Communication architecture and operations for SDR-enabled UAVs network in disaster-stressed areas

Unmanned aerial vehicle (UAV) is an aircraft without a human pilot onboard and a sort of unmanned vehicle. UAV Network research activities in the real world can pose several challenges, especially in urban environments or in densely populated environments. This is due to the nature of the UAV which requires a large area of operation. Therefore, digital simulation is vital in conducting research related to UAV and its networks. One of the UAV simulators recently developed is FlyNetSim. FlyNetSim is an open-source UAV Network Simulator based on NS-3 and Ardupilot. In the application, UAV can have some network-related problems. One of them is when a UAV is in an environment that has a lot of interfering networks because it is contending a network channel. This can cause network delay. These delays will raise with the increase in the number of nodes that interfere with the UAV. To overcome the problem of interfering nodes, we propose to add a priority set module for UAV and Ground Control Station (GCS). The proposed module will set priority for the UAV and GCS socket. This module is intended to give priority to packets sent by UAVs or GCS. Later the packet that has priority will be sent first to reduce the delay caused by the appearance of interfering nodes. Setting priority in the socket may reduce the average delay caused by contending nodes. Test result shows that there is a 16.2% reduction of average delay. Test result also shows that there is a 4% raise in packet delivery ratio.

In this paper, cyberattack detection and isolation is studied for a network of formation-flying unmanned aerial vehicles. As the unmanned aerial vehicles communicate to reach consensus on their states while making the formation, the communication network among the unmanned aerial vehicles makes them vulnerable to a potential attack from malicious adversaries. Two types of attacks pertinent to a network of unmanned aerial vehicles have been considered: a node attack on the unmanned aerial vehicles and a deception attack on the communication between the unmanned aerial vehicles. Unmanned aerial vehicle formation control involves using a consensus algorithm to maintain a prespecified formation. Two kinds of cyberattacks in the unmanned aerial vehicle network (node and communication path deception) are considered with their respective models in the formation setup. A bank of unknown input observer-based distributed fault detection schemes as well as with a rule based on residual generated using the bank of unknown input observers are proposed to detect the attacks and to identify the compromised unmanned aerial vehicle in the formation. Furthermore, an algorithm is developed to remove the faulty unmanned aerial vehicle from the network and to keep the compromised unmanned aerial vehicle isolated once an attack is detected, while maintaining the flight formation with a missing unmanned aerial vehicle node.

The ever-increasing demand for wireless communication has driven the development of innovative technologies to address the issues that conventional communication networks meet. To support autonomous devices in the sixth-generation (6G) and provide seamless wireless connectivity in inaccessible and hard-to-reach areas, the combination of software-defined networking (SDN) with unmanned aerial vehicles (UAVs) has already shown promises. As the traditional UAV-based cellular network encompasses challenges such as lack of centralized supervision, robust decision-making ability, and dynamic network management, the SDN platform could be an excellent alternative to offer aerial networking for future 6G ecosystem. However, the SDN-assisted UAV paradigm faces a set of challenges and critical issues. For instance, the network management and orchestration, resource allocation and optimization, handover mechanism, spectrum management, energy efficiency, integration of heterogeneous networks, and reliability, thus causing the decrease of quality of services of the SDN-enables UAV networks. To effectively address all these issues and identify the trends of software-defined UAV (SDUAV) networks, more research activities are needed. Unfortunately, there are not any specific guidelines in the literature that address each of these requirements, enabling technologies, emerging techniques, and future research directions in the context of SDN-based UAV networks to enable next-generation 6G systems. Therefore, motivated by this fact, this paper offers a comprehensive evaluation of the state of the SDUAV network and architecture, highlighting its various requirements and different prospects and applications in 6G systems. Besides, this paper also addresses why the combination of SDN and UAV-enabled networks is becoming essential in the 6G networking context and how it can offer more flexibility, scalability, reliability, and efficient connectivity than the conventional approaches to provide features like intelligence, automation, programmability, and centralized controllability. This paper furthermore outlines the difficulties of implementing SDUAV networks, such as security, adaptability, interference management, interoperability, and lack of standardization. Finally, to accomplish the goals, this article provides a roadmap for future trends and research directions in SDUAV networks to support the 6G systems.

Non-orthogonal multiple access (NOMA) is indeed an important technique of 5G and beyond 5G (B5G) communication systems, as it can increase the spectrum efficiency and system capacity under the same bandwidth. Long-distance unmanned aerial vehicle (UAV) networks, comprising a base station and multiple UAVs, are commonly used for reconnaissance purposes due to the extended flight range of the UAVs, which can provide broad coverage and satisfy the requirements for real-time communication. To alleviate spectrum underutilization of single channel orthogonal multiple access, we investigate resource allocation in UAV networks by utilizing NOMA and then formulate an optimization problem that maximizes the network capacity. The formulated problem can be decomposed into two sub-problems, namely UAV grouping and power allocation optimization. Accordingly, we present a UAV grouping method with low complexity according to the difference of channel gain between UAVs and base station, in which the UAVs with larger difference will be in the same cluster such that they can transmit signals over the same channel. Then, a power allocation optimization algorithm based on Lagrange multiplier method is then used to optimize the transmit power of UAVs utilizing the same channel. This algorithm considers the quality of service requirements for each UAV, and assigns power levels that maximize the overall network capacity. Simulation results have shown that the presented algorithms improve network capacity by approximately 19% compared with that of orthogonal multiple access, which demonstrates the effectiveness of utilizing NOMA in long-distance UAV networks.

Interference modeling and analysis in 3-dimensional directional UAV networks based on stochastic geometry

Wireless communication is an essential technology to unlock the full potential of unmanned aerial vehicles (UAVs) in numerous applications and has thus received unprecedented attention recently. Although technologies such as direct link, WiFi, and satellite communications are still useful in some remote scenarios where cellular services are unavailable, it is believed that exploiting the thriving 5G and beyond cellular networks to support UAV communications is the most promising and cost-effective approach, especially when the number of UAVs grows dramatically. On the one hand, to guarantee safe and efficient flight operations of multiple UAVs, it is of paramount importance to provide secure and ultra-reliable communication links between the UAVs and their ground pilots or control stations for conveying command and control signals, especially in beyond-visual-line-of-sight (BVLOS) scenarios. On the other hand, because of advances in communication equipment miniaturization as well as UAV manufacturing, mounting compact and lightweight base stations (BSs) or relays on UAVs becomes increasingly feasible. This has led to two promising research paradigms for UAV communications, namely, UAV-assisted cellular communications and cellular-connected UAVs, where UAVs are integrated into cellular networks as aerial communication platforms and aerial users, respectively. As such, integrating UAVs into cellular networks is believed to be a win-win technology for both UAV-related industries and cellular network operators, which not only creates plenty of new business opportunities but also benefits the communication performance of 3-D wireless networks. In addition, UAV related sensing and computing are also helpful for achieving efficient and reliable communication (e.g., in avoiding coverage holes) as well as smart UAV coordination, positioning, and trajectory design. However, 5G and beyond wireless networks with UAVs significantly differs from traditional communication systems, because of the high altitude and high maneuverability of UAVs, the unique UAV-ground channels, the diversified quality of service (QoS) requirements for downlink command and control (C&C) and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the new design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

Unmanned aerial vehicles (UAVs) are being used extensively in various fields. UAVs provide various services to users, including monitoring, logistics, and sensing, because of their flexible deployment and dynamic reconfigurability. However, UAV networks have become more susceptible to malicious threats because of their multiconnectivity and openness. A great effort has been made to develop an effective intrusion detection system (IDS) based on machine-learning approaches for UAVs. Unfortunately, existing methods were unable to identify real time and zero-day attacks for UAV networks. This is due to that existing methods have still used obsolete data sets and past knowledge-based detection. Also, the shortcomings of standalone IDS render them unsuitable for defending UAV networks from potential security risks. Further, the lack of precise identification for compromised UAV nodes in UAV networks poses a critical security gap, risking the entire network’s integrity with the compromise of a single node. Therefore, in this work, we propose an autonomous collaborative IDS (UAV-CIDS) with a feedforward convolutional neural network (FFCNN), which accurately identifies zero-day with high accuracy. The proposed solution takes into account encoded Wi-Fi traffic logs of three popular UAVs types: 1) DBPower UDI; 2) parrot Bebop; and 3) DJI spark. Evaluation results indicate that our FFCNN model has produced outstanding results based on the UAVIDS data set with 98.23% accuracy compared to existing models. After the detection of attacks, their mitigation is equally significant. In addition, we also design and implement real-time incident response handling against cyber-attacks on UAV Networks. The incident response handling will assist in minimizing the effects of a security breach, remediate vulnerabilities and systematically secure the entire UAV networks.

Information acquisition and rescue operation management can be facilitated by the use of unmanned aerial vehicles (UAVs) equipped with cameras and sensors that are capable of aerial photography and acquire important information from disaster-stricken areas. UAV technology is a relatively low-cost, flexible, and safe solution in missions that are difficult for humans or other systems. UAV usage in pre-and post-disaster assessment, response, and management is an active area of research. There are two important technical aspects of UAV applications in disaster situations. The first is the limited communication range. Information acquired and photography produced by UAVs are only transmitted in real-time to the ground control station (GCS) within its communication range. Remote real-time monitoring of distant areas beyond the UAV's communication range is not possible using a single UAV. It is only possible through communication among several UAVs. The second is the relatively short flight time due to limited battery capacity. UAV replacement is required in order to carry out a persistent monitoring mission. In this paper, we propose a new remote real-time monitoring system of a distant disaster area using multiple UAVs. Wireless communications between multiple UAVs constitutes a UAV network in our proposed system. Moreover, the proposed system replaces the UAVs in-flight with standby UAVs according to the remaining energy levels. We compare the performance of the proposed system with an existing UAV system. Our simulation results show that the proposed system can achieve efficient remote real-time monitoring.

The unmanned aerial vehicles (UAVs) have been widely considered as one of the key applications for future wireless communication systems, where UAVs can be used as aerial base stations (BSs) for coverage extension, transmission improvement, emergency communication, and etc. Against this background, each UAV BS is expected to select the optimal codeword to form directional analog beams, and it is capable of achieving concurrent transmissions from multiple other UAV BSs simultaneously. However, in such a kind of UAV networks, due to the vast number of connected mobile users (MUEs), UAV BSs cannot timely and preciously select the codeword from the pre-defined codebook. Fortunately, machine learning (ML) is suitable for decreasing complexity in codeword selection, because ML could extract features from the data samples acquired in real environments. In this paper, we propose an ML approach to achieve an efficient and low complexity codeword selection for UAV networks. Specifically, we first derive the probabilities that multiple UAV BSs serve one MUE to obtain the average sum rate (ASR) in UAV networks. On that basis, we develop an ML approach to maximize the ASR, where we design a classifier based on support vector machine (SVM), where our ML approach is used for selecting the optimal codeword and maximizing the ASR in UAV networks. Third, we proposed an iterative sequential minimal optimization (SMO) training algorithm to train the data of all links between UAV BSs and MUEs, where the algorithm convergence is also discussed. Finally, we show the comparison between our proposed algorithm and the traditional methods by the simulation results. The simulation at last demonstrate our method is a more efficient solution for obtain a higher performance, where a much lower computational complexity can achieved than the traditional algorithm based on channel estimation.

Introduction Autonomous unmanned aerial vehicles (UAVs) are considered with increasing interest in a diverse set of civil and commercial applications, such as environmental and natural disaster monitoring, border surveillance, emergency assistance, search and rescue missions, delivery of goods, and construction [208, 132, 362, 265, 434, 110, 280, 163]. Use of single or multiple UAVs as communication relays or aerial base stations for network provisioning in emergency situations and for public safety communications has been of particular interest due to UAVs fast deployment and large coverage capabilities [331, 193, 206, 429]. The deployed aerial platform size and the number of necessary platforms are key factors in determining the on-board communication system in addition to the coverage and capacity requirements [73]. Small-scale multi-rotors are of particular interest in practice due to their ease of deployment, low acquisition and maintenance costs, high maneuverability, and ability to hover. Research and development of small-scale UAVs focused initially on control issues, such as flight stability, maneuverability, and robustness, followed by designing autonomous vehicles capable of waypoint flights with minimal user intervention. Considering the limited flight time and payload capacity of these vehicles and the area sizes the aforementioned applications might span, the interest is shifting more and more toward collaborative multiple UAV systems for efficient and successful missions. The number of UAVs might be from tens to hundreds traveling over tens of meters to kilometers for different applications (see Figure 3.1 for an illustration). Wireless communication is an essential factor not only for network provisioning with UAVs but also for successful deployment of systems consisting of multiple small-scale UAVs. It is highly likely that high-performance links and connectivity in three-dimensional space will be required for several applications (e.g., monitoring certain areas) with data delivery meeting a certain quality of service demands [35].The question arises as to which wireless technology should be employed for aerial networks that consist of air-to-ground and air-to-air links, and data needs to be delivered regardless of significant height and orientation differences. Taking into account the quality of service to be satisfied over diverse links and the high node mobility, it is not yet clear as to whether networking protocols developed for ground networks are readily deployable on UAV networks.

Autonomous formation flight has been a fast growing research area during the last decade. Unmanned aerial vehicles (UAVs) can achieve a wide range of benefits from formation flight, such as fuel saving and aerial refueling. In this paper, the development of a small UAV system, called Swallow UAV system, as testbed of formation flight, is presented. Swallow UAV system includes Swallow UAV itself and a Hardware-In-the-Loop Simulation Environment (HILSE). Swallow UAV is a fixed wing, pusher-type, electric motor driven aircraft with 1.4 meter wing span. Its avionics system includes an onboard computer and a sensor system with GPS, IMU, air data sensor, and motor monitoring unit to provide position, velocity, inertial data, and system health. Communications between UAVs and the ground control station (GCS) are conducted by wireless modem. A fuzzy navigation and control system (FNCS) is developed for the waypoint navigation and attitude control of the aircraft. GPS velocity based pseudo attitude and fuzzy logic control are applied to FNCS for attitude estimation and fast implementation of controllers. HILSE is built to verify FNCS with onboard computer on the ground. X-Plane flight simulator is applied to simulate aircraft dynamics and output sensor data. GCS software, built with data distributor, transmits these data to the onboard computer and receive control signal from it so the onboard computer can control the aircraft in X-Plane. HILSE can perform pre-flight test for software debug, which improves safety and efficiency of flight test. The design of Swallow UAV is verified by flight test and the result shows that it has sufficient flight performance with waypoint navigation and attitude control capability to be used as a formation flight testbed.

We propose a distributed unmanned aerial vehicle (UAV) network performing collaborative radar sensing for multi-target localization and motion parameter estimation. Two UAV network topologies are considered for data propagation and information fusion. In the former, we form a sequential UAV node chain, whereas in the latter, the UAV nodes are grouped into clusters and the information among different clusters is propagated through the cluster master nodes. Sparse reconstruction methods are used to fuse the target state information from previous nodes or clusters with the data measured in the underlying nodes or clusters, depending on the adopted topology, to achieve improved target state estimates. In order to minimize the communication traffic in the UAV network, each node transmits the estimated Doppler signatures or sparse target state estimates to the next UAV node in the network or the cluster master node, in lieu of the large volume of raw sampled data. Simulation results verify the effectiveness of the proposed approaches and compare the performance between the two UAV network topologies.

With the development of airborne equipment and integrated avionics technology, the unmanned aerial vehicle (UAV) network replaces human beings in many fields. To improve the durability of the UAV network, we propose a nondisruptive wireless rechargeable UAV network (WRUN) model, in which UAVs can be charged by wireless static chargers (WSCs) without returning back to the charging platform. Under the nondisruptive WRUN model, a baseline algorithm is proposed to solve the nondisruptive charging time schedule problem (nCTSP), in which chargers do not release energy all the time and can ensure UAVs do not run out of energy. Then to improve the energy utilization rate of WSCs, we propose an efficient charging time scheduling algorithm (ECTSA), in which the flight time and paths of UAVs are discretized and nCTSP is transformed into a linear binary integer programming (LBIP) problem to calculate the efficient charging time periods of WSCs. Finally, experiments are conducted to verify that ECTSA can improve the energy utilization of WSCs.

Routing in Unmanned Aerial Vehicle (UAV) networks is critical for effective data transfer and overall network performance. However, current UAV routing algorithms exhibit high latency, poor route selection, excessive energy consumption, and limited flexibility in changing network topologies. To overcome these limitations, this paper proposes a new routing strategy that uses the Shuffled Frog Leaping Algorithm (SFLA) to improve UAV network routing. Using a two-phase optimization approach considering Quality of Service (QoS), our system combines global exploration with local exploitation, unlike previous techniques. This hybrid method enables UAVs to dynamically change their trajectories, helping to choose the best path even in fast-changing surroundings. Our approach’s self-adaptive population-based search mechanism accelerates convergence and removes a common weakness in traditional metaheuristic algorithms—premature standstill elimination—which determines its effectiveness. By constantly adjusting UAV routing patterns depending on energy economy, latency, and throughput characteristics, SFLA guarantees that UAV networks transmit effectively and consistently. Based on experimental data, our method outperforms benchmark alternatives in terms of energy use by 3.11%, latency by 5.14%, and network lifetime by 2.25%. These developments make our approach ideal for real-time applications including aerial surveillance and disaster response that call for high data transfer speeds and great energy economy.

A Survivable Communication Game based Approach for a network of cooperative UAVs