Aerial Networks Research Articles

An efficient Protocol for Enhanced Flynet Communication in Presence of Partitioning

This paper presents an adaptive fault-tolerant resource allocation protocol tailored for FlyNet, a dynamic aerial network characterized by its mobility and three-dimensional operational space. Addressing the challenges of network partitioning and resource allocation in aerial networks, the proposed algorithm dynamically adjusts to the network's changing topology, ensuring consistent and efficient resource management. Through robust fault tolerance mechanisms, the protocol enhances FlyNet's reliability, maintaining seamless communication and optimal resource utilization even amid node mobility and disruptions. Simulation results demonstrate the algorithm's effectiveness in adapting to dynamic environments, maximizing resource utilization, and minimizing communication delays.

Grid-Connected Photovoltaic System for Generating Energy to Dairy Farm Activities

Minas Gerais is the leading milk-producing state in Brazil, and many dairy activities are supplied with electricity by the cable aerial network of the electrical energy state company. Given that the Brazilian energy matrix relies primarily on hydroelectric power, studies on the potential of other renewable energy sources in the context of dairy farms are innovative. Additionally, frequent disturbances and interruptions have motivated the installation of solar energy systems on dairy farms. However, the scarcity of technical and scientific studies demonstrating the benefits of electricity generated from photovoltaic panels in Minas Gerais has caused dairy farmers to fear potential financial losses. In this study, grid-connected photovoltaic systems in dairy farms were evaluated, considering three fixed tilt angles for the panels (latitude - 10°, latitude, and latitude + 10°) and electrical energy demands of dairy activities associated with two herd sizes (100 and 150 cows). Meteorological databases and mathematical models were used to predict daily global radiation on tilted panels in dairy farms of two Brazilian municipalities of Minas Gerais State (Lavras and Paracatu). Energy consumption related to dairy activities was monitored for 12 months. For both municipalities, the best results were verified with the tilt angle of panels equal to the local latitude - 10°. The municipality with the lowest latitude (Paracatu) showed higher electricity generation potential. In all simulated scenarios, a greater energy surplus was found from late spring to early autumn (Southern Hemisphere), when the higher electricity generation by the photovoltaic panels met the elevated electrical energy demand of the dairy farms. A high energy surplus was also verified during midwinter, where lower energy consumption by the dairy farms compensated for the reduced availability of solar radiation. The proposed methodology can be adapted to other dairy farms, agricultural and urban buildings.

UAV Inspections of Power Transmission Networks with AI Technology: A Case Study of Lesvos Island in Greece

The inspection of overhead power transmission lines is of the utmost importance to ensure the power network’s uninterrupted, safe, and reliable operation. The increased demand for frequent inspections implementing efficient and cost-effective methods has emerged, since conventional manual inspections are highly inaccurate, time-consuming, and costly and have geographical and weather restrictions. Unmanned Aerial Vehicles are a promising solution for managing automatic inspections of power transmission networks. The project “ALTITUDE (Automatic Aerial Network Inspection using Drones and Machine Learning)” has been developed to automatically inspect the power transmission network of Lesvos Island in Greece. The project combines drones, 5G data transmission, and state-of-the-art machine learning algorithms to replicate the power transmission inspection process using high-resolution UAV data. This paper introduces the ALTITUDE platform, created within the frame of the ALTITUDE project. The platform is a web-based, responsive Geographic Information System (GIS) that allows registered users to upload bespoke drone imagery of medium-voltage structures fed into a deep learning algorithm for detecting defects, which can be either exported as report spreadsheets or viewed on a map. Multiple experiments have been carried out to train artificial intelligence (AI) algorithms to detect faults automatically.

Slicing-Aware Aerial Networks for Integrated Sensing and Communication: 3D Placement and Adaptive Allocation of Resources

Slicing-Aware Aerial Networks for Integrated Sensing and Communication: 3D Placement and Adaptive Allocation of Resources

Secure UAV adhoc network with blockchain technology.

Recent advances in aerial robotics and wireless transceivers have generated an enormous interest in networks constituted by multiple compact unmanned aerial vehicles (UAVs). UAV adhoc networks, i.e., aerial networks with dynamic topology and no centralized control, are found suitable for a unique set of applications, yet their operation is vulnerable to cyberattacks. In many applications, such as IoT networks or emergency failover networks, UAVs augment and provide support to the sensor nodes or mobile nodes in the ground network in data acquisition and also improve the overall network performance. In this situation, ensuring the security of the adhoc UAV network and the integrity of data is paramount to accomplishing network mission objectives. In this paper, we propose a novel approach to secure UAV adhoc networks, referred to as the blockchain-assisted security framework (BCSF). We demonstrate that the proposed system provides security without sacrificing the performance of the network through blockchain technology adopted to the priority of the message to be communicated over the adhoc UAV network. Theoretical analysis for computing average latency is performed based on queuing theory models followed by an evaluation of the proposed BCSF approach through simulations that establish the superior performance of the proposed methodology in terms of transaction delay, data secrecy, data recovery, and energy efficiency.

A cloud-edge collaborative computing framework using potential games for space-air-ground integrated IoT

As a critical component of space-air-ground integrated IoT, the aerial network provides highly reliable, low-latency and ubiquitous information services to ground users by virtue of their high mobility, easy deployment and low cost. However, the current computation and resource management model of air-ground integrated networks are insufficient to meet the latency demanding of emerging intelligent services such as autonomous systems, extended reality and haptic feedback. To tackle these challenges, we propose a computation offloading and optimization method based on potential game. First, we construct an cloud-edge collaborative computing model. Secondly, we construct Offloading Decision Objective Functions (ODOF) with the objective of minimum task processing latency and energy consumption. ODOF is proved to be a Mixed Inferior Nonlinear Programming (MINLP) problem, which is hard to solve. ODOF is converted to be a full potential game, and the Nash equilibrium solution exists. Then, a computational resource allocation algorithm based on Karush–Kuhn–Tucker (KKT) conditions is proposed to solve resource allocation problem. On this basis, a distributed game-based computational offloading algorithm is proposed to minimize the offloading cost. Extensive simulation results demonstrate that the convergence performance of the proposed algorithm is reduced by 50%, the convergence time is reduced by 13.3% and the average task processing delay is reduced by 10%.

Hierarchical aerial offload computing algorithm based on the Stackelberg-evolutionary game model

Aerial access networks have been envisioned as a promising 6 G technology to enhance the service experience in underserved areas where terrestrial base stations do not exist. In such scenarios, a hierarchical model of high-altitude platforms (HAPs) and unmanned aerial vehicles (UAVs) is considered to provide aerial computing services for ground Internet of Things (IoT) devices. In this study, we investigate a hierarchical aerial computing system to optimally orchestrate the limited computation resources in both HAPs and UAVs. For offloading services, we formulate a joint resource allocation problem to maximize service satisfaction for terrestrial IoT devices. To solve this problem, we employ the ideas of game theory with centralized decision and decentralized execution. Through the Stackelberg-evolutionary game model, the HAP works as a leader, and selects its price strategy based on the evolutionary learning process. As followers, individual UAVs make decisions to partially offload their computing tasks by considering different objectives. According to the interactive control paradigm, our proposed method can get reciprocal advantages for HAPs, UAVs, and ground IoT devices while adaptively handling dynamic aerial network conditions. Finally, extensive simulation results verify the efficiency of our proposed algorithm to increase the usability of edge servers’ computational resources. Compared with other existing state-of-the-art aerial network offloading protocols, we can improve the profits of system throughput, resource usability and UAV fairness up to 10 %, 10 %, and 15 %, respectively.

Energy-Efficient Hybrid Federated and Centralized Learning for Edge-Based Wireless Traffic Prediction in Aerial Networks

Energy-Efficient Hybrid Federated and Centralized Learning for Edge-Based Wireless Traffic Prediction in Aerial Networks

User-Centric Communication With Aerial Network for 6G: A Reinforcement Learning Approach

User-Centric Communication With Aerial Network for 6G: A Reinforcement Learning Approach

Joint Resource Management and Deployment Optimization for Heterogeneous Aerial Networks With Backhaul Constraints

Joint Resource Management and Deployment Optimization for Heterogeneous Aerial Networks With Backhaul Constraints

Framework for Optimized Resource Allocation in Multi-User, Multi-Service, Multi-Device Aerial Networks

Framework for Optimized Resource Allocation in Multi-User, Multi-Service, Multi-Device Aerial Networks

Direction Estimation in 3D Outdoor Air–Air Wireless Channels through Machine Learning

UAVs need to communicate along three dimensions (3D) with other aerial vehicles, ranging from above to below, and often need to connect to ground stations. However, wireless transmission in 3D space significantly dissipates power, often hindering the range required for these types of links. Directional transmission is one way to efficiently use available wireless channels to achieve the desired range. While multiple-input multiple-output (MIMO) systems can digitally steer the beam through channel matrix manipulation without needing directional awareness, the power resources required for operating multiple radios on a UAV are often logistically challenging. An alternative approach to streamline resources is the use of phased arrays to achieve directionality in the analog domain, but this requires beam sweeping and results in search-time delay. The complexity and search time can increase with the dynamic mobility pattern of the UAVs in aerial networks. However, if the direction of the receiver is known at the transmitter, the search time can be significantly reduced. In this work, multi-antenna channels between two UAVs in A2A links are analyzed, and based on these findings, an efficient machine learning-based method for estimating the direction of a transmitting node using channel estimates of 4 antennas (2 × 2 MIMO) is proposed. The performance of the proposed method is validated and verified through in-field drone-to-drone measurements. Findings indicate that the proposed method can estimate the direction of the transmitter in the A2A link with 86% accuracy. Further, the proposed direction estimation method is deployable for UAV-based massive MIMO systems to select the directional beam without the need to sweep or search for optimal communication performance.

Distributed user pairing and effective computation offloading in aerial edge networks

Future Sixth-Generation (6G) mobile communication networks extremely require the global connectivity and the ground Base Stations (BSs) are difficult to develop in some specific areas, such as mountainous areas. Therefore, the aerial networks are the key framework for the future communications, in which the aerial vehicle could act as the access point. Additionally, Mobile Edge Computing (MEC) is also essential to enhance the data processing capabilities of aerial networks. In this paper, we study a comprehensive communication-computation resource management problem for jointly optimizing user pairing, power and time allocation in the MEC aided Cognitive Radio (CR) aerial networks, namely CR-MEC aerial networks. Explicitly, this multilevel optimization problem could be decomposed into the user pairing and power allocation as well as time allocation sub-problems. In the conceived CR-MEC aerial networks, we propose a User Pairing and Computation Offloading Management Algorithm (UPCOMA) based on three-sided matching theory, aiming to minimize the system’s energy consumption. At the first step of UPCOMA, multiple Primary Users (PUs) and Cognitive Users (CUs) on the ground negotiate to each other with the suitable power allocation coefficients and construct the stable user pairs. Moreover, the stable user pairs would match to a high altitude platform who act as the base station, which is for appropriately allocating Transmission Time Slots (TSs) at the second step of UPCOMA. Additionally, a hybrid offloading mode is proposed in our conceived networks in order to support ground users to offload their tasks adaptively according to their individual deadlines. Furthermore, the simulation results reveal that the relative performance of UPCOMA is close to that of the Brute-Force Search Algorithm (BFSA) with low complexity. Meanwhile, the hybrid offloading mode with less energy consumption supports much more ground user pairs to offload tasks compared to the binary mode.

Coverage Strategy for Small-Cell UAV-Based Networks in IoT Environment

In wireless communication, small cells are low-powered cellular base stations that can be used to enhance the coverage and capacity of wireless networks in areas where traditional cell towers may not be practical or cost-effective. Unmanned aerial vehicles (UAVs) can be used to quickly deploy and position small cells in areas that are difficult to access or where traditional infrastructure is not feasible. UAVs are deployed by telecommunication service providers to provide aerial network access in remote rural areas, disaster-affected areas, or massive-attendance events. In this paper, we focus on the scheduling of beaconing periods as an efficient means of energy consumption optimization. The conducted study provides a sub-modular game perspective of the problem and investigates its structural properties. We also provide a learning algorithm that ensures convergence of the considered UAV network to a Nash equilibrium operating point. Finally, we conduct extensive numerical investigations to assist our claims about the energy and data rate efficiency of the strategic beaconing policy (at Nash equilibrium).

Learning-Based Collaborative Computation Offloading in UAV-Assisted Multi-Access Edge Computing

Unmanned aerial vehicles (UAVs) have gained considerable attention in the research community due to their exceptional agility, maneuverability, and potential applications in fields like surveillance, multi-access edge computing (MEC), and various other domains. However, efficiently providing computation offloading services for concurrent Internet of Things devices (IOTDs) remains a significant challenge for UAVs due to their limited computing and communication capabilities. Consequently, optimizing and managing the constrained computing, communication, and energy resources of UAVs are essential for establishing an efficient aerial network infrastructure. To address this challenge, we investigate the collaborative computation offloading optimization problem in a UAV-assisted MEC environment comprising multiple UAVs and multiple IODTs. Our primary objective is to obtain efficient offloading strategies within a multi-heterogeneous UAV environment characterized by limited computing and communication capabilities. In this context, we model the problem as a multi-agent markov decision process (MAMDP) to account for environmental dynamics. We employ a multi-agent deep deterministic policy gradient (MADDPG) approach for task offloading. Subsequently, we conduct simulations to evaluate the efficiency of our proposed offloading scheme. The results highlight significant improvements achieved by the proposed offloading strategy, including a notable increase in the system completion rate and a significant reduction in the average energy consumption of the system.

GREENSKY: A fair energy-aware optimization model for UAVs in next-generation wireless networks

Unmanned Aerial Vehicles (UAVs) offer a strategic solution to address the increasing demand for cellular connectivity in rural, remote, and disaster-hit regions lacking traditional infrastructure. However, UAVs’ limited onboard energy storage necessitates optimized, energy-efficient communication strategies and intelligent energy expenditure to maximize productivity. This work proposes a novel joint optimization model to coordinate charging operations across multiple UAVs functioning as aerial base stations. The model optimizes charging station assignments and trajectories to maximize UAV flight time and minimize overall energy expenditure. By leveraging both static ground base stations and mobile supercharging stations for opportunistic charging while considering battery chemistry constraints, the mixed integer linear programming approach reduces energy usage by 9.1 ​% versus conventional greedy heuristics. The key results provide insights into separating charging strategies based on UAV mobility patterns, fully utilizing all available infrastructure through balanced distribution, and strategically leveraging existing base stations before deploying dedicated charging assets. Compared to myopic localized decisions, the globally optimized solution extends battery life and enhances productivity. Overall, this work marks a significant advance in UAV energy management by consolidating multiple improvements within a unified coordination framework focused on joint charging optimization across UAV fleets. The model lays a critical foundation for energy-efficient aerial network deployments to serve the connectivity needs of the future.

Multi-Objective Robust Beamforming for Integrated Satellite and Aerial Networks Supporting Heterogeneous Services

An integrated satellite and aerial network (ISAN) is considered a promising candidate to provide seamless connectivity for future wireless communication systems. In this paper, we propose a multi-objective based robust beamforming (BF) scheme for an ISAN to support heterogeneous services with high flexibility, where the satellite network serves various heterogeneous satellite terminals through multicast non-orthogonal multiple access (MC-NOMA), while the aerial network offers services to many internet of things devices using layered division multiplexing (LDM). Specifically, we first formulate a multi-objective optimization problem (MOOP) to achieve a good trade-off between sum rate maximization and total transmit power minimization. To tackle this mathematically intractable problem, we exploit the weighted Tchebycheff approach to transform the MOOP into a single-objective problem. Since only the angular information based channel state information is available, we exploit the angular discretization method and sequential convex approximation to design a robust BF algorithm to obtain the Pareto optimal solutions. Finally, simulation results demonstrated that our proposed scheme can achieve a optimal trade-off between multiple performance metrics with high spectrum and energy efficiency, so as to support heterogeneous services in the ISAN and fill the gap of only single type of serivce in the existing ISAN works.

MULTI-AGENT MODEL OF GROUP CONTROL POINT OF UNMANNED AIRCRAFT AND UNMANNED GROUND VEHICLE WHEN PERFORMING SPECIAL MISSIONS

Unmanned aerial vehicles (UAVs) are becoming very important platforms for conducting combat operations in the russian-ukrainian war, as they significantly reduce the risk of human life during special and combat missions. The UAV group can be used to perform a wide range of military tasks: destruction of enemy equipment, reconnaissance (monitoring) of the area, construction of aerial networks, etc.
 In the conditions of a change in the tactical situation, countermeasures by the enemy, effective management of the UAV group is impossible without an appropriate management system. The UAV group can include both unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UAVs). The following main tasks are assigned to the management system: determination and coordination of the order of joint actions of autonomous UAV groups; formation of flight (ground) tasks and flight (movement) trajectories of the UAV group; dynamic adjustment of the flight task and the role of each UAV during its use; organization of the communication network between the control center and between UAV groups, etc. The tasks and target functions of management of each of their subsystems are determined. The features of the UAV group control cycle are described.
 The main stages of the synthesis of the control system are considered: the choice of the control method (centralized, hierarchical, decentralized and mixed); selection of algorithms (protocols) and organization of the process of data exchange between UAVs. A functional model of the UAV group management system is proposed. Tasks and main management functions of each of the subsystems are defined. Features of the UAV group management cycle are described. The main stages of the synthesis of the control system are considered: the selection of algorithms (protocols) and the organized process of information exchange between UAVs; variants of formation and swarm models of UAV movement in groups; multi-agent decision-making models for the organization of task performance by the UAV group.

Aerial Social Spaces and State Power

This article suggests that aerial networks can be usefully conceptualized as broad “aerial social spaces” that can (and often do) extend, produce, and reproduce state power. They are not easily created: their creation and maintenance require large resources both on land and in the air. The same aerial network can be embodied in multiple aerial social spaces that produce and reproduce state power in different ways. This article highlights two such pathways: the first through cultural and the second through logistical impact. In support it presents two case studies based on the expansion of civil aviation in the developing world in the 1940s and 1950s: the International Civil Aviation Authority's interventions in the Middle East and US involvement in Saudi Arabian aviation.

Taming Aerial Communication with Flight-Assisted Smart Surfaces in the 6G Era: Novel Use Cases, Requirements, and Solutions

Aerial communication is gradually taking an assertive role within common societal behaviors by means of unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and fixed-wing aircrafts (FWAs). Such devices can assist general operations in a diverse set of heterogeneous applications, such as video surveillance, remote delivery, and connectivity provisioning in crowded events and emergency scenarios. Given their increasingly higher technology penetration rate, telco operators started looking at the sky as a new potential direction to enable a three-dimensional (3D) communication paradigm. However, designing flying mobile stations involves addressing a daunting number of challenges, such as an excessive onboard control overhead, variable battery drain, and advanced antenna design. To this end, the newly born smart surfaces technology may come to help: reconfigurable intelligent surfaces (RISs) may be flexibly installed on board to control the terrestrial propagation environment from an elevated viewpoint by involving low-complex and battery-limited solutions. In this article, we focus on identifying the key optimization aspects to be considered when designing RIS-based aerial networks, and in particular the associated control architecture, by shedding light on novel use cases, corresponding requirements, and potential solutions.