A system-level methodology for the design and deployment of reliable low-power wireless sensor networks
Bibliographic record
Abstract
Innovative Internet of Things (IoT) applications with strict performance and energy consumption requirements and where the agile collection of data is paramount are rousing. Wireless sensor networks (WSN) represent a promising solution as they can be easily deployed to sense, process, and forward data. The large number of Sensor Nodes (SNs) composing a WSN are expected to be autonomous, with a node's lifetime dictated by the battery's size. As the form factor of the SN is critical in various use cases such as industrial and building automation, minimizing energy consumption while ensuring availability becomes a priority. Moreover, energy harvesting techniques are increasingly considered as a viable solution for building an entirely green SN and prolonging its lifetime. In the process of building a SN and in the absence of a clear and well-rounded methodology, the designer can easily make unfounded decisions about the right hardware components, their configuration and data reliable data communication techniques such as automatic repeat request (ARQ) and forward error correction (FEC). In this thesis, a methodology to better optimize the design, configuration and deployment of reliable ultra-low power WSNs is proposed. Comprehensive and realistic energy and path-loss (PL) models of the sensor node are also established. Through estimations and measurements, it is shown that following the proposed methodology, the designer can thoroughly explore the design space and make most favorable decisions when choosing commercial off-the-shelf (COTS) components, configuring the node, and deploying a reliable and energy-efficient WSN.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".