In this paper, we attempt to deal with the routing problem in a cognitive unmanned aerial vehicle (UAV) swarm (CU-SWARM), which applies the cognitive radio into a swarm of UAVs within a three-hierarchical aerial-ground integrated network architecture for emergency communications. In particular, the flexibly converged architecture utilizes a UAV swarm and a high-altitude platform to support aerial sensing and access, respectively, over the disaster-affected areas. We develop a Q-learning framework to achieve the intelligent routing with maximum utility for CU-SWARM. To characterize the reward function, we take into account both the routing metric design and the candidate UAV selection optimization. The routing metric is determined by maximizing the utility, which jointly captures the achievable rate of UAV pair and the residual energy of UAV. Besides, under the location, arc, and direction constraints, the circular sector is modeled by properly choosing the central angle and the acceptable signal-to-noise ratio for the UAV. With this setup, we further propose a low-complexity iterative algorithm using the dynamic learning rate to update Q-values during the training process for achieving a fast convergence speed. Extensive simulation results are provided to assess the potential of the Q-learning framework of intelligent routing as well as to verify our overall iterative algorithm via the dynamic learning rate for training procedure. Our findings reveal that the proposed algorithm can significantly increase the accumulated rewards significantly with practical complexity compared to other benchmark schemes with fixed and decaying learning rates.

This paper brings these two technologies together to investigate the current state of AI-powered BC. We begin with an introduction to BC and an overview of the AI algorithms employed in BC. Then, we delve into the recent advances in AI-based BC, covering key areas such as backscatter signal detection, channel estimation, and jammer control to ensure security, mitigate interference, and improve throughput and latency. We also explore the exciting frontiers of AI in BC using B5G/6G technologies, including backscatter-assisted relay and cognitive communication networks, backscatter-assisted MEC networks, and BC with RIS, UAV, and vehicular networks. Finally, we highlight the challenges and present new research opportunities in AI-powered BC. This survey provides a comprehensive overview of the potential of AI-powered BC and its insightful impact on the future of IoT.

The recent development of metasurfaces, which may enable several use cases by modifying the propagation environment, is anticipated to have a substantial effect on the performance of 6G wireless communications. Metasurface elements can produce essentially passive sub-wavelength scattering to enable a smart radio environment. STAR-RIS, which refers to reconfigurable intelligent surfaces (RIS) that can transmit and reflect concurrently (STAR), is gaining popularity. In contrast to the widely studied RIS, which can only reflect the wireless signal and serve users on the same side as the transmitter, the STAR-RIS can both reflect and refract (transmit), enabling 360-degree wireless coverage, thus serving users on both sides of the transmitter. This paper presents a comprehensive review of the STAR-RIS, with a focus on the most recent schemes for diverse use cases in 6G networks, resource allocation, and performance evaluation. We begin by laying the foundation for RIS (passive, active, STAR-RIS), and then discuss the STAR-RIS protocols, advantages, and applications. In addition, we categorize the approaches within the domain of use scenarios, which includes increasing coverage, enhancing physical layer security (PLS), maximizing sum rate, improving energy efficiency (EE), and reducing interference. Next, we will discuss the various strategies for resource allocation and measures for performance evaluation. We aimed to elaborate, compare, and evaluate the literature in terms of setup, channel characteristics, methodology, and objectives. In conclusion, we examine the open research problems and potential future prospects in this field.

The connected and autonomous vehicles   (CAV)   applications and services-based traffic make an extra burden on the already congested cellular networks. Offloading is envisioned as a promising solution to tackle cellular networks’  traffic explosion problem. Notably, vehicular traffic offloading leveraging different vehicular communication network (VCN) modes is one of the potential techniques to address the data traffic problem in cellular networks. This paper surveys the state-of-the-art literature for vehicular data offloading under a communication perspective,  i.e.,  vehicle to vehicle  (V2V),  vehicle to roadside infrastructure  (V2I),  and vehicle to everything  (V2X). First, we pinpoint the significant classification of vehicular data/traffic offloading techniques, considering whether data is to download or upload. Next, for better intuition of each data offloading’s category,  we sub-classify the existing schemes based on their objectives. Then, the existing literature on vehicular data/traffic is elaborated, compared, and analyzed based on approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends.

Integrating digital twins (DTs) and multi-access edge computing (MEC) is a promising technology that realizes edge intelligence in 6G, which has been recognized as the key enabler for Industrial Internet of Things (IIoT). In this paper, we explore a DT-assisted MEC system for the IIoT scenario where a DT server is created as a digital counterpart of the MEC server, via estimating the computation state of the MEC server within the DT modelling cycle. To achieve energy and spectrally efficient offloading, we consider that IIoT devices communicate with industrial gateways (IGWs) through a non-orthogonal multiple access (NOMA) protocol. Each IIoT device has an industrial computation task that can be executed locally or fully offloaded to IGW. We aim to minimize the total task completion delay of all IIoT devices by jointly optimizing the IGW’s subchannel assignment as well as the computation capacity allocation, edge association, and transmit power control of IIoT device. The resulting problem is shown to be a mixed integer non-convex optimization problem, which is NP-hard and challenging to solve. We decompose the original problem into four solvable sub-problems, and then propose an overall alternating optimization algorithm to solve the sub-problems iteratively until convergence. Validated via simulations, the proposed scheme shows superiority to the benchmarks in reducing the total task completion delay and increasing the percentage of offloading IIoT devices.

Combination of the industrial Internet of Things (IIoT) and federated learning (FL) is deemed as a promising solution to realizing Industry 4.0 and beyond. In this paper, we focus on a hierarchical collaborative FL architecture over the IIoT systems, where the three-layer architectural design is conceived for supporting the training process. To effectively balance among the learning speed, energy consumption, and packet error rate for edge aggregation with regard to the participating IIoT devices, a weighted learning utility function is developed from the perspective of the fusing multiple performance metrics. An optimization problem is formulated to maximize the weighted learning utility by jointly optimizing the edge association as well as the allocations of resource block (RB), computation capacity, and transmit power of each IIoT device, under the practical constraints of the FL training process. The resulting problem is a non-convex and mixed integer optimization problem, and consequently it is difficult to solve. By resorting to the block coordinate descent method, we propose an overall alternating optimization algorithm to solve this problem in an iterative way. Specifically, in each iteration, for given transmit power and computation capacity, the sub-problem of joint RB assignment and edge association is transformed to a three-uniform weighted hypergraph model, which is solved by the local search-based three-dimensional hypergraph matching algorithm. Second, given RB assignment, edge association, and computation capacity, we employ the successive convex approximation method to tackle the sub-problem for optimizing the transmit power by turning it into a convex approximation problem. After the proposed alternating optimization algorithm converges to a tolerance threshold, a locally optimal solution of the original problem can be found. Numerical results reveal that our proposed joint optimization scheme can increase the system-wide learning utility and achieve significant performance gains over the four benchmark schemes.

In this paper, a joint spectrum allocation and device association problem is investigated for a federated learning aided hierarchical Industrial Internet of Things (IIoT) system for smart factory. To achieve the optimization jointly, we design a weighted learning utility maximization problem, which is a 0-1 integer linear programming problem. To solve this problem, we convert it into a weighted 3D hypergraph model by capturing the 3D mapping relation for IIoT device, subchannel, and edge server. A local search algorithm is then presented to find a 3D hypergraph matching with maximum total weights as the suboptimal solution. Simulation results demonstrate the superior performance of the proposed algorithm compared with the greedy algorithm in the system learning utility.

Multi-access edge computing (MEC) has been recently considered in challenging environments lacking available terrestrial infrastructures by extending the computing resources to the air for further enhancing the computation capability of the new aerial user equipment (AUE). Additionally, wireless power transfer (WPT) is a promising solution to prolong the battery lifetime of energy-constrained wireless devices like AUEs. In this paper, we investigate the integration of laser-beamed WPT in the high-altitude platform (HAP) aided MEC systems for the HAP-connected AUEs. By discretizing the three-dimensional coverage space of the HAP, we present a multi-tier tile grid-based spatial structure to provide aerial locations for laser charging. With this setup, we identify a new privacy vulnerability caused by the openness during the air-to-air transmission of WPT signaling messages in the presence of a terrestrial adversary. A privacy-aware laser-powered aerial MEC framework is developed that addresses this vulnerability and enhances the location privacy of AUEs for laser WPT. Specifically, the interaction between the HAP as a defender and the adversary in their tile grid allocation as charging locations to AUEs is formulated as a Colonel Blotto game, which models the competition of the players for limited resources over multiple battlefields for a finite time horizon. Moreover, we derive the mixed-strategy Nash equilibria of the tile grid allocation game for both symmetric and asymmetric tile grids between the defender and the adversary. Simulations results show that the proposed framework significantly outperforms the design baselines with a given privacy protection level in terms of system-wide expected total utilities.