Strategic Use of AI in Multimodal Edge Environments: Leveraging Artificial Intelligence for Enhanced Performance, Real-Time Analytics, and Scalability in Distributed, Resource-Constrained Systems

Abstract

This paper explores the strategic integration of multimodal AI in edge systems, aiming to enhance real-time data processing and decision-making capabilities closer to data sources. Multimodal AI, which processes and understands diverse data types such as text, images, audio, and video, is combined with edge computing to reduce latency, increase efficiency, and improve data privacy. By leveraging deep learning models like CNNs and transformers, and employing advanced data fusion techniques, multimodal AI can provide richer interpretations of complex data. Edge systems, featuring distributed architecture and localized data processing, are crucial for applications demanding immediate insights, such as autonomous vehicles and smart cities. This research identifies key strategies for integrating these technologies, examines hardware advancements, and addresses challenges like managing multiple data streams and limited computational resources. Through a detailed literature review, methodology, and case studies, the paper provides comprehensive insights and practical recommendations for optimizing multimodal AI in edge environments, ultimately driving innovation across various domains.