Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Uppal, Khushi; Vidushi; Shoran, Preety Title A name given to the resource AI Meets the Edge: Optimizing Computation Through Intelligent Offloading Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Studies in Computational Intelligence;Volume;1267;pp.149-161 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-16711-8_7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-16711-8_7</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038040683?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038040683?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Uppal K., Christ University, Bengaluru, India; Vidushi, Christ University, Bengaluru, India; Shoran P., Christ University, Bengaluru, India Description An account of the resource The chapter looks into the developing bond between artificial intelligence (AI) and edge computing. In particular, the idea of using AI to intelligently offload computations. As the number of latency-sensitive applications have increased and the use cases for smart devices has widened, resource allocation at the edge has become critical. We discuss AI-based methods that intelligently determine what and when to transfer compute-intense tasks from resource-constrained edge devices to nearby edge servers or cloud environments. Pragmatic methods, RL optimization procedures and ML research exercises are the main focus of the standardized testing. To illustrate real-world examples, smart cities, autonomous vehicles, and industrial IoT are further explored. This chapter focuses on the development of a new hybrid offloading framework, synthesizing some of the greatest qualities of the predictive analytic and real-time learning to put into practice. These challenges including device heterogeneity, network variability, privacy, etc., are elaborated. Finally, in the concluding chapter, we argue the need for open problems that inform the path toward a sustainable, secure, AI-enabled edge computing. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Artificial intelligence; Computation offloading; Edge computing; IoT; Reinforcement learning; Resource optimization; Smart cities Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 1860949X; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online