Smart education is now a typical feature in education emerging from information communications technologies (ICT) and the constant introduction of new technologies into institutional learning. The smart classroom aims users to develop skills, adapt, and use technologies in a learning context that produces elevated learning outcomes which leads to big data. The internet of things (IoT) is a new technology in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose. The technologies are rapidly changing, and designing for these situations can be complex. Designing the IoT applications is a challenging issue. The existing standardization activities are often redundant IoT development. The reference architecture provides a solution to smart education for redundant design activities. The purpose of this chapter is to look at the requirements and architectures required for smart education. It is proposed to design a scalable and flexible IoT architecture tor smart education (IoTASE).
Electronic learning or e-learning is the use of technology to enable learners to learn from anywhere and anytime. The delivery involves the use of electronic devices in some way to make available learning contents. Today, e-learning has drastically changed the educational environment. The e-learning methodology is a good example of green computing. Green computing refers to the study and practice of using computing resources in an eco-friendly manner. It is the practice of using computing resources in an energy efficient and environmentally friendly manner. In order to reduce costs, education services can be provided using cloud technology. The green cloud computing solutions save energy, reduce operational costs, and reduce carbon footprints on the environment. Hence, the objective is to provide a green cloud architecture to e-learning solutions. This architecture is addressing the issues such as improving resource use and reducing power consumption.
A smart education system uses emerging technologies and generates a vast amount of heterogeneous data in the learning environment. The conventional methods presently used by the educational administrators for decision-making are minimal and take more time to generate the results. The educational administrators could not be able to predict the results quickly and advance for better decision-making. Today, artificial intelligence approaches are widely used in educational systems for automating educational processes. These approaches achieve a better, efficient, and effective modern education system. Integrating machine learning deep learning techniques with a smart education system can automatically analyze the generated data for better decision-making and provide recommendations to students and educational administrators. This chapter aims to introduce a machine learning model to predict the outcomes in a smart education system.
The agricultural sector has witnessed significant technological transformations over the last few decades. The state-of-the-art technologies are transforming the traditional agriculture models into digital agriculture. From these technologies, conventional agriculture has evolved and shifted towards a smart agriculture system. In a smart agriculture system, farmers can collect and analyze the collected data to fertilize and tend their crops. The smart agriculture system provides economical and more accurate ways to predict and protect crop growth. The incorporation of these technologies digitalizes the agricultural industry by increasing profits, reducing waste, improving efficiency, and becoming sustainable. This chapter aims to study the state-of-the-art technologies used in the agriculture sector and proposes a smart agriculture model using these technologies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.