A Comprehensive Survey of Machine Learning Techniques in Next-Generation Wireless Networks and the Internet of Things
Bibliographic record
Abstract
The advent of next-generation wireless networks and the Internet of Things (IoT) has introduced numerous challenges in terms of quality of service (QoS), user data rates, throughput, and security.These challenges necessitate innovative solutions to optimize performance and ensure robust security.Machine Learning (ML) has emerged as an influential tool in this regard, offering the potential to fully harness the capabilities of nextgeneration wireless networks and the IoT.With an ever-increasing number of connected devices and the commensurate data proliferation, ML presents an effective means of analyzing and processing this data.One significant challenge addressed by ML is network optimization.Through the analysis of network traffic patterns, congestion points are identified, and potential network performance issues are predicted.Security, a critical concern in next-generation wireless networks and the IoT, is another facet where ML proves instrumental by detecting and mitigating security breaches.This is achieved by analyzing data to identify anomalous behaviour and potential threats.Moreover, ML facilitates informed decision-making in IoT systems.By scrutinizing real-time data generated by IoT devices, ML algorithms reveal valuable insights, trends, and correlations.This capability enables IoT-enabled systems to make data-driven decisions, thus enhancing the efficiency of various applications such as smart cities, industrial automation, healthcare, and environmental monitoring.This study undertakes a systematic review of the impact of ML techniques, such as reinforcement learning, deep learning, transfer learning, and federated learning, on next-generation wireless networks, placing a particular emphasis on the IoT.The literature is reviewed systematically and studies are categorized based on their implications.The aim is to highlight potential challenges and opportunities, providing a roadmap for researchers and scholars to explore new approaches, overcome challenges, and leverage potential opportunities in the future.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".