Energy Harvesting WSNs with Adaptive Modulation: Inter-delivery-aware Scheduling Algorithms
Bibliographic record
Abstract
In this paper, we deal with the regularity of status updates in a monitoring system. Specifically, we consider a system consisting of independent energy harvesting nodes with adaptive modulation capabilities that transmit status updates to a non energy harvesting sink over a fading channel. Due to the randomness of the energy arrival and the channel time variations, a node may have difficulties maintaining regular status updates. Hence, the objective of this work is to design scheduling algorithms that minimize the number of violations of inter-delivery time over a finite time horizon. An inter-delivery violation event occurs when the time duration between two consecutive status updates exceeds a given time limit. We focus on online modulation and power adaptation policies where the transmitting sensor node adjusts the M-ary modulation level and transmission power based on both the channel state and the battery level. Specifically, we propose both deterministic and randomized algorithms to efficiently solve the considered scheduling problem for the onenode system. Deterministic solutions were extended to the multi-node system. The numerical results show that the proposed algorithms realize significant gain in terms of violations events compared to the benchmark fixed modulation solutions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".