Design and prototyping of low‐power wide area networks for critical infrastructure monitoring
Bibliographic record
Abstract
Low‐energy critical infrastructure monitoring (LECIM) networks is essential for the monitoring of infrastructure facilities in smart cities. One critical requirement of an LECIM network is its wide coverage of up to several kilometres by using a star topology instead of the tree or mesh networks. In meeting this requirement, this study develops a system with a transceiver of extremely high receiver sensitivity based on the IEEE 802.15.4k physical layer specifications. To reduce the energy consumption, the modulation schemes suitable for low complexity detection are chosen for the data transmission in the design. Also, an efficient parallel preamble and payload data detection are adopted at the access point of the proposed LECIM to acquire concurrent packets from respective nodes. Meanwhile, a data‐aided dynamic timing adjustment scheme is proposed for data field detection to rapidly and adaptively synchronise to the long duration of data packet. Furthermore, a testbed is implemented using a software‐defined radio to demonstrate the effectiveness of the proposed system design.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".