Unmanned aerial vehicles (UAVs) are an emerging technology in the 5G-and-beyond systems with the promise of assisting cellular communications and supporting IoT deployment in remote and density areas. Safe and secure navigation is essential for UAV remote and autonomous deployment. Indeed, the opensource simulator can use commercial software-defined radio tools to generate fake global positioning system (GPS) signals and spoof the UAV GPS receiver to calculate wrong locations, deviating from the planned trajectory. Fortunately, the existing mobile positioning system can provide additional navigation for cellular-connected UAVs and verify the UAV GPS locations for spoofing detection, but it needs at least three base stations (BSs) at the same time. In this article, we propose a novel deep-ensemble-learning-based, mobile-network-assisted UAV monitoring and tracking system for cellular-connected UAV spoofing detection. The proposed method uses path losses between BSs and UAVs communication to indicate the UAV trajectory deviation caused by GPS spoofing. To increase the detection accuracy, three statistics methods are adopted to remove environmental impacts on path losses. In addition, deep ensemble learning methods are deployed on the edge cloud servers and use the multilayer perceptron (MLP) neural networks to analyze path losses statistical features for making a final decision, which has no additional requirements and energy consumption on UAVs. The experimental results show the effectiveness of our method in detecting GPS spoofing, achieving above 97% accuracy rate under two BSs, while it can still achieve at least 83% accuracy under only one BS.

Internet of Things (IoT) systems are of paramount importance to provide ubiquitous wireless connectivity for smart cities. However, such systems are facing security challenges due to the broadcast and openness nature of wireless channels. This article studies the performance of covert communication under a scenario consisted of a source-destination pair, a passive warden, and multiple relays. We first propose two relay selection schemes, one is random selection and another is superior-link selection. Based on these two schemes, we then examine the transmission strategy design for the source and thus define the necessary condition that the source can transmit covert messages. We further derive the detection error probability of warden and covert capacity based on two relay selection schemes and also explore the covert capacity maximization through efficient numerical searches. Finally, numerical results are provided to illustrate our theoretical findings and the performance of covert communication in such systems. Remarkably, the superior-link selection scheme has 108% improved to the random selection scheme for the maximum covert capacity performance under the same transmission power at the source.

This paper investigates a wireless powered mobile edge computing (WP-MEC) network with multiple hybrid access points (HAPs) in a dynamic environment, where wireless devices (WDs) harvest energy from radio frequency (RF) signals of HAPs, and then compute their computation data locally (i.e., local computing mode) or offload it to the chosen HAPs (i.e., edge computing mode). In order to pursue a green computing design, we formulate an optimization problem that minimizes the long-term energy provision of the WP-MEC network subject to the energy, computing delay and computation data demand constraints. The transmit power of HAPs, the duration of the wireless power transfer (WPT) phase, the offloading decisions of WDs, the time allocation for offloading and the CPU frequency for local computing are jointly optimized adapting to the time-varying generated computation data and wireless channels of WDs. To efficiently address the formulated non-convex mixed integer programming (MIP) problem in a distributed manner, we propose a T wo-stage M ulti- A gent deep reinforcement learning-based D istributed computation O ffloading (TMADO) framework, which consists of a high-level agent and multiple low-level agents. The high-level agent residing in all HAPs optimizes the transmit power of HAPs and the duration of the WPT phase, while each low-level agent residing in each WD optimizes its offloading decision, time allocation for offloading and CPU frequency for local computing. Simulation results show the superiority of the proposed TMADO framework in terms of the energy provision minimization.

This paper investigates the fundamental covert rate performance in a wireless relaying system consisting of a source-destination pair, a full-duplex (FD) relay and a warden, where the relay can work at either the FD mode or the half-duplex (HD) mode. We first provide theoretical modeling for the instantaneous/average covert rate when the system works solely under the FD mode or HD mode, and then explore the corresponding optimal transmit power control of relay for the covert rate maximization. For an improvement of covert rate, we further propose a joint FD/HD mode that flexibly switches between the FD and HD modes depending on channel state of the relay self-interference channel. Under the joint FD/HD mode, we also examine the related problems of theoretical modeling for covert rate and optimal transmit power control of relay for covert rate maximization. Finally, extensive numerical results are provided to illustrate the covert rate performances of the relaying system under the FD, HD and joint FD/HD modes.

This paper investigates the covert communications via cooperative jamming and relay selection in a wireless relay system, where a source intends to transmit a message to its destination with the help of a selected relay, and a warden attempts to detect the existence of wireless transmissions from both the source and relay, while friendly jammers send jamming signals to prevent warden from detecting the transmission process. To this end, we first propose two relay selection schemes, namely random relay selection (RRS) and max-min relay selection (MMRS), as well as their corresponding cooperative jamming (CJ) schemes for ensuring covertness in the system. We then provide theoretical modeling for the covert rate performance under each relay selection scheme and its CJ scheme and further explore the optimal transmit power controls of both the source and relay for covert rate maximization. Finally, extensive simulation/numerical results are presented to validate our theoretical models and also to illustrate the covert rate performance of the relay system under cooperative jamming and relay selection.

The widespread adoption of Internet of Things (IoT) technology has introduced new cybersecurity challenges. Encryption services are being offloaded to cloud and fog platforms to mitigate these risks. Encryption as a Service (EaaS) emerges as a remedy, offering cryptographic solutions tailored to the resource constraints of IoT devices. This study thoroughly examines existing EaaS platforms, categorizing them based on encryption algorithms and service offerings. Additionally, we outline various EaaS architecture types depending on the placement of key components. Practical implementations of these platforms are explored through different testbeds. A key focus lies in dissecting the challenges that EaaS faces, particularly in the context of IoT, while suggesting potential remedies. This work stands out as an all-encompassing exploration, bridging the gap left by previous surveys.