A Comprehensive Survey on Edge Computing, Analytics, and AI Model Optimization for Industrial and Healthcare Applications
Siva Subramanian R, Beneta Mary A., R. Abitha, Sivaramakrishnan Rajendar, D. Mangaiyarkarasi, L. A. Anto Gracious, P. Girija · 2025
Real time data processing of data in intelligent analytics, decision making and AI model training and updating is an area where edge computing can be put to effective use. This chapter focuses on their deployment with machine intelligence, sensors, and networking in IIoT and smart health care particularly concentrating on edge computing. It explores deploying AI models at the edge level and how to overcome some areas of constraint computational resources such as memory devices. Approaches for incorporating the sensor and the fusion of data to improve efficiency in edge devices are presented. Central concepts such as decentralised, distributed, and distributed intelligence accentuate the collaborative model building; federal learning acknowledges the model training co-operation but with the data protection interior. This chapter also explores large-scale edge-cloud systems, security, and resources in hybrid models. Last, the chapter explores use cases in industry across verticals and technology trends such as AI, 5G and quantum computing that define the future of edge intelligence.