The number of Internet of Things (IoT) devices is increasing exponentially. They process high volumes of data and require lots of computing power. A recent paper on arXiv.org looks for a way to reduce these costs.
Researchers propose a framework based on a set of open-source tools which let using the available computational power of computers designated for other purposes.
The investigated architecture manages, analyzes, and transforms raw data into valuable insights in several stages by several distributed or local workers. The system is scalable and can split the workload among multiple machines.
The approach was tested in a real-world case study where current and vibration sensors were connected to washing machines and refrigerators. Results confirm that the presented system could be advantageous for tackling real-world big-data IoT scenarios in a cost-effective way.
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 works a distributed and scalable platform architecture that can be introduced 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.
Research article: Chaves, P., “An IoT Cloud and Big Data Architecture for the Maintenance of Home Appliances”, 2022. Link: https://arxiv.org/abs/2211.02627