FPGA based wireless sensor node with customizable event-driven architecture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Abstract This article presents the design and implementation of modular customizable event-driven architecture with parallel execution capability for the first time with wireless sensor nodes using stand alone FPGA. This customizable event-driven architecture is based on modular generic event dispatchers and autonomous event handlers, which will help WSN application developers to quickly develop their applications by adding the required number of event dispatchers and event handlers as per the need of a WSN application. This architecture can handle multiple events in parallel, including high priority ones. Additionally, it provides non-preemptive operation which removes the timing uncertainty and overhead involved with interrupt-driven processor-based sensor node implementation, which is required in real-time wireless sensor networks (WSNs). Thus, higher computation power of FPGAs combined with the non-preemptive modular event-driven architecture with parallel execution capability enables a variety of new WSN applications and facilitates rapid prototyping of WSN applications. In this article, the performance of FPGA-based sensor device is compared with general purpose processor-based implementations of sensor devices. Results show that our FPGA-based implementation provides significant improvement in system efficiency measured in terms of clock cycle counts required for typical sensor network tasks such as packet transmission, relay and reception.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it