Scalable Neural Architectures for Real-Time Decision Making in Edge Environments

Abstract

The rapid proliferation of Internet of Things (IoT) devices and smart sensors has catalyzed the need for scalable and efficient real-time decision-making mechanisms in edge computing environments. Traditional cloud-based neural networks often suffer from high latency, increased bandwidth usage, and privacy issues. Consequently, the integration of scalable neural architectures capable of operating locally at the edge has become essential. This paper explores the development and deployment of lightweight, adaptable neural networks that can perform inference tasks in real-time without relying heavily on centralized cloud resources. The research addresses architectural design, model compression techniques, and real-world performance on edge hardware platforms. We also present experimental validations conducted on Raspberry Pi and Jetson Nano devices using datasets from autonomous driving and industrial monitoring systems. The results demonstrate that properly optimized neural architectures can maintain high accuracy while significantly reducing response time and computational load. This paper concludes by discussing the trade-offs in scalability, accuracy, and energy efficiency, and outlines future directions for dynamic neural adaptation in highly constrained edge environments.