Internet of Everything: Enabling Technologies, Applications, Security and Challenges
Bibliographic record
Abstract
Internet of Everything (IoE) connects billions of people, devices, objects and things to the Internet for autonomous services provisioning. IoE benefits from the value created by the compound impacts of connecting people, processes, things, and data. The interoperability among heterogeneous components facilitates seamless communication exchange for service provisioning in almost all the fields of life. Apart from providing immense benefits in autonomous services provisioning, it suffers from implementation issues, architectural considerations, and security and privacy vulnerabilities. There is a lack of surveys that comprehensively discuss all the aspects of IoE. Therefore, we design this survey to discuss key effective implementation of IoE. We discuss enabling technologies, architectural components, and expectations for an efficient realization of IoE. We discuss state-of-the-art use cases, synergies, and implementations of IoE that could be used as a road-map for and effective implementation of IoE-enabled systems. We highlight key security, privacy, and trust issues. We identify lethal attack scenarios and highlight countermeasures against these attacks. Finally, we discuss current challenges, their solutions, and future research directions. This survey provides key insights for implementing future IoE systems.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".