2022/10/25 by Pedro Chaves, Tiago Fonseca, Chaves, Pedro +14
Computer Science · Engineering · #Architecture #Artificial Intelligence (cs.AI) #Big data #Cloud computing #Computer science #Data Stream Mining Techniques #Database #Distributed computing #Embedded system #FOS: Computer and information sciences #FOS: Electrical engineering #Internet of Things #IoT and Edge/Fog Computing #IoT-based Smart Home Systems #Machine Learning (cs.LG) #Operating system #Process (computing) #Real-time computing #Scalability #Signal Processing (eess.SP) #The Internet #World Wide Web #cs.AI #cs.LG #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2211.02627
published in arXiv (Cornell University) (Cornell University) · 6 pages, 6 figures, IECON 2022
arxiv created 2022/10/25 · openalex publication_date 2022/10/25 · arxiv updated 2022/11/07 · openalex created_date 2022/11/12 · openalex updated_date 2026/08/01
Billions of interconnected Internet of Things (IoT) sensors and devices collect tremendous amounts of data from real-world scenarios. Big data is generating increasing interest in a wide range of industries. Once data is analyzed through compute-intensive Machine Learning (ML) methods, it can derive critical business value for organizations. Powerfulplatforms are essential to handle and process such massive collections of information cost-effectively and conveniently. This work introduces a distributed and scalable platform architecture that can be deployed for efficient real-world big data collection and analytics. The proposed system was tested with a case study for Predictive Maintenance of Home Appliances, where current and vibration sensors with high acquisition frequency were connected to washing machines and refrigerators. The introduced platform was used to collect, store, and analyze the data. The experimental results demonstrated that the presented system could be advantageous for tackling real-world IoT scenarios in a cost-effective and local approach.