Phoenix: Transformative Reconfigurability for Edge IoT Devices in Small-Scale IoT Systems
Bibliographic record
Abstract
Transformative reconfigurability refers to the ability of changing the current software stack of a configurable device by fully replacing its existing one. In the context of IoT systems, such major device reconfigurations can be used to change the role, to adapt new functionality, and to keep reconfigurable IoT devices compatible with the IoT systems requirements as the ambient technology around them evolve, thus fostering a thriving and continuously-connected IoT environment. In this paper, we introduce Phoenix, an IoT device configuration management system that is designed to automate transformative reconfigurability for edge IoT devices at small scales. Edge IoT devices are typically computationally capable and configurable devices that have enough processing power to run user programs and control sensors and embedded devices in an IoT environment. Enabling transformative reconfigurability for such devices at small scales can increase IoT systems flexibility, efficiency, and adaptability in small IoT environments, for example, agri-farms, smart homes, micro grids, and the like. Phoenix manages the life cycle of edge IoT devices configuration and uses bare-metal provisioning to provide unattended installation of new software stacks that are defined by user intents that instruct the reconfiguration process. We implemented a Phoenix proof-of-concept system and deployed it on the SAVI testbed where we evaluated its performance in reconfiguring a variety of edge IoT devices under different network conditions. Our results indicate that Phoenix can meet the requirements of small-scale heterogeneous IoT systems in various application environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".