An SDN approach to route massive data flows of sensor networks
Bibliographic record
Abstract
Summary With the advent of the Internet of Things (IoT), more and more devices can establish a connection with local area networks and use routing protocols to forward all information to the sink. But these devices may not have enough resources to execute a complex routing protocol or to memorize all information about the network. With proactive routing protocols, each node calculates the best path, and it needs enough resources to memorize the network topology. With reactive routing protocols, each node has to broadcast the message to learn the right path that the packets must follow. In all cases, in large networks such as IoT, this is not an appropriate mechanism. This paper presents a new software‐defined network (SDN)–based network architecture to optimize the resource consumption of each IoT object while securing the exchange of messages between the embedded devices. In this architecture, the controller is in charge of all decisions, and objects only exchange messages and forward packets among themselves. In the case of large networks, the network is organized into clusters. Our proposed network architectures are tested with 1000 things grouped in five clusters and managed by one SDN controller. The tests using OpenDayLight and IoT embedded applications have been implemented on several scenarios providing the ability and the scalability from dynamic reorganization of the end‐devices. This approach explores the network performance issues using a virtualized SDN‐clustered environment which contributes to a new model for future network architectures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".