Real-Time Decision Making with Edge AI Technologies: Advanced Techniques for Optimizing Performance, Scalability, and Low-Latency Processing in Distributed Computing Environments

Abstract

This paper explores the transformative potential of Edge AI technologies in enhancing real-time decision-making across various industries. Edge AI refers to the deployment of artificial intelligence algorithms on localized hardware devices, such as smartphones, IoT devices, and autonomous vehicles, enabling immediate data processing at the edge of the network. This approach mitigates latency, enhances privacy, and reduces bandwidth usage, addressing the limitations of traditional cloud-based AI models. The paper examines the evolution and core concepts of Edge AI, the benefits of reduced latency, enhanced data privacy and security, and scalability. It delves into the synergistic relationship between Edge AI and emerging technologies like 5G, while also considering ethical and privacy implications. By analyzing case studies and specific applications in sectors such as healthcare, manufacturing, and smart cities, the paper highlights the practical benefits and future trajectory of Edge AI. The research aims to provide comprehensive insights, equipping stakeholders with the knowledge necessary to leverage Edge AI effectively for real-time decision-making.