Complementing IoT Services Using Software Defined Information Centric Networks: A Comprehensive Survey
Bibliographic record
Abstract
IoT connects a large number of physical objects with the Internet that capture and exchange real-time information for service provisioning.Traditional network management schemes face challenges to manage vast amounts of network traffic generated by IoT services.Software-Defined Networking (SDN) and Information-Centric Networking (ICN) are two complementary technologies that could be integrated to solve the challenges of different aspects of IoT service provisioning.ICN offers a clean-slate design to accommodate continuously increasing network traffic by considering content as a network primitive.It provides a novel solution for information propagation and delivery for large-scale IoT services.On the other hand, SDN allocates overall network management responsibilities to the central controller, where network elements act merely as traffic forwarding components.An SDN-enabled network flexibly supports ICN without deploying ICN-capable hardware.Therefore, the integration of SDN and ICN provides benefits for large-scale IoT services.This paper provides a comprehensive survey on Software-Defined Information-Centric Internet of Things (SDIC-IoT) for IoT services provisioning.We present critical enabling technologies of SDIC-IoT, discuss its architecture, and elaborate its benefits for IoT services provisioning.We elaborate on key IoT service provisioning requirements and discuss how SDIC-IoT supports different aspects of IoT services.We develop different taxonomies of SDIC-IoT literature based on various performance parameters.Furthermore, an extensive discussion on different use cases, synergies, and advances is presented to envision the SDIC-IoT concept.Finally, we provide current challenges, causes, and future research directions of IoT services provisioning using SDIC-IoT. Index Terms-Internet of things,
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".