Unmanned aerial vehicles (UAVs) are becoming increasingly prevalent in modern society, but are often insecure by design or limited in security capability due to computational constraints. The growing open-source UAV ecosystem has further enabled custom software and hardware development, frequently without adherence to established best practices. At the centre of this ecosystem is the MAVLink protocol, used primarily, but not exclusively, for communication between UAVs and their associated Ground Control Stations (GCSs). Since the adoption of MAVLink 2.0 in 2017, limited research has been conducted on its in-built security mechanisms. Although MAVLink 2.0 supports message signing, its signature scheme remains highly vulnerable. This study presents a novel attack that exploits GPS-based timestamp manipulation without the knowledge of a UAV’s secret key. The attack is evaluated in simulation, hardware-in-the-loop tests, and hardware environment tests. This paper also outlines potential countermeasures and briefly discusses the broader applicability of the attack to other GPS-synchronised systems beyond UAVs.

Intrusion detection in Industrial Control Systems (ICS) remains a significant challenge due to strict real-time constraints and evolving cyber threats. Although deep learning models like autoencoders have shown promise, they often require large datasets and are limited in their ability to generalize across scenarios. In this paper, we propose a novel intrusion detection method based on Variational Quantum Classifiers (VQC). By leveraging quantum circuits for feature encoding and classification, our method demonstrates strong anomaly detection capabilities, even with limited data. We evaluate the VQC model on a subset of the WUSTL-IIOT-2018 dataset and compare its performance to a classical Conditional Variational Autoencoder (CVAE) model. The VQC approach achieves $99.00 \%$ accuracy, perfect recall, and 0.9947 AUC-ROC, showcasing its potential as a lightweight and accurate anomaly detector for resourceconstrained ICS environments. This work provides one of the first explorations of quantum machine learning for ICS security and outlines key directions for future research on real quantum hardware and larger-scale deployments.

Abstract The 2D mesh topology-based Network-on-Chip (NoC) is a prevalent structure in System-on-Chip (SoC) designs, offering implementation and fabrication benefits. However, increased NoC scale leads to longer communication paths, more hops, and higher end-to-end latency and energy consumption. To mitigate these issues, Wireless NoC (WiNoC) integrates wireless communication, enhancing data rates, energy efficiency, and routing flexibility. Despite several mapping algorithms for NoCs, optimal techniques for hybrid WiNoCs are underexplored. This study proposes two novel application mapping methods for 2D mesh topology-based hybrid WiNoCs, using quadratic programming (QP) and simulated annealing (SA). Our goal is to minimize communication-related energy consumption. We evaluated these methods across various wireless router configurations, benchmarks, and custom application graphs. The QP-based method excels in smaller problems, while the SA-based approach yields optimal or near-optimal results for larger sizes within practical runtimes.

Quality of Service (QoS) within the Internet of Things (IoT) by facilitating decentralized processing and bringing the computation and storage resources closer to the network edge. It reduces the latency of application responses to the end-users when compared to the cloud computing only-based approaches. One of the major challenges that hinder the ubiquitous adoption of IoT technologies is the efficient handling of resource allocation. There is a necessity for a smart layer that would facilitate efficient and adaptive scheduling between the edge and fog nodes, especially in light of the dynamic and heterogeneous nature of today's fog/cloud-based IoT systems. In this study, we present a theoretical model for such an intermediary layer middleware, with an objective to enable QoS-aware allocation of fog resources.

Shrinking technology sizes of the CMOS circuits makes it possible to place more transistors on a single chip at each technology generation. On the other hand, circuits become more vulnerable to radiation effects due to lower supply and threshold voltage levels; thus, the number of transient faults in circuits tends to increase. Moreover, energy reduction techniques also negatively affect the reliability of circuits. Traditional high-level synthesis (HLS) methods usually consider only area and latency along with either energy or reliability. Especially the effect of using different voltage levels on reliability is completely ignored by previous studies. In this article, we present two new HLS methods for application-specific integrated circuit (ASIC) design under area and timing constraints with the objectives of low energy consumption and high reliability. For the mapping and scheduling steps of HLS, we propose integer linear programming (ILP) and genetic algorithm (GA)-based optimization methods. While ILP provides the optimum results, the CPU time increases exponentially with the number of application nodes. On the other hand, GA-based metaheuristic is faster and determines optimum or near-optimum results in shorter times than ILP. Additionally, we use a selective duplication method to further improve the overall reliability.

Advances in wireless communication, digital systems and micro-electronic-mechanical system technologies led to the development of wireless sensor networks (WSNs) which are used in various critical real-world applications. The fact that WSNs are low cost and eliminate the need for infrastructure led to their replacing traditional networks in area/event monitoring and tracking applications. WSNs consist of small and resource-limited sensor nodes, due to which several problems arise in the WSN development process. One of these problems is coverage. Providing the best coverage with a minimum number of sensor nodes is an NP-hard problem known as the maximum coverage sensor deployment problem (MCSDP). Genetic Algorithms (GAs) have been proved effective in solving optimization problems in many different disciplines (increasing coverage in WSNs, image processing, route planning, etc.). In this study, a GPU-based parallel GA solution for increasing the coverage of a given homogeneous WSN topology in a 2-D Euclidean area is proposed which is the first time this technique is used and parallelized on GPUs to the best of our knowledge. Finally, performance results of the proposed algorithm are compared to the previous work with the emphasis on the achieved performance improvement.

Internet of Things (IoT) can be seen as a pervasive network of networks: numerous heterogeneous entities both physical and virtual interconnected with any other entity or entities through unique addressing schemes, interacting with each other to provide/request all kinds of services. IoT technology is expected to pave the way for groundbreaking applications in a diversity of areas such as healthcare, security and surveillance, transportation, and industry, and integrate advanced technologies of communication, networking, cloud computing, sensing and actuation. Given the enormous number of connected devices that are potentially vulnerable, highly significant risks emerge around the issues of security, privacy, and governance; calling into question the whole future of IoT. IoT applications are expected to affect many aspects of people's lives, bringing about many conveniences; however, if security and privacy cannot be ensured, this can lead to a number of undesired consequences. This survey focuses on the security aspects of IoT, and discusses up-to-date IoT security solutions.