A Novel Dual-Layer Quantum-Resilient Encryption Strategy for UAV–Cloud Communication Using Adaptive Lightweight Ciphers and Hybrid ECC–PQC

\n Unmanned Aerial Vehicles (UAVs) are being employed in many fields such as precision agriculture, environmental monitoring, disaster response support, and others. Following the technological development, these vehicles are now being connected to other devices and systems such as the Internet of Things (IoT), allowing them to provide and consume services as one of the things in the environment. The IoT is believed to bring new unprecedented development to our society and will impact and enable new, more powerful applications in many areas. This integration does not come without challenges. The intrinsic IoT heterogeneity regarding devices, platforms, and communication protocols makes the task of service provision and management much harder to be done in the application layer. Security is one of the most critical challenges in IoT-enabled applications, being sometimes put in after the main development is done, if at all. This work demonstrates how to leverage UAVs to be connected in the IoT using SEMU SErvice Management for UAVs. SEMU comprises (a) communication security, including contextaware security; (b) service management (registration, discovery, request and consumption) for different types and data requirements and (c) autonomous task management that allows the UAVs to perform their mission without human intervention. Its functionality and feasibility is demonstrated by its use in different applications simulating real-life scenarios such as a UAV-enabled delivery service and a UAV-enabled data collection from IoT sensors. Future research will be able to leverage on SEMU and its prototype to further investigate and advance the state-of-art regarding IoT-enabled UAV applications and their security mechanisms.\n

The integration of Blockchain technology into Internet of Things (IoT) ecosystems has emerged as a transformative approach to address critical security and privacy challenges in connected devices. This chapter explores the innovative convergence of Blockchain and IoT, focusing on the enhancement of data security, privacy, and trust through decentralized solutions. By leveraging the immutable and transparent nature of Blockchain, IoT networks can achieve secure, efficient, and automated data exchanges. The chapter delves into key Blockchain trends that are shaping IoT security, including decentralized identity management, smart contracts, and consensus mechanisms, while addressing the specific challenges of resource-constrained IoT devices. The use of advanced cryptographic techniques such as encryption, Zero-Knowledge Proofs, and homomorphic encryption ensures that sensitive data remains private and secure. The application of Blockchain in facilitating secure data sharing, device authentication, and access control was discussed, alongside the potential of permissioned blockchains to balance privacy with scalability. The chapter concludes by identifying the future directions for Blockchain and IoT innovations, emphasizing their role in the evolution of secure, autonomous, and scalable connected environments.

Abstract: This review paper offers a comprehensive exploration of the multifaceted applications of Unmanned Aerial Vehicles (UAVs) in various domains, showcasing their transformative impact in addressing complex challenges. The evaluation of cloud-based UAV systems' stability reveals their robustness and reliability, underlining their significance in numerous industries. Additionally, their role in enhancing robot navigation in intricate environments signifies a substantial advancement in robotics and automation. The integration of blockchain technology for secure Internet of Things (IoT) data transfer emphasizes the critical importance of data integrity and confidentiality in the IoT era. Furthermore, the optimization of energy-efficient data collection in IoT networks through UAVs demonstrates their potential to revolutionize data-driven decision-making processes, particularly in fields reliant on data accuracy and timeliness. The paper also highlights the application of deep reinforcement learning to enhance UAV-assisted IoT data collection, showcasing the synergy between advanced machine learning techniques and UAV technology. Finally, the discussion underscores the pivotal role of UAVs in precision agriculture, where they facilitate ecological farming practices and monitor environmental conditions, contributing to the pursuit of sustainable and efficient agriculture. This review reaffirms UAVs' status as transformative tools, reshaping industries and unlocking new frontiers of innovation and problem-solving. With ongoing technological advancements, UAVs are poised to play an increasingly central role in a wide range of applications, promising a future marked by ground breaking possibilities. Key findings include the dominance of the United States and China in the field, exploration of characteristics such as crop production, and innovative UAV-based methods for grassland mapping, maize growth assessment, and Arctic plant species monitoring. The research underscores the potential of UAVs in bridging field data and satellite mapping, providing valuable insights into diverse applications, from soil analysis to yield predictions, highlighting their transformative role in environmental monitoring and precision agriculture

Manipulating objects using arms mounted to unmanned aerial vehicles (UAVs) is attractive because UAVs may access many locations that are otherwise inaccessible to traditional mobile manipulation platforms such as ground vehicles. Historically, UAVs have been employed in ways that avoid interaction with the environment at all costs. The recent trend of increasing small UAV lift capacity and the reduction of the weight of manipulator components make the realization of mobile manipulating UAVs imminent. Despite recent work, several major challenges remain to be overcome before it will be common practice to manipulate objects from UAVs. Among these challenges, the constantly moving UAV platform and compliance of manipulator arms make it difficult to position the UAV and end-effector relative to an object of interest precisely enough for reliable manipulation. Solving this challenge will bring UAVs one step closer to being able to perform meaningful tasks such as infrastructure repair, disaster response, law enforcement, and personal assistance. Toward a solution to this challenge, this thesis describes a way forward that uses the UAV as a means to crudely position a manipulator within reach of the end-effector's goal position in the world. The manipulator then performs the fine positioning of the end-effector, rejecting position perturbations caused by UAV motions. An algorithm to coordinate the redundant degrees of freedom of an aerial manipulation system is described that allows the motions of the manipulator to serve as inputs to the UAV's position controller. To demonstrate this algorithm, the manipulator's six degrees of freedom are servoed using visual sensing to drive an eye-in-hand camera to a specified pose relative to a target while treating motions of the host platform as perturbations. Simultaneously, the host platform's degrees of freedom are regulated using kinematic information from the manipulator. This ultimately drives the UAV to a position that allows the manipulator to assume a pose relative to the UAV that maximizes reachability, thus facilitating the arm's ability to compensate for undesired UAV motions. Maintaining this loose kinematic coupling between the redundant degrees of freedom of the host UAV and manipulator allows this type of controller to be applied to a wide variety of platforms, including manned aircraft, rather than a single instance of a purpose-built system. As a result of this loose coupling, careful consideration must be given to the manipulator design so that it can achieve useful poses while minimally influencing the stability of the host UAV. Accordingly, the novel application of a parallel manipulator mechanism is described.

Due to flexible scheduling, and better transmission channel, unmanned aerial vehicle (UAV) can improve transmit performance of Internet of Things (IoT). In this paper, we propose an UAV-enabled multichannel IoT based on intelligent fog computing, where UAV as a relay forwards IoT's information to the data center under the control of fog computing base station in the case of terrestrial channel fading. The IoT's throughput are maximized by jointly optimizing subcarrier, power of IoT, and UAV, and UAV trajectory, subject to the constraints of information causality, maximum transmit power, and maximum UAV speed. The subcarriers are dynamically allocated according to their channel gains, the water filling algorithm is adopted to optimize the power for UAV, and IoT by fixing UAV trajectory, and the optimal UAV trajectory is achieved with successive convex approximation under the fixed power allocation. Then a jointly iterative optimization on subcarrier, power, and trajectory is presented to get the optimal solution. In addition, we propose a fairness optimization scheme to maximize the minimum transmit rate of IoT nodes. The simulations indicate the IoT with mobile UAV, and dynamic subcarrier allocation may achieve better transmit performance, and the fairness optimization can decrease the rate difference of nodes effectively.

Recent advancements in technology have led to a great interest in the use of Unmanned Aerial Vehicles (UAVs) for a vast array of applications such as real time site monitoring, target search and destroy and UAVs being used as mobile sinks to collect data from Internet of Things (IoT) devices. This is mainly due to their autonomy, high mobility, ease of deployment and affordable nature. A group of UAVs can be used collectively to bring a coordinated effort in task execution, allowing more tasks to be completed in a wider area and in the shortest possible time. However, using multiple UAVs presents some challenges for efficient cooperation. UAVs are resource constrained due to being battery powered and this limits the permissible flight time. Therefore, it is necessary to intelligently manage their operation given the limited resources and other constraints associated with the mission. In this paper, we propose a multi-objective UAV task assignment model to support spatio-temporally distributed events raised by static IoT devices, using a discrete Non- Dominated Sorting Genetic Algorithm II (NSGA-II). This model assigns the most suitable UAV(s) to serve at the different event locations ensuring that none of the constraints are violated. The performance of the algorithm was evaluated through numerical simulations and compared to a similar implementation using Mixed Integer Linear Programming (MILP). Results show an improvement of 7.9% in the total energy consumption for all UAVs while ensuring that all the temporal constraints are not violated.

Incorporating software-defined networking (SDN) technology into Internet of Things (IoT) can improve network utilization. In this paper, we consider SDN-IoT networks, in which IoT devices use Zigbee protocol. A machine-learning- assisted minimum end-to-end delay (MaMED) routing scheme for IoT monitoring services is proposed; and this scheme requires no prior knowledge of input traffic. Under different SDN- IoT simulation scenarios, results show that our routing scheme outperforms the shortest path first (SPF) algorithm in terms of end-to-end delay (E2E) performances (e.g., average E2E delay and jitter), and gives more consistent delay performance when the input IoT traffic increases.

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.

Energy optimization for full-duplex wireless-powered IoT networks using rotary-wing UAV with multiple antennas

Предметом вивчення в статті є розгляд особливостей використання безпілотних літальних апаратів (БПЛА) при проведенні екологічного моніторингу для оптимізації проведення спостережень техногенних об’єктів та виконання екологічних завдань в системі екологічного моніторингу. Це можливо здійснити за допомогою висвітлення позитивних та врахування негативних властивостей при застосуванні БПЛА в системі екологічного моніторингу. Метою є розкриття особливостей використання БПЛА в екологічному моніторингу та визначення напрямків їх застосування для проведення контролю за параметрами стану навколишнього природного середовища заданих територій. Завдання: провести аналіз можливості використання БПЛА в системі екологічного моніторингу; висвітлити особливі властивості застосування БПЛА в системі спостереження; визначити можливості БПЛА для формування процедури проведення екологічного моніторингу та визначити коло екологічних завдань для їх вирішення за допомогою БПЛА; розглянути можливості застосування БПЛА в системі спостереження та знімання параметрів навколишнього середовища, що є підґрунтям для проведення робіт, що пов’язані з проведенням екологічного моніторингу. Використовуваними методами є: системний підхід до розробки складних систем, проведення порівняльного аналізу параметрів та властивостей БПЛА, математичні моделі оптимізації для побудови складних систем. Отримані такі результати. Проведено порівняльний аналіз властивостей БПЛА, висвітлення процесу обробки інформації в системі екологічного моніторингу при здійсненні вибору оптимальної структури системи екологічного моніторингу за допомогою використання БПЛА. На основі системного аналізу різних технічних показників та властивостей БПЛА можливо здійснити вивчення структурно-параметричних характеристик та дослідити режими роботи літальних апаратів в різних умовах при побудові системи екологічного моніторингу. Висновки. Наукова новизна отриманих результатів полягає в наступному: проведено аналіз властивостей БПЛА, визначено характеристики та параметри, що впливають на спостереження в системі екологічного моніторингу, також висвітлено коло питань, які необхідно враховувати при побудові системи екологічного моніторингу з використанням дистанційно керованих літальних апаратів.

The Internet of Things is defined as a network of identifiable and unique elements that communicate without human interaction using IP connectivity. This new concept covers several domains: energy, smart home, agriculture, healthcare. The integration of IoT in the healthcare sector is giving rise to a new paradigm called the Internet of Medical Things (IoMT). These benefits can be extended when IoMT get integrated to Unmanned Aerial Vehicles (UAVs), since it is very useful in supporting logistics and pave the way to revolutionizing healthcare. This chapter aims to shad the light on coupling different disciplines of IoMT and UAVs in healthcare along with their capabilities, advantages, applications, challenges, and conclude with some recommendations and future work. This contribution is a noticeable shift from exiting work in the literature, where it provides a panoramic view on the proposed integration between IoMT and UAVs, especially in isolated areas with difficult terrines where it could be economically infeasible or may be impossible to provide wireless services using traditional systems.

As unmanned aerial vehicle (UAV) technology has evolved, these systems are being increasingly utilized across diverse industries. However, controlling UAVs faces significant problems owing to several environmental circumstances and obstacles, making path planning a critical initial step for UAV operation. This paper offers an overview of UAV path planning research founded on intelligent algorithms, which are divided into three categories: computational intelligence (CI), machine learning (ML), and hybrid methods. Each category has been analyzed in depth to show its strengths, limits, and where it may be applied to UAV-related problems. The methodology includes a comparative analysis based on multiple performance metrics such as path length, flight time, collision avoidance, complexity, and environmental adaptability. Furthermore, the research covers the latest publications that deal with solving essential challenges of UAV path planning by using new hybrid algorithms and enhanced optimization methods. The results indicate that although each strategy offers specific strengths suited to particular scenarios, hybrid strategies are more likely to deliver greater flexibility and robustness, particularly in uncertain, and dynamic environments. These findings are significant for guiding future research in adaptive path planning and for supporting practical UAV applications such as autonomous delivery, aerial surveillance, disaster response, and environmental monitoring.

With the rapid development of unmanned aerial vehicles assisted applications enabled with a communication system, they are open to various malicious attacks. As a new form of flying things, they can access the network for better communication via the aerial base station. Most of the Unmanned aerial vehicles assisted flying objects' optimal path selection schemes does not consider the path deviation. In path deviation attacks, secure data transmission are not addressed in existing works. The secure communication process between Unmanned aerial vehicles and base station are exploited through security-based attacks. Moreover, path loss issue leads to multicast packet loss and unsecured broadcast. The existing network architecture setup does not fulfill the secure data communication and privacy issues. In this paper, Blockchain is utilized to investigate the secure communication between Unmanned aerial vehicles to Wireless Unmanned aerial vehicles Base stations. Since the destination information is dynamic under an uncertain environment, it will cause a delay in data communication. Unmanned aerial vehicles are more vulnerable to security attacks. The proposed blockchain-based architecture supports secure data communication in Unmanned aerial vehicles uncertain environments. To improve network security, this paper designs a modified particle swarm optimization method for better path selection. Through these experimental results, a blockchain-based data communication scheme is outer performed concerning network security.

In many emerging Internet of Things (IoT) applications, the freshness of the is an important design criterion. Age of Information (AoI) quantifies the freshness of the received information or status update. This work considers a setup of deployed IoT devices in an IoT network; multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages and the devices' energy consumption. The solution accounts for the UAVs' battery lifetime and flight time to recharging depots to ensure the UAVs' green operation. The complex optimization problem is efficiently solved using a deep reinforcement learning algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower ergodic age and ergodic energy consumption when compared with benchmark algorithms such as greedy algorithm (GA), nearest neighbour (NN), and random-walk (RW).

- An Unmanned Aerial Vehicle (UAV), commonly known as a drone, is an aircraft that operates without a human pilot onboard and is controlled either remotely or autonomously. UAVs serve various applications, from defence to civilian purposes, and are categorized into fixed-wing, rotary-wing, hybrid, nano, tactical, target, and commercial drones. For this project, we selected a quadcopter due to its versatility, stability, and efficiency, making it ideal for applications like surveillance, aerial photography, emergency response, and environmental monitoring. The prototype is modeled in Fusion 360 for precise design and analyzed using ANSYS to assess structural integrity and durability under varying load conditions. The UAV frame is fabricated using lightweight materials such as carbon fiber and integrates key components like brushless DC motors, LiPo batteries, GPS modules, and a Pixhawk flight controller. Advanced features such as real-time data acquisition, autonomous flight capabilities, and efficient payload handling enhance its functionality. Additionally, 3D printing is used for custom parts, ensuring an optimized weight-to-strength ratio. To validate performance, the UAV undergoes simulation of flight dynamics and control systems using SolidWorks, followed by extensive ground and flight testing. The results demonstrate reliable performance in real-world scenarios such as medical supply delivery, environmental condition monitoring, and disaster relief efforts. By leveraging artificial intelligence, real-time data processing, and robust communication networks, this project contributes to the advancement of UAV technology, providing a cost-effective, efficient, and sustainable solution for public services and community needs. Key Words: Unmanned Aerial Vehicle (UAV), Quadcopter, Prototype Development, Structural Integrity, Environmental Monitoring.