Energy-Efficient Aerial Data Aggregation for IoT: From Prototyping to Large-Scale Implementation
Bibliographic record
Abstract
The integration of unmanned aerial vehicles (UAVs) within the Internet of Things (IoT) framework has emerged as a compelling solution to address the energy constraints that impede the full realization of IoT potential. As IoT applications continue to proliferate across industries, the dependence on battery-powered devices poses challenges in terms of longevity, maintenance, and sustainability. UAVs have been widely promoted as efficient enablers for aerial data aggregation in IoT networks due to their high capability of approaching hard-to-reach areas. Recently, UAVs have been integrated with the wake-up radio (WuR) technology to further extend the lifetime of IoT networks. In this article, we explore the theoretical foundations, technical intricacies, and practical implications of the integration of UAVs and WuR technology, showcasing its capability to extend network coverage, prolong operational lifespans, and enhance data reliability. More specifically, this work proposes a comprehensive design and implementation of a UAV-enabled WuR and data collection (U-WuRIoT) system, with a focus on achieving extended wake-up range and low power consumption. Based on the obtained measurements, we compare the U-WuRIoT system against the traditional duty-cycling (DCY) scheme. The results highlight the efficiency of U-WuRIoT in overcoming the trade-off between energy consumption and data-collection reliability. Furthermore, we go beyond prototyping to test U-WuRIoT in large-scale deployments by developing a comprehensive analytical framework using stochastic geometry. Drawing insights from both analytical constructs and experimental validation, this work envisions a future where UAV-WuR synergy unleashes the full potential of IoT applications while surmounting the challenges of energy constraints.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".