iCache: An Intelligent Caching Scheme for Dynamic Network Environments in ICN-Based IoT Networks
Bibliographic record
Abstract
Advanced network technologies and ubiquitous connected devices are boosting the development of the Internet of Things (IoT) at an unprecedented pace. However, as most of the connected IoT devices are battery powered, the energy consumption issue has become the bottleneck of the IoT’s development. Caching is a promising approach to reducing the energy consumption of the battery-powered devices since the requested data packets can be retrieved from intermediate nodes in the network, e.g., routers, instead of from the remote battery-powered IoT devices, which allows the IoT devices to spend more time in the sleep mode. To realize in-network caching and overcome the IP-based networks’ inefficiency support for IoT, building IoT over information-centric networking (ICN) is a promising approach advocated by researchers. However, existing works in this area assume the network environments are static, which hinders the development of existing approaches in the real dynamic network environments. In this article, we leverage the deep$Q$-networks (DQNs) to propose an intelligent caching scheme (named as iCache) that can automatically adjust the caching nodes’ caching parameters to make caching decisions for the dynamic network environments. Extensive evaluations were conducted and the results show that the proposed iCache outperforms the existing approaches in terms of the total energy consumption (e.g., more than 29% reduction compared to the caching transient data (CTD) caching scheme) and the average number of hops (e.g., more than 20% reduction compared to the CTD caching scheme).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".