Intelligent Surfaces for Internet of Unmanned Air Vehicles Communications

Dear Colleagues,

Main Focus

Providing the seamless integration of sensing, communication, localization, and computing services in a real-time communication environment, while assuring low latency, high capacity, and high dependability, is the goal of sixth generation (6G) mobile networks. New technologies must be created that can satisfy a variety of quality of service (QoS) standards in order to accomplish sustained capacity development at an acceptable cost and energy consumption. Internet of Things (IoTs) applications that are quickly emerging in fields like smart cities, intelligent transportation, and entertainment services like augmented and virtual reality have accelerated the need for new technologies that can meet the demanding specifications of future mobile networks. The wireless channel, however, presents a serious difficulty and performance constraint. The uncontrolled nature of the wireless propagation channel has an inescapable detrimental effect on the accuracy and flexibility of IoTs networks. This highlights the need for reconfigurable intelligent surfaces (RISs) to enable IoTs applications with low energy consumption, low cost, and low latency.

Internet of Things (IoTs) applications in smart cities, intelligent transportation, and entertainment-focused services like augmented and virtual reality or holographic telepresence are quickly emerging and accelerating the development of new technologies to meet the strict requirements of future mobile networks. However, the wireless channel is an unavoidable factor and a primary hindrance to performance. In addition to being uncontrolled, the wireless propagation channel has an unavoidable detrimental impact on the precision and adaptability of IoTs networks. Such limitations motivate a need to reconfigure intelligent surfaces (RISs) to facilitate IoTs applications with low energy consumption, low cost, and low latency.

Flexible air-to-ground communication utilizing unmanned aerial vehicles (UAVs) is one newly popular technique. The flexibility, cost-effectiveness, and agility of UAVs in deployment make this strategy appealing. UAVs can occasionally establish a line-of-sight (LoS) link when flying at great altitudes. But creating such linkages is difficult for them, especially when dealing with concurrent connections from a lot of ground IoTs devices. In these circumstances, mounting RISs on UAVs can deliver 360-degree full-angle reflection, improving coverage and network performance while introducing flexibility. Flexible RIS-assisted UAV technology has the potential to enhance mobile networks' capacity for user-to-IoTs device connectivity.

Scope:

UAVs can occasionally establish a LoS link when flying at a high elevation. However, UAVs are incapable of establishing such LoS links, especially when considering concurrent connections with a massive number of ground IoT devices. Therefore, combining RIS with UAVs boosts the reliability, energy efficiency, and other performance metrics of wireless communication channels. RISs mounted on airships, high-altitude platforms (HAPs), or UAVs are a promising technology for future IoT applications in order to overcome the limitations of the wireless propagation environment and meet various QoS requirements. The deployment of RIS on UAVs can improve the communication links, network coverage, and throughput of future IoTs or vehicular networks.

This Special Issue aims to tackle the challenges associated with flexible communication in UAVs enabled by reconfigurable intelligent surfaces (RIS) for IoT networks. The topics of interest cover a broad spectrum and include, but are not limited to:

• Prototypes, test beds, and applications for intelligent surface-enabled air–ground IoT networks;
• Privacy protocols, security mechanisms, and blockchain in RIS-enabled air–ground communication;
• Techniques for channel estimation, modeling, and pilot decontamination in RIS air-to-ground communication;
• Swarm intelligence, localization, deep/reinforcement learning, joint optimization, and efficient approaches in RIS-enabled air–ground communication;
• ML-driven architectures for intelligent IoT air–ground communication;
• Integrated technologies for RIS-enabled air–ground IoT, covering sensing, localization, computing, and communication, along with privacy protocols and blockchain-based security.

Purpose of the Special Issue:

Research on the RIS-assisted UAV for future IoTs network is still in its very early stage. The design of beamforming for IoTs networks (passive/active beamforming at the RIS and passive/active beamforming at the UAV), scheduling for IoTs transmissions, maximizing the secrecy rate of vulnerable IoTs devices, optimizing the transmit power of UAVs, residual power of nodes, the phase shifters of RIS for energy-limited IoTs devices, the UAV's trajectory design, and iterative procedures to resolve a non-convex sum-rate problem are just some examples of the serious challenges of IoTs communication using RIS-assisted UAVs not yet tackled properly for targeted scenarios.

We present this proposal with the aim to use RIS-enabled UAV communications to resolve the most recent future IoTs communication challenges. Radio engineering, mobile communications, robotics, and computer science are professions and disciplines that will contribute to this topic. We anticipate receiving a large number of contributions due to the popularity of the research on RIS as well as UAV communications and the subsequent inclusion of RIS and UAVs in mobile network standards.

Dr. Ishtiaq Ahmad
Dr. Lina Mohjazi
Dr. Elli Kartsakli
Dr. Manar Mohaisen
Guest Editors

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• machine learning
• Internet of Things
• unmanned air vehicles
• resource management
• privacy protocols and security mechanisms
• channel estimation, channel modeling, and pilot decontamination techniques
• prototypes and test beds for intelligent surfaces
• intelligence of swarm and localization
• deep/reinforcement learning for intelligent surfaces
• joint optimization
• offloading optimization in edge machine learning
• spectrum management
• energy-efficient techniques