Edge AI-Enabled IoT Architecture for Low-Latency Smart Environment Monitoring

Abstract

The rapid evolution of the Internet of Things (IoT) has enabled large-scale deployment of sensor networks for environmental monitoring in domains such as smart cities, agriculture, industrial automation, and disaster management. However, traditional cloud-centric IoT architectures face significant challenges, including high latency, bandwidth limitations, data privacy concerns, and unreliable connectivity. These limitations hinder real-time decision-making, which is critical for applications such as air quality monitoring, flood prediction, and wildfire detection. To address these challenges, Edge Artificial Intelligence (Edge AI) has emerged as a transformative paradigm that integrates AI capabilities directly at or near the data source. This research proposes an Edge AI-enabled IoT architecture designed to achieve low-latency and efficient smart environment monitoring. The architecture leverages distributed intelligence by deploying lightweight machine learning models on edge devices such as microcontrollers, gateways, and embedded AI processors. By performing real-time data processing and inference locally, the system significantly reduces dependency on cloud infrastructure, minimizes communication delays, and enhances system responsiveness. The proposed system adopts a multi-layer architecture consisting of the sensing layer, edge processing layer, communication layer, and cloud layer. Environmental data such as temperature, humidity, air quality index (AQI), and noise levels are captured using IoT sensors. Edge nodes process this data using optimized AI models (e.g., TinyML-based classifiers) to detect anomalies and generate alerts in real time. Only relevant or aggregated data is transmitted to the cloud for long-term storage, advanced analytics, and model updates. Experimental results demonstrate that the proposed architecture reduces latency by up to 60–80% compared to traditional cloud-based systems, while maintaining high prediction accuracy (>92%). Furthermore, bandwidth consumption is reduced significantly due to localized processing. The system also enhances data privacy by minimizing the transmission of sensitive raw data. The integration of communication protocols such as MQTT and LoRaWAN ensures efficient data transmission in resource-constrained environments. This study contributes to the advancement of intelligent IoT systems by providing a scalable, energy-efficient, and low-latency architecture for smart environment monitoring. The findings indicate that Edge AI is a viable solution for next-generation IoT applications requiring real-time analytics and adaptive decision-making.