5G-Enabled Internet of Things: Latency Optimization through AI-Assisted Network Slicing
Annotatsiya
The confluence of Fifth-Generation (5G) wireless technology and the Internet of Things (IoT) heralds a new era of hyper-connectivity, enabling transformative applications from autonomous vehicles and industrial automation to extended reality and remote surgery. A critical performance indicator for many of these applications is ultra-reliable low-latency communication (URLLC), where delays must be bounded within milliseconds. However, the heterogeneous and dynamic nature of IoT traffic presents a monumental challenge to consistently meeting these stringent latency requirements. Traditional network management paradigms, which are largely static and reactive, are ill-suited for this task.This paper posits that the synergy of two cornerstone 5G technologies—Network Slicing and Artificial Intelligence (AI)—provides the foundational architecture and intelligent control mechanism necessary to achieve dynamic latency optimization at scale. Network Slicing allows for the creation of multiple logical, end-to-end virtual networks on a shared physical infrastructure, each tailored to specific service requirements. Meanwhile, AI and Machine Learning (ML) offer the predictive and adaptive capabilities to manage these slices proactively.This comprehensive review and analytical paper delves into the architecture of 5G-standalone (SA) systems to elucidate the enablers of low latency. It then provides a detailed exposition of network slicing as a resource isolation mechanism. The core of the paper is a thorough investigation into how various AI/ML paradigms—including supervised learning, reinforcement learning, and deep learning—can be integrated into the network control loop to predict traffic surges, dynamically allocate resources, and proactively reconfigure slices. We present a conceptual framework for an AI-assisted Network Slicing orchestration system, detailing its functional components and operational workflow. Furthermore, we analyze the significant challenges impeding widespread deployment, such as data collection, model training, security, and standardization. Through this analysis, we demonstrate that AI-assisted network slicing is not merely an enhancement but a critical imperative for realizing the full potential of latency-critical 5G-IoT ecosystems.