A Self-Powered IoT Platform with Security Mechanisms for Smart Agriculture
Bibliographic record
Abstract
In response to escalating global challenges posed by climate change and resource scarcity, an innovative Internet of Things (IoT) framework has been developed which is specifically designed for smart agriculture applications.This work integrates a self-powered system with advanced security mechanisms to manage water resources effectively.System central are sensing node and an (ESP32+WIFI) base station, leveraging NRF24L01 technology for efficient data communication.The architecture of the platform is characterized by its integration of hardware components.which are facilitates seamless data collection from multiple sensing nodes.These nodes transmit information to a base station, where data consolidation occurs before secure transmission to a server via Wi-Fi.A key aspect of the framework is its emphasis on security (incorporating robust encryption, authentication) and access control strategies to mitigate risks, which associated with IoT deployments in agricultural system.Furthermore, the system's power management strategy is meticulously designed to enhancing energy efficiency and to extending the operational lifespan of the platform.This system combination (hardware and software elements) results in a reliable and secure IoT solution.Which it enabling real-time data acquisition, analysis, and decision-making processes for sustainable smart agriculture practices.This allencompassing strategy not only satisfies present agricultural demands, but also coincides with environmental aims.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".