Research Title: A Novel Traffic characteristics aware and Context Prediction Protocol for Intelligent Connected Vehicles

Author: Maram Bani Younes, Published Year: 2023

IEEE Transactions on Vehicular Technology , Early Access

Faculty: Information Technology

Abstract: Digital maps have been installed and attached to vehicles recently. They help with the GPS receivers to determine the relative locations of vehicles to other existing traffic and objects over the road network such as entrance/exit points, obstacles, road intersections, etc. This helps drivers or autonomous vehicles to decide the most appropriate reaction, in terms of speed, take-over, or stop operations ahead of time. Several daily traveling vehicles do not have digital maps. Besides, digital maps are vulnerable to being destroyed or inaccurate. They require regular updates due to the continuous construction and re-design of the road networks. These constructions aimed to enhance the road design and the traffic efficiency there. Moreover, accidents, broken vehicles, traffic congestion, or other ad-hoc obstacles appear unpredictably over the road network. In this paper, we aim to introduce a prediction protocol that gathers and analyzes the traffic characteristics of vehicles over the investigated road scenario using wireless transceivers in vehicles. Then, it predicts the physical and traffic context based on the analyzed traffic data. This protocol can replace the absent or broken digital maps in vehicles. It also can be used to verify the correctness of the digital map in vehicles. From the experimental results, we can infer that the proposed protocol has succeeded in predicting the road context over highways and downtown scenarios. More accurate and better predictions are acquired by increasing the percentage of wireless transceiver-equipped vehicles.

Keywords: Roads , Road transportation , Protocols , Accidents , Vehicles , Ad hoc networks , Wireless communication

Research Title: SmartLight: A smart efficient traffic light scheduling algorithm for green road intersections

Author: Maram Bani Younes, Published Year: 2023

Ad Hoc Networks, 140

Faculty: Information Technology

Abstract: Traveling vehicles participate in emphasizing the global warming problem due to the gases produced by them. The exponential increase in the number of daily traveling vehicles has exaggerated the world pollution problem threatening the life on the planet. This encourages several environmental organizations to look for designing green vehicles. Moreover, several countries have forced green driving rules and technologies. Road intersections are considered high fuel consumption areas over the road network. This is due to the required power to stop moving vehicles and restart stopped vehicles at these intersections. This work introduces an efficient traffic light scheduling algorithm (SmartLight) that controls the competing traffic flows at the road intersections. It is designed to reduce the total consumed fuel of vehicles and thus it reduces their produced gases. The topology of the road intersection, the context of the competing traffic flows, and the real-time traffic characteristics of each flow are mainly considered to schedule the phases of each located traffic light. The phases of the traffic light cycle are primly set to allow emergency vehicles to pass through the intersection without stopping. Then, the traffic flow that contains heavy vehicles or has the highest weight among the competing traffic flows is assigned the highest priority to pass through the signalized intersection. Finally, the average waiting delay time of each flow on the signalized intersection should not exceed a pre-determined threshold to guarantee fair sharing of the signalized intersection. The scheduling time of each phase is set based on the lengths of platoons of vehicles that are scheduled during that phase from different un-conflicted traffic flows. An experimental study has evaluated the performance of the proposed algorithm (SmartLight) compared to previous traffic light scheduling algorithms in terms of total fuel consumption, gas emission, the average queuing delay time of vehicles, and the throughput of the road intersection.

Keywords: SmartLight: A smart efficient traffic light scheduling algorithm for green road intersections

Research Title: Information Security and Data Management for IoT Smart Healthcare

Author: Maram Bani Younes, Published Year: 2023

Faculty: Information Technology

Abstract: International legislation and health authorities urge and promote healthcare providers to adopt meaningful use of becoming network integrated. As a result, healthcare services are intelligently provided using the Internet of things (IoT)-based principle. However, transiting healthcare providers and organizations to electronic-based systems are vulnerable to information security attacks and cybercrimes [1]. Information security techniques protect information and systems from illegal and unauthorized admission, usage, disclosure, interference, or conversion. This is accomplished by processing the three main elements: confidentiality, integrity, and availability of information. Confident information is available or disclosed only to legal processes and only by authorized people from a healthcare perspective. Therefore, only authorized users can modify and control the integrity and protection of electronic data.

Keywords: Information Security and Data Management for IoT Smart Healthcare

Research Title: Safe and Efficient Advising Traffic System Around Critical Road Scenarios

Author: Maram Bani Younes, Published Year: 2023

International Journal of Intelligent Transportation Systems Research, 21

Faculty: Information Technology

Abstract: Vehicles travel daily over the road networks toward their targeted destinations. The context of the road varies in terms of the geometric design and existing traffic. Accidents repeatedly occur among traveling vehicles. Some areas of road segments over the road network witness a higher rate of traffic accidents compared to other road scenarios. This is usually affected by the geometric design and pavement quality of the road, including its winding and slope. These roads that witness a higher rate of accident occurrence are referenced as critical road scenarios. In this work, an advising traffic system is proposed to recommend the best speed and basic driving behavior around these scenarios. This system considers the geometric design of the road scenario, the weather conditions, and the real-time traffic characteristics (e.g., traffic density and traffic speed) to obtain optimal recommendations for the traveling vehicles there. From the experimental results, we can infer that the proposed system enhances the safety conditions and reduces the accidental rate over the critical road. The proposed system also enhances traffic efficiency in terms of reducing fuel consumption and gas emission over the investigated critical road scenarios.

Keywords: Critical road Curvature Slope Weather conditions Traffic characteristics Traffic recommendation

Research Title: An Object Classification Approach for Autonomous Vehicles Using Machine Learning Techniques

Author: Maram Bani Younes, Published Year: 2023

World Electric Vehicle Journal, 14

Faculty: Information Technology

Abstract: An intelligent, accurate, and powerful object detection system is required for automated driving systems to keep these vehicles aware of their surrounding objects. Thus, vehicles adapt their speed and operations to avoid crashing with the existing objects and follow the driving rules around the existence of emergency vehicles and installed traffic signs. The objects considered in this work are summarized by regular vehicles, big trucks, emergency vehicles, pedestrians, bicycles, traffic lights, and traffic signs on the roadside. Autonomous vehicles are equipped with high-quality sensors and cameras, LiDAR, radars, and GPS tracking systems that help to detect existing objects, identify them, and determine their exact locations. However, these tools are costly and require regular maintenance. This work aims to develop an intelligent object classification mechanism for autonomous vehicles. The proposed mechanism uses machine learning technology to predict the existence of investigated objects over the road network early. We use different datasets to evaluate the performance of the proposed mechanism. Accuracy, Precision, F1-Score, G-Mean, and Recall are the measures considered in the experiments. Moreover, the proposed object classification mechanism is compared to other selected previous techniques in this field. The results show that grouping the dataset based on their mobility nature before applying the classification task improved the results for most of the algorithms, especially for vehicle detection.

Keywords: autonomous vehicle; object detection; object classification; Udacity dataset; BDD100K dataset; machine learning; road network

Research Title: Review of Security Challenges in Mobile Cloud Computing Applications

Author: Maram Bani Younes, Published Year: 2023

2022 International Conference on Emerging Trends in Computing and Engineering Applications (ETCEA, Karak, Jordan

Faculty: Information Technology

Abstract: Mobiles and smartphones recently store huge amounts of valuable information such as personal information, financial transactions, social applications, and call records. These devices allow the transmission of data, by voice, text, or video, at anytime and everywhere. They enable easy access to the required information. The huge development of mobile devices has introduced the ability to use new and advanced applications. In several scenarios, the advanced applications require a connection to the cloud services. This affects the general environment, infrastructure, and security challenges of mobile applications. In this paper, we mainly aim to investigate the security challenges and issues of Mobile Cloud Computing (MCC) applications. First, we discuss the most popular applications of MCC. Then, we present an adversary and threat model that determines the main security challenges and issues in these applications. We summarize some security techniques, that have been used to tackle these challenges and determine their main requirements. Finally, clear recommendations regarding the directions and required research in this field are given in the paper.

Keywords: Wireless communication , Threat modeling , Cloud computing , Market research , Mobile handsets , Malware , Mobile applications

Research Title: Machine learning based prediction with parameters tuning of multi-label real road vehicles characteristics

Author: Maram Bani Younes, Published Year: 2022

ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks, canada

Faculty: Information Technology

Abstract: The real-time traffic characteristics on the road network highly affect the safety conditions and the driving behaviors there. Early detection of crowded areas or hazardous conditions on the road network should affect the drivers' decisions and behavior to guarantee smooth and comfortable trips. Machine learning mechanisms have been mainly used for general prediction after extensive training processes. Over the road networks, trained machines could be really helpful to obtain instant predictions that assist drivers and autonomous vehicles there. However, the quality and efficiency of these machines are affected by several criteria including the quality of the used dataset and the tuning of the parameters of the regression algorithm. In this work, we investigate the performance of the most popular regression algorithms in terms of temporal prediction of the traffic characteristics in a real road scenario. Moreover, we optimize the regression algorithm by tuning the parameters using the grid search technique. From the experimental results, we can clearly notice the enhancements in predicting the traffic characteristics for different periods of time. We have observed that the number of neighbors, the distance, and the metric parameters' values are best tuned with the values of 4, 'Manhattan', and 'Distance', respectively, for the K-Nearest Neighbor (KNN) regression algorithm.

Keywords: traffic prediction, machine learning

Research Title: Towards green driving: A review of efficient driving techniques

Author: Maram Bani Younes, Published Year: 2022

World Electric Vehicle Journal, 13

Faculty: Information Technology

Abstract: The exponential increase in the number of daily traveling vehicles has exacerbated global warming and environmental pollution issues. These problems directly threaten the continuity and quality of life on the planet. Several techniques and technologies have been used and developed to reduce fuel consumption and gas emissions of traveling vehicles over the road network. Here, we investigate some solutions that assist drivers to follow efficient driving tips during their trips. Advanced technologies of communications or vehicle manufacturing have enhanced traffic efficiency over road networks. In addition, several advisory systems have been proposed to recommend to drivers the most efficient speed, route, or other decisions to follow towards their targeted destinations. These recommendations are selected according to the real-time traffic distribution and the context of the road network. In this paper, different high fuel consumption scenarios are investigated over the road networks. Next, the details of efficient driving techniques that were proposed to tackle each case accordingly are reviewed and categorized for downtown and highway driving. Finally, a set of remarks and existing gaps are reported to researchers in this field.

Keywords: green driving; road context; driving assistance; traffic situation

Research Title: Toward Creating Software Architects Using Mobile Project-Based Learning Model (Mobile-PBL) for Teaching Software Architecture

Author: Lamis Al-Qoran, Published Year: 2023

Faculty: Information Technology

Abstract: Project-based learning (PBL) promotes increased levels of learning, deepens student understanding of acquired knowledge, and improves learning motivation. Students develop their ability to think and learn independently through depending on themselves in searching for knowledge, planning, exploration, and looking for solutions to practical problems. Information availability, student engagement, and motivation to learn all increase with mobile learning. The teaching process may be enhanced by combining the two styles. This paper proposes and evaluates a teaching model called Mobile Project-Based Learning (Mobile-PBL) that combines the two learning styles. The paper investigates how significantly Mobile-PBL can benefit students. The traditional lecture method used to teach the software architecture module in the classroom is not sufficient to provide students with the necessary practical experience to earn a career as software architects in the future. Therefore, the first author tested the use of the model for teaching the software architecture module at Philadelphia University’s Software Engineering Department on 62 students who registered for a software architecture course over three semesters. She compared the results of using the model for teaching with those results that were obtained when using the project-based learning (PBL) approach alone. The students’ opinions regarding the approach, any problems they had, and any recommendations for improvement were collected through a focus group session after finishing each semester and by distributing a survey to students to evaluate the effectiveness of the used model. Comments from the students were positive, according to the findings. The projects were well-received by the students, who agreed that it gave them a good understanding of several course ideas and concepts, as well as providing them with the required practical experience. The students also mentioned a few difficulties encountered while working on the projects, including student distraction from social media and the skills that educators and learners in higher education institutions are expected to have.

Keywords: software architecture education; Jordanian higher education; project-based learning; mobile learning

Research Title: State of the Art of Mobile Learning in Jordanian Higher Education: An Empirical Study

Author: Lamis Al-Qoran, Published Year: 2023

Multimodal Technologies and Interaction, 7

Faculty: Information Technology

Abstract: A new approach to learning is mobile learning (m-learning), which makes use of special features of mobile devices in the education sector. M-learning is becoming increasingly common in higher education institutions all around the world. The use of mobile devices for education and learning has also gained popularity in Jordan. Unlike studies about Jordan, there are many studies that thoroughly analyze the situation of m-learning in other countries. Thus, it is important to understand the current situation of m-learning at Jordanian universities, especially in light of the COVID-19 pandemic. While there have been some studies conducted prior to COVID-19 and a few studies after COVID-19, there is a need for a comprehensive study that provides an in-depth exploration of the current situation, student adoption, benefits, disadvantages, and challenges, particularly following COVID-19. Therefore, this study utilizes a sequential exploratory mixed research method to investigate the current state of the art of m-learning in Jordanian higher education with a particular focus on student adoption, benefits, disadvantages, and challenges. Firstly, the study explores the existing literature on m-learning and conducts 15 interviews with educators and learners in three Jordanian universities to gain insights into their experiences with m-learning. The study then distributes a survey to students at four Jordanian universities, representing both public and private universities, to generalize the results from the qualitative study. Additionally, the study investigates the relationship between student enrollment in public/private universities and the adoption of m-learning. The study came to the conclusion that students have a positive opinion of m-learning and are also willing to use it. However, there are a number of disadvantages and challenges to its adoption. Additionally, there is a relationship between student enrolment in public/private universities and the adoption of m-learning. These findings have important implications for institutions that want to incorporate m-learning into their undergraduate and graduate degree programs, as they aid decision-makers in these universities in creating frameworks that may be able to meet the needs of m-learning.

Keywords: educational mobile applications; mobile learning; Jordanian higher education; perceptions; adoption