Internet of Things and Heterogeneous Networks Technologies: Concepts, Challenges and Perspectives
Bibliographic record
Abstract
The IoT network is used to provide an object with connectivity to the Internet to allow the feedback of information. The IoT has the ambition to make objects interact with each other and with people. It promises to be the engine of great transformations in the lives of individuals by democratizing new uses and services in the mobility sector. This is why it is compared to the internet of the future. The global size of the IoT, the huge number of Things in the IoT, the changing topology of the IoT, and the resource constraints make routing in the IoT difficult. Routing is a necessary process in the Internet of Things because it enables the interchange of data between Things by quickly guiding and reliably delivering data through the network from its origins to its destinations. The Internet of Things is enabled by key pillars: radio frequency identification (RFID) and wireless sensor networks (WSNs) (IoT), NFC (Near Field Communication), BLE (Bluetooth Low-Energy technology), LoRa and Sigfox. This paper examines these different technologies as well as the barriers and challenges that must be solved.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".