An IoT Cloud and Big Data Architecture for the Maintenance of Home Appliances

TL;DR

This paper presents a scalable IoT and big data platform architecture designed for predictive maintenance of home appliances, enabling efficient data collection, storage, and analysis for real-world IoT scenarios.

Contribution

It introduces a novel distributed platform architecture tailored for IoT big data analytics, specifically applied to predictive maintenance of home appliances.

Findings

The system effectively collected and analyzed sensor data from appliances.

Experimental results showed cost-effectiveness and practicality in real-world IoT scenarios.

The platform demonstrated scalability and efficiency in handling large data volumes.

Abstract

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.