Editorial: IEEE Transactions on Sustainable Computing , Special Issue on Smart Data and Deep Learning in Sustainable Computing

The twelve papers in this special section focus on smart data and deep learning in sustainable computing. We are living in a data-driven era in which numerous infrastructure can be connected and the interconnected systems can perform “smart” when the large pool of the data are well utilized. Finding the way of well utilizing the large volume of data has an urgent demand in multiple realms, including academics, industries, and education. The force behind the data can be pushed out from a variety of data-driven techniques, such as machine learning and deep learning, which is a great potential for generating successful model, framework, and method for achieving sustainable computing. Therefore, gathering recent achievements in smart data and deep learning in sustainable computing is meaningful and valuable for powering the capability of datadriven domain and the various applications, implementations, and innovations in different disciplines and fields. This special issue focuses on two aspects considering the perspective of sustainable computing, which include smart data and deep learning. The smart data covers all dimensions of data usage lifecycles, such as data selections and collections, data preprocessing, data mining, and data analytics, in various application scenarios. The other aspect, deep learning, emphasizes the intelligent performance of applying data-driven techniques in practices and research explorations. Thus, this special issue aims at collecting updated outstanding papers that illustrate the latest achievements and development updates concerning the smart data and deep learning solutions, issues, applications, trends, and implementations in sustainable computing.