AoI-Aware Trajectory Planning for Smart Agriculture Using Proximal Policy Optimization
Bibliographic record
Abstract
In the realm of smart agriculture, the integration of Internet of Things Devices (IoTDs) for crop surveillance is pivotal for enhancing agricultural quality and output. However, the limited processing and transmission capabilities of these devices require alternative mechanisms for efficient data collection and transfer. Unmanned Aerial Vehicles (UAVs) emerge as a proficient solution, bridging the gap between IoTDs and more sophisticated systems, such as Multi-access Edge Computing (MEC) servers. Within this framework, the meticulous design of UAV flight paths is imperative to circumvent the processing of obsolete data. Such precision ensures timely access to vital data, for instance, in pest detection, and prevents erroneous actions such as unsuitable pesticide dispensation. This study delves into a UAVMEC-integrated system tailored for data acquisition, transfer, and processing from a multitude of IoTDs dispersed throughout a smart farm. The flight path of the UAV is traced by employing the Proximal Policy Optimization (PPO) approach, based on the data’s timeliness in the IoTDs, quantified by the average Age of Information (AoI). Furthermore, we analyze the potential advantages of this optimized UAV trajectory concerning its battery longevity. Simulations reveal that our model adeptly formulates an effective UAV trajectory, leading to a reduction in the average AoI and simultaneously conserving the UAV’s propulsion energy in designated scenarios.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".