Bibliographic record
Abstract
Internet of Things (IoT) is ubiquitous, which includes objects communicating through heterogenous networks.One of the challenges in mobile IoT is vertical handover decision (VHD) between heterogenous networks for seamless connectivity.Conventional VHD approach is based on received signal strength (RSS), which is limited to only RSS Quality and hence inefficient to decide best network for vertical handover.This thesis proposes multi-criteria based VHD (MCVHD) algorithm for efficient VHD between Wi-Fi, Radio and Satellite network.The experiment results show that MCVHD outperforms the conventional RSS Quality based VHD by minimizing handover failures, unnecessary handovers, handover time and cost of service by selecting best available network using multi-criteria parameters.The proposed IoT solution also employs Docker container based microservices architecture.The results show that the Docker container produces negligible resource overhead and can be used on resource constrained IoT devices like Raspberry Pi 3, for efficiently managing IoT application and services.TinyOS nesC 1 Yes Partial Yes Contiki C 2 Yes Yes Yes LiteOS C 4 Yes Yes Yes Riot OS C/C++ 1.5 No Yes Yes Android Java _ Yes Yes Yes 2.1.2IoT Common Standards World Wide Web Consortium (W3C), Internet Engineering Task 17 Force (IETF), EPCglobal, Institute of Electrical and Electronics Engineers (IEEE) and the European Telecommunications Standards Institute are some of the groups responsible to provide IoT protocols and standardization for the programmers and the service providers.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".