An Energy-Aware SDN/NFV Architecture for the Internet of Things
Bibliographic record
Abstract
The Internet of Things (IoT) is an essential component of emerging network applications like smart cities, where billions of IoT devices are connected to transport massive network traffic. A programmable network such as softwaredefined networking (SDN) can cope with such data explosion and constrained network resources. Furthermore, Network Function Virtualization (NFV) can enable on-demand network functions deployment. Thus, SDN and NFV can complement each other to empower an architecture for the IoT. In this paper, we first design an SDN/NFV enabled IoT node for dynamic deployment of network functions. The proposed IoT node is then used to develop an SDN/NFV architecture to realize on-demand network functions like data aggregation. Next, we define an Integer Linear programming (ILP) problem to optimize the energy consumption of the IoT nodes by activating an optimal number of NFV nodes and optimally assigning regular nodes to those activated NFV nodes. Finally, we design a heuristic and evaluate it in the Cooja simulator. Extensive evaluation confirms that the proposed solution outperforms its counterparts in communication energy consumption and packet delivery ratio.
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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".