Smart homes are an element of developing smart cities. In recent years, countries around the world have spared no effort in promoting smart cities. Smart homes are an interesting technological advancement that can make people’s lives much more convenient. The development of smart homes involves multiple technological aspects, which include big data, mobile networks, cloud computing, Internet of Things, and even artificial intelligence. Digital information is the main component of signal control and flow in a smart home, while information security is another important aspect. In the event of equipment failure, the task of safeguarding the system’s information is of the utmost importance. Since smart homes are automatically controlled, the problem of mobile network security must be taken seriously. To address these issues, this paper focuses on information security, big data, mobile networks, cloud computing, and the Internet of Things. Security efficiency can be enhanced by using a Secure Hash Algorithm 256 (SHA-256), which is an authentication mechanism that, with the help of the user, can authenticate each interaction of a given device with a WebServer by using an encrypted username, password, and token. This framework could be used for an automated burglar alarm system, guest attendance monitoring, and light switches, all of which are easily integrated with any smart city base. In this way, IoT solutions can allow real-time monitoring and connection with central systems for automated burglar alarms. The monitoring framework is developed on the strength of the web application to obtain real-time display, storage, and warning functions for local or remote monitoring control. The monitoring system is stable and reliable when applying SHA-256.
Summary Climate change is one of the main challenges faced by the development of every country. For countries producing agricultural commodities, the climate affects the quantity and quality of products. Many methods have been proposed to keep track of climate. One traditional method is the weather station model, which indicates the temperature, wind speed, and direction and extent of cloud cover. However, this method of predicting climate change has low accuracy due to geographical variation, for example, mountainous or forested areas. Recently, a combination of wireless sensor networks (WSN) and machine learning (ML) has been considered for prediction with the Internet of Things (IoT), for instance, through a wireless body area network. For climate change prediction, we design and develop a control system that uses node sensors to collect data in sandhills and beaches, with data management conducted via a web application with three components. The first component is designed to collect data from the node sensors. The second component is mainly used to control the system through a web application. The third component uses linear regression in ML to analyze the data to predict weight and volume. The complete system has been tried and tested in real time on a 10‐m2 area of a beach at Binh Thuan province, Vietnam, where sensor node data were wirelessly collected over a cloud using a web application. This enabled assessment of the current state of the land at a coastal sandy beach, as well as prediction of the risk level of desertification and natural disasters.
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