Vehicular ad hoc networks (VANETs) are envisioned as the future of intelligent transportation systems, which enable various kinds of applications aiming at improving road safety and transportation efficiency. Uni-cast routing is required for many of these applications. As VANET is expected to be massive in terms of number of nodes and amount of generated information, geographic routing protocols are considered the most suitable for such network owing to their scalability. Due to VANETs' extremely dynamic topology and variable channel condition, multiple metrics related to vehicles' mobility, link quality, and bandwidth availability need to be considered to make more informed and reliable routing decisions. However, some of these metrics might oppose each other. While the main forwarding strategy in geographic routing selects nodes closer to the destination to maximize distance progress, these nodes are most probably located at the border of the communication range where the probability of link breakage increases. Furthermore, the continuous selection of these nodes without considering their available bandwidth might result in higher packet delays and losses. This paper proposes a novel routing protocol based on fuzzy logic systems, which can help in coordinating and analyzing contradicting metrics. The proposed routing protocol combines multiple metrics considering vehicles' position, direction, link quality, and achievable throughput to select the most suitable next-hop for packet forwarding. Results from our simulation experiments of relatively dense urban environments show remarkable performance improvements in terms of packet delivery ratio, end-to-end delay, and total network throughput.

Deaf and hard-of-hearing people mainly communicate using sign language, which is a set of signs made using hand gestures combined with facial expressions to make meaningful and complete sentences. The problem that faces deaf and hard-of-hearing people is the lack of automatic tools that translate sign languages into written or spoken text, which has led to a communication gap between them and their communities. Most state-of-the-art vision-based sign language recognition approaches focus on translating non-Arabic sign languages, with few targeting the Arabic Sign Language (ArSL) and even fewer targeting the Saudi Sign Language (SSL). This paper proposes a mobile application that helps deaf and hard-of-hearing people in Saudi Arabia to communicate efficiently with their communities. The prototype is an Android-based mobile application that applies deep learning techniques to translate isolated SSL to text and audio and includes unique features that are not available in other related applications targeting ArSL. The proposed approach, when evaluated on a comprehensive dataset, has demonstrated its effectiveness by outperforming several state-of-the-art approaches and producing results that are comparable to these approaches. Moreover, testing the prototype on several deaf and hard-of-hearing users, in addition to hearing users, proved its usefulness. In the future, we aim to improve the accuracy of the model and enrich the application with more features.

Smart cities utilize advanced technologies to enhance quality of life by improving urban services, infrastructure, and environmental sustainability. Effective pothole detection and repair strategies are essential for improving quality of life as they directly impact the comfort and safety of road users. In addition to causing serious harm to residents’ lives, potholes can also cause costly vehicle damage. In this study, a pothole detection system utilizing unmanned aerial vehicles, called PDS-UAV, is developed. The system aids in automatically detecting potholes using deep learning techniques and managing their status and repairs. In addition, it allows road users to view an overlay of the detected potholes on the maps based on their selected route, enabling them to avoid the potholes and increase their safety on the roads. Two data collection methods were used, an interview and a questionnaire, to gather data from the target system users. Based on the data analysis, the system’s requirements, design, and implementation were completed. For the pothole detection, a deep learning model using YOLOv8 was developed, which achieved an overall performance of 95%, 98%, and 92% for F1 score, precision, and recall, respectively. Different types of testing has been performed on the target users to ensure the system’s validity, effectiveness, and ease of use, including unit testing, integration testing, and usability testing. As a future work, more features will be added to the system in addition to improving the deep learning model accuracy.

Deaf-mute people have much potential to contribute to society. However, communication between deaf-mutes and non-deaf-mutes is a problem that isolates deaf-mutes from society and prevents them from interacting with others. In this study, an information technology intervention, intelligent gloves (IG), a prototype of a two-way communication glove, was developed to facilitate communication between deaf-mutes and non-deaf-mutes. IG consists of a pair of gloves, flex sensors, an Arduino nano, a screen with a built-in microphone, a speaker, and an SD card module. To facilitate communication from the deaf-mutes to the non-deaf-mutes, the flex sensors sense the hand gestures and connected wires, and then transmit the hand movement signals to the Arduino nano where they are translated into words and sentences. The output is displayed on a small screen attached to the gloves, and it is also issued as voice from the speakers attached to the gloves. For communication from the non-deaf-mutes to the deaf-mute, the built-in microphone in the screen senses the voice, which is then transmitted to the Arduino nano to translate it to sentences and sign language, which are displayed on the screen using a 3D avatar. A unit testing of IG has shown that it performed as expected without errors. In addition, IG was tested on ten participants, and it has been shown to be both usable and accepted by the target users.

Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. Different approaches to effectively modeling complex spatio-temporal correlations within traffic data have been proposed. These approaches often rely on a single model to capture temporal dependencies, which neglects the varying influences of different time periods on traffic flow. Additionally, these models frequently utilize either static or dynamic graphs to represent spatial dependencies, which limits their ability to address complex and overlapping spatial relationships. Moreover, some approaches struggle to fully capture spatio-temporal variations, leading to the exclusion of critical information and ultimately resulting in suboptimal prediction performance. Thus, this paper introduces the Adaptive Spatio-Temporal Attention-Based Multi-Model (ASTAM), an architecture designed to capture spatio-temporal dependencies within traffic data. The ASTAM employs multi-temporal gated convolution with multi-scale temporal input segments to model complex non-linear temporal correlations. It utilizes static and dynamic parallel multi-graphs to facilitate the modeling of complex spatial dependencies. Furthermore, this model incorporates a spatio-temporal self-attention mechanism to adaptively capture the dynamic and long-term spatio-temporal variations in traffic flow. Experiments conducted on four real-world datasets reveal that the proposed architecture outperformed 13 baseline approaches, achieving average reductions of 5.0% in MAE, 13.28% in RMSE, and 6.46% in MAPE across four datasets.