The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in the UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate more than $45 Billion market value of UAV usage. In this paper, we present UAV civil applications and their challenges. We also discuss the current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including charging challenges, collision avoidance and swarming challenges, and networking and security-related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider an Air-to-Ground path loss model, which assumes that the users are outdoor and they are located on a 2D plane. In this paper, we propose using a single UAV to provide wireless coverage for indoor users inside a high-rise building under disaster situations (such as earthquakes or floods), when cellular networks are down. We assume that the locations of indoor users are uniformly distributed in each floor and we propose a particle swarm optimization algorithm to find an efficient 3D placement of a UAV that minimizes the total transmit power required to cover the indoor users.

In unmanned aerial vehicle (UAV)-assisted wireless networks, the potential of the high probability of line-of-sight links could be a disadvantage for other nearby wireless networks because of interference caused by a UAV. For this reason, using interference mitigation techniques (IMTs) for UAV-assisted wireless networks is necessary to minimize interference effects. For instance, the mobility of UAVs gives a chance to manage interference better to avoid being a source of interference or being susceptible to interference. After the first-generation mobile network was introduced in the early 1980s, mobile communication systems were developed through several stages of evolution over the following few decades to satisfy rising demand and more stringent specifications. Different IMTs are needed due to these generations’ differences. Unlike previous research, this research presents a systematic review of IMTs in the current and future UAV-assisted wireless networks and discusses their challenges. We also review the current research trends of UAV IMTs and their future insights. Our motivation for conducting this research is the need for a systematic review addressing these issues. In this systematic review, we use a more precise classification of the IMTs for UAV-assisted wireless networks. We classify them into four different techniques, namely, adaptive modulation and coding schemes, dynamic antenna pattern adjustment, dynamic transmit power control, and sub-channel scheduling. Moreover, we discuss the IMTs for UAVs using B5G wireless technologies such as intelligent reflecting surfaces, rate-splitting, super modular massive multiple-input multiple-output, Terahertz communications, holographic beamforming, blockchain-based spectrum sharing, and artificial intelligence and machine learning schemes. Furthermore, we identify the challenges of UAV IMTs and their open research directions such as wireless radio frequency spectrum scarcity, lack of networking scalability, beam distortion, resource-constrained management, and new sources of interference. We believe this research will help researchers choose the best technique to mitigate the interference effects in UAV-assisted wireless networks.

Machine learning algorithms represent the intelligence that controls many information systems and applications around us. As such, they are targeted by attackers to impact their decisions. Text created by machine learning algorithms has many types of applications, some of which can be considered malicious especially if there is an intention to present machine-generated text as human-generated. In this paper, we surveyed major subjects in adversarial machine learning for text processing applications. Unlike adversarial machine learning in images, text problems and applications are heterogeneous. Thus, each problem can have its own challenges. We focused on some of the evolving research areas such as: malicious versus genuine text generation metrics, defense against adversarial attacks, and text generation models and algorithms. Our study showed that as applications of text generation will continue to grow in the near future, the type and nature of attacks on those applications and their machine learning algorithms will continue to grow as well. Literature survey indicated an increasing trend in using pre-trained models in machine learning. Word/sentence embedding models and transformers are examples of those pre-trained models. Adversarial models may utilize same or similar pre-trained models as well. In another trend related to text generation models, literature showed effort to develop universal text perturbations to be used in both black-and white-box attack settings. Literature showed also using conditional GANs to create latent representation for writing types. This usage will allow for a seamless lexical and grammatical transition between various writing styles. In text generation metrics, research trends showed developing successful automated or semi-automated assessment metrics that may include human judgement. Literature showed also research trends of designing and developing new memory models that increase performance and memory utilization efficiency without validating real-time constraints. Many research efforts evaluate different defense model approaches and algorithms. Researchers evaluated different types of targeted attacks, and methods to distinguish human versus machine generated text.

In this paper, the dynamic deployment of a single UAV as an aerial base station in providing wireless coverage for mobile outdoor and indoor users is studied. The problem of finding the efficient UAV trajectory is formulated with the objective to minimize the required UAV transmit power that satisfies the users' minimum data rate. The proposed solution to the problem considers the users' movement in a search and rescue (SAR) operation. More specifically, the outdoor rescue team members are considered to move in a group with the reference point group mobility (RPGM) model. Whilst, the indoor rescue team members are considered to move individually and in a group with random waypoint and RPGM models, respectively. The efficient UAV trajectory is developed using two approaches, namely, heuristic and optimal approaches. The employment of the heuristic approach, namely particle swarm optimization (PSO) and genetics algorithm (GA), to find the efficient UAV trajectory reduced the execution time by a factor of $\simeq ~1/60$ and $\simeq ~1/9$ compared to that when using the optimal approach of brute-force search space algorithm. Furthermore, the use of PSO algorithm reduced the execution time by a factor of $\simeq ~1/7$ compared to that when the GA algorithm is invoked.The performance of the dynamic UAV deployment also outperformed the static UAV deployment in terms of the required transmit power. More specifically, the dynamic UAV deployment required less total transmit power by a factor of about 1/2 compared to the static UAV deployment, in providing wireless coverage for rescue team to perform SAR operation within a rectangular sub-region.

Unmanned aerial vehicles (UAVs) can be deployed as backup aerial base stations due to cellular outage either during or post natural disaster. In this paper, an approach involving multi-UAV three-dimensional (3D) deployment with power-efficient planning was proposed with the objective of minimizing the number of UAVs used to provide wireless coverage to all outdoor and indoor users that minimizes the required UAV transmit power and satisfies users’ required data rate. More specifically, the proposed algorithm iteratively invoked a clustering algorithm and an efficient UAV 3D placement algorithm, which aimed for maximum wireless coverage using the minimum number of UAVs while minimizing the required UAV transmit power. Two scenarios where users are uniformly and non-uniformly distributed were considered. The proposed algorithm that employed a Particle Swarm Optimization (PSO)-based clustering algorithm resulted in a lower number of UAVs needed to serve all users compared with that when a K-means clustering algorithm was employed. Furthermore, the proposed algorithm that iteratively invoked a PSO-based clustering algorithm and PSO-based efficient UAV 3D placement algorithms reduced the execution time by a factor of ≈1/17 and ≈1/79, respectively, compared to that when the Genetic Algorithm (GA)-based and Artificial Bees Colony (ABC)-based efficient UAV 3D placement algorithms were employed. For the uniform distribution scenario, it was observed that the proposed algorithm required six UAVs to ensure 100% user coverage, whilst the benchmarker algorithm that utilized Circle Packing Theory (CPT) required five UAVs but at the expense of 67% of coverage density.