Drones have been widely used in Internet of Things tasks, and rational planning of UAV paths can improve mission efficiency while reducing the risk of potential third-party impacts. Existing of existing UAV data acquisition efforts take all efficiency and safety goals into a single decision, And view it as a multi-objective optimization problem, While the single-decision model ignores the connection between the optimization goals and multiple decision-makers, For this purpose, for the first time, the efficiency-based DM (decision-maker, DM) and safe DM UAV multi-decision multi-target trajectory optimization (BP-UAVTO, Biparty Multiobjective UAV Trajectory Optimization) Model, For this problem, Improvement to the existing multi-target immunization algorithms, BP-series algorithm to find the Pareto optimal solution of the BP-UAVTO model, As close as possible to the Pareto front of each decision. Then it is decoupled into a problem of data acquisition time minimization in the wide area Internet of Things without accurate SN location information, and a greedy algorithm based on the division unit (Greedy algorithm based on dividing units, GA-BODN) is proposed to minimize the completion time of parallel data acquisition of multiple drones in a large range. Simulation results show that the Pareto optimal solution of BP-algorithm is closer to the Pareto front of each decision than the ordinary multi-objective evolution algorithm such as NSGA-II and the multi-decision multi-objective evolution algorithm such as OptMPNDS and OptMPNDS2. The GA-BODN algorithm outperformed the average simulation time by the statistical simulation method and was close to the lower bound on the completion time of data acquisition.

Traditional communication Optical Cable Line Inspections (OCLI) often use labor inspection method, but traditional method will take long inspection time and high labor consumption. In contrast, OCLI by unmanned aerial vehicle (UAV) inspections have many advantages, such as good visibility, low cost, flexibility and high efficiency. There are many key issues and technologies need to be addressed, which significantly impact the execution of OCLI tasks. This paper proposes a UAV OCLI task planning method based on the combination of the Analytic Hierarchy Process and Genetic Algorithm, and its feasibility and practicality are verified through simulation experiments.

The Connected and Autonomous Vehicles (CAVs) is considered to be a promising technology to improve the traffic congestion. However, to realize its expected benefits, the real-time, accurate and forward-looking guidance message are required. Based on this, considering various practical constraints and the characteristics of the CAVs fully, multiple distributed edge computing servers are deployed at the network edge to provide the real-time storage and computation support in our paper. Furtherly, based on the fruitful deployment for edge computing servers, an efficient traffic flow online prediction model is constructed, which can adaptively modify according to the dynamic changes of the actual road traffic state, thus providing more accurate and forward-looking results. The simulation results based on MATLAB platform show the proposed deployment scheme for edge computing servers only needs more cost than the enumeration method. Moreover, compared with other baseline methods, the online prediction model proposed can improve 37.5%–76.9% in terms of prediction accuracy.

Due to its high flexibility and other advantages,aerial drone base stations can expand the communication support range for ground users and solve the mobile coverage problem of ground mobile users in battlefield environments.Considering the impact of high-level terrain features on radio signals in the battlefield environment,the differentiated communication needs of users,and the energy consumption issues of UAV(Unmanned Aerial Vehicle)base stations,a reinforcement learning communication coverage algorithm based on heterogeneous mobile users(ABS-RL)is proposed to model the channel capacity of ground users,the energy consumption of UAV,and the dynamic deployment of UAV base stations,aiming to provide high-quality communication services for ground users.The simulation results demonstrate that the algorithm offers significant advantages in enhancing the channel capacity for ground users and reducing the total energy consumption of UAV base stations.

To realize the benefits expected and ensure user privacy and data security simultaneously, a cost-efficient edge federated learning (FL) architecture over multiple base stations (BSs) is proposed for the intelligent transportation system (ITS) based on connected and autonomous vehicles (CAVs). Firstly, in the proposed FL architecture, the road side units (RSUs) are designed to train the machine learning (ML) model with the BSs equipped with edge servers collaboratively. In this way, since the autonomous vehicles do not participate in model training, the negative impact of unreliable communication caused by vehicle mobility can be eliminated. Then, considering that the limited amount of data involved within the coverage of a single base station (BS), the FL architecture over multiple BSs at network edge is proposed for better learning performance. Along this line, the joint edge aggregation and association problem is studied, and a set function optimization problem is formulated with the objective of minimizing the costs considering latency and energy consumption comprehensively. Finally, after analyzing the complexity, monotonicity, and modularity of the problem formulated, the NP-hardness optimization problem is further decomposed and transformed, and an innovative solution is proposed. The abundant simulation results demonstrate the superior performance of the cost-efficient FL architecture proposed.

Millimeter wave (mmWave) unmanned aerial vehicle (UAV)-aided networks have enormous application potential due to their large bandwidth and ultra-high speed, being regarded as an effective technology for improving the reliability of military and civilian fields. However, due to their complex electromagnetic spectrum environment and the sensitivity of mmWaves to blocking effects, its performance analysis faces certain difficulties. This article investigates the coverage and network capacity of mmWave UAV-aided networks under significant blocking effects and complex electromagnetic environments; for this purpose, we equipped each UAV with mmWave antennas featuring adjustable beamwidth and direction. A Matérn hard-core point process (MHCPP) with repulsion constraints was also employed to reflect the minimum distance constraints to isolate the mutual interference between UAVs. Then, using a stochastic geometric analysis, we derived the coverage and capacity characteristics and further obtained a closed-form expression for the network coverage probability. Finally, the simulation results showed that the network throughput could reach 86% when the density of UAVs was half of that of ground base stations (GBSs) in the city center, validating the efficiency and accuracy of our theoretical derivations.

Based on the reality of UAV communication optical cable line patrol technology, and the application status of patrol system and operation platform, this paper systematically discusses the key problems of UAV patrol, summarizes its key technologies from three aspects of flight platform, communication link platform and ground command and control platform, and points out the technical development and design requirements and the next development trend of UAV patrol. It has certain reference value for deepening the reform of value dimension mode in the communication industry.

A trajectory optimization algorithm based on user priority for multi UAV collaborative conditions (TOUP) is proposed to address the trajectory problem of unmanned aerial vehicle (UAV) base stations collecting data from users of different priority levels. We divided the combat area and calculated the optimal data collection path. Based on the number of drone base stations, we divided the tasks and constructed a utility function based on user priority, drone energy consumption, and task completion time factors to optimize the movement trajectory of drone base stations. Simulation analysis shows that, considering user priority, the algorithm effectively balances the overall performance of drone energy consumption and task completion time.

In the application environment of UAV Ad-Hoc network with frequent topology changes and unstable communication links, in order to improve the stability of network routing, this paper proposes SZLS-GPSR (Safe zone link stability GPSR) routing protocol based on the stability of communication safe zone links, aiming at the problems of unstable communication edges and routing repair holes that appear in traditional GPSR algorithms. By setting up a communication safe zone, the projection length, cumulative communication duration and flight direction of nodes in the communication safe zone are comprehensively measured to select the relay node with the most stable link. Moreover, in the peripheral forwarding stage, the problem of path redundancy and routing holes is improved since the factors of the forwarding angle and the degree of adjacent nodes are both taken into account. With the comparison and analysis of NS-3 network simulation platform, it proves that the proposed protocol has more advantages in packet delivery success rate, end-to-end delay and average hops compared with the traditional GPSR and MM-GPSR protocols.

When the large number of sensors in wireless rechargeable sensor network (WRSNS), for the traditional one-to-one charging mode of low efficiency, put forward an adaptive hover charging algorithm AHPA, to minimize the charging time in the target area, using the improved particle swarm algorithm for path planning.First, the sensor network in the target area is fully covered based on the minimum circle coverage algorithm, and the initial charging and suspension position of UAV is determined by the minimum circle coverage algorithm.During the process of charging, UAV automatically adjusts the hover position in 3D space according to the sensor position and power state in the region.In the simulation experiments, this algorithm is compared with the one-to-one charging method, the cluster partition algorithm and the node hover genetic algorithm, the results show that this algorithm has obvious advantages in the charging completion time.

计算机网络课程是各个院校理工科专业普遍开设的一门专业基础课，其教学内容也是理论教学与实践教学相结合。 常规的实验教学内容通常由抓包分析、商用路由器配置和网络编程等组成，这些内容存在着难度差异大，参与度受限，实验效果 不佳等问题。而 SDN（软件定义网络）作为近些年出现的网络新技术，具有使用灵活、控制方便、参与深入的特点。本文分析了 现有实验方法存在的问题和 SDN 的特点，初步分析了利用 SDN 搭建实验系统的可行性和有效性，并初步设计了 SDN 实验的基本 内容，以供读者借鉴。

无人机辅助的移动边缘计算被认为是在下一代移动通信网络中能高效灵活处理时延敏感的计算密集型任务的潜力技术之一.本文研究了基于无人机的空地协同移动边缘计算的服务布置问题，具体而言，如何在满足任务时延需求和其他资源约束的情况下，通过联合优化无人机和地面基站的服务布置、无人机航迹、任务卸载和计算资源分配，以最小化所有用户的总能耗.由于问题的非凸和各种变量的复杂耦合，该问题属于一个非凸混合整数非线性规划问题，较难求解.本文针对多无人机和多地面基站协同提供计算服务的场景，提出了一种基于交替优化的服务布置算法.该算法通过迭代求解三个不同的子问题来获得具有收敛性保证的次优解决方案.首先，采用分支定界法求解联合服务布置和任务卸载的子问题.其次，采用连续凸逼近方法求解无人机航迹优化的子问题.然后，利用计算资源分配子问题的特性得到该子问题的闭式最优解.最后，对上述过程重复迭代，得到问题的一个次优解.仿真结果表明，相比随机布置策略、贪心布置策略、本地计算策略，所提布置策略能够大大减少用户总能耗.

无人集群是未来的发展趋势之一,它需要可靠稳定的通信网络实现内部互联互通,为复杂的集群行动提供有效的信息传输渠道.为组成该网络,满足集群特点和需求的网络架构是关键.首先,本文提出了一种适用于无人集群的全分布式软件定义网络架构,论证了其全分布、无中心、实时性强、架构动态和可扩充等诸多优点;其次,介绍了全分布式软件定义网络架构在无人集群系统组网以及实现与现有系统互联方面的优势;最后,以无人机集群里的延迟容忍网络作为一个应用实例,介绍了该架构在无人集群系统里的应用,并验证了架构的有效性.

In this article, we study the problem of task selection and scheduling in unmanned aerial vehicle (UAV)-enabled multiaccess edge computing for reconnaissance (ASSUMER). Specifically, taking into account the time-varying priorities of reconnaissance tasks, we investigate how to maximize the overall reconnaissance utility by selecting an appropriate set of tasks and scheduling their execution sequence in the multiaccess edge computing server of the UAV. The ASSUMER problem is a mixed-integer nonlinear programming (MINLP) problem, which includes both integer and continuous variables and is proved to be NP-hard. To address this challenging problem, we first model the task scheduling subproblem as a single machine scheduling problem with the deterioration effect. We find out that the optimal task scheduling can be solved efficiently given any task selection variables and propose an optimal scheduling algorithm. Second, using the proposed scheduling algorithm, the ASSUMER problem is equivalent to a binary integer programming problem with respect to the task selection variable only. We prove that the objective function falls into the category of the submodular function and transform the original problem into the problem of maximizing submodular function with the energy constraint. Third, combining the proposed scheduling algorithm with submodularity, we design an effective approximation algorithm for the ASSUMER problem and prove that the algorithm has (1 - e(-1))/2 bicriterion approximation guarantee. Finally, simulation results show that the proposed algorithm can improve the overall reconnaissance utility and energy efficiency compared to five benchmark algorithms.

为使汽轮发电机轴流风扇产生的气动摩擦损耗不直接引入到电机内部,研制了大容量350MW抽风式空冷汽轮发电机.基于该电机在试验条件下轴流风扇安装角为29.2°的运行数据,开展CFD仿真计算,研究扇叶的安装角(29.2°、31.2°)和叶片数对静压效率、压升、温升等风扇性能参数的影响.通过数据分析发现,在保证风扇数目足够的情况下,电机风量较大时,可采用风扇安装角度31.2°,同时适当提高空冷器冷却能力;在电机风量较少时,建议采用风扇安装角29.2°.

信息化时代,智能设备在人们生活中发挥着重要作用.无论是LTE网络还是Wi-Fi网络,都为给用户提供更好更优质的服务而在不断发展.然而,尽管有很多技术已经用于提高频谱利用率,在授权频段工作的LTE网络仍然面临频谱资源紧缺的问题.将LTE的工作频段扩展到未授权频段是一个可行的解决方案,而当前已经有多种技术为LTE与Wi-Fi在未授权频段的共存提供技术支持,其中基于先听后发的授权频段辅助接入技术是目前比较合理且合适的一种共存机制.因此,全面分析该共存机制的关键技术及参数配置,并基于竞争接入信道的思路,提出通过时域-频域二维表的方式来交互控制信息,从而实现竞争接入信道的效果,为LTE与Wi-Fi的共存提供一种新思路.

为了解决无人机(unmanned aerial vehicle,UAV)协作通信网络在完成任务时由于高机动性而会影响链路状态的问题,提出了一种基于UAV位置预测的信道中继选择算法.根据卡尔曼算法预测出UAV下一时刻的位置,提前判断链路优劣性,使更换UAV中继节点的时机更为精准.通过UAV源节点到UAV中继节点以及UAV中继节点到UAV目的节点的瞬时信道状态信息(channel state information,CSI)选出备选UAV中继节点集合.最优的备选UAV中继节点由贪婪算法计算而得.仿真结果证明了此方法的有效性,链路中断的概率比随机选择算法降低了10％,且链路更加稳定.

The explosive growth of mobile data makes mobile operators seek the capacity of their cellular network. Besides, the scarcity of spectrum resources brings a great challenge to the development of wireless communication. Therefore, the mobile operator intends to extend LTE technology to the unlicensed spectrum, which is mainly used by Wi-Fi. LAA and LTE-U are two major unlicensed LTE technologies developed for the coexistence with Wi-Fi. Due to different access mechanisms, LAA and LTE-U have different performance trends with the change of the number of Wi-Fi stations. In this paper, we model and analyze the two coexistence modes using the Markov model, and analyze the throughput of the system, the access probability T off and collision probability of LTE and Wi-Fi in the system where the two coexistence methods are applied to the coexistence network. The results show that LAA coexistence method is suitable for coexistence scenarios with low Wi-Fi density, while LTE-U is more suitable for coexistence scenarios with relatively high Wi-Fi density.

With the rapid development of the information age, the licensed frequency bands used by mobile cellular communications have become more crowded, while unlicensed frequency bands still have more spectrum resources available. Therefore, mobile cellular communication operators hope to provide better services to users by shifting some services to unlicensed bands. The current mechanisms to realize the coexistence of WiFi and LTE mainly include License Assisted Access(LAA) technology based on listening before talk(LBT) and LTE-U system by using Carrier-Sensing Adaptive Transmission(CSAT) algorithm. In general, the CSAT algorithm adjusts the duty cycle by increasing or decreasing a fixed length of time of each cycle, which results in some cases that only one cycle cannot accurately set the duty cycle accurately, requiring multiple long enough periods to adjust the duty cycle, thus making the duty cycle setting more reasonable. In this paper, the duty cycle configuration of CSAT, is optimized by Cournot game model. At the same time, using this model, a dynamic algorithm for solving duty cycle based on Cournot game is proposed, which provides an idea for CSAT technology to configure duty cycle. Compared with the original CSAT technology to adjust the duty cycle, the algorithm can shorten the adjustment period, reduce the time delay of 1-4 transmission cycles, and can better adapt to the dynamic network scenarios.

In a multi-UAV system, some collected data should be transmitted to relevant UAVs or ground station for processing when energy resources are insufficient or computation consumption is large. In this paper, we researched the energy-saving strategies for data aggregation on the combination of store-carry-forward (SCF) routing and hop-by-hop routing, which are suitable for large-scale multi-UAV networks, and proposed Single Coalition Strategy (SCS) and Coalition Formation Strategy (CFS) based on the coalition game theory. In SCS, all source UAVs transmit data individually or can form a coalition. The coalition aggregates all data to a ferry UAV for transmission, the ferry choose between SCF routing mode and hop-by-hop routing mode with the help of predicted energy-cost calculation. In CFS, all source UAVs form a coalition structure by coalition formation game (CFG) algorithm according to data amount, location, and network topology. Each coalition can aggregate data to its own ferry, and then the ferry transmits data in the optimized mode. We analyze the performance of SCS and CFS by simulations, the average transmission energy consumption of a UAV in SCS and CFS are about 30% of hop-by-hop routing mode. Based on the results, we recommend that SCS should be used when the application scenario is small and the number of UAVs is limited. In the case where the application scenario is great and the number of UAVs is large, CFS with the Utilitarian order and limited message propagation hops is recommended.