AI-Enabled Context-Aware IoT Architecture for Intelligent Smart Campus Ecosystems

Abstract

: The combination of IoT (Internet of Things), edge computing, and AI (Artificial Intelligence) has brought intelligent and adaptive smart campuses. The present paper proposes an AI-based context-aware IoT that is based on the edge and cloud computing to simplify real-time campus operations, enhance the utilization of the available resources, and provide intelligent decision-making. The proposed architecture will address the main problem of current smart campus systems, such as high latency, reduced computational power at the edges, and absence of services integration. The architecture also supports edge computing, where local processing and context inference with AI are done on an edge node, and long-term optimization and cross-campus analysis, which is done by cloud computing through a hybrid approach of edge-cloud architecture. The architecture enables real time solutions to the services such energy management, safety monitoring and optimization of mobility of people, making the campus sustainable and adaptable to the requirements of dynamic users. A group of performance metrics, including latency, accuracy, energy efficiency, and scalability is used to test the recommended system. Experimentally, it is proved that the hybrid model is more responsive and resource-efficient when compared with the solutions based on clouds and edges only. This paper is a supplement to the smart campus systems by providing a scalable, adaptive, and efficient architecture that may be applied to different campus services. Future directions of this work will encompass enhancement of privacy saving mechanisms, integration of the digital twin technologies and a multi-campus architecture.