Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather conditions. Navigating the intricacies of modeling the intricate interaction among these elements, creating universal representations, and employing them to address transportation issues presents a significant endeavor. Yet, these intricacies comprise just one facet of the multifaceted trials confronting contemporary ITS. This paper offers an all-encompassing survey exploring Deep learning (DL) utilization in ITS, primarily focusing on practitioners' methodologies to address these multifaceted challenges. The emphasis lies on the architectural and problem-specific factors that guide the formulation of innovative solutions. In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. Besides, we identify several future challenges and research directions that can push the boundaries of ITS, including the critical aspects of explainability, transfer learning, hybrid models, privacy and security, and ultra-reliable low-latency communication. Our aim for this survey is to bridge the gap between the burgeoning DL and transportation communities. By doing so, we aim to facilitate a deeper comprehension of the challenges and possibilities within this field. We hope that this effort will inspire further exploration of fresh perspectives and issues, which, in turn, will play a pivotal role in shaping the future of transportation systems.

This paper aims to explore integrating various ML techniques into ISAC systems, covering various applications. These applications span intelligent vehicular networks, encompassing unmanned aerial vehicles (UAVs) and autonomous cars, as well as radar applications, localization and tracking, millimeter wave (mmWave) and Terahertz (THz) communication, and beamforming. The contributions of this paper lie in its comprehensive survey of ML-based works in the ISAC domain and its identification of challenges and future research directions. By synthesizing the existing knowledge and proposing new research avenues, this survey serves as a valuable resource for researchers, practitioners, and stakeholders involved in advancing the capabilities of ISAC systems in the context of 6G networks.

The number of disasters has increased over the past decade where these calamities significantly affect the functionality of communication networks. In the context of 6G, airborne and spaceborne networks offer hope in disaster recovery to serve the underserved and to be resilient in calamities. Therefore, this paper surveys the state-of-the-art literature on post-disaster wireless communication networks and provides insights for the future establishment of such networks. In particular, we first give an overview of the works investigating the general procedures and strategies for counteracting any large-scale disasters. Then, we present the possible technological solutions for post-disaster communications, such as the recovery of the terrestrial infrastructure, installing aerial networks, and using spaceborne networks. Afterward, we shed light on the technological aspects of post-disaster networks, primarily the physical and networking issues. We present the literature on channel modeling, coverage and capacity, radio resource management, localization, and energy efficiency in the physical layer and discuss the integrated space-air-ground architectures, routing, delay-tolerant/software-defined networks, and edge computing in the networking layer. This paper also presents interesting simulation results which can provide practical guidelines about the deployment of ad hoc network architectures in emergency scenarios. Finally, we present several promising research directions, namely backhauling, placement optimization of aerial base stations, and the mobility-related aspects that come into play when deploying aerial networks, such as planning their trajectories and the consequent handovers.

Edge computing is one of the key driving forces to enable Beyond 5G (B5G) and 6G networks. Due to the unprecedented increase in traffic volumes and computation demands of future networks, Multi-access Edge Computing (MEC) is considered as a promising solution to provide cloud-computing capabilities within the radio access network (RAN) closer to the end users. There has been a huge amount of research on MEC and its potential applications; however, very little has been said about the key factors of MEC deployment to meet the diverse demands of future applications. In this article, we present key considerations for edge deployments in B5G/6G networks including edge architecture, server location and capacity, user density, security etc. We further provide state-of-the-art edge-centric services in future B5G/6G networks. Lastly, we present some interesting insights and open research problems in edge computing for 6G networks.

Artificial neural network (ANN) ability to learn, correct errors, and transform a large amount of raw data into useful medical decisions for treatment and care have increased its popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients’ healthcare decisions and efficient disease diagnosis. We thoroughly review different types of ANNs presented in the existing literature that advanced ANNs adaptation for complex applications. Moreover, we also investigate ANN’s advances for various disease diagnoses and treatments such as viral, skin, cancer, and COVID-19. Furthermore, we propose a novel deep Convolutional Neural Network (CNN) model called ConXNet for improving the detection accuracy of COVID-19 disease. ConXNet is trained and tested using different datasets, and it achieves more than 97% detection accuracy and precision, which is significantly better than existing models. Finally, we highlight future research directions and challenges such as complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.

Future small satellites require the development of reconfigurable antennas. Designing such antennas, especially single port patch antennas with circular polarization (CP) is a challenging task. Therefore, we propose both right-hand/left- hand circularly polarized (RHCP/LHCP) antenna which can reconfigure. The proposed antenna follows patch topology with E-shape that is single-layer and single-feed with two RF switches. The switches can alter the polarization in real-time. We also show various properties of the proposed antenna, such as radiation pattern, impedance matching, axial ratio, and bandwidth through simulations and measurements. The proposed model shows excellent performance and agrees well with the measurements. The performance of the antenna shows an effective bandwidth of 2.45 GHz to 2.82 GHz with a maximum gain of 9.88 dB at 2.55 GHz. The symmetry of the antenna radiation is preserved by switching between the LHCP and RHCP polarization modes.

Localization of sensor nodes in the Internet of Underwater Things (IoUT) is of considerable significance due to its various applications, such as navigation, data tagging, and detection of underwater objects. Therefore, in this paper, we propose a hybrid Bayesian multidimensional scaling (BMDS) based localization technique that can work on a fully hybrid IoUT network where the nodes can communicate using either optical, magnetic induction, and acoustic technologies. These communication technologies are already used for communication in the underwater environment; however, lacking localization solutions. Optical and magnetic induction communication achieves higher data rates for short communication. On the contrary, acoustic waves provide a low data rate for long-range underwater communication. The proposed method collectively uses optical, magnetic induction, and acoustic communication-based ranging to estimate the underwater sensor nodes’ final locations. Moreover, we also analyze the proposed scheme by deriving the hybrid Cramer-Rao lower bound (H-CRLB). Simulation results provide a complete comparative analysis of the proposed method with the literature.

Following recent advancements in Terahertz (THz) technology, THz communications are currently being celebrated as key enablers for various applications in future generations of communication networks. While typical communication use cases are over medium-range air interfaces, the inherently small beamwidths and transceiver footprints at THz frequencies support nano-communication paradigms. In particular, the use of the THz band for in-body and on-body communications has been gaining attention recently. By exploiting the accurate THz sensing and imaging capabilities, body-centric THz biomedical applications can transcend the limitations of molecular, acoustic, and radio-frequency solutions. In this paper, we study the use of the THz band for body-centric networks, by surveying works on THz device technologies, channel and noise modeling, modulation schemes, and networking topologies. We also promote THz sensing and imaging applications in the healthcare sector, especially for detecting zootonic viruses such as Coronavirus. We present several open research problems for body-centric THz networks.

Given the increasing number of space-related applications, research in the emerging space industry is becoming more and more attractive. One compelling area of current space research is the design of miniaturized satellites, known as CubeSats, which are enticing because of their numerous applications and low design-and-deployment cost. The new paradigm of connected space through CubeSats makes possible a wide range of applications, such as Earth remote sensing, space exploration, and rural connectivity. CubeSats further provide a complementary connectivity solution to the pervasive Internet of Things (IoT) networks, leading to a globally connected cyber-physical system. This paper presents a holistic overview of various aspects of CubeSat missions and provides a thorough review of the topic from both academic and industrial perspectives. We further present recent advances in the area of CubeSat communications, with an emphasis on constellation-and-coverage issues, channel modeling, modulation and coding, and networking. Finally, we identify several future research directions for CubeSat communications, including Internet of space things, low-power long-range networks, and machine learning for CubeSat resource allocation.