A Survey on Beyond 5G Network Slicing for Smart Cities Applications
Bibliographic record
Abstract
Beyond fifth generation (B5G) is expected to tremendously improve network capabilities by using a higher frequency band compared to 5G, capable of delivering higher network capacity with much lower latency. It is expected that there will be around 30 billion connected objects by 2030, approximately 3.5 times the population then which underscores the pressing need for advanced network capabilities to support diverse applications ranging from smart transportation and energy management to healthcare and public safety. Network slicing enables sharing of network resources by transforming the physical network into logically independent networks, each specifically tailored to meet the requirements of heterogeneous services (e.g., Internet of Things applications, gaming services, holographic communication). Each slice is an end-to-end logical network comprising network, compute, and storage resources. Softwarization and virtualization are the main drivers for innovation in B5G, enabling network developers and operators to develop network-aware applications to match customer demands. Smart cities vertical offers unique service characteristics, performance requirements, and technical challenges in B5G network slicing. Therefore, this paper provides a comprehensive survey on B5G network slicing use cases, synergies, practical implementations and applications based on their quality of service parameters for smart cities applications. The paper gives a detailed taxonomy of the B5G network slicing framework requirements, design, dynamic intra-slice and inter-slice resource allocation techniques, management and orchestration, artificial intelligence/machine learning-empowered network slicing designs, implementation testbeds, 3GPP specifications and projects/standards for B5G network slicing. Furthermore, the paper provides a thorough discussion on the technical challenges that can arise when implementing B5G network slicing for smart cities applications and offers potential solutions. Finally, the paper discusses B5G network slicing current and future research directions for smart cities applications.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".