Energy Efficient Software Update Mechanism for Networked IoT Devices
Bibliographic record
Abstract
Due to security issues and incremental user requirements, software in IoT devices needs to be changed frequently. Recently, advanced IoT devices employ the component-based software architecture in which components can be updated at run-time. In such IoT networks, devices can download updated components from neighbor nodes, enabling quick deployment of updates in the entire network. A key operation which consumes a significant amount of energy in the update process is flash re-writing, in which the order of re-writing components into the memory is decisive for energy consumption. In this paper, we propose a mechanism that schedules updates on all devices in an IoT network to minimize the energy consumption, taking into account the deadline constraint for updating the entire network. We introduce a novel energy model of the update process, then propose an algorithm to approximate the optimal schedule for updating all devices in the network. Simulation results show that our algorithm can obtain a near optimal which is, on average, 7.1% different from the global minimum.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".