Vertical Handover Decision for Mobile IoT Edge Gateway Using Multi-Criteria and Fuzzy Logic Techniques
Bibliographic record
Abstract
Internet of Things (IoT) is ubiquitous, including objects or devices communicating through heterogenous wireless networks. One of the major challenges in mobile IoT is an efficient vertical handover decision (VHD) technique between heterogenous networks for seamless connectivity with constrained resources. The conventional VHD approach is mainly based on received signal strength (RSS). The approach is inefficient for vertical handover, since it always selects the target network with the strongest signal without taking into consideration of factors such as quality of service (QoS), cost, delay, etc. In this paper, we present a hybrid approach by integrating the multi-cri- teria based VHD (MCVHD) technique and an algorithm based on fuzzy logic for efficient VHD among Wi-Fi, Radio and Satellite networks. The MCVHD provides a lightweight solution that aims to achieving seamless connectivity for mobile IoT Edge Gateway over a set of heterogeneous networks. The proposed solution is evaluated in real time using a testbed containing real IoT devices. Further, the testbed is integrated with lightweight and efficient software techniques, e.g., microservices, containers, broker, and Edge/Cloud techniques. The experimental results show that the proposed approach is suitable for an IoT environment and it outperforms the conventional RSS Quality based VHD by minimizing handover failures, unnecessary handovers, handover time and cost of service.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".