Attribute-Based Proxy Signature Scheme Supporting Flexible Threshold Predicate for UAV Networks

Digital signature with proxy delegation, which is a secure ownership enforcement tool, allows an original signer to delegate signature rights to a third party called proxy, so that the proxy can sign messages on behalf of the original signer. In today’s modern society, many applications use this mechanism. Several types of delegations are quite prevalent and the delegation of signing authority is one of them. In a traditional digital signature scheme, signer transmits signature along with message for verification, which leads to additional communication, computation cost and requires extra bandwidth. To resolve these issues, in this paper, we present an efficient ID-based proxy signature scheme with message recovery using bilinear pairings. Because of the message recovery feature, the proxy signer need not send the message to the verifier, so that the proposed scheme reduces the bandwidth requirement and communication cost. Our proposed scheme is proven secure against existential forgery under adaptively chosen message and identity attacks in the random oracle model (ROM) with the assumption that the Computational Diffie-Hellman Problem (CDHP) is intractable. We compare our scheme with related schemes. The efficiency analysis shows that the scheme is computationally efficient. Thus the proposed ID-based proxy signature scheme is secure and efficient both in terms of computation and communication costs than the related existing schemes.

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.

As there has been an advancement in avionic systems in recent years, the enactment of unmanned aerial vehicles (UAV) has upgraded. As compared to a single UAV system, multiple UAV systems can perform operations more inexpensively and efficiently. As a result, new technologies between user/control station and UAVs have been developed. FANET (Flying Ad-Hoc Network) is a subset of the MANET (Mobile Ad-Hoc Network) that includes UAVs. UAVs, simply called drones, are used for collecting sensitive data in real time. The security and privacy of these data are of priority importance. Therefore, to overcome the privacy and security threats problem and to make communication between the UAV and the user effective, a competent anonymous mutual authentication scheme is proposed in this work. There are several methodologies addressed in this work such as anonymous batch authentication in FANET which helps to authenticate a large group of drones at the same time, thus reducing the computational overhead. In addition, the integrity preservation technique helps to avoid message alteration during transmission. Moreover, the security investigation section discusses the resistance of the proposed work against different types of possible attacks. Finally, the proposed work is related to the prevailing schemes in terms of communication and computational cost and proves to be more efficient.

With the popularity of unmanned aerial vehicles (UAVs), more and more valuable data can be collected by UAVs. In order to balance the data usage and communication cost, the data can be preprocessed in UAVs rather than directly transmitting to the data centre in the edge computing paradigm. Users can obtain information of interest by accessing the data centre remotely by authenticating themselves to the data centre using the most pervasive password authentication method. Unfortunately, the data centre becomes the main attack target because it not only stores the data but also maintains the passwords of all the users. Aiming at protecting the data as well as the password in the UAV‐enabled mobile edge computing environment, the authors combine the advantages of gateway‐oriented password authenticated key exchange (PAKE) protocols and two‐server PAKE protocols and put forward an efficient gateway‐oriented two‐server PAKE protocol. The security of the proposed protocol is given in the random oracle model. The performance comparison shows their proposal has comparable efficiency in computation and communication costs. Their protocol provides better protection to the password without sacrificing efficiency. Consequently, their protocol is more suitable for real applications in UAV‐enabled mobile edge computing environment.

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.

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.

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

A server-aided verification signature scheme consists of a digital signature scheme and a server-aided verification protocol. By executing the server-aided verification protocol with the server, one can perform the verification of signatures with less computational cost compared to the original verification algorithm. This mechanism is therefore indispensable for low-power devices such as smart cards. The contributions of this paper are manyfold. Firstly, we introduce and define the existential unforgeability of server-aided verification signatures. We prove that the new notion includes the existing security requirements in server-aided verification signatures. Then, we analyze the Girault-Lefranc scheme in Asiacrypt 2005 and show that their scheme can be made secure in our model, but the computational cost is more than the claimed in the original scheme. After that, we propose the first server-aided verification BLS, which is existentially unforgeable in the random oracle model under the Bilinear Diffie-Hellman assumption. Finally, we consider the collusion and adaptive chosen message attack in server-aided verification signatures. For the first time in the literature, the security of server-aided verification signatures against such attacks is defined. We provide a concrete construction of a server-aided verification BLS secure against the collusion and chosen message attack.

Unmanned aerial vehicles (UAVs), commonly known as drones, have grown in popularity and utility in a variety of applications, including surveillance, agriculture, disaster management, and communication. Path planning for unmanned aerial vehicles (UAVs) in a networked environment is a significant factor that influences the efficiency and efficacy of UAV operations. This chapter presents an overview of UAV network path planning, concentrating on essential principles, problems, and cutting-edge technologies. Because of technological breakthroughs and a wide range of applications, the use of unmanned aerial vehicles (UAVs) has grown quickly. Multiple UAVs collaborate to achieve specified objectives in UAV networks, demanding effective path-planning algorithms to enhance their performance. Path planning entails deciding on a series of actions that will allow a UAV to reach its destination while avoiding obstacles and sticking to stated limits. A UAV network is made up of many UAVs that communicate and work together to complete tasks.

In the last few years, a considerable amount of research effort has been put into creating networks of Unmanned Aerial Vehicles (UAVs). Flying Ad Hoc Networks (FANETs) and Internet of Drones (IoDs) are the result of such efforts. FANETs assume that the member UAV nodes belong to different users. However, in UAV networks that would find their applications in areas such as agriculture, environment monitoring, crowd surveillance and natural disaster situations, all UAVs would belong to the same organization, creating a private UAV network. The number of UAVs used in private UAV networks is relatively small compared to that of FANETs due to the cost. If the packet routing approaches designed for FANETs are used in such networks, the quality of video transmitted through the network will suffer. This is because, while there can be many neighbors for each UAV in a FANET, the movements of UAVs in a private UAV network may cause some UAVs not to have any neighbors. To overcome this problem, a UAV in such a network needs to be controlled differently and an appropriate method of selecting the next hop node is required. The new approach presented in this paper uses mathematical analysis to find the optimum next hop node in a private UAV network by reducing a global optimization problem to a local optimization problem at each UAV node along the path from the source to the destination. Once the best next hop node is found, the UAVs can be controlled accordingly.

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.

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.

Objective: The aim of the article is to present the state of the art on threats to UAVs posed by sudden changes of weather conditions, involving in particular storms and hail. Identifying such threats is likely to facilitate the development of hardware and software solutions aimed at improving the safety of the UAV missions. Introduction: Severe weather phenomena strongly affect the safety of unmanned aerial vehicle (UAV) missions. The article describes in detail the implementation concept of atmospheric discharge and precipitation images using wireless Internet connections for an exemplary mobile application for multi-rotor flight control. The concept was developed to reduce risks associated with flights in storm and precipitation areas, and to maximise the performance of electric multi-rotor UAVs with a maximum take-off mass of no more than 25 kg. This is dealt with in the introductory section and in three substantive sections. UAV capabilities are discussed in the first section, while the second one analyses specific weather conditions which merit particular attention when constructing new UAVs, and the third one outlines some of the solutions employed in UAV steering systems control apps. A number of concluding remarks are presented in the end. Conclusions: The analysis of usefulness has proven electric UAVs suitability for professional missions. However, these vehicles display a number of limitations, one of the most important being their high sensitivity to weather conditions. With no hermetically sealed hatch, on-board equipment is exposed to penetrating damp caused by various types of precipitation, including drizzle, rain and snow. These can cause a short-circuit and equipment damage, while hailstones can result in UAV mechanical damage. Severe turbulences and strong ascending or descending air currents, which are likely to occur during low-altitude flights, pose a threat of UAV's collision with overground objects. Using more resistant composite materials in the construction of hermetic UAVs could increase the application range of these vehicles. The wide range of possible uses of UAVs encourages further investigations aimed at ensuring their best possible working conditions.

Multi-antenna unmanned aerial vehicle (UAV) networks can improve throughput by spatial multiplexing and UAVs dominant line-of-sight (LOS) connections, which will intensify interference and exacerbate user fluctuation. This paper proposes user-centric interference cooperation in multi-antenna UAV networks, where multi-antenna beamforming and joint transmission are performed among cooperative UAVs, and derives a semi-closed expression of the ergodic rate using stochastic geometry. Specifically, a dynamic UAV group with adjustable radius <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${R}$</tex-math></inline-formula> is organized for each user that forms a 3D conical region to guarantee seamless service, which provides spatial multiplexing freedom via cooperative multi-antenna beamforming to achieve interference mitigation. In addition, both signal power and inter-cluster interference power are approximated by Gamma distributions with appropriate parameters experiencing generic Nakagami-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> fading in air-to-ground (AtG) channel. Furthermore, zero-forcing (ZF) beamforming is adopted with equal power allocation among beams, which is across the distributed set of UAVs within the cluster and considering different antenna loading. Numerical results show that the average ergodic rate can achieve 86.01% gain compared to the uncoordinated single-cell multi-antenna UAV network when UAV density is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${100\mathrm{/km^{2}}}$</tex-math></inline-formula> and UAV group radius <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${R}$</tex-math></inline-formula> is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${20\mathrm{m}}$</tex-math></inline-formula>. The optimal network parameters are investigated to maximize ergodic rate, thus providing guidelines for practical network configuration. For instance, when UAV density is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$50\mathrm{/km^{2}}$</tex-math></inline-formula> and loading factor is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K/M=2/16$</tex-math></inline-formula>, the optimal deployment altitude for UAVs is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ h=20m$</tex-math></inline-formula>, which can achieve a rate gain of 144% compared to the case of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$h=0\mathrm{m}$</tex-math></inline-formula>.

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.