Reconfigurable intelligent surface (RIS) has emerged as a promising technology to enhance both achievable rate and energy efficiency in next-generation wireless networks. This article investigates a novel energy-efficient UAV-RIS-assisted architecture for simultaneous wireless information and power transfer (SWIPT) in integrated ground–aerial–space networks, where a satellite cooperates with multiple UAV-RISs to provide downlink wireless energy transfer (WET) and support uplink wireless information transmission (WIT) for energy-constrained IoT devices in remote environments. We formulate an alternating iterative joint optimization problem (AIJOP) that aims to maximize the system sum achievable rate while ensuring causality of device energy via jointly optimizing UAV-RIS trajectories, RIS phase shift matrices, and satellite power allocation and time allocation between WET and WIT. The problem is highly non-convex due to the strong coupling among variables. To address this challenge, we propose a trajectory–phase–power–time alternating optimization algorithm (TPPTAOA), which decomposes the original problem into four tractable subproblems and solves them iteratively. Specifically, the UAV-RIS trajectories is first optimized via using a device scheduling and TSP-based path planning approach to solve the first subproblem, followed by the proposition of a two-stage heuristic phase optimization algorithm under fixed parameters to solve the second subproblem. Subsequently, the satellite power allocation is solved using a Lagrangian dual method, while the time allocation is optimized through a two-stage strategy combining grid-based coarse search with gradient-based refinement. Simulation results under various system settings verify the fast convergence, robustness, and superior performance of the proposed TPPTAOA, showing significant improvements in both energy efficiency and uplink achievable rate compared with benchmark schemes.

The advancement of wireless networks has spurred an increasing demand for high-quality maritime communication services. This study presents an innovative unicast-multicast access and backhaul maritime communication network (UMABMCN), in which a high-altitude platform (HAP) provides HAP-to-vessel (H2V) unicast services to vessels and backhaul support to unmanned aerial vehicles (UAVs) through HAP-to-UAV (H2U) links. Additionally, multiple UAVs are deployed to deliver UAV-to-vessel (U2V) multicast transmission services to vessels. Specifically, we formulate a HAP-UAV-assisted unicast-multicast cooperation multi-objective optimization problem (UMCMOP) aimed at maximizing the sum achievable rate of base stations (BS)-to-vessel (B2V), maximizing the sum backhaul rate of H2U, and minimizing the energy consumption of UAVs via jointly optimizing communication connection between BSs and vessels, power allocations of UAVs, along with the placement of UAVs. The formulated UMCMOP is a mixed integer non-linear programming (MINLP) problem. To address this, we propose an enhanced multi-objective multi-verse optimization (EMOMVO-CGD) algorithm, which integrates achaos probability operator,gray wolf exploitation operator, anddiscrete update operator. To further validate the performance of EMOMVO-CGD, a joint communication connection, power allocation and placement optimization (JCCPAPO) method is proposed. Simulation results demonstrate that the two proposed algorithms outperform benchmark strategies in optimizing the aforementioned objectives.

Given that the locations of base stations (BSs) remain fixed after installation, direct data forwarding to remote user equipment (UE) becomes challenging. Unmanned aerial vehicles (UAVs) offer a hopeful solution as mobile relays for next generation wireless communications to realize data forwarding with the flexible and cost-effective deployment. However, the limited onboard energy of UAVs and slow progress in energy storage technology pose significant challenges to achieving energy-efficient communication. Therefore, in this article, we investigate a wireless communication network utilizing a UAV as a high-altitude relay for data forwarding, and formulate a UAV relay deployment optimization problem (URDOP) to minimize the energy consumption of data forwarding and UAV hovering by optimizing UAV deployment, including the locations and number of UAV hover points. Given that the URDOP is a mixed-integer programming problem, conventional gradient-based approaches face limitations. To address this, we propose a self-adaptive differential evolution with a variable population size (SaDEVPS) algorithm to solve the URDOP. The performance of proposed SaDEVPS is verified through simulations, and the results show that it can successfully decrease the energy consumption of system when compared to other benchmark algorithms.

Internet of Things (IoT) has gradually been applied to various fields, including industries and agriculture, and plays an increasingly important role in society. However, the limited coverage of terrestrial IoT network restricts the communication performance of IoT devices, making the network inefficient. Unmanned aerial vehicles (UAVs) have the potential to be an efficient solution to improve the communication efficiency of the terrestrial IoT devices. Thus, we formulate a UAV-assisted data collection multi-objective optimization problem (UAVDCMOP) to jointly maximize the average transmission rate, minimize the total time of UAVs, and minimize the average energy consumed by UAVs via determining the optimal positions of UAVs. To this end, we propose an improved multi-objective grey wolf-based optimization (IMOGWO) algorithm with chaotic mapping initialization operator and inversion opposition generation operator, making it suitable for optimizing the formulated UAVDCMOP. Simulation results demonstrate that the proposed approach contributes to enhance the system average transmission rate and energy efficiency, and it has superior performance compared to other approaches.

Visible light communication (VLC) based on unmanned aerial vehicles (UAVs) can simultaneously transmit data and lighting, which has been considered as a promising technology for the next generation wireless networks. In this paper, we construct a system consisting of UAV elements to fairly communicate with receiving plane. However, the unreasonable layout of UAVs may lead to the uneven distribution of the received optical power on the same receiving plane, which cannot guarantee the fairness of the communication between the UAVs and receiving plane. Besides, the transmission power of the UAVs has direct effects on the strength of the received optical signal in VLC communication. Therefore, we formulate an optical power coverage optimization problem (OPCOP) to achieve more uniform received optical power coverage by jointly considering the positions and appropriate power adjustment factor of transmission power of UAVs. Then, an improved cuckoo search with c haotic solution initialization operation and m utation mechanisms (ICSCM) algorithm is proposed to solve the formulated optimization problem. ICSCM introduces the chaotic solution initialization operation for increasing the performance of initial solution, and employs two mutation mechanisms, which are mutation operator of differential evolution (DE) algorithm and Gaussian perturbation to enhance the exploration ability of conventional cuckoo search (CS). Simulations are conducted and the results verify that the received optical power of receivers distributed on the same receiving plane obtained by ICSCM can be more uniform than other comparison methods.

In recent years, the dramatic increase in vehicles and the limited resources of VEC servers make it challenging for vehicles to execute intensive and sensitive tasks on the local own CPU. The mobile edge computing (MEC) is viewed as a promising paradigm by deploying the cloud resources on roadside road side units (RSU). However, compared to cloud server, MEC servers have limited resources. Moreover, the vehicular tasks with different priorities have different requirements on the edge resources. In this work, we propose a priority -aware collaborative task offloading and resource allocation approach for vehicular edge computing networks (VECN). Specifically, we propose a variant grey wolf optimizer (VGWO) algorithm for resource optimization and a dynamic task offloading strategy (DOS) algorithm for task offloading. Simulation results show that the proposed VGWO algorithm outperforms the basic swarm intelligence optimization algorithm, and the collaborative offloading method is able to effectively reduce the task processing latency and energy consumption.

The fifth generation (5G) mobile technology is one of the means to support wireless communication capabilities in Internet of Things (IoT), which has been widely used in multifarious scenarios. However, the insufficient terrestrial networks limit the deployment of IoT devices, which makes the integration between the devices and the looming 5G infrastructures more difficult. Unmanned Aerial Vehicles (UAVs) have the potential to facilitate the integration of them and overcome the limitations of terrestrial infrastructures since they can be deployed as the air auxiliary base stations (AABS) for IoT. In this work, we aim to determine the optimal number of UAVs while considering communication-related parameters such as average throughput of UAVs and association between the devices and UAVs. First, we formulate a joint deployment optimization problem of UAVs (JDOPUAV) to simultaneously minimize the number of UAVs, maximize the average throughput of UAV-device pairs and maximize the lowest throughput of UAV-device pairs by optimizing the positions of UAVs. Then, an improved biogeography-based optimization with flexible local selection, chaos mechanism and intrusion operator (IBBOFCI) is proposed to solve the formulated JDOPUAV. Simulation results verify that the proposed IBBOFCI is more effective for the problem compared to other methods.

Unmanned-aerial-vehicle (UAV)-aided wireless communication in Internet of Things (IoT) applications is becoming the focus of attention of researchers. This article investigates a UAV-assisted communication system for serving IoT, in which multiple rotary-wing UAVs are employed to communicate with multiple terrestrial IoT devices. Specifically, we formulate a UAV deployment multiobjective optimization problem (UAVDMOP) to simultaneously maximize the minimum throughput of the UAV–device pairs, maximize the total throughput of the whole system, and minimize the total energy consumptions of UAVs via a joint optimization of the locations of UAVs, transmission power of UAVs, and association relationship between the UAVs and IoT devices. UAVDMOP consists of both discrete and continuous solution spaces, which is difficult to be solved. Thus, we propose an improved discrete and continuous multiobjective evolutionary algorithm based on decomposition (IDCMOEA/D) with a hybrid solution initialization operation and a hybrid solution reproduction operation for increasing the performance of the algorithm so that making it more suitable for dealing with the UAVDMOP. Simulation results demonstrate that the proposed method is effective to enhance the throughput and energy efficiency of the system and it has superior performance compared to other methods.

This article is concerned with the problem of the number and dynamical properties of equilibria for a class of connected recurrent networks with two switching subnetworks. In this network model, parameters serve as switches that allow two subnetworks to be turned ON or OFF among different dynamic states. The two subnetworks are described by a nonlinear coupled equation with a complicated relation among network parameters. Thus, the number and dynamical properties of equilibria have been very hard to investigate. By using Sturm's theorem, together with the geometrical properties of the network equation, we give a complete analysis of equilibria, including the existence, number, and dynamical properties. Necessary and sufficient conditions for the existence and exact number of equilibria are established. Moreover, the dynamical property of each equilibrium point is discussed without prior assumption of their locations. Finally, simulation examples are given to illustrate the theoretical results in this article.

Wireless power transfer based on charging unmanned aerial vehicles (CUAVs) is a promising method for enhancing the lifetime of wireless rechargeable sensor networks (WRSNs). However, how to deploy the CUAVs so that enhancing the charging efficiency is still a key issue. In this work, we formulate a CUAV deployment optimization problem (CUAVDOP) to jointly increase the number of the sensor nodes that within the charging scopes of CUAVs, improve the minimum charging efficiency in the network and reduce the motion energy consumptions of CUAVs. Moreover, the formulated CUAVDOP is analyzed and proofed as NP-hard. Then, we propose an improved firefly algorithm (IFA) to solve the formulated CUAVDOP. IFA introduces two improved items that are the attraction model and adaptive step size factor to enhance the performance of conventional firefly algorithm, so that making it more suitable for CUAVDOP. Simulation results demonstrate that the proposed algorithm is effective for the formulated joint optimization. Moreover, the performance of IFA is better than some other algorithms.