Bibliographic record
Abstract
The Internet of Things (IoT) has enabled unprecedented interactions with our physical world, with the aim to deliver a wide range of customizable services in many domains. With recent advancements in IoT technology, users are increasingly expecting these services to be intelligent and context aware. Nevertheless, there is still no framework capable of delivering personalized IoT services on a large scale. For such a framework to be conceived, it is likely that technologies from many domains have to be utilized. This paper examines the readiness of the leading state-of-the-art technologies in several key fields for realizing the goal of a truly scalable and personalized IoT experience. We discuss the important requirements and challenges for realizing this goal. Then, we identify the major approaches that can contribute to this goal and categorize them into: technologies for adaptive personalized sensing, scalable solutions for user-centric networking, and intelligence techniques that leverage context awareness and adaptability at the application and system levels. In the first category, our discussion centers around virtualization and reprogrammability at the sensing layer. In the second category, we investigate the readiness of Fog computing and information-centric networking to develop scalable personalized IoT infrastructures. These approaches were chosen for their combined ability to match dynamic user requirements with available system resources, while guaranteeing overall efficient utilization. Finally, in the third category, we examine context awareness, reasoning, and machine learning techniques as well as semantic technologies for realizing proactive and adaptive intelligent IoT systems and applications. This paper offers a focused discussion of the key topics that drive the research in the important and timely topic of scalable and personalized IoT networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".