Energy-aware Multicast, Multi-channel Supports For Over-The-Air Software Update in Component-based IoT Networks
Bibliographic record
Abstract
Over-The-Air Programming (OTA) is an emerging framework for updating IoT devices deployed at scale and in remote areas, mitigating the need for manual intervention post-deployment. Prior research focused mainly on optimizing the update order of software components in memory to achieve energy efficiency. So far, the literature has not yet considered the major amount of energy consumed by data transmission during the update process. In this paper, we propose using multicast, multi-radio, and multi-channel communications for supporting OTA updates in component-based IoT networks, in addition to an optimized memory replacement schedule for each IoT device. We formulate an optimization model named joint multicast multi-radio multi-channel software update (M3U), considering both the data dissemination process and software replacement in memory. This model minimizes the overall energy consumption across all devices during updates. Since M3U is a combinatorial optimization problem, which is NP-hard, we propose a linearization, a relaxation, and a heuristic algorithm to find a near-optimal solution with a much lower computational time. Our experimental results demonstrate that the proposed solution outperforms prior works and approximates the optimal solution (i.e., with only 2% difference).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".