An Energy-Efficient SDN Controller Architecture for IoT Networks With Blockchain-Based Security
Bibliographic record
Abstract
Internet of Things (IoT) is a disruptive technology in many aspects of our society, ranging from communications to financial transactions to national security (e.g., Internet of Battlefield / Military Things), and so on. There are long-standing challenges in IoT, such as security, comparability, energy consumption, and heterogeneity of devices. Security and energy aspects play important roles in data transmission across IoT and edge networks, due to limited energy and computing (e.g., processing and storage) resources of networked devices. Whether malicious or accidental, interference with data in an IoT network potentially has real-world consequences. In this article, we explore the potential of integrating blockchain and software-defined networking (SDN) in mitigating some of the challenges. Specifically, we propose a secure and energy-efficient blockchain-enabled architecture of SDN controllers for IoT networks using a cluster structure with a new routing protocol. The architecture uses public and private blockchains for Peer to Peer (P2P) communication between IoT devices and SDN controllers, which eliminates Proof-of-Work (POW), as well as using an efficient authentication method with the distributed trust, making the blockchain suitable for resource-constrained IoT devices. The experimental results indicate that the routing protocol based on the cluster structure has higher throughput, lower delay, and lower energy consumption than EESCFD, SMSN, AODV, AOMDV, and DSDV routing protocols. In other words, our proposed architecture is demonstrated to outperform classic blockchain.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".