A new way of controlling and managing smart homes, built on the analysis of large amounts of data, is developed to boost their efficiency. In order to gather data from a smart house, visualise such data, and set off alarms with a buzzer, the fundamental hardware of control and management is created. This hardware includes smoke sensors, temperature and humidity sensors, and infrared sensors. Data acquired from smart homes are communicated over an indoor wireless network comprising gateway equipment, with data being stored using a distributed cache architecture informed by big data analysis. Scheduling the control and management chores of a smart home is accomplished with the help of a hybrid particle swarm optimization algorithm based on the necessary data. From our experiments, we can conclude that this approach improves upon the state-of-the-art in terms of device control and scenario management, as well as communication performance and usefulness in the real world. Many studies are being conducted to find the best way for an application to handle a large data set. This article provides an overview of the use of Big Data in Smart Homes and discusses the problems and solutions that have arisen throughout the introduction of Big Data ideas and technologies. The primary problems and obstacles are also mentioned, including things like slow or unreliable Internet, slow or unreliable sensors, high costs, a lack of reliable device maintenance or reliable vendors, and problems with converting data formats. A smart home framework is also proposed in this study, comprised of three parts: an application module, a device configuration module, and a big data module. This framework makes use of big data ideas and technologies in order to store and analyze data for more informed decisions and notifications.

Big Data, Smart Home, IoT, Hadoop, Cloud Computing.