ItemBook Chapter

AI-Driven Real-Time Decision Making at the Edge: Overcoming Latency, Bandwidth, and Scalability Challenges for Smarter Data-Intensive Applications in Healthcare, Manufacturing, and Smart Cities

Title
AI-Driven Real-Time Decision Making at the Edge: Overcoming Latency, Bandwidth, and Scalability Challenges for Smarter Data-Intensive Applications in Healthcare, Manufacturing, and Smart Cities
Creator
Bajpai, Annanya; Srivastava, Shilpa
Description
Aim and Purpose: This chapter explores the crucial role of artificial intelligence (AI) in enabling real-time decision-making at the edge, particularly within data-intensive applications. It aims to identify and address fundamental challengessuch as latency, limited bandwidth, and scalabilitythat frequently hinder the efficient deployment of AI models near data sources. The objective is to propose a coherent and implementable framework to mitigate these obstacles, thereby facilitating the development of intelligent, responsive systems. The chapter emphasizes the transformative potential of edge AI across three key sectors: healthcare, manufacturing, and smart cities, illustrating how localized intelligence can enhance performance, efficiency, and autonomy in time-sensitive environments. Methodology: We adopt a comprehensive methodological approach that includes studying optimization techniques such as model compression, quantization, and distributed inference. Special attention is given to federated learning, which supports collaborative training without the need to transfer raw dataenhancing both privacy and scalability. The examination of edge-optimized hardware accelerators (e.g., NPUs, FPGAs) and streamlined software frameworks will highlight their role in overcoming processing bottlenecks and ensuring low-latency performance. Limitations: Despite the promise of edge AI, challenges persist. These include limited processing and energy resources, security vulnerabilities, and device heterogeneity. Managing updates and maintaining consistency across distributed systems complicate widespread implementation further. Applications and Novelty: This chapters novelty lies in its integrated focus on practical, real-world applications of edge AI in healthcare, manufacturing, and smart cities. By presenting targeted solutions to known constraints, it contributes a practical, implementation-ready perspective to the growing body of edge AI research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Source
Studies in Computational Intelligence;Volume;1267;pp.1-19
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Bandwidth; Edge AI; Healthcare; Latency; Manufacturing; Real-time decision making; Scalability; Smart cities
Coverage
Bajpai A., Christ University, Bengaluru, India; Srivastava S., Christ University, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 1860949X;
Format
online
Language
English
Type
Book chapter

Citation

Bajpai, Annanya; Srivastava, Shilpa, “AI-Driven Real-Time Decision Making at the Edge: Overcoming Latency, Bandwidth, and Scalability Challenges for Smarter Data-Intensive Applications in Healthcare, Manufacturing, and Smart Cities,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/24107.

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