Recent years witness a great deal of interest in artificial intelligence (AI) tools in the area of optimization. AI has developed a large number of tools to solve the most difficult search-and-optimization problems in computer science and operations research. Indeed, metaheuristic-based algorithms are a sub-field of AI. This study presents the use of the metaheuristic algorithm, that is, water cycle algorithm (WCA), in the transportation problem. A stochastic transportation problem is considered in which the parameters supply and demand are considered as random variables that follow the

Automatic lane detection is a classical task in autonomous vehicles that traditional computer vision techniques can perform. However, such techniques lack reliability for achieving high accuracy while maintaining adequate time complexity in the context of real-time detection in complex and dynamic road scenes. Deep neural networks have proved their ability to achieve competing accuracy and time complexity while training them on manually labeled data. Yet, the unavailability of segmentation masks for host lanes in harsh road environments hinders fully supervised methods’ operability on such a

This paper studies the frequency/time selective K-user Gaussian interference channel with secrecy constraints. Two distinct models, namely the interference channel with confidential messages and the interference channel with an external eavesdropper, are analyzed. The key difference between the two models is the lack of channel state information (CSI) of the external eavesdropper. Using interference alignment along with secrecy precoding, it is shown that each user can achieve non-zero secure degrees of freedom (DoF) for both cases. More precisely, the proposed coding scheme achieves K-2/2K-2

A virtualized radio access network (V-RAN) is considered one of the key research points in the development of 5G and the interception of machine learning algorithms in the Telecom industry. Recent technological advancements in Network Function Virtualization (NFV) and Software Defined Radio (SDR) are the main blocks towards V-RAN that have enabled the virtualization of dual-site processing instead of all BBU processing as in the traditional RAN. As a result, several types of research discussed the trade-off between power and bandwidth consumption in V-RAN. Processing at remote locations

This paper aims to help self-driving cars and autonomous vehicles systems to merge with the road environment safely and ensure the reliability of these systems in real life. Crash avoidance is a complex system that depends on many parameters. The forward-collision warning system is simplified into four main objectives: detecting cars, depth estimation, assigning cars into lanes (lane assign) and tracking technique. The presented work targets the software approach by using YOLO (You Only Look Once), which is a deep learning object detector network to detect cars with an accuracy of up to 93%

This article proposes a new problem which is called the Stochastic Travelling Advisor Problem (STAP) in network optimization, and it is defined for an advisory group who wants to choose a subset of candidate workplaces comprising the most profitable route within the time limit of day working hours. A nonlinear binary mathematical model is formulated and a real application case study in the occupational health and safety field is presented. The problem has a stochastic nature in travelling and advising times since the deterministic models are not appropriate for such real-life problems. The

Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Traditionally, clustering algorithms aim at generating a number of disjoint clusters that satisfy some criteria. In this paper, we formulate a novel clustering problem that aims at generating overlapping multihop clusters. Overlapping clusters are useful in many sensor network applications, including intercluster routing, node localization, and time synchronization protocols. We also propose a randomized, distributed multihop clustering algorithm (KOCA) for solving the overlapping

In recent years, many probability models proposed to calculate the collision probability for each vehicle and those models used in collision avoidance algorithms and intersection management algorithms. In this paper, we introduce a method to calculate the collision probability of vehicles at an urban intersection. The proposed model uses the current position, speed, acceleration, and turning direction then each vehicle shares its required information to the roadside unit (RSU) via the Vehicle to Infrastructures (V2I). RSU can predict each vehicle's path in intersections by using the received

With the development of the 5G and Internet of things (IoT) applications, which lead to an enormous amount of data, the need for efficient data-driven algorithms has become crucial. Security concerns are therefore expected to be raised using state-of-the-art information technology (IT) as data may be vulnerable to remote attacks. As a result, this paper provides a high-level overview of machine-learning use-cases for data-driven, maintaining security, or easing telecommunications operating processes. It emphasizes the importance of analyzing the role of machine learning in the

The rising interest in assistive and autonomous driving systems throughout the past decade has led to an active research community in perception and scene interpretation problems like lane detection. Traditional lane detection methods rely on specialized, hand-tailored features which is slow and prone to scalability. Recent methods that rely on deep learning and trained on pixel-wise lane segmentation have achieved better results and are able to generalize to a broad range of road and weather conditions. However, practical algorithms must be computationally inexpensive due to limited resources