Internet of things 

About: Internet of things is an academic journal published by Elsevier BV. The journal publishes majorly in the area(s): Computer science & Biology. It has an ISSN identifier of 2542-6605. Over the lifetime, 358 publications have been published receiving 1038 citations.

AI for Next Generation Computing: Emerging Trends and Future Directions

Abstract: Autonomic computing investigates how systems can achieve (user) specified control outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.

Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review

Abstract: Internet of Things (IoT) and Unmanned Aerial Vehicles (UAVs) are two hot technologies utilized in cultivation fields, which transform traditional farming practices into a new era of precision agriculture. In this paper, we perform a survey of the last research on IoT and UAV technology applied in agriculture. We describe the main principles of IoT technology, including intelligent sensors, IoT sensor types, networks and protocols used in agriculture, as well as IoT applications and solutions in smart farming. Moreover, we present the role of UAV technology in smart agriculture, by analyzing the applications of UAVs in various scenarios, including irrigation, fertilization, use of pesticides, weed management, plant growth monitoring, crop disease management, and field-level phenotyping. Furthermore, the utilization of UAV systems in complex agricultural environments is also analyzed. Our conclusion is that IoT and UAV are two of the most important technologies that transform traditional cultivation practices into a new perspective of intelligence in precision agriculture.

HealthCloud: A system for monitoring health status of heart patients using machine learning and cloud computing

Abstract: In the context of the global health crisis of 2020, the tendency of many people to self-diagnose at home virtually, prior to any physical interaction with medical professionals, has been increased. Existing self-diagnosis systems include those accessible via the Internet, which involve entering one’s symptoms. Several other methods do exist, for example, people read medical blogs or notes, which are often wrongly interpreted by them and they arrive at a completely different assumption regarding the cause of their symptoms. In this paper, a system called HealthCloud is proposed, for monitoring health status of heart patients using machine learning and cloud computing. This study aims to offer the ‘best of both worlds’, by combining the information required for the person to understand the disease in sufficient detail, with an accurate prediction as to whether they may have (in this case) heart disease or not. The presence of heart disease is predicted using machine learning algorithms such as Support Vector Machine, K-Nearest Neighbours, Neural Networks, Logistic Regression and Gradient Boosting Trees. This paper evaluates these machine learning algorithms to obtain the most accurate model, in compliance with Quality of Service (QoS) parameters. The performance of these machine learning models is measured and compared using the metrics such as Accuracy, Sensitivity (Recall), Specificity, AUC scores, Execution Time, Latency, and Memory Usage. For better establishment of the results, these machine learning algorithms have been cross validated with 5-fold cross validation technique. With an accuracy rate of 85.96%, it has been found that Logistic Regression is the most responsive and accurate model amongst those models assessed. The Precision, Recall, Cross Validation mean and AUC Score for this model were 95.83%, 76.67%, 81.68% and 96% respectively. The algorithm and the mobile application were tested on Google Cloud Firebase with existing user inputs from the dataset, as well as with unseen new data. The use of this system can assist patients, both in reaching self-diagnosis decisions and in monitoring their health.

Smart City Data Science: Towards data-driven smart cities with open research issues

Abstract: Cities are undergoing huge shifts in technology and operations in recent days, and ‘data science’ is driving the change in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting useful knowledge or actionable insights from city data and building a corresponding data-driven model is the key to making a city system automated and intelligent. Data science is typically the scientific study and analysis of actual happenings with historical data using a variety of scientific methodologies, machine learning techniques, processes, and systems. In this paper, we concentrate on and explore “Smart City Data Science”, where city data collected from various sources such as sensors, Internet-connected devices, or other external sources, is being mined for insights and hidden correlations to enhance decision-making processes and deliver better and more intelligent services to citizens. To achieve this goal, artificial intelligence, particularly, machine learning analytical modeling can be employed to provide deeper knowledge about city data, which makes the computing process more actionable and intelligent in various real-world city services. Finally, we identify and highlight ten open research issues for future development and research in the context of data-driven smart cities. Overall, we aim to provide an insight into smart city data science conceptualization on a broad scale, which can be used as a reference guide for the researchers, industry professionals, as well as policy-makers of a country, particularly, from the technological point of view.