A Comprehensive and Effective Framework for Traffic Congestion Problem Based on the Integration of IoT and Data Analytics

This research was funded by the Deanship of Scientific Research, Islamic University of Madinah, Saudi Arabia, under Tamayuz research grant number 2/710.Traffic congestion is still a challenge faced by most countries of the world. However, it can be solved most effectively by integrating modern technologies such as Internet of Things (IoT), fog computing, cloud computing, data analytics, and so on, into a framework that exploits the strengths of these technologies to address specific problems faced in traffic management. Unfortunately, no such framework that addresses the reliability, flexibility, and efficiency issues of smart-traffic management exists. Therefore, this paper proposes a comprehensive framework to achieve a reliable, flexible, and efficient solution for the problem of traffic congestion. The proposed framework has four layers. The first layer, namely, the sensing layer, uses multiple data sources to ensure a reliable and accurate measurement of the traffic status of the streets, and forwards these data to the second layer. The second layer, namely, the fog layer, consumes these data to make efficient decisions and also forwards them to the third layer. The third layer, the cloud layer, permanently stores these data for analytics and knowledge discoveries. Finally, the fourth layer, the services layer, provides assistant services for traffic management. We also discuss the functional model of the framework and the technologies that can be used at each level of the model. We propose a smart-traffic light algorithm at level 1 for the efficient management of congestion at intersections, tweet-classification and image-processing algorithms at level 2 for reliable and accurate decision-making, and support services at level 4 of the functional model. We also evaluated the proposed smart-traffic light algorithm for its efficiency, and the tweet classification and image-processing algorithms for their accuracy.Deanship of Scientific Research, Islamic University of Madinah, Saudi Arabia 2/71

A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data

The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines. One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials. In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications. Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains

Optimizing Plant Water Use Efficiency for a Sustainable Environment

The rising shortage of water resources in crop-producing regions worldwide and the need for irrigation optimisation call for sustainable water savings. The allocation of irrigation water will be an ever-increasing source of pressure because of vast agricultural demands under changing climatic conditions. Consequently, irrigation has to be closely linked with water-use efficiency with the aim of boosting productivity and improving food quality, singularly in those regions where problems of water shortages or collection and delivery are widespread. The present Special Issue (SI) showcases 19 original contributions, addressing water-use efficiency in the context of sustainable irrigation management to meet water scarcity conditions. These papers cover a wide range of subjects including (i) interaction mineral nutrition and irrigation in horticultural crops, (ii) sustainable irrigation in woody fruit crops, (iii) medicinal plants, (iv) industrial crops, and (v) other topics devoted to remote sensing techniques and crop water requirements, genotypes for drought tolerance, and agricultural management. The studies were carried out in both field and laboratory surveys, with modelling studies also being conducted, and a wide range of geographic regions are also covered. The collection of these manuscripts presented in this SI updates on and provides a relevant contribution for efficient saving water resources