Survey of Important Issues in UAV Communication Networks

The use of Unmanned Aerial Vehicles (UAVs) or drones has grown rapidly in both civilian and military operations over the last few decades. Multi-UAV systems are preferred to single-UAV systems, as they are more efficient and cost-effective when completing missions collaboratively. However, like other ad-hoc networks, UAV networks face the challenge of effective communication. Existing research in mobile ad-hoc networks (MANETs) and vehicular ad-hoc networks (VANETs) does not fully address the unique characteristics of UAV networks, which can exhibit varying levels of dynamism, intermittent links, and fluid topology. Moreover, drones need to communicate with each other and ground stations in Flying Ad-hoc Networks. Therefore, routing protocols in such networks must select the most effective paths for communication while ensuring reliable and stable data transmission. This article examines several recently developed routing protocols in UAV communication, detailing their construction methods. As routing is quite challenging in UAVs, where communication performance depends on various factors, this research introduces performance metrics to analyze the efficiency of these protocols. Routing protocols in UAV networks must adapt to high mobility, dynamic topology, intermittent links, power constraints, and changing link quality. Since the lifespan of UAV nodes is limited, seamless handovers are crucial, and the energy efficiency of protocols at different layers should also be considered. Although the reviewed protocols address several aspects of designing routing protocols for UAV communication, some challenges remain. Thus, this article provides a comprehensive exploration of routing protocols in UAV communication, along with a discussion of some open research areas.

Due to the flying nature of Unmanned Aerial Vehicles (UAVs), it is very attractive to deploy UAV network as aerial base stations, relays or scouters. However, the management of UAV airborne network is not easy due to the following two facts. First, massive information exits in UAV networks including the flight and control information of UAVs, the protocol stack information in the UAV network, the sensing information of UAVs, and the obtained information from ground terminals. Second, the wireless links and network topologies are changing result from the UAV motion. How to manage and utilize the information, deal with the intermittent links, and at the same time maintain a fluid topology therefore become a challenging problem. Software Defined Networking (SDN) could facilitate flexible deployment and management of network, which helps reduce cost, increase availability in networks. Moreover, the SDN controller can fuse and learn from the information of the UAV network itself and gathered by UAVs to make optimal decision intelligently. In this paper, we design a SDN framework for UAV backbone network. In this framework, a monitoring platform is proposed in the SDN controller to effectively manage and analyze the information from UAV networks. Based on the analysis results, a load balancing algorithm is further proposed to maintain a desirable network service. The algorithm considers the power limit of UAV, both global and local dynamic status in the network. Computer simulation validates the proposed framework and algorithms. The reported SDN framework in this paper is scalable to contribute in the development of UAV backbone network towards Knowledge Centric Networking (KCN).

Geographic Position based Hopless Opportunistic Routing for UAV networks

The increasing adoption of unmanned aerial vehicle (UAV) communication networks has introduced new cybersecurity challenges, particularly in detecting and mitigating distributed denial-of-service (DDoS) attacks. This study evaluates the effectiveness of multiple machine learning models, including Random Forest, Gradient Boosting, XGBoost, Logistic Regression, and Support Vector Machine (SVM), for DDoS attack detection in UAV networks. The dataset, derived from a simulated UAV communication network, incorporates key network parameters such as signal strength, packet loss rate, round-trip time, and base station load. Data preprocessing steps, including feature selection, normalization, and synthetic minority over-sampling (SMOTE), were applied to enhance model performance. Among the evaluated models, Random Forest demonstrated the highest classification accuracy with an F1-score of 0.839 and an AUC score of 0.912, outperforming other models in precision-recall trade-offs. Gradient Boosting and XGBoost exhibited moderate classification ability, whereas Logistic Regression and SVM struggled with capturing complex network patterns. The results highlight the effectiveness of ensemble learning in intrusion detection for UAV networks. This study provides valuable insights into optimizing machine learning-based intrusion detection systems and paves the way for further advancements in UAV cybersecurity. Future work will focus on integrating additional feature engineering techniques and validating models on real-time network traffic datasets.

In order to improve the performance of cooperative unmanned aerial vehicle (UAV) networks under the condition of a UAV can only carry one single antenna in the complex wireless environment, a novel case is decode-and-forward relaying method for 4 UAVs based on the distributed space-time block code (DSTBC). It can achieve 3 times diversity gains in quasi-static Rayleigh fading channel. Moreover, to obtain a better BER performance, polar codes are used in the UAV networks. The Polar-UAV networks can be equivalent to a single transmission channel for each polar code bit. By comparing the various BER simulation results, it can be concluded that polar codes can tremendously improve the coding gain and guarantee high reliable fly control in the proposed cases.

Chapter Four - A survey study on MAC and routing protocols to facilitate energy efficient and effective UAV-based communication systems

For large unmanned aerial vehicle (UAV) networks, the timely communication is needed to accomplish a series of missions accurately and effectively. The relay technology will play an important role in UAV networks by helping drones communicating with long-distance drones, which solves the problem of the limited transmission power of drones. In this paper, the relay selection is seen as the entry point to improve the performance of self-organizing network with multiple optimizing factors. Different from the ground relay models, the relay selection in UAV communication networks presents new challenges, including heterogeneous, dynamic, dense and limited information characteristics. More effective schemes with distributed, fast, robust and scalable features are required to solve the optimizing problem. After discussing the challenges and requirements, we find that the matching game is suitable to model the complex relay model. The advantages of the matching game in self-organizing UAV communications are discussed. Moreover, we provide extensive applications of matching markets, and then propose a novel classification of matching game which focuses on the competitive relationship between players. Specifically, basic preliminary models are presented and some future research directions of matching game in UAV relay models are discussed.

The reliability and transmission quality in any wireless communication network is paramount. Unmanned Aerial Vehicle (UAV) communication networks are no exception to this fact. Hence, the need to investigate various communication technologies that will improve and be able to support the various applications of the UAV communication network. Correlated based stochastic models (CBSM) have been used to assess theoretical performances in UAV communication networks. CBSM has insufficient precision in a practical system. Geometry based stochastic channel models (GBSM) on the other hand, displays realistic channel features. These realistic channel features include pathloss, angle of arrival (AoA), angle of departure (AoD), etc. GBSM is better and ideal for channel modeling. This paper analyses the UAV communication networks in terms of their reliability and quality in transmission. MIMO-OFDM technology has been proposed to improve the UAV communication network. In this UAV network, the transmitters are modeled as cylindrical array (CA) because of its attribute of good regulation in 3-D space among others. A 3-D GBSM is proposed in the analysis. Also, an interference model has been presented in the UAV communication network. Results from this research show that MIMO-OFDM improves the reliability and quality of the UAV communication network. Generally, the capacity and BER increased with increasing number of antennas and SINR. However, beyond SINR of 25dB, we observed an irreducible error floor, that is, BER remained constant with increasing SINR. Successive Interference Cancellation (SIC) was therefore employed to minimize the irreducible error floor in the UAV communication network. This increased the average capacity and BER to about 25bits/s/Hz and \(10^{-8}\) respectively. KeywordsUAV communication networksMIMO-OFDMCylindrical arrayCBSMGBSMSuccessive interference cancellation

Abnormal power emission poses serious interference and security threats to unmanned aerial vehicle (UAV) communication networks. To tackle this problem, in this letter, we first develop a cloud-based drone surveillance framework, where a closed-loop cognitive control cycle is formed with power requirement and allocation information exchanged between a UAV network and a management cloud, and allocation information and abnormal power emission exchanged between the cloud and a surveillance center. Then, the detection problem of abnormal power emission is mathematically formulated as a ternary-hypothesis test. To achieve the tradeoff between false alarm and missed detection, a generalized Neyman-Pearson test criterion is proposed, where the detection probability is maximized under two false-alarm constraints. Following the criterion, the test rules are derived for local detection and cooperative detection, where local decision regions and the global decision threshold are optimized, respectively. Finally, the simulation results are provided to verify the proposed scheme.

Unmanned aerial vehicles (UAVs) can gather data in the air and transmit the data to the ground station. Multi-UAV systems have been used in an increasing number of mission scenarios and routing protocols play a critical role in UAV network communications. It is now well established that unstable link quality and frequently changing network topology pose significant challenges for messages forwarding in UAV networks. Hence, traditional mobile ad-hoc network routing protocols do not fit well in UAV networks. In many UAV applications, the flight paths of UAVs are planned in advance before performing missions. The positions and motion information of UAVs are available through Global Positioning System (GPS) and inertial sensors, which can be utilized to calculate the future positions of UAVs. Therefore, the future topology of the UAV network is also available. However, existing work does not take advantage of this information. Based on the trajectory, location and motion information of the UAVs, this paper proposes a future network topology-aware routing (FNTAR) protocol, which uses future location information to make superior routing decisions. Moreover, to mitigate data loss problems caused by unstable links and highly dynamic topology, FNTAR can forward messages to multiple excellent next-hop UAVs based on future network topology, and these UAVs can deliver messages to destinations faster. We implement FNTAR in the simulation experiment, the simulation results demonstrate that FNTAR can achieve lower latency and higher delivery ratio than DTN geo protocol.

The development of Unmanned Aerial Vehicle (UAV) networks has revolutionized several sectors, such as emergencies, environmental monitoring, and intelligent agriculture. UAV networks suggest significant benefits in real-time information gathering, remote surveillance, and automation. Yet, achieving optimal communication efficiency in UAV networks is still a major challenge with dynamic network topology, energy constraints, and interference from environmental variables like weather conditions and signal obstructions. Preserving untainted and reliable connectivity for UAV networks is essential for their effective deployment in mission-critical scenarios. The study provides a Hybrid Snow Ablation Shark Nose Optimizer (HSASNO) method to enhance communication performance in UAV networks with focus on performance parameters including latency, energy efficiency, and throughput.The suggested method makes use of nature-based and bio-based optimization methods to adaptively modify the network parameters so as to guarantee adaptive and auto-optimizing communication by UAVs. HSASNO aims to optimize UAV path planning, power transmission management, and resource planning, which will result in a remarkable network performance and stability improvement.Simulations were conducted in diverse scenarios, such as different densities of UAVs, movement styles, and weather conditions, to estimate the robustness of the suggested method. Results show that there is considerable enhancement in network performance with reduced latency, better energy efficiency, and increased data throughput in comparison to conventional schemes. In particular, the HSASNO scheme recorded less energy consumption (24.40), less end-to-end delay (0.98), and increased throughput (0.992), which shows its superiority. The outcomes reflect the effectiveness of meta-heuristic methods in overcoming the inherent complexity of UAV communication in networks. The system as proposed guarantees efficient and reliable communication and flexibility with respect to changes in climatic and operational environments, hence highly suitable for real-world applications, including disaster response, environmental surveillance, and smart transportation systems.

With the advances in computation, sensor, communication and networking technologies, utilization of Unmanned Aerial Vehicles (UAVs) for military and civilian areas has become extremely popular for the last two decades. Since small UAVs are relatively cheap, the focus is changing, and usage of several small UAVs is preferred rather than one large UAV. This change in orientation is dramatic, and it is resulting to develop new networking technologies between UAVs, which can constitute swarm UAV teams for executing specific tasks with different levels of intra and inter vehicle communication especially for coordination and control of the system. Setting up a UAV network not only extends operational scope and range but also enables quick and reliable response time. Because UAVs are highly mobile nodes for networking, setting up an ad-hoc network is a challenging issue, and this networking has some requirements, which differ from traditional networks, mobile ad-hoc networks (MANETs) and vehicular ad-hoc networks (VANETs) in terms of connectivity, routing process, services, applications, etc. In this paper, it is aimed to point out the challenges in the usage of UAVs as mobile nodes in an ad-hoc network and to depict open research issues with analyzing the opportunities and future works.

Unmanned Aerial Vehicles (UAVs) are an emerging technology that can be utilized in military, public and civil applications. Multi-UAV systems can collaboratively complete missions more efficiently and economically as compared to single UAV systems. However, UAV networks in multi-UAV systems are still based on TCP/IP, which is not efficient and scalable to provide stable and reliable communication. Therefore, novel UAV networks via Named Data Networking (NDN) is proposed in this paper. Meanwhile, the simulation conducted on ndnSIM tests the transmission interference time and the maximum end-to-end transmission delay of this novel UAV networks, which are vital to the whole system. The result indicates the novel UAV networks via NDN satisfies the current network requirements, which has advantages such as good network adaptability, low latency, high security, etc.

Unmanned aerial vehicles (UAVs) are gaining tremendous attention due to their flying nature. To complete the task efficiently, multi-UAV systems are a good choice as compared to a single UAV system. However, multi-UAV systems introduce issues, such as high dynamics, limited battery, and frequent changes in topology. Software control is required to solve these issues. Thus, software-defined networking (SDN) is an excellent candidate to separate control logic from forwarding elements and provide high-level programming abstractions. However, due to architectural constraints, applying SDN introduces some new challenges, including uneven load on multiple links between source and destination. This irregular load also affects UAVs’ battery consumption, necessitating an adequate solution to meet these challenges fully. This article proposes an SDN-based framework for UAV elements that monitors frequent changes in the network topology. Based on this monitoring, an algorithm is designed, which distributes traffic load evenly on different links of multi-UAV systems. UAV networks have limited resources; therefore, battery limitations are also considered, and traffic is shifted to a path where elements have more battery. Moreover, a flight control mechanism is proposed to avoid collisions due to the high dynamics of UAVs. Extensive simulation results show that the traffic load is distributed evenly on multiple links connecting different systems with less battery consumption.

This paper proposes an orthogonal frequency division multiplexing (OFDM) relaying wireless power transfer based protocol for energy-constrained unmanned aerial vehicle (UAV) communication network. At the first step of the proposed protocol, the energy-constrained UAV separates the received signals into two disjoint subcarrier groups to perform energy harvesting (EH) and information decoding (ID). At the second step, UAV forwards the received information signals with the energy harvested a priori. Based on the proposed protocol, this paper aims to find the joint optimization of subcarrier grouping and power allocation for maximizing the transmission rate under the EH constraint. We further solve such a joint resource allocation problem via dual decomposition after transforming it into an equivalent convex optimization problem. Simulation results indicate that the performance of our proposed protocol can be significantly improved for the OFDM relaying based UAV communication network.