In commercial unmanned aerial vehicle (UAV) applications, one of the main restrictions is UAVs' limited battery endurance when executing persistent tasks. With the mature of wireless power transfer (WPT) technologies, by leveraging ground vehicles mounted with WPT facilities on their proofs, we propose a mobile and collaborative recharging scheme for UAVs in an on-demand manner. Specifically, we first present a novel air-ground cooperative UAV recharging framework, where ground vehicles cooperatively share their idle wireless chargers to UAVs and a swarm of UAVs in the task area compete to get recharging services. Considering the mobility dynamics and energy competitions, we formulate an energy scheduling problem for UAVs and vehicles under practical constraints. A fair online auction-based solution with low complexity is also devised to allocate and price idle wireless chargers on vehicular proofs in real time. We rigorously prove that the proposed scheme is strategy-proof, envy-free, and produces stable allocation outcomes. The first property enforces that truthful bidding is the dominant strategy for participants, the second ensures that no user is better off by exchanging his allocation with another user when the auction ends, while the third guarantees the matching stability between UAVs and UGVs. Extensive simulations validate that the proposed scheme outperforms benchmarks in terms of energy allocation efficiency and UAV's utility.

Due to the fading characteristics of wireless channels and the burstiness of data traffic, how to deal with congestion in Ad-hoc networks with effective algorithms is still open and challenging. In this paper, we focus on enabling congestion control to minimize network transmission delays through flexible power control. To effectively solve the congestion problem, we propose a distributed cross-layer scheduling algorithm, which is empowered by graph-based multi-agent deep reinforcement learning. The transmit power is adaptively adjusted in real-time by our algorithm based only on local information (i.e., channel state information and queue length) and local communication (i.e., information exchanged with neighbors). Moreover, the training complexity of the algorithm is low due to the regional cooperation based on the graph attention network. In the evaluation, we show that our algorithm can reduce the transmission delay of data flow under severe signal interference and drastically changing channel states, and demonstrate the adaptability and stability in different topologies. The method is general and can be extended to various types of topologies.

The true-time delay (TTD) units are critical for solving beam squint and frequency selective fading in Wideband Large-Scale Antenna Systems (LSASs). In this work, we propose a TTD array architecture for wideband multi-beam tracking that eliminates the beam squint phenomenon and filters out interference signals by applying a spatial filter and time delay estimations (TDEs). The paper presents a novel approach to spatial filter design by introducing a transformation matrix that can optimize the beam response in a specific direction and at a specific frequency. Using the variable fractional delay (VFD) filters, we propose a TDE algorithm with a Newton-Raphson iteration update process that corrects the arrival time delay difference between sensors. Simulations and examples have demonstrated that the proposed architecture can achieve beam tracking within 10 ms at the low signal-to-noise ratio (SNR) and demodulation loss is less than 0.5 dB in wideband multi-beam scenarios.

In this paper, we focus on providing data provenance auditing schemes for distributed denial of service (DDoS) defense in intelligent internet of things (IoT). To achieve effective DDoS defense, we introduce a two-layer collaborative blockchain framework to support data auditing. Specifically, using data scattered among intelligent IoT devices, switch gateways self-assemble a layer of blockchain in the local autonomous system (AS), and the main chain with controller participation can be aggregated by its associated layer of blocks once a cycle, to obtain a global security model. To optimize the processing delay of the security model, we propose a process of data pre-validation with the goal of ensuring data consistency while satisfying overhead requirements. Since the flood of identity spoofing packets, it is difficult to solve the identity consistency of data with traditional detection methods, and accountability cannot be pursued afterwards. Thus, we proposed a $Packet \; Traceback \; Telemetry $ (PTT) scheme, based on in-band telemetry, to solve the problem. Specifically, the PTT scheme is executed on the distributed switch side, the controller to schedule and select routing policies. Moreover, a tracing probabilistic optimization is embedded into the PTT scheme to accelerate path reconstruction and save device resources. Simulation results show that the PTT scheme can reconstruct address spoofing packet forward path, reduce the resource consumption compared with existing tracing scheme. Data tracing audit method has fine-grained detection and feasible performance.

The emergence of various commercial and industrial Internet of Things (IoT) devices has brought great convenience to people's life and production. Both low-power, massively connected mMTC devices (MDs) and highly reliable, low-latency URLLC devices (UDs) play an important role in different application scenarios. However, when dense MDs and UDs periodically initiate random access (RA) to connect the base station and send data, due to the limited preamble resources, preamble collisions are likely to occur, resulting in device access failure and data transmission delay. At the same time, due to the high-reliability demands of UDs, which require smooth access and fast data transmission, it is necessary to reduce the failure rate of their RA process. To this end, we propose an intelligent preamble allocation scheme, which uses hierarchical reinforcement learning to partition the UD exclusive preamble resource pool at the base station side and perform preamble selection within each RA slot at the device side. In particular, considering the limited processing capacity and energy of IoT devices, we adopt the lightweight Q-learning algorithm on the device side and design simple states and actions for them. Experimental results show that the proposed intelligent scheme can significantly reduce the transmission failure rate of UDs and improve the overall access success rate of devices.

Integration of digital twin (DT) and wireless channel provides new solution of channel modeling and simulation, and can assist to design, optimize and evaluate intelligent wireless communication system and networks. With DT channel modeling, the generated channel data can be closer to realistic channel measurements without requiring a prior channel model, and amount of channel data can be significantly increased. Artificial intelligence (AI) based modeling approach shows outstanding performance to solve such problems. In this work, a channel modeling method based on generative adversarial networks is proposed for DT channel, which can generate identical statistical distribution with measured channel. Model validation is conducted by comparing DT channel characteristics with measurements, and results show that DT channel leads to fairly good agreement with measured channel. Finally, a link-layer simulation is implemented based on DT channel. It is found that the proposed DT channel model can be well used to conduct link-layer simulation and its performance is comparable to using measurement data. The observations and results can facilitate the development of DT channel modeling and provide new thoughts for DT channel applications, as well as improving the performance and reliability of intelligent communication networking.

As the sixth generation network (6G) emerges, the Internet of remote things (IoRT) has become a critical issue. However, conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks, and the Space-Air-Ground integrated network (SAGIN) holds promise. We propose a novel setup that integrates non-orthogonal multiple access (NOMA) and wireless power transfer (WPT) to collect latency-sensitive data from IoRT networks. To extend the lifetime of devices, we aim to minimize the maximum energy consumption among all IoRT devices. Due to the coupling between variables, the resulting problem is non-convex. We first decouple the variables and split the original problem into four subproblems. Then, we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation (SCA) techniques and slack variables. Finally, simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency, providing valuable insights.

With the rapid development of the Industrial Internet of Things (IIoT), the traditional centralized cloud processing model has encountered the challenges of high communication latency and high energy consumption in handling industrial big data tasks. This paper aims to propose a low-latency and low-energy path computing scheme for the above problems. This scheme is based on the cloud-fog network architecture. The computing resources of fog network devices in the fog computing layer are used to complete task processing step by step during the data interaction from industrial field devices to the cloud center. A collaborative scheduling strategy based on the particle diversity discrete binary particle swarm optimization (PDBPSO) algorithm is proposed to deploy manufacturing tasks to the fog computing layer reasonably. The task in the form of a directed acyclic graph (DAG) is mapped to a factory fog network in the form of an undirected graph (UG) to find the appropriate computing path for the task, significantly reducing the task processing latency under energy consumption constraints. Simulation experiments show that this scheme's latency performance outperforms the strategy that tasks are wholly offloaded to the cloud and the strategy that tasks are entirely offloaded to the edge equipment.