| Abstract |
The 6G wireless technology is emerging as a boom and gives a huge expectation in the wireless environment by resolving the problems like latency, data-rate, QOS and system capacity of 5G technology. A transition from “linked things” to “linked intelligence” is expected in the future. The end result would be an ultra-smart, self-sustaining network that could provide cutting-edge, self-managed services. This technology is 1000 times faster than the 5G technology. Edge computing is a distributive form of computing and it provides high security, smart applications, efficient bandwidth usage and high response time. The 6G network's infrastructure will enable various types of AI data analytics, comprising diagnostic, descriptive, prescriptive, and predictive analytic approaches. In recent times, with the emergence of ubiquitous computing and the Internet of Things (IoT), innumerable gadgets have been linked to the web, producing tremendous amounts of raw information at the network end. Due to this development, it is crucial to expand the boundaries of artificial intelligence to the network edge in order to effectively utilize the advantages of edge data. Edge computing, a recently created architecture that moves computing activities and services from the network core to the network edge, may be able to meet this need. Edge AI is a method for running AI operations close to users at the network's edge. In order to enable resource allocation that is service-driven in the 6G network, AI offers a new paradigm for optimisation algorithms. New integrative technology must be created in order to support the smooth deployment of such programmes and to fulfill their rigorous standards. The algorithms of edge AI and 6G are to be codesigned or combined to get a communication efficient training system. The 6G inspired Edge AI seamlessly integrates sensing,communication and intelligence for intelligent applications such as Automatic driving, Industrial IOT, smart healthcare,smart city etc. The deep edge node architecture blends artificial intelligence (AI), network capabilities, and edge computing. The 6G edge network's edge AI serves as a solution for gathering data, analyzing it, transmission, and reception. The different possibilities of 6G inspired edge AI enhanced the data security, Personalized user experience, reduced latency and bandwidth. With developments in the Metaverse, Deep Learning, security, automation, and breakthroughs across a massive automated industry, edge AI is predicted to continue its stratospheric growth. These methods obtain profound insights into the network and accurately forecast a wide range of parameters to carry out numerous tasks. They are capable of carrying out a number of functions, including information recognition, resource management, and traffic classification. To enhance networks’ adaptability, they automatically perform network rerouting, control of congestion, and Quality of Experience (QOE) improvement. In the end, 6G networks will consist of pervasive AI solutions at both the network hub and edge devices. The possibilities and opportunities in 6 G inspired Edge artificial intelligence (AI) provide a variety of growth prospects as a field that is continually developing. The field of artificial intelligence (AI) offers a variety of opportunities such as consultant, researcher, practitioner, machine learning engineer, computer vision engineer,or even to develop personalized AI services. This article mainly concentrates on possibilities and opportunities of Edge AI in 6G. © 2025 |
