Faculty of Computing

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Subject: search.filters.subject.IoT

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    SmartSphere: Bridging AI-Powered Automation and Tesla Coil Wireless Energy for Smarter Living
    (Institute of Electrical and Electronics Engineers Inc., 2025) Jurison Jenaraj, J.K; Umasuthan, A; Vigneswaran, V; Raveendran, K; Thiruthanigesan, K; Kasthuriarachchi, S
    This paper presents SmartSphere, an intelligent, secure, and wire-free home automation system that synergizes artificial intelligence, multi-factor access control, and resonant wireless power transmission. Built on an edge-computing architecture using ESP32 and Arduino Mega 2560 microcontrollers, SmartSphere integrates facial recognition, fingerprint authentication (96.7% accuracy), and IR-based presence detection to reduce false activations by 75%. The system employs OpenCV and TensorFlow Lite for real-time anomaly detection and environmental personalization, including weather-responsive adjustments and emotion-aware lighting via facial expression analysis. A key innovation is the incorporation of a Tesla coil-based wireless power transmission module, which eliminates conventional wiring constraints and reduces installation cabling by 86%. Experimental validation demonstrates a 14% improvement in energy efficiency, 75% faster response time (0.3 s), and seamless compatibility with 92% of tested IoT devices. Through comprehensive testing and validation, this research establishes SmartSphere as a secure, intelligent, and sustainable solution for next-generation smart homes, addressing the limitations of traditional wired systems while enhancing the user experience through AI-driven personalization.
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    Bovitrack:Animal behavior monitoring using Machine learning and IoT
    (Institute of Electrical and Electronics Engineers Inc., 2025) Viraj, H; Wijesekara, S; Tharuka, K; Fernando, S; Jayakody, A; Wijesiri, P
    Analyzing dairy cattle behavior and anomalies is a critical component of precision livestock farming, allowing farmers to remotely monitor animals for health and behavior. In order to accomplish this task better, the use of IoT technology and machine learning algorithms is more appropriate as per the time. The YOLO (you only look once) object recognition algorithm is more suitable for that, and the use of this algorithm allows these processes to be performed automatically and in real time with high accuracy. YOLO's ability to recognize multiple objects in images or videos makes Yolo ideal for cattle detection and tracking.
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    Multispectral Images and IoT Based Tea Plantation Monitoring: A Proposed System Architecture
    (Institute of Electrical and Electronics Engineers Inc., 2025) Kariyawasam K.P.W.D.V.; Kahandagamage P.N.; Fernando M.R.R.; Fernandopulle J.M; Jayasinghearachchi, V; Dissanayaka, K
    In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments.