Smart Embedded Systems for Real-Time Event Detection and Analysis

Abstract

The proliferation of smart embedded systems has revolutionized real-time event detection and analysis across domains such as surveillance, healthcare, industrial automation, and environmental monitoring. These systems, equipped with low-power processors, sensor fusion capabilities, and machine learning algorithms, offer rapid and adaptive responses to dynamic environments. This paper explores the architectural components, data processing techniques, and real-time analytics frameworks that empower smart embedded platforms. Furthermore, we assess their application-specific design considerations, including latency constraints, energy efficiency, and fault tolerance. A comparative analysis of different embedded system configurations is presented to highlight performance trade-offs. The findings underscore the transformative potential of intelligent embedded solutions in creating context-aware, autonomous systems.