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Title Edge Computing Revolution: Unleashing Artificial Intelligence Potential In The World Of Edge Intelligence
ID_Doc 21762
Authors Chandrasekaran S.; Athinarayanan S.; Masthan M.; Kakkar A.; Bhatnagar P.; Samad A.
Year 2025
Published Edge of Intelligence: Exploring the Frontiers of AI at the Edge
DOI http://dx.doi.org/10.1002/9781394314409.ch6
Abstract The emergence of Edge Intelligence (EI) is causing a revolution in the field of data processing. By incorporating potent Artificial Intelligence (AI) capabilities right at the network's edge, where data originates from devices and sensors, this approach goes beyond the constraints of conventional edge computing. Through this convergence, the latency and bandwidth limitations that come with only cloud-centric AI are overcome, enabling real-time analysis and decision-making. The following are three ways that edge intelligence encourages a paradigm shift: First, by using localized processing, it enables real-time data analysis and prompt responses. Second, latency is significantly decreased by shifting large-scale data transfers to centralized cloud resources. Third, by lessening the load on centralized systems, edge intelligence's distributed design increases infrastructure resilience and scalability and paves the way for exciting new advancements. Imagine autonomous automobiles making decisions almost instantaneously based on real-time sensor data, or smart cities utilizing edge-based analysis to improve traffic flow. The difficulty lies in the fact that robust security measures are required to safeguard data processed at the network's edge. Developing efficient AI algorithms for resource-constrained edge devices is also essential. By addressing these problems and opening the door for intelligent, nearly instantaneous decision-making at the network's edge, edge intelligence has the potential to revolutionize a wide range of sectors. While Edge Computing (EC) addressed this problem by bringing processing closer to data sources, the integration of AI at the network edge heralds in the Edge Intelligence era. This creative approach makes use of edge devices' AI capabilities directly to allow functionality that goes beyond data processing. EI makes real-time learning, decision-making, and on-device inferencing possible at the edge of the network. Significant benefits from this integration include decreased latency, increased efficiency, and the empowerment of intelligent devices. This article addresses the need for a strong infrastructure to manage artificial intelligence (EI) and integrates cutting-edge AI models to optimize Edge Devices (ED). Motivated by the remarkable outcomes of artificial intelligence (AI) in several domains, researchers at EC are progressively delving into its possibilities, with a particular emphasis on Machine Learning (ML), a subset of AI that has experienced substantial expansion in the past few years. This page explores EC as well, including a formal description and a summary of the reasons why it is a preferred computing paradigm. Next, we look at the main issues that EC has addressed and assess the shortcomings of conventional methods. This article seeks to act as a springboard for finding new research topics that take advantage of the synergistic link between AI and EC by exhibiting research on using AI to optimize EC and applying AI inside the EC framework for additional areas. © 2025 Scrivener Publishing LLC. All rights reserved.
Author Keywords Artificial intelligence; Cloud computing; Deep learning; Edge computing; Edge intelligence; Fog computing; Machine learning; Optimization


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