In the area of unmanned aerial vehicles (UAVs) and the Internet of Things (IoT), enormous technological advancement has transformed the way we choose to live. UAVs link everything in the modern world, and it is an attractive area for academia to focus on. UAVs are proven to be a boon for the whole world as it provides flying services in critical and medical emergencies. UAVs are futuristic technology that enriches human needs by providing numerous benefits or services to proceed toward the smart world. In UAV‐assisted traditional networks, multiple sensors and flying nodes consume a lot of energy resources, and Fifth Generation (5G)/Beyond 5G enabled devices to excrete high radiations, which may cause damage to our environment as well as our society. So, it is very important to maintain a balance between the environment and modern technologies for a better lifestyle. To maintain this, many steps need to be put further to reduce the energy consumption of nodes, consume fewer data resources, and encourage effective energy‐use techniques. Green UAV‐based fog computing is a promising solution to balance environmental issues and provide energy‐efficient data computation and aerial to ground network communication for building an intelligent world with improved Quality of Services (QoS). This article provides a comprehensive survey on green technologies and applications for UAV‐Fog. We also highlight research issues, current challenges, lessons learned, and the future direction of the UAV‐Fog network.

A survey on green unmanned aerial vehicles‐based fog computing: Challenges and future perspective

The evolution of future networks into fully autonomous entities appears inevitable. The need for greater operational agility in supporting a wide range of services and use cases together with increasing network complexity creates, in all likelihood, unforeseen new challenges. 6G and beyond networks will have a disruptive architecture imposing some crucial requirements: 1) Support universal access to intelligence sustained by a distributed computing, sensing, and control infrastructure; 2) manage with a unified framework of a heterogeneous communication, computing, caching, and control infrastructure; 3) support highly dynamic and possibly intermittent networks of vertical networks. This study first advocates the merits of integrated vertical heterogeneous network (VHetNet) architecture in support of 6G infrastructure. We then review the current literature on network management of vertical networks and discuss a unified network management framework to analyze new architecture-dependent challenges and their coordination with existing network management services and requirements. Third, we investigate recent advancements in artificial intelligence (AI)/machine learning (ML) methods in current VHetNets and discuss the core challenges of integrating AI/ML in VHetNets. Finally, we discuss potential future research directions for advancing the autonomous and evolving capabilities of VHetNets.

Self-Evolving Integrated Vertical Heterogeneous Networks.

In order to improve the intelligent monitoring level and performance of transmission lines, a mobile telephone system for UAV performance monitoring and scheduling based on multi-sensor fusion is proposed. In order to collect the observation data and better understand the situation of the battlefield, the battlefield can be equipped with 2 UAVs for multi-mission flight.This document describes major communications/university applications for flight surveillance, navigation and landing at different locations; and Built in 12 bit intelligent charging counter to realize the address of charging remote control system; Adjust GPS / RTK dual-mode navigation system, identify the defect signals with one click, and the intelligent analysis APP uses the deepest program defined by front end and back end to eliminate the defect. In some areas, 253000 square kilometers of the operation control area has been greatly developed, and the transmission lines and streams of 20,1409 kilometers have been carefully analyzed. and the transmission lines and channels of 20,1409 kilometers have been analyzed. 225 defects and 66 hidden dangers were found. Remove. These things. Inspection work is 60 times higher than that of manual work, saving 2200 hours of work time, more than 4 million yuan of labor cost, about 300000 yuan of maintenance cost, and the cost of machinery, drones and other equipment. Conclusion: Mobile log was used in some areas with good results.

The Mobile Command System of UAV Operation Inspection and Dispatching Oriented to Fusion Multi Sensors

In order to improve the intelligent monitoring level and performance of transmission lines, a mobile telephone system for UAV performance monitoring and scheduling based on multi-sensor fusion is proposed. In order to collect the observation data and better understand the situation of the battlefield, the battlefield can be equipped with 2 UAVs for multi-mission flight.This document describes major communications/university applications for flight surveillance, navigation and landing at different locations; and Built in 12 bit intelligent charging counter to realize the address of charging remote control system; Adjust GPS / RTK dual-mode navigation system, identify the defect signals with one click, and the intelligent analysis APP uses the deepest program defined by front end and back end to eliminate the defect. In some areas, 253000 square kilometers of the operation control area has been greatly developed, and the transmission lines and streams of 20,1409 kilometers have been carefully analyzed. and the transmission lines and channels of 20,1409 kilometers have been analyzed. 225 defects and 66 hidden dangers were found. Remove. These things. Inspection work is 60 times higher than that of manual work, saving 2200 hours of work time, more than 4 million yuan of labor cost, about 300000 yuan of maintenance cost, and the cost of machinery, drones and other equipment. Conclusion: Mobile log was used in some areas with good results. 

6G and beyond networks tend towards fully intelligent and adaptive design in order to provide better operational agility in maintaining universal wireless access and supporting a wide range of services and use cases while dealing with network complexity efficiently. Such enhanced network agility will require developing a self-evolving capability in designing both the network architecture and resource management to intelligently utilize resources, reduce operational costs, and achieve the coveted quality of service (QoS). To enable this capability, the necessity of considering an integrated vertical heterogeneous network (VHetNet) architecture appears to be inevitable due to its high inherent agility. Moreover, employing an intelligent framework is another crucial requirement for self-evolving networks to deal with real-time network optimization problems. Hence, in this work, to provide a better insight into network architecture design in support of self-evolving networks, we highlight the merits of integrated VHetNet architecture while proposing an intelligent framework for self-evolving integrated vertical heterogeneous networks (SEI-VHetNets). The impact of the challenges associated with SEI-VHetNet architecture, on network management is also studied considering a generalized network model. Furthermore, the current literature on network management of integrated VHetNets along with the recent advancements in artificial intelligence (AI)/machine learning (ML) solutions are discussed. Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are identified. Finally, the potential future research directions for advancing the autonomous and self-evolving capabilities of SEI-VHetNets are discussed. 

Self-Evolving Integrated Vertical Heterogeneous Networks

Unmanned aerial vehicles (UAVs) as base stations (BSs) are capable of offering wireless connectivity for users without new terrestrial infrastructures. However, fewer antennas can be placed in the UAV-based BS due to its limited space, which also limits the transmission rate of the UAV-based BS. Millimeter wave (mmWave) bands enable large scale antennas to be packed into very small areas to serve multi-users. However, the existence of the interference is non-negligible in the UAV-based BS with mmWave system. The instantaneous channel state information (CSI), which plays a key role in the interference elimination, is difficult to obtain due to the UAV mobility. Compared to the instantaneous CSI, statistical CSI, such as the channel covariance, can be easily acquired by exploiting the channel statistical reciprocity. In this paper, we propose a novel joint optimization problem of the user scheduling, the statistical precoding, and the UAV trajectory in the UAV-based BS with mmWave system to maximize the sum rate of users. The statistical precoding is utilized to alleviate the multi-users interference. Due to the non-convex objective function and constraints, the optimization problem is decomposed into two subproblems. The goal of the first subproblem is to mitigate multi-users interference using statistical CSI and to select the optimal users, while the goal of the second subproblem is to adjust the UAV trajectory to maximize the sum rate of users via transforming the non-convex subproblem into convex optimization. An iterative algorithm is proposed to optimize two subproblems alternatively. The simulation results demonstrate that the proposed joint optimization algorithm is able to achieve good performance.

Joint Optimization of UAV Trajectory Statistical Precoding and User Scheduling

Due to their maneuverability, unmanned aerial vehicles (UAVs) have grown into a promising enabler of the Internet of Things (IoTs). In addition to the benefits of the bandwidth and communication quality of millimeter-wave (mmWave) systems, a UAV-aided mmWave multiple-input and multiple-output (MIMO) communication system is investigated in this paper for the data collection of IoT systems, in which single-antenna IoT devices are divided into several clusters, and the UAV aided mmWave base station (UAV-BS) collects data from each cluster using the time division scheme. The joint optimization of the beam selection, UAV trajectory, user clustering, power allocation and transmission duration is studied in this paper to improve the data collection efficiency. The solution of the problem is then given in three steps. Firstly, the incremental K-means clustering and ant colony optimization algorithm are utilized to handle the UAV trajectory planning and user clustering problem. Secondly, an incremental beam selection scheme is employed to ensure that all the devices in each cluster can communicate with the UAV. Thirdly, an iterative algorithm is proposed by alternately optimizing the power allocation and transmission duration of the IoT devices. Finally, the simulation results demonstrate the effectiveness of the proposed solution for the UAV-aided mmWave communication system.

Xingxuan Zuo

Papers

Joint Optimization of UAV Trajectory Statistical Precoding and User Scheduling

Optimization of UAV-Aided Millimeter-Wave IoT Systems