The emerging Internet-of-Things (IoT) network is expected to connect billions of devices, creating intelligent environments and transforming our daily lives. However, due to large-scale deployment, the broadcast nature of wireless channels, and the limited energy and computation capabilities of IoT devices, such networks are highly vulnerable to cyber-attacks and malware infiltration. In the presence of eavesdroppers, establishing a giant connection where devices can securely and privately communicate is particularly challenging. This motivates our study of a physical layer security (PLS) approach that introduces artificial noise (AN) into transmissions. Using tools from percolation theory, we analyze the probability of legitimate devices forming a giant component in both signal-to-noise ratio (SNR)- and signal-to-interference-plus-noise ratio (SINR)-based connectivity and intrinsically secure communication graphs. We show that by carefully designing the AN power fraction, legitimate devices can be protected from eavesdropping regardless of the eavesdroppers’ density and capabilities, leading to a significant reduction in the critical percolation density. We also prove the existence of an optimal AN fraction that minimizes this critical density, which remains consistent across both SNR and SINR graph models. Furthermore, we characterize the percolation behavior in SINR-based graphs and reveal that, while AN reduces the percolation threshold, efficient interference cancellation is necessary to counteract the reduced data transmission power and maintain robust connectivity.

Unmanned Aerial Vehicles (UAVs) are extensively used to collect sensing data from Internet of Things (IoT) networks. Facing with widely distributed Sensor Nodes (SNs), multiple UAVs collaborate to accomplish coverage access of SNs. Age of Information (Aol) is utilized to evaluate the timeliness of sensing data. However, SNs encounter a trade-off between remaining inactive to extend their lifespan and data generation to ensure task sensing requirements. Limited coverage capabilities lead to UAVs frequently traveling between SNs, consuming energy and reducing collection efficiency. Delays in information updates arise from the unsynchronized between UAV cruising times and the dynamic sensing frequencies of SNs. To tackle these challenges, we propose an multi-UAV collaboration framework that integrates coordinated data generation from SNs with UAV data collection. Additionally, we design an AoI and Energy Consumption Minimization (AECM) algorithm to jointly optimize sensing frequencies, UAV state switching, trajectory planning, and SN association. Simulation results demonstrate that our algorithm reduces energy consumption by 30 % while achieving a significantly lower AoI compared to traditional methods.

Urban air mobility (UAM) relies on developing aerospace industries, where safe aviation and efficient communication are critical features of aircraft. However, it is challenging for aircraft to sustain efficient air-ground communication in urban circumstances. Without continuous air-ground communication, aircraft may experience course deviation and safety accidents. To address these problems, a reconfigurable intelligent surface (RIS)-aided trajectory optimization scheme is proposed enabling efficient air-ground communication and safe aviation in UAM with a layered airspace structure. This article first devises a dual-plane RIS communication scheme for layered airspace. It fully engages the omnidirectional and directional signal attributes to reduce the transmission delay of the air-ground communication. Based on the dual-plane RIS configuration, we jointly develop the intra- and interlayer trajectory scheme to optimize communication and safe aviation. In the intralayer trajectory optimization, we propose a dual-time-scale flight scheme to improve communication capacity and horizontal flight safety. Meanwhile, we propose a safe layer-switching method to ensure collision avoidance during vertical flight in the interlayer trajectory optimization. The communication load of the proposed scheme can be improved 40% and the time of safe separation restoration can be lessened 66% compared with the benchmarks in the layered airspace.

Urban Air Mobility (UAM), powered by flying cars, is poised to revolutionize urban transportation by expanding vehicle travel from the ground to the air. This advancement promises to alleviate congestion and enable faster commutes. However, the fast travel speeds mean vehicles will encounter vastly different environments during a single journey. As a result, onboard learning systems need access to extensive environmental data, leading to high costs in data collection and training. These demands conflict with the limited in-vehicle computing and battery resources. Fortunately, learning model sharing offers a solution. Well-trained local Deep Learning (DL) models can be shared with other vehicles, reducing the need for redundant data collection and training. However, this sharing process relies heavily on efficient vehicular communications in UAM. To address these challenges, this paper leverages the multi-hop Reconfigurable Intelligent Surface (RIS) technology to improve DL model sharing between distant flying cars. We also employ knowledge distillation to reduce the size of the shared DL models and enable efficient integration of non-identical models at the receiver. Our approach enhances model sharing and onboard learning performance for cars entering new environments. Simulation results show that our scheme improves the total reward by 85% compared to benchmark methods.

The terahertz spectrum has emerged as a solution to alleviate the exhaustion of low-frequency resources, and meet the high-speed, large bandwidth transmission requirements of applications such as vehicular entertainment. However, the limited transmitting power of terahertz modules underscores the importance of researching high energy-efficient green communication schemes. The inherently low gain of terahertz phased arrays and Intelligent Reflecting Surfaces (IRS) fails to meet the energy efficiency demands, while economical high-gain directional antennas face challenges in achieving beam alignment within rapidly changing mobile environments. This paper addresses the challenges of utilizing high-gain directional antennas for terahertz communication in high-speed mobile scenarios. Unlike traditional network solutions that place antennas near the service area, our proposed green communication method positions the antennas at locations distant from the service area. This strategy leverages distance to expand the projection of narrow beams on the plane, thereby increasing the beam coverage area. Furthermore, through performance analysis, we demonstrate that our method not only offers a cost-effective advantage but also surpasses conventional method that employ state-of-the-art IRS or phased arrays in terms of energy efficiency. We also analyze the signal obstruction issues within the proposed scheme, calculate the coverage probability, and validate the derived expressions through simulations.

Urban Air Mobility (UAM) expands roads from the ground to the near-ground space, envisioned as a revolution for congestion alleviation and fast commutes. However, UAM is still in its infancy, encountering the safety flying challenge: high environmental perception requirements for Autonomous Air Vehicles (AAVs) conflict with limited onboard computing and poor communication quality. The primary purpose of this paper is attempted to facilitate the perception of AAVs by exploring collaborative learning through learning model propagation and integration. Specifically, we leverage well-trained perception learning models of local AAVs to boost the onboard training of new entrants by Knowledge Distillation (KD) technology. Therefore, a knowledge distillation-based model propagation protocol is proposed to obtain the well-trained learning models of nearby AAVs. This protocol also takes transmission errors between AAVs into account. Moreover, we develop an error repair scheme to reduce the upper bound delay of model propagation. Simulation results verify the efficiency of the proposed scheme to improve the AAV onboard learning performance whenever it encounters new environments.

Urban air mobility (UAM) expands vehicles from the ground to the near-ground space, envisioned as a revolution for transportation systems. Comprehensive scene perception is the foundation for autonomous aerial driving. However, UAM encounters the intelligent perception challenge: high-perception learning requirements conflict with the limited sensors and computing chips of flying cars. To overcome the challenge, federated learning (FL) and other collaborative learning have been proposed. It enables resource-limited devices to conduct onboard deep learning (DL) collaboratively. But traditional FL relies on a central integrator for DL model aggregation, which is difficult to deploy in dynamic UAM environments. The fully decentralized learning schemes may be the intuitive solution while the convergence of decentralized learning cannot be guaranteed. Accordingly, this article explores reconfigurable intelligent surfaces (RISs)-empowered decentralized FL (DFL), taking account of topological attributes to facilitate the DFL performance with convergence guarantee. Several DFL topological criteria are proposed for optimizing the transmission delay and convergence rate. Subsequently, we innovatively leverage the RIS link construction and deconstruction ability to remold the current network based on the proposed topological criteria. This article rethinks the functions of RIS from the perspective of the network layer. Furthermore, a deep deterministic policy gradient-based RIS phase shift control algorithm is developed to reshape the communication network. Simulation experiments are conducted over MobileNet-based multiview learning to verify the efficiency of the DFL framework.

Multirobot system for manufacturing is an Industry Internet of Things (IIoT) paradigm with significant operational cost savings and productivity improvement, where unmanned aerial vehicles (UAVs) are employed to control and implement collaborative productions without human intervention. This mission-critical system relies on 3-dimension (3-D) scene recognition to improve operation accuracy in the production line and autonomous piloting. However, implementing 3-D point cloud learning, such as Pointnet, is challenging due to limited sensing and computing resources equipped with UAVs. Therefore, we propose a digital twin (DT) empowered knowledge distillation (KD) method to generate several lightweight learning models and select the optimal model to deploy on UAVs. With a digital replica of the UAVs preserved at the edge server, the DT system controls the model-sharing network topology and learning model structure to improve recognition accuracy further. Moreover, we employ network calculus to formulate and solve the model sharing configuration problem toward minimal resource consumption, as well as convergence. Simulation experiments are conducted over a popular point cloud data set to evaluate the proposed scheme. Experiment results show that the proposed model-sharing scheme outperforms the individual model in terms of computing resource consumption and recognition accuracy.

Wireless communication in the TeraHertz band (0.1--10 THz) is envisioned as one of the key enabling technologies for the future sixth generation (6G) wireless communication systems scaled up beyond massive multiple input multiple output (Massive-MIMO) technology. However, very high propagation attenuations and molecular absorptions of THz frequencies often limit the signal transmission distance and coverage range. Benefited from the recent breakthrough on the reconfigurable intelligent surfaces (RIS) for realizing smart radio propagation environment, we propose a novel hybrid beamforming scheme for the multi-hop RIS-assisted communication networks to improve the coverage range at THz-band frequencies. Particularly, multiple passive and controllable RISs are deployed to assist the transmissions between the base station (BS) and multiple single-antenna users. We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss. To improve the convergence of the proposed DRL-based algorithm, two algorithms are then designed to initialize the digital beamforming and the analog beamforming matrices utilizing the alternating optimization technique. Simulation results show that our proposed scheme is able to improve 50\% more coverage range of THz communications compared with the benchmarks. Furthermore, it is also shown that our proposed DRL-based method is a state-of-the-art method to solve the NP-hard beamforming problem, especially when the signals at RIS-assisted THz communication networks experience multiple hops.

With the rapid development of autonomous driving technologies, it becomes difficult to reconcile the conflict between ever-increasing demands for high process rate in the intelligent automotive tasks and resource-constrained on-board processors. Fortunately, vehicular edge computing (VEC) has been proposed to meet the pressing resource demands. Due to the delay-sensitive traits of automotive tasks, only a heterogeneous vehicular network with multiple access technologies may be able to handle these demanding challenges. In this article, we propose an intelligent task offloading framework in heterogeneous vehicular networks with three Vehicle-to-Everything (V2X) communication technologies, namely Dedicated Short Range Communication (DSRC), cellular-based V2X (C-V2X) communication, and millimeter wave (mmWave) communication. Based on stochastic network calculus, this article firstly derives the delay upper bounds of different offloading technologies with certain failure probabilities. Moreover, we propose a federated Q-learning method that optimally utilizes the available resources to minimize the communication/computing budgets and the offloading failure probabilities. Simulation results indicate that our proposed algorithm can significantly outperform the existing algorithms in terms of resource cost and offloading failure probability.

Internet-of-Vehicles (IoV) has the potentials of enhancing automatic driving in various transportation environment. However, there is very little investigation on quantifying the potential influence of automatic driving applications with the road efficiency in IoV. This paper studies the connection of safety distance to the road congestion under different IoV resource conditions. We propose an elastic wave equation model to reveal the relation between safety distance and road congestion. It can be found that the propagation speed of road congestion is largely affected by the safety distance. To recoup the efficient road safety and alleviate road congestion, an optimization problem is formulated with cooperative communication and computing via platoons that aims to minimize the total safety distance. Since the optimization is a complicated 0-1 programming problem, we propose a practical resource allocation algorithm and solve the problem through Lagrangian relaxation. Simulation experiments show that the proposed algorithm leads to near-optimal results with low complexity but no overhead of vehicular information exchange.