Sheikh Salman Hassan

The proliferation of intelligent transportation systems (ITS) has led to increasing demand for diverse network applications. However, conventional terrestrial access networks (TANs) are inadequate in accommodating various applications for remote ITS nodes, i.e., airplanes and ships. In contrast, satellite access networks (SANs) offer supplementary support for TANs, in terms of coverage flexibility and availability. In this study, we propose a novel approach to ITS data offloading and computation

Non-terrestrial networks (NTNs), which integrate space and aerial networks with terrestrial systems, are a key area in the emerging sixth-generation (6G) wireless networks. As part of 6G, NTNs must provide pervasive connectivity to a wide range of devices, including smartphones, vehicles, sensors, robots, and maritime users. However, due to the high mobility and deployment of NTNs, managing the space-air–sea (SAS) NTN resources, i.e., energy, power, and channel allocation, is a major challenge.

Satellite systems face a significant challenge in effectively utilizing limited communication resources to meet the demands of ground network traffic, characterized by asymmetrical spatial distribution and time-varying characteristics. Moreover, the coverage range and signal transmission distance of low Earth orbit (LEO) satellites are restricted by notable propagation attenuation, molecular absorption, and space losses in sub-terahertz (THz) frequencies. This paper introduces a novel approach t

Next-generation networks need to meet ubiquitous and high data-rate demand. Therefore, this paper considers the throughput and trajectory optimization of terahertz (THz)-enabled unmanned aerial vehicles (UAVs) in the sixth-generation (6G) communication networks. In the considered scenario, multiple UAVs must provide on-demand terabits per second (TB/s) services to an urban area along with existing terrestrial networks. However, THz-empowered UAVs pose some new constraints, e.g., dynamic THz-chan

Non-terrestrial networks (NTN), encompassing space and air platforms, are a key component of the upcoming sixth-generation (6G) cellular network. Meanwhile, maritime network traffic has grown significantly in recent years due to sea transportation used for national defense, research, recreational activities, domestic and international trade. In this paper, the seamless and reliable demand for communication and computation in maritime wireless networks is investigated. Two types of marine user eq

With the emergence of sixth-generation (6G) mobile communication technologies, intelligent gadgets are expanding. Meanwhile, due to the fast rise of marine operations for trade, research, military, oil drilling, and recreational activities, the number of maritime internet-of-things (MIoT) devices is also expanding. On-demand deployment of multiaccess edge computing (MEC) empowered unmanned aerial vehicles (UAVs) to meet the network coverage demand for MIoT devices at the seaside is presented. Th

Maritime network traffic is increasing due to the ongoing need for trade and tourism, thus increasing the demand for convenient, reliable, energy-efficient, and high-speed network access at sea that could be analogous to terrestrial networks. Therefore, to ensure the concept of a connected world under the umbrella of sixth-generation (6G) networks, we propose the next-generation integrated space-oceanic network, which consists of a set of LEO satellites and marine user equipments (MUE). This pap

Sixth-generation (6G) communication networks will fulfill users’ requests for high data speeds and low latency without causing network outages throughout the world. However, marine communication in deep-sea waters is expanding as maritime traffic grows. To serve mission-critical applications, a growing number of maritime end-users require high throughput and low latency. Although deep-sea satellite connections will enable 6G networks, however due to limited service capacity owing to the long pro

We propose using federated learning (FL) in loiv Earth orbit (LEO) satellite networks for the Internet of Remote Things (IoRTs) to enable adaptive learning in massively networked devices while reducing costly traffic in satellite communication (SatCom). In this resource-constrained space setting, FL techniques in LEO satellite-based learning can improve system energy efficiency and save time. However, FL raises security and risk concerns, as local model updates can be used to infer device inform

Non-terrestrial networks (NTNs), which integrate space and aerial networks with terrestrial systems, are a key area in the emerging sixth-generation (6G) wireless networks. As part of 6G, NTNs must provide pervasive connectivity to a wide range of devices, including smartphones, vehicles, sensors, robots, and maritime users. However, due to the high mobility and deployment of NTNs, managing the space-air-sea (SAS) NTN resources, i.e., energy, power, and channel allocation, is a major challenge.

Earth observation satellites generate large amounts of real-time data for monitoring and managing time-critical events such as disaster relief missions. This presents a major challenge for satellite-to-ground communications operating under limited bandwidth capacities. This paper explores semantic communication (SC) as a potential alternative to traditional communication methods. The rationality for adopting SC is its inherent ability to reduce communication costs and make spectrum efficient for

In this paper, we study the minimum input energy for an unmanned aerial vehicle (UAV) while maneuvering in a smart factory. The deployment of UAV in a factory environment for wireless communication between sensors to the central controller promises to provide these services of efficient data collection. A UAV has to traverse the path for data collection from sensors and monitors the production line in a factory. The trajectory optimization of UAV is an important parameter for energy efficiency.

Space-air-ground integrated networks (SAGINs) are emerging as a fundamental architecture for 6G systems to enable massive connectivity, novel applications, extreme data rates, ultra-low latency, and multi-dimensional networking. The complex nature of SAGINs has driven researchers to explore classical machine learning (CML) as a powerful tool for modeling and optimizing these multi-layer networks. Although CML offers many benefits, it will suffer from exponential complexity for SAGIN design, main

Space-air-ground integrated networks (SAGINs) are emerging as a fundamental architecture for 6G systems to enable massive connectivity, novel applications, extreme data rates, ultra-low latency, and multi-dimensional networking. The complex nature of SAGINs has driven researchers to explore classical machine learning (CML) as a powerful tool for modeling and optimizing these multi-layer networks. Although ML offers many benefits, it will suffer from exponential complexity for SAGIN design mainly

The proliferation of intelligent transportation systems (ITS) has led to increasing demand for diverse network applications. However, conventional terrestrial access networks (TANs) are inadequate in accommodating various applications for remote ITS nodes, i.e., airplanes and ships. In contrast, satellite access networks (SANs) offer supplementary support for TANs, in terms of coverage flexibility and availability. In this study, we propose a novel approach to ITS data offloading and computation