Mobile ad hoc networks (MANETs) and vehicular ad hoc networks (VANETs) are considered in a review  without taking into account the peculiarities of UAV networks, which can vary from slow dynamic to dynamic and have intermittent connections. It is noted that multi-UAV networks has been an understudied area. High mobility, dynamic topology, intermittent links, power constraints, changing link quality, latency and fault tolerance should be considered when designing communication systems for RPAS/UAV swarms.
An overview of the characteristics and requirements for communications in UAV networks is carried out in a work . General networking related requirements such as connectivity, safety, privacy, security, and scalability are considered. Suitability of existing communication technologies for supporting reliable aerial networking is examined.
UAVs must communicate effectively with each other using UAV-to-UAV (U2U) communications and with existing network infrastructure using UAV-to-infrastructure (U2I) communications. This requirement for effective communication is caused by errors in navigation, guidance and control systems, introduced at each step. First, navigation systems introduce errors to the current coordinates and attitude parameters determination [5 - 8]. Then guidance and control system may have correspondent imperfections in placing single drone to the required position . Therefore, effective and reliable communication inside of UAVs’ swarm plays a crucial role. The article  defines the functions, services and requirements for communication systems based on UAVs. Network architectures, basic structures and requirements for data traffic in these systems are presented. Services of the middleware level for uninterrupted communication and support of heterogeneous network interfaces are discussed. A new area of research is considered, which includes the use of UAVs to collect data from Wireless Sensor Networks (WSN).
Single small UAV has limited payloads, flight times and requires external control. Coordinating multiple drones into swarm increases their functionality. A swarm is defined as a group of behaving entities that coordinate their actions together to achieve the desired result. A swarm of UAVs can distribute tasks in principle without operator intervention. The article  provides a review of UAV swarms and proposes a swarm architecture using cellular communication. The authors note that the use of cellular mobile infrastructure removes the limiting factors of drone use, including range and network problems.
Multi-UAVs are predicted to be an important element in the development of advanced cyber-physical systems (CPS) with synergistic interactions between computing and physical capabilities. The main advantages of using UAVs in the CPS application are their exceptional characteristics (mobility, dynamism, ease of deployment, adaptive height, maneuverability, adjustability and effective assessment of real functions anytime, anywhere). The review  describes the fundamental problems of designing systems with several UAVs for CPS applications. Various algorithms for fixed and mobile coverage and target tracking have been investigated, and comparisons have been made between them on complexity, share of open area and number of surveillance cameras.
Understanding how multiple drones can coordinate and interact is essential for the advancement of multi-agent robotics. The review  illustrates existing flight control and communication systems for multi-agent drone deployments. Articles with experimental results and analysis of the used communication equipment are considered. It is noted that most of the work in this area remains at the modeling stage, since the coordination of UAVs is a complex issue. Choosing communication and coordination strategies is very difficult and designers must consider range, throughput, data rate, power requirements, payload weight, compatibility, and cost.
The review article  presents a study of UAVs with a discussion of the mechanics, functionality, organization, modeling, applications and aspects of drones’ autonomy.
Communications play an important role in the control and coordination of RPASs/UAVs swarm. The communication architecture defines the exchange of information between drones and the control center. Routing protocols help ensure reliable end-to-end communications. The review article  describes four communication architectures and provides a systematic overview and feasibility study of routing protocols. It is concluded that layered architecture, combined with mesh architecture within the swarm, is currently the most applicable communication architecture.
The article  provides an overview of typical Swarm Intelligence (SI) algorithms and summarizes their application in the Internet of Things (IoT). The focus is on analyzing SI-enabled applications for the WSN and discussing research issues at WSN. Authors generally divide the UAV-aided wireless network into three categories according to their principles, and their applications based on SI are analyzed.
When using UAVs, there are many problems that need to be solved, and the main one is communication. The review  explores the latest UAV communication technologies by examining suitable task modules, antennas, resource processing platforms, and network architectures. Explored techniques such as machine learning and path planning. Encryption methods to ensure long-term and secure communication are discussed. Applications of UAV networks are investigated for a variety of contextual purposes, from navigation to surveillance, URLLC (Ultra-Reliable Low Latency Communication), edge computing and work related to artificial intelligence. The complex interaction between UAVs, cellular communications and the IoT is one of the main topics of this article. This literature review demonstrates the need for additional research in the field of drone-to-drone and drone-to-device communications.
There are many complex issues in the design of UAV swarm networks, such as the integration of hardware and software for large-scale UAV network management, long distance data transmission between UAVs, swarm shape/formation control, and intelligent UAV mobility/position prediction. Engineering developments and designs of network protocols for dynamic large-scale UAV networks are considered in the book . It provides technical models/algorithms and protocol specifications for the practical deployment of UAV swarms.
Automating swarms’ management is challenging as every drone operates under fluctuating wireless, networking and environment constraints. In the review , drone swarms are considered as Network Control Systems (NCS), in which the control of the entire system is carried out within the wireless communication network. This is based on a tight interconnection between the networking and computational systems, aiming efficiently support data collection, information exchanging, decision-making, and the distribution of commands. The development of self-organized drone swarms as NCS through the integration of networking and computing systems is described. Their integration is analyzed to improve the performance of drone swarms.
UAVs swarm is usually used to solve the problems of finding survivors, monitoring and tracking several targets. This requires complex mechanisms for their control, communication and coordination. However, these mechanisms are difficult to test and analyze in the context of flight dynamics. Such multi-UAV scenarios are inherently well suited to be simulated as multi-agent systems. The article  presents an approach for modeling the UAV as an agent in terms of multi-agent system. Sensors and communication devices allow interaction with other drones in the swarm and the environment. Proposed flight dynamics model reflects limitations and uncertainties.
Data congestion control is used to expand network capabilities, improve the reliability of VANETs by reducing packet loss and communication delays. The study  proposes a distributed congestion control strategy based on an intelligent swarm. This maintains channel utilization below the network failure threshold and maintains high quality of service. Experiments have shown that the proposed strategy improves network throughput, channel utilization, and link stability when compared to other competing congestion management strategies.
Due to the uncertainty of wireless links, communications between UAVs experience transmission delays that impair the swarm's ability to stabilize the system. The article  examines the problem of joint communication and control for a group of three UAVs connected by cellular communication. A new approach is proposed to optimize the swarm operation while taking into account the wireless network latency and the stability of the control system. The maximum allowable delay required to prevent swarm instability is determined. The simulation results help to get recommendations for the formation of a stable UAV swarm.
Traffic monitoring is considered in the paper  using a swarm that continuously monitors traffic in SwarmCity. It is a simulated city built on the Unity game engine, where drones and cars are modeled realistically. The swarm control algorithm is based on six modes of behavior with twenty-three parameters that are configurable. Parameters optimization is performed using a genetic algorithm in a simplified and fast simulator. The best resulting configurations are tested at SwarmCity and perform well in terms of the number of vehicles monitored versus the total number of vehicles over time windows.
Mini-UAVs should be grouped using swarm coordination algorithms to perform tasks in a scalable and reliable manner. The article  uses biological mechanisms to coordinate unmanned aerial vehicles searching for a target with imperfect sensors. Coordination can be achieved by combining stigmergic and flocking behavior. Stigmergia occurs when a drone releases a digital pheromone when it detects a potential target. Such pheromones can aggregate and spread between flocking drones, creating a spatially attractive potential field.
A multi-layer model of network communication and message management is proposed in the article  developing a communication system for UAVs swarm. The model implements the communication infrastructure of the swarm in the form of a communication node, in which scheduling algorithms and message management schemes are applied. Experimental results show that communication node meets the requirements of swarm communications with unstable bandwidth changes.
The work  considered the scenario, in which several UAVs with one antenna simultaneously exchange data with a ground station (GS) equipped with a large number of antennas. The achievable performance of the uplink communication (UAV - GS) throughput in the case of line-of-sight conditions is discussed. The geometric model includes an arbitrary orientation of the GS and UAV antenna elements and estimates the polarization mismatch losses that arise due to the UAV's movement and orientation. For homogeneous linear and rectangular arrays, the optimal distance between the antennas has been determined.
The data transfer from the RPAS swarm was modeled using MATLAB Simulink in our work . RLOS and Beyond Radio Line of Sight (BRLOS) link models included: 1) “Base Station Transmitter”; 2) RLOS channel: “Uplink Path”, “RPAS Receiver”; 3) BRLOS channel: “Uplink Path”, “Satellite Transponder”, “Downlink Path”; “RPAS Receiver”. The dependences of the BER on the SNR were obtained for different levels of BS transmitter nonlinearity, its gain, diameters of BS and satellite transponder antennas.
Models of "Base Station - Satellite - RPASs" communication channels were built using the NetCracker Professional 4.1 software . We analyzed the dependences of average utilization on the size of the transaction, satellite channels with different bandwidths and the number of RPAS, as well as the impact of the likelihood of satellite failure.
The article  proposes the UAV-Edge-Cloud model as a new hybrid computing platform to provide powerful resources for supporting resource-intensive applications and real-time tasks in edge networks. Potential applications of the model for smart cities and the routing problem for latency-critical applications are discussed. Simulation results show that this approach can improve Quality of Service (QoS).
The paper  describes experiments with small drones, a real-time big data platform, and an operating system that interacts with 4G cellular mobile services. The purpose of the experiment is to collect data for testing obstacle avoidance algorithms and to evaluate communication performance.
In the literature, there is generally no data on the loss of data packets when exchanging information with drones in swarms. In the article  we published the first packet losses estimation for single drone. The article  is actually the first publication containing numerical experimental data on the traffic of single drone.