Age of information (AoI)-sensitive bidirectional computation tasks quality of service (QoS) for unmanned surface vehicles (USVs) is a critical issue in realizing ship-shore cooperative systems. In this paper, a reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV)-USV mobile edge computing (MEC) network architecture is proposed, where one RIS-carried tethered UAV (TUAV) and rotary-wing UAVs (RUAVs) are cooperatively dispatched to serve USVs bidirectional data computation with average AoI (AAoI) constraint. The minimization of weighted sum USVs AAoI and RUAVs flight energy is formulated by jointly considering RUAVs service duration indicators, TUAV-mounted RIS phase shift, TUAV hovering altitude, and RUAVs' trajectories. A heursitic solution is proposed to address this minimized issue. In particular, a novel mixed linear quadratic Lyapunov framework is utilized to transform the original long-term stochastic problem into a list of deterministic single-slot problems. Then, each single-slot problem is divided into two subproblems. First, the subproblem of RUAVs' trajectories is tackled by an enhanced whale optimization algorithm. Second, the subproblem of RUAVs service duration indicators, TUAV-mounted RIS phase shift and TUAV hovering altitude is addressed by an enhanced alternating optimization algorithm. The results demonstrate that the proposed heuristic solution reduces long-term RUAVs flight energy consumption by approximately 50% while maintaining satisfactory USVs AAoI.

One of the most significant technical research challenges of space-air-ground-sea integrated networks is to realize satisfactory wireless data transmission in underwater environments. However, although numerous types of communications, e.g., radio frequency, optical and acoustic, have been investigated, the performance of wireless underwater communications still cannot satisfy the ever-increasing high data rate and long communication distance requirements. This paper demonstrates an early attempt regarding performance analysis of acoustic intelligent reflecting surface (RIS)-assisted wireless underwater communication. The minimization of transmission power is formulated subject to a list of constraints. The analytic scheme is proposed, where two key system parameters, e.g., reflection angle and acoustic signal frequency, are investigated. Numerical results verify that acoustic RIS-assisted wireless underwater communication can significantly enhance signal transmission quality and decrease interference. The results obtained in this paper can be applied to future autonomous underwater vehicles and robot system designs.

Unmanned surface vehicles (USVs) are becoming increasingly significant in fulfilling integrated sensing, computing, and communication with the emergence of bidirectional computation tasks. However, Quality-of-Service provisioning is still challenging since USVs are restricted with limited onboard resources and direct links between them and shore-based terrestrial base stations (TBSs) are frequently blocked. This article proposes a novel reconfigurable intelligent surface (RIS)-assisted cooperative unmanned aerial vehicle (UAV)–USV mobile-edge computing (MEC) network architecture, where RIS-mounted tethered UAV (TUAV) and rotary-wing UAVs (RUAVs) are collaboratively utilized to serve USVs. RUAVs energy minimization is formulated by jointly considering TUAV hovering altitude, RIS phase-shift vector, RUAV service selection indicator, and RUAVs turning points. A heuristic solution is proposed to tackle the formulated problem, where the original problem is first decoupled into three subproblems, e.g., the joint optimization of RIS phase-shift vector and TUAV hovering altitude subproblem, RUAVs service selection indicator subproblem, and RUAVs turning points subproblem, each of which is solved by the proposed modified alternative direction method of multiplier (ADMM) algorithm, the proposed enhanced simulated annealing (ESA) algorithm and the proposed successive convex approximation (SCA)-based algorithm. In this way, the challenging problem can be efficiently solved iteratively. The results show that the proposed solution can decrease RUAVs energy consumption by nearly 29% compared to numerous selected advanced algorithms. Moreover, the performance of the proposed solution regarding typical penalty coefficients and number of RIS reflecting elements is investigated.

Unmanned Surface Vehicles (USVs) in inland waterways have drawn increasing attention for their excellent capability to serve maritime time-consuming missions such as autonomous navigation and intelligent monitoring. However, USVs struggle to accomplish emerging computation-intensive tasks (e.g., sensor, telemetry, etc) timely due to the limited on-board resources. This paper proposes a novel reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) multi-access edge computing (MEC) network architecture to support low-latency USVs data computation with time window. Aiming to enhance USVs task processing efficiency, the minimization of USVs task processing time is formulated by jointly considering UAVs flight route selection, USVs execution mode selection, UAVs hovering coordinates and RIS phase shift vector. A heuristic solution is proposed to tackle the formulated challenging problem iteratively. The original problem is decoupled into three subproblems: an enhanced deferred acceptance algorithm is proposed to solve UAVs flight route selection subproblem; an enhanced Lagrangian relaxation method is proposed to solve USVs execution mode selection subproblem; a joint alternating direction method of multipliers (ADMM)-successive convex approximation (SCA)-based algorithm is proposed to solve UAVs hovering coordinates subproblem. Experiment results demonstrate that the proposed solution can decrease task processing time by approximately 54% compared with numerous selected advanced algorithms. Moreover, the performance of the proposed solution under typical UAVs caching capability and the number of UAVs has been investigated.

360-degree video streaming has gained significant popularity and has been employed in a variety of autonomous driving scenarios such as road traffic awareness and interactions between autonomous vehicles and pedestrians. However, maintaining an appropriate buffer size poses a major challenge for smooth playback and a high Quality of Experience (QoE). In this paper, we propose an adaptive buffer management scheme (BMQoE) that dynamically computes the optimal video bitrate based on the current buffer occupancy and available bandwidth. The proposed BMQoE scheme aims to improve QoE and bandwidth utilization by considering the client-side field of interest, bandwidth conditions, and buffer sizes. Experimental results show that the BMQoE scheme significantly reduces the duration of video stalls and improves the QoE of 360-degree video streaming by 16.7%, which is crucial for autonomous vehicles.

This paper proposes an intelligent reflecting surface (IRS)-assisted wireless inland ship multi-access edge computing (MEC) network architecture with time windows, where UAV is deployed to serve time-constrained unmanned surface vehicles (USVs) with IRS. The task execution efficiency maximization optimization problem is formulated by joint considering IRS phase-shift vector, UAVs hovering coordinates and the task execution indicator. To tackle the formulated challenging problem, a heuristic solution is proposed. First, an enhanced differential evolution algorithm is proposed to optimize UAVs hovering coordinates. Moreover, IRS phase-shift vector and task execution indicator are jointly optimized in an iterative manner by the proposed modified deferred acceptance algorithm. Numerical results verify the effectiveness of the proposed algorithm in comparison with some selected advanced algorithms in terms of task execution efficiency.

Non-terrestrial wireless communications have evolved into a technology enabler for seamless connectivity and ubiquitous computing services in the beyond fifth-generation (B5G) and sixth-generation (6G) networks, aiming to provision reliable and energy efficient communications among aerial platforms and ground mobile users. This paper considers intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV)-empowered wireless communication, which exploits both the high mobility of UAV and passive beamforming gain brought by IRS. The energy minimization of rotary-wing UAV is formulated by jointly considering numerous quality of service (QoS) constraints with intricately coupled variables. To tackle the formulated challenging problem, a heuristic algorithm is proposed. First, we decouple it into several subproblems. Moreover, we jointly investigate offloading decisions of Internet of Thing (IoT) devices by the proposed enhanced differential evolution algorithm. Then, minorization-maximization algorithm (MMA) is utilized to solve the optimization of IRS phase shift-vector. Moreover, ant colony optimization (ACO) algorithm is proposed to optimize UAV flight route indicator matrix. Numerical results validate the effectiveness of the proposed algorithm. The results show that the proposed solution can remarkably decrease UAV flight distance while improving the network energy efficiency in comparison with numerous advanced algorithms.

Non-terrestrial communications have emerged as a technological enabler for seamless connectivity and ubiquitous computation services in the upcoming beyond fifth generation (B5G) and sixth generation (6G) networks. However, there exist numerous practical technical limitations, such as high deployment cost, massive energy consumption, high probability of information transmission blockage and dynamic propagation environments and so forth. Thanks to the rapid developments of meta-materials, the cost-effective and energy-efficiency intelligent reconfigurable surface (IRS) has been globally recognized as a revolutionized technology to construct smart radio environments. In this paper, a novel double-IRS assisted unmanned aerial vehicles (UAV)-swarm-enabled communication network architecture is proposed, where two UAV swarms are integrated with the main IRS reflector and subreflector, respectively. The energy minimization problem of UAV swarm carried main IRS is formulated, subject to a list of quality of service (QoS) constraints. To tackle the formulated challenging problem, we first decouple the original problem into two subproblems. Then, a heuristic algorithm is proposed, where the enhanced differential evolution (DE) algorithm is proposed to optimize the UAV swarm trajectory and the alternate optimization algorithm is utilized to optimize the cooperative reflect beamforming vector. Numerical results validate that the proposed algorithm outperforms several selected advanced algorithms regarding UAV swarm energy consumption. Moreover, the network performance under the different number of IRS elements is investigated.

Owing to the high flexibility and mobility, unmanned aerial vehicles (UAVs) have attracted significant attention from both academia and industry communities, especially in the UAVempowered mobile edge computing (MEC) networks. However, the repetitiveness of tasks generated by user equipments (UEs) has not been fully analyzed. In this paper, a UAV-empowered MEC network architecture is proposed. Computation tasks are divided into two categories, i.e., private tasks and public tasks, which can be executed locally or offloaded to UAVs utilized as flying MEC servers for task execution. The aim of this paper is to optimize task execution latency and network energy consumption by jointly considering UEs’ offloading decisions and UAVs’ route planning. To solve the challenging formulated optimization problem, an enhanced block coordinate descent algorithm is proposed, which is utilized in conjunction with the differential evolution and penalty function method. The simulation results demonstrate that the proposed scheme outperforms the random offloading strategy and fixed route strategy regarding the cumulative cost and time cost.

In this paper, we aim to minimize in-body sensor node energy consumption and to prolong the network lifetime by utilizing a relay strategy based on our proposed flexible quality of service (QoS) radio frequency communication module and advanced In-to-out body path loss (PL) model. A selection algorithm is derived and investigated in which the relay nodes with lower energy consumption and minimum distance to the coordinator will be chosen in each round. In this way, time division multiple access (TDMA) can be investigated to schedule data transmission from implants to the corresponding relays, and thus minimize the overall length of communication links. Moreover, a linear programming network lifetime model is proposed along with various subjective functions. Followed by constraints, simulations are conducted on a proposed topology network assuming two commercial transceivers. Results show that the existing two-relay based protocol achieves higher transmission data rates due to the availability of redundant communication pathways. However, our proposed incremental relay-based cooperative routing protocol outperforms the existing two-relay based scheme regarding total network lifetime, overall throughput, average power consumption and propagation delay.