT) that monitors multiple phenomena, due to the limited power supply and complicated observation. In this paper, taking into account the power consumptions of observation, coding, and communication, we propose a joint sparse observation and coding scheme for energy-efficient monitoring of multiple phenomena using IoT. Through the analysis of outage performance, we find that the sparse observation and coding scheme can achieve the performance of the full observation scheme in which all nodes observe all phenomena with lower power consumption due to the dynamic and selective observation and coding. With the derived achievable rates and network power consumption, we study the trade-off between achievable rates and network power consumption that is determined by both the observation matrix and the coding matrix. For given rate constraints, we propose an optimization problem to minimize the network power consumption by jointly designing the observation and coding matrices. To solve this NP-hard problem efficiently, we propose a low-complexity algorithm with the convex-concave procedure. Moreover, to improve performance in high noise environment, we adopt collaboration among nodes to suppress observation noises and equalize bad observations by utilizing observation diversity. Finally, simulation results illustrate the superior performance of the proposed schemes.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".