Efficient Wake-Up Strategy: UAV-Enabled Opportunistic Sensing in IoT Networks
Bibliographic record
Abstract
This paper proposes an unmanned aerial vehicle (UAV)-enabled wake-up radio (WuR) and data collection strategy for an Internet of Things (IoT) network. The considered model involves employing UAVs to awaken IoT devices from an ultralow power sleep mode through the transmission of WuR signals. Diverging from traditional methods where all IoT devices engage in data collection post-wake-up, the proposed framework centers on a more streamlined strategy. In the system model, a single UAV continuously traverses a specific region without hovering for the sake of waking-up nearby IoT devices. Specifically, solely IoT devices identifying valuable or unusual information transmit data back to the UAV upon a successful wake-up, which we refer to as opportunistic sensing. Hence, for reliable communication, it becomes important to determine the optimal height and velocity of the UAV while traversing the IoT region so as to maximize the probability that the UAV successfully wakes-up enough IoT devices to cover the field, followed by successful data collection of valuable sensed information. To this end, the paper proposes a low-complexity, fast in convergence, simple to implement efficient method to solve the non-convex optimization problem. The proposed algorithm relies on multidimensional bisection method, and is specifically tailored to determine the UAV's velocity and height, along with optimizing the device density. The numerical results in the paper validate the superior performance of the proposed algorithm when compared to conventional baselines.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".