Authors : Simbarashe Fani; Tichaona Phillip Sumbureru

Volume/Issue : Volume 9 - 2024, Issue 7 - July

Google Scholar : https://tinyurl.com/34bptsbd

Scribd : https://tinyurl.com/36na3u9c

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUL1949

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The swift evolution of the Internet of Things (IoT) has led to the generation of immense sums of data that require effective processing and storage. Old cloud computing methods often struggle to meet the real-time processing and low latency necessities of IoT applications. To discourse these encounters, fog computing has developed as a proficient model that carries computing resources closer to the data sources.  This paper presents an energy-efficient monitoring system for a school campus that integrates fog computing and IoT technologies to improve resource management. The projected system contains three main components:  IoT sensor nodes positioned across the campus to collect real-time data on energy consumption, environmental conditions, and occupancy levels.  Fog computing nodes that process the sensor data locally, do analytics, and make smart decisions to augment energy usage.  A cloud-based platform that provides unified monitoring, reporting, and long-term data storage.  The Key Features of the System Comprise:  Real-time monitoring and analysis of energy consumption designs  Automated control of lighting, HVAC, and other building systems based on occupancy and environmental conditions  Predictive maintenance of equipment to increase energy efficiency  Centralized control panel for campus-wide resource management  Secure and privacy-conserving data processing at the fog layer The paper summarizes 10 main results related to energy-efficient building management through the integration of fog computing and IoT. This work fills a major gap in the literature by presenting a holistic system that combines fog-based data processing, intelligent decision-making, and cloud-based reporting for energy optimization in an educational campus. Simulations and real-world deployment in a small- scale setting show that the proposed system yields substantial gains in energy savings, reduced operational costs, and enhanced user comfort compared to traditional building management approaches. This study contributes new findings on the solutions for sustainable campus management and technology adoption in the education sector, building upon previous studies that have employed fog computing and IoT.

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