Proceedings of the 6th International Conference on Big Data and Internet of Things


Book Description

This book is a collection of papers that were presented at the 6th International Conference on Big Data Cloud and Internet of Things, BDIoT 2022. The conference took place on October 25-27, 2022, Tangier, Morocco. The book consisted of 49 chapters, which correspond to the four major areas that are covered during the conference, namely Big Data & Cloud Computing, Cybersecurity, Machine Learning, Deep Learning, E-Learning, Internet of Things, Information System and Natural Language Processing. Every year BDIoT attracted researchers from all over the world, and this year was not an exception – the authors received 98 submissions from 7 countries. More importantly, there were participants from many countries, which indicates that the conference is truly gaining more and more international recognition as it brought together a vast number of specialists who represented the aforementioned fields and share information about their newest projects. Since the authors strived to make the conference presentations and proceedings of the highest quality possible, the authors only accepted papers that presented the results of various investigations directed to the discovery of new scientific knowledge in the area of Big Data, IoT and their applications. All the papers were reviewed and selected by the Program Committee, which comprised 96 reviewers from over 58 academic institutions. As usual, each submission was reviewed following a double process by at least two reviewers. When necessary, some of the papers were reviewed by three or four reviewers. Authors’ deepest thanks and appreciation go to all the reviewers for devoting their precious time to produce truly through reviews and feedback to the authors.




Perspectives and Considerations on the Evolution of Smart Systems


Book Description

Smart systems are rapidly evolving and finding ways to influence different aspects of human life, industry, and the environment. Smart systems based on available data should have the ability to predict and be adaptive, which leads to performing reliable, smart actions. Smartness and learning capabilities are essential characteristics describing smart systems besides connectivity and digital virtual cloudification technologies. Perspectives and Considerations on the Evolution of Smart Systems discusses the latest edge development that informs and facilitates the next level of development. It highlights how the evolving technologies and techniques are going to impact the developments in the field considering climate, environment, circular economy, and ecosystems. Covering topics such as dynamic difficulty adjustment, intelligent control, and serious games, this premier reference source is an excellent resource for engineers, computer scientists, IT professionals, developers, data analysts, students and educators of higher education, librarians, researchers, and academicians.







Smart Robust Operation and Trading of Integrated Energy Systems with Low Pollution Goals


Book Description

To mitigate two major environmental concerns of global warming and air pollution, renewable energies with uncertainty are increasingly deployed in power systems, which challenge the system's secure operation. A single system usually has limited adjusting ability. In contrast, integrated energy systems such as electricity-gas, electricity-traffic, electricity-heat, and transmission-distribution coordinated systems enhance the regulating ability of renewable energy accommodation and environmental protection. The operation of integrated energy systems will meet three essential requirements: low-pollution attribute, robustness, and cooperativity. However, the diversity of uncertainty conditions, the complementarity of new energy accommodation among systems, the conflict of interest between systems, and the dispatch autonomy of systems challenge the requirements mentioned above. The main goal of this Research Topic includes: 1. Propose more effective trading mechanisms or control strategies for carbon and air pollutant emissions. 2. Fully use complementary effects between electric power, natural gas, heat, hydrogen, and traffic systems. 3. Realize the coordinated operation of integrated energy systems with limited information interaction and ensured dispatch autonomy. 4. Improve the robustness of integrated energy systems under diversified uncertainty conditions. 5. Apply data-based reinforcement learning methods for the dynamic decision of smart integrated energy systems under complex environments.




IoT as a Service


Book Description




Deep Learning for Marine Science, volume II


Book Description

This Research Topic is the second volume of this collection. You can find the original collection via https://www.frontiersin.org/research-topics/45485/deep-learning-for-marine-science Deep learning (DL) is a critical research branch in the fields of artificial intelligence and machine learning, encompassing various technologies such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), Transformer networks and Diffusion models, as well as self-supervised learning (SSL) and reinforcement learning (RL). These technologies have been successfully applied to scientific research and numerous aspects of daily life. With the continuous advancements in oceanographic observation equipment and technology, there has been an explosive growth of ocean data, propelling marine science into the era of big data. As effective tools for processing and analyzing large-scale ocean data, DL techniques have great potential and broad application prospects in marine science. Applying DL to intelligent analysis and exploration of research data in marine science can provide crucial support for various domains, including meteorology and climate, environment and ecology, biology, energy, as well as physical and chemical interactions. Despite the significant progress in DL, its application to the aforementioned marine science domains is still in its early stages, necessitating the full utilization and continuous exploration of representative applications and best practices.




Artificial Intelligence and Internet of Things for Smart Agriculture


Book Description

Smart agriculture combines modern science and technology with agricultural cultivation, to achieve unmanned, automatic, intelligent management of agricultural production, such as intelligent irrigation, intelligent fertilization, and intelligent spraying. It is the application of artificial intelligence (AI) and Internet of Things (IoTs) in the field of modern agriculture. Agricultural AI (AAI) is the application of various information technologies and their cross-application in the field of agriculture, including intelligent equipment, IoTs, agricultural unmanned aerial vehicle, intelligent perception, deep learning, digital twin network, expert systems, agricultural cognitive computing, etc. With the rapid development of smart agriculture, agricultural applications combined with deep learning are quite common, such as crop disease-pest detection, growth environment monitoring, automatic crop picking, unmanned farm management, etc. Edge computing can provide efficient and reliable new data processing solutions for multi-scenario and complex tasks in agriculture. At present, cloud computing, deep learning and digital twinning have been widely used in agricultural fields, such as plant identification and detection, pest diagnosis and recognition, remote sensing regional classification and monitoring, fruit carrier detection and agricultural product classification, animal identification and posture detection, etc.