Published Paper Details:

In this research paper, a detailed overview of Edge Artificial Intelligence (Edge AI) is given in which processing capabilities of AI are embedded into edge devices near the sources of data generation. This paper discusses the history, processing models, and implementations of Edge AI in a variety of industries and how it can support real-time and low-latency decision-making as well as minimize bandwidth reliance and improve data privacy. The paper also explores resource limitation, security vulnerability and scalable issue as some of the major challenges that limit the implementation of Edge AI at present. Moreover, the future research directions are outlined, such as improved specialized hardware, lightweight, and adaptable AI models, federated learning, strong security frameworks, and edge-cloud infrastructure coordination. It will be used to guide continued innovation and deployment of Edge AI technologies to develop smarter, more efficient, and privacy-vaulting intelligent systems, and revolutionize industries and make AI-driven applications ubiquitous.