Benefiting from smartphones' powerful computing and sensing capabilities, biometric authentication is widely applied to them for conveniently verifying users' identities. However, most biometric features can be easily acquired or reproduced, making them vulnerable to replay and impersonation attacks. To address this issue, we propose DBreathLock, a non-contact deep breath-based authentication system that utilizes a smartphone emitting inaudible frequency-modulated continuous waves (FMCW)-based sonar signals and synchronously records breath sounds and sonar echoes of chest-abdominal-joint (C-A-joint) movements. Then, we implement a dual-protection barrier to defend against advanced replay attacks (ARAs). First, by analyzing the energy features of C-A-joint movements, we develop a Deep Breath Activity Detection method to detect deep breath fragments alongside the capability of resisting ARAs. Second, we take C-A-joint movements and smartphone vibrations caused by holding a smartphone as features and design a liveness detection mechanism to further fortify the resistance to ARAs. Furthermore, a multi-stream identity authentication model is designed to verify legitimate users by fusing biometric features from C-A-joint movements, deep breath sounds, and correlation sequences of both. Extensive real-world experiments with 40 users demonstrate DBreathLock's authentication accuracy of 95.97%. Additionally, it successfully defends against advanced replay, impersonation, simple hybrid, and advanced hybrid attacks, achieving the AUC of 0.9792, and FPRs of 2.17%, 2%, and 4.17%, respectively.

For the computation offloading via device-to-device (D2D) terminals and edge servers in a resource-constrained wireless network (RCWN), mobile users can choose to offload their tasks to nearby D2D terminals or edge servers according to quality of service (QoS) requirements (e.g., load balancing at the network edge) by mobile edge computing. To this end, we first formulate computation offloading as a multi-user collaborative resource dynamic management optimization problem that aims to maximize user satisfaction utility function, carefully considering critical issues like the non-uniform distribution of computational resources, user's risk awareness, and the dynamic changes between computing-intensive regions and computing-sparse regions. This is a nonlinear and nonconvex optimization problem, which is generally difficult to be solved. We then construct a resource management scheme for resource allocation of the edge server based on convex optimization. Furthermore, we propose a dynamic offloading update strategy achieving the maximum of user satisfaction utility function based on game theory. The simulation results are presented to show that our proposed method can increase the total system satisfaction utility by nearly 20% and reduce the system energy consumption by nearly 10% compared to the benchmark methods.

By exploiting the high directivity of directional antennas and severe propagation loss, covert Terahertz (THz) communication can better suppress detection by wardens while achieving high-performance legitimate transmission. This emerging technology can achieve a higher level of covertness and can be applied in Internet of Things networks, such as smart senior care. In this work, we explore a one-hop system of covert THz communication, comprising a transmitter-receiver pair and multiple randomly distributed wardens. Utilizing the high directivity of antennas and the molecular absorption effect of THz signals, we can achieve transmissions with high covertness over THz bands. We divide the propagation area around Alice into several regions and propose a covert THz communication protocol in the considered scenario. Specifically, a circular area around Alice is divided into three regions based on a transmission model of directional antennas and the distance to Alice, and Alice decides to conduct transmissions if no warden exists in the insecure region (IR). Meanwhile, the theoretical expression of detection error probability (DEP) at a warden is derived and given. Numerical results demonstrate that our proposed protocol can increase the overall DEP of multiple wardens in both noncolluding and colluding modes compared to the case where no protocol is used.

This article investigates covert communications in a multirelay Internet of Things (IoT) system with multiple energy harvesting jammers, where a transmitter (Alice) attempts to covertly transmit confidential messages to its destination (Bob) through relay forwarding, while a warden (Willie) detects the existence of Alice’s transmission. Specifically, we employ a harvesting-then-jamming protocol with which the jammers first harvest energy from Alice and then send jamming signals to interfere with Willie’s detection. We propose a relay and jammer selection strategy, namely quality of service (QoS)-aware selection, and use the random selection strategy as a comparison strategy. Under these two selection strategies, we derive the optimal detection threshold and minimum detection error probability at Willie, respectively. We then model the covert throughput performance and obtain the maximum covert throughput by jointly optimizing covert transmit power and jamming transmit power. Extensive numerical results are provided to illustrate the impacts of system parameters on covert throughput performance.

Wireless edge networks (WENTs) can provide edge services to support various time-critical Internet of Things (IoT) applications, like autonomous vehicles, where cache content updates are significant to maintaining information freshness quantified as Information of Age (AoI). However, frequent content updates result in high energy consumption at the edge nodes. This article investigates the cache content updates in WENTs, aiming to ensure information freshness and low energy consumption. To this end, we propose a rainbow deep reinforcement learning-based cache content update scheme (RB-DRN). In the RB-DRN scheme, we first establish a Markov decision process (MDP) to characterize the process of cache update. By fully taking advantage of R-Learning empowered Rainbow DQN, we then make optimal strategy to obtain the minimum long-term average overhead associated with energy consumption and information freshness. Extensive simulation results are presented to validate our proposed RB-DRN scheme and also to illustrate that our RB-DRN scheme outperforms the benchmark scheme in terms of information freshness and energy consumption.

In vehicular edge computing networks (VECNs), service migration among edge servers is critical to addressing the challenge of service interruption caused by high mobility of vehicles and limited coverage of each edge server. In this article, we tackle this challenge by optimizing service migration among edge servers through a joint management of resource scheduling and dynamic server selection. Specifically, we aim to minimize system overhead consisting of system time and energy consumption taking account for resource scheduling and dynamic server selection, which is formulated as a constrained optimization problem. To solve this optimization problem, we propose a learning-driven joint resource scheduling and dynamic server selection strategy (LD-JRS3) based on deep reinforcement learning. Under the LD-JRS3 strategy, we first model joint resource scheduling and dynamic server selection as a Markov decision process (MDP). Then, we adopt a recurrent neural network (RNN)-empowered feedback mechanism based on historical information to achieve the optimal system performance. We fully consider the advantages of the soft actor-critic (SAC) algorithm to obtain the optimal decision (i.e., computational resources allocation and servers selection). Notably, we employ an improved SAC algorithm, which takes into account prioritized experience replay and automatic tuning of temperature parameters. Extensive simulation results are presented to verify the effectiveness of our proposed LD-JRS3 algorithm, and also to illustrate the advantage of our algorithm on improving the time consumption and energy consumption compared with the baseline schemes. LD-JRS3 has 19%, 24%, and 11% higher utility values than DDRN, DQN-based, and multiarmed bandit-based systems, respectively.

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 aTwo-stageMulti-Agent deep reinforcement learning-basedDistributed computationOffloading (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 explores the joint covert and secure communications in a device-to-device (D2D)-enabled cellular system (DCS) consisting of a base station BS, an eavesdropper Eve, and two user equipments UE and UR. To conduct secure communications with UE against Eve, BS works either under the cellular mode using direct transmission or under the D2D mode replying through UR, while UR is greedy since it opportunistically transmits its own covert message to UE against the detection from BS. To understand the fundamental performance of secrecy rate and covert rate in DCS, we first develop theoretical models to depict the detection probability/secrecy rate of BS and covert rate of UR under different modes (i.e., underlay, overlay, or cellular). Based on these models, we further explore the secrecy rate maximization (SRM) for BS subject to the constraints of detection probability at BS and transmit power at both BS and UR, as well as the covert rate maximization (CRM) for UR subject to the constraints of covertness requirement and covert transmit power. Finally, we employ the Newton-based searching method to solve the SRM/CRM problems and illustrate via numerical results the achievable secrecy rate and covert rate of BS and UR under various DCS scenarios.

In this paper, we explore covert communications in a device-to-device (D2D) network consisting of an intelligent reflecting surface (IRS), a base station, a cellular user, a D2D pair, and an adversary warden. With the help of the IRS, the D2D pair attempts to perform covert communication, while the warden also tries to detect the very existence of such a transmission. To investigate the covert performance under the scenario, we derive the detection error probability at Warden, the optimal detection threshold for minimizing the probability, and the transmission outage probabilities for D2D and cellular communications, respectively. We further jointly optimize the transmission powers of the cellular user and the D2D transmitter, the reflection phase shifts, and the amplitudes of the IRS reflecting elements to improve covert communication performance. Finally, we provide numerical results to reveal the impact of system parameters on the covert performance and also to exhibit the merits of IRS-enabled D2D networks for achieving covert communications.

The main goal of Encryption as a Service (EaaS) is to deliver cryptography services to limited-resource devices. However, due to the massive number of devices connecting EaaS platforms, they face challenging issues, such as high service delays and uncovered requests. The existing EaaS architectures lack in adequately taking advantage of both cloud and fog layers, by which the performance can be improved. Therefore, this article proposes a novel EaaS architecture called full-cloud-fog that focuses on increasing the EaaS throughput by locating the frequently accessed components on the fog layer and resolving resource allocations utilizing the cloud nodes. We have analyzed the security aspects of the proposed architecture and then implemented it in a real testbed. The evaluation results show that the proposed full-cloud-fog architecture improves the EaaS throughput by 81%.

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.

This article investigates sum-rate maximization while achieving seamless coverage with the minimum number of unmanned aerial vehicles (UAVs) in a device-to-device (D2D)-enabled UAV network. Toward this end, we formulate it as a nonlinear and nonconvex optimization problem, and then propose a max-rate-min-number (MRMN) scheme to solve this optimization problem. First, we derive UAV’s coverage radius which can depict the maximum coverage for user equipments, and then implement the optimal deployment for UAV swarm by exploiting the disk covering theory. Furthermore, we apply the coalitional game theory to design the cooperative strategy between UAV swarm and ground equipments. Finally, a coalition formation algorithm is presented for achieving maximum system sum-rate while reducing the number of UAVs under seamless coverage constraint. Extensive simulation results are provided to validate the effectiveness of our proposed MRMN scheme, and also illustrate that the scheme can improve the system sum-rate and reduce the number of deployed UAVs. Meanwhile, we further conduct a performance comparison between our scheme and the existing benchmark schemes.

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.

This paper investigates the fundamental covert rate performance in a D2D-enabled cellular network consisting of a cellular user Alice, a base station BS, an active warden Willie, and a D2D pair with a transmitter$D_{t}$and a full-duplex receiver$D_{r}$. To conduct covert communication between Alice and BS, the full-duplex$D_{r}$transmits jamming signal to confuse the active Willie and also receives signal from$D_{t}$simultaneously. With spectrum sharing,$D_{t}$can operate over either an underlay mode reusing cellular spectrum or an overlay mode using dedicated spectrum. With power control,$D_{r}$can send jamming signal to confuse Willie's detection of the transmission from Alice. We first provide theoretical results for the outage probabilities of the cellular and D2D transmissions, the average minimum detection error probability at Willie, and the achievable covert rate from Alice to BS. We then explore the power control for covert rate maximization (CRM) under the underlay mode as well as the joint designs of power control and spectrum partition for CRM under the overlay mode. We further consider a mode selection that flexibly switches between these two modes with a probability, and also investigate the covert rate modeling and joint designs of power control, spectrum partition and mode selection probability for CRM. Finally, numerical results are presented to illustrate the covert rate performances of the network under the underlay mode, overlay mode and mode selection.

This paper investigates the fundamental traffic steering issue for cellular-enabled unmanned aerial vehicles (UAVs), where each UAV needs to select one from different Mobile Network Operators (MNOs) to steer its traffic for improving the Quality-of-Service (QoS). To this end, we first formulate the issue as an optimization problem aiming to minimize the maximum outage probabilities of the UAVs. This problem is non-convex and non-linear, which is generally difficult to be solved. We propose a solution based on the framework of deep reinforcement learning (DRL) to solve it, in which we define the environment and the agent elements. Furthermore, to avoid sharing the learned experiences by the UAV in this solution, we further propose a federated deep reinforcement learning (FDRL)-based solution. Specifically, each UAV serves as a distributed agent to train separate model, and is then communicated to a special agent (dubbed coordinator) to aggregate all training models. Moreover, to optimize the aggregation process, we also introduce a FDRL with DRL-based aggregation (DRL2A) approach, in which the coordinator implements a DRL algorithm to learn optimal parameters of the aggregation. We consider deep Q-learning (DQN) algorithm for the distributed agents and Advantage Actor-Critic (A2C) for the coordinator. Simulation results are presented to validate the effectiveness of the proposed approach.

This paper investigates the joint secrecy and covert communication in a two-hop relaying system consisting of a transmitter Alice, a receiver Bob, an eavesdropper Eve and a Relay. Eve always overhears the secret message from Alice. Meanwhile, Alice transmits covert message on top of secret message without being detected by Relay. First, we propose a covert transmission scheme that Alice will transmit covert message only when the signal-to-interference-plus-noise-ratio (SINR) of the secret message is greater than a threshold. Then, we provide the closed-form expressions of covert performance, i.e., the optimal detection threshold of Relay and the corresponding minimal detection error probability as well as the average minimum detection error probability. Finally, extensive numerical results are presented to illustrate the impacts of the system parameters on covert performances. Remarkably, the corresponding theoretical results well match with the simulation ones indicating that our theoretical analysis can accurately model the covert performance of the considered system.

Multi-services are of fundamental importance in Unmanned Aerial Vehicle (UAV)-enabled aerial communications for the Internet of Things (IoT). However, the multi-services are challenging in terms of requirements and use of shared resources such that the traditional solutions for a single service are unsuitable for the multi-services. In this paper, we consider a UAV-enabled aerial access network for ground IoT devices, each of which requires two types of services, namely ultra Reliable Low Latency Communication (uRLLC) and enhanced Mobile Broadband (eMBB), measured by transmission delay and effective rate, respectively. We first consider a communication model that accounts for most of the propagation phenomena experienced by wireless signals. Then, we derive the expressions of the effective rate and the transmission delay, and formulate each service type as an optimization problem with the constraints of resource allocation and UAV deployment to enable multi-service support for the IoT. These two optimization problems are nonlinear and nonconvex and are generally difficult to be solved. To this end, we transform them into linear optimization problems, and propose two iterative algorithms to solve them. Based on them, we further propose a linear program algorithm to jointly optimize the two service types, which achieves a trade-off of the effective rate and the transmission delay. Extensive performance evaluations have been conducted to demonstrate the effectiveness of the proposed approach in reaching a trade-off optimization that enhances the two services.

This article investigates the covertness and secrecy of wireless communications in an untrusted relay-assisted device-to-device (D2D) network consisting of a full-duplex base station (BS), a user equipment (UE), and an untrusted relay${R}$. For the covertness, we attempt to prevent Willie from detecting the very existence of communications via a D2D link from UE to R and cellular link from R to BS, while for the secrecy, we aim to prevent the untrusted relay from eavesdropping the UE message. To explore the fundamental covertness and secrecy in such a network, we first provide theoretical modelings for the average minimum detection error rate of Willie, and the average covert/secrecy rate from UE to BS under the underlay and overlay modes, respectively. Based on these models, th we further explore the optimal power control at UE, R, and BS to achieve the average covert rate maximization (MCR) for UE with the constraints of covertness and security requirements under the underlay mode. We also identify the optimal transmit powers and the optimal spectrum partition factor for MCR under the overlay mode. Finally, the exhaust searching method is adopted to solve the MCR problems, and extensive numerical and simulation results are presented to validate our theoretical analysis and to illustrate the average covert rate and secrecy rate of UE under various scenarios.

Device-to-device (D2D) communications underlaid cellular networks have emerged as a promising network architecture to provide extended coverage and high data rate for various Internet of Things (IoT) applications. However, because of the inherent openness and broadcasting nature of wireless communications, such networks face severe risks of data privacy disclosure. This paper investigates the covert communications in such networks for providing enhanced privacy protection. Specifically, this paper explores the critical covert rate performance in a full-duplex D2D communication underlaid cellular network consisting of a base station, a cellular user, a D2D pair with a transmitter and a full-duplex receiver, and a warden, where the D2D receiver can operate over either the full-duplex (FD) mode or the half-duplex (HD) mode. We first derive transmission outage probabilities of cellular and D2D links under the FD and HD modes, respectively. Based on these probabilities, we further provide theoretical modelling for the covert rate under each mode and explore the corresponding covert rate maximization by jointly optimizing the transmit powers of the D2D pair and the cellular user. To improve the covert rate performance, we propose a general mode in which the D2D receiver can flexibly switch between these two modes. Under the general mode, we also investigate the theoretical modelling and maximization problems of covert rate. Finally, we present extensive numerical results to illustrate the covert rate performances under the FD, HD, and general modes.

Protecting large-scale networks, especially Software-Defined Networks (SDNs), against distributed attacks in a cost-effective manner plays a prominent role in cybersecurity. One of the pervasive approaches to plug security holes and prevent vulnerabilities from being exploited is Moving Target Defense (MTD), which can be efficiently implemented in SDN as it needs comprehensive and proactive network monitoring. The critical key in MTD is to shuffle the least number of hosts with an acceptable security impact and keep the shuffling frequency low. In this paper, we have proposed an SDN-oriented Cost-effective Edge-based MTD Approach (SCEMA) to mitigate Distributed Denial of Service (DDoS) attacks at a lower cost by shuffling an optimized set of hosts that have the highest number of connections to the critical servers. These connections are named edges from a graph-theoretical point of view. We have proposed a three-layer mathematical model for the network that can easily calculate the attack cost. We have also designed a system based on SCEMA and simulated it in Mininet. The results show that SCEMA has lower complexity than the previous related MTD field with acceptable performance.

This paper proposes the concept of Ahead-Me Cov-erage (AMC) aiming to get the coverage of a cellular network ahead of the mobile users for maintaining enhanced Quality- of-Service (QoS) in cellular-connected unmanned aerial vehicle (UAV) networks. In such networks, each base station (BS) with an intelligent logic can automatically tilt the direction of its radio antennas based on the trajectory of UAV s. For this purpose, we first formulate AMC as an integer optimization problem for maximizing the minimum transmission rate of UAVs by jointly optimizing the angles of the different radio antenna, the resource allocation and the selection of the appropriate serving BS for the UAVs throughout their path. For this complex optimization problem, we then propose a solution based on Deep Reinforcement Learning (DRL) to solve it. Under this solution, we adopt a multi-heterogeneous agent-based approach (MHA-DRL) including two types of agents, namely the UAV agents and the BS agents. Each agent implements an Advantage Actor Critic (A2C) to learn optimal policies. Specifically, the BS agents aim to tilt their antennas to get ahead of the UAV s throughout their mobility, and the UAV agents target selecting the appropriate serving BSs along with resource allocation. Performance evaluations are presented to validate the effectiveness of the proposed approach.

This paper considers the scenario of Unmanned Aerial Vehicles (UAVs) acting as flying base stations (UAV-BSs) to provide network connectivity to ground Internet of Things (IoT) devices. More precisely, we investigate the issue where a UAV-BS needs to be replaced by a new one in a seamless way. First, we formulate the issue as an optimization problem aiming to maximize the minimum transmission rate of the served IoT devices during the UAV-BS replacement process. This is translated into jointly optimizing the trajectory of the source UAV-BS (the one to be replaced) and the target UAV-BS (the replacing one), while pushing the IoT devices to seamlessly transfer their connections to the target UAV-BS. We therefore consider a target replacement zone where the UAV-BS replacement can happen, along with IoT connections transfer. Furthermore, we propose a solution based on Deep Reinforcement Learning (DRL). More precisely, we introduce a Multi-Heterogeneous Agent-based approach (MHA-DRL), where two types of agents are considered, namely the UAV-BS agents and the IoT agents. Each agent implements a DQN (Deep Q-Learning) algorithm, where UAV-BS agents learn optimal policies to perform replacement while IoT agents learn optimal policies to transfer their connections to the target UAV-BS. The conducted performance evaluations show that the proposed approach can achieve near optimal optimization.

Edge computing (EC) is a promising technology to support a variety of performance-sensitive intelligent applications, especially in the Industrial Internet of Things (IIoT). The sensing-data-driven applications whose task processing requires sensing data from various sensors are typical applications in IIoT systems. The placement of caching and computing edge service functions for such applications is vital to ensure system performance and resource utilization in EC-enabled IIoT systems. Therefore, this paper investigates the joint caching and computing edge service placement (JCCESP) for multiple sensing-data-driven IIoT applications in an EC-enabled IIoT system. The JCCESP problem is formulated as a Markov Decision Process (MDP). Then, a deep reinforcement learning (DRL)-based approach is proposed to address the challenges like limited prior knowledge and the heterogeneity of such IIoT systems. Under such an approach, the policy network of the DRL agent is constructed based on an encoder-decoder model to tackle various applications requiring different numbers of service functions. A REINFORCE-based method is further employed to train the policy network. Simulation results indicate that the performances achieved by our proposed approach can converge after training and are significantly superior to benchmarks.

Unmanned Aerial Vehicles (UAVs) are set to become an integral part of 5G and beyond systems with the promise of assisting cellular communications and enabling advanced applications and services, such as public safety, caching, and virtual/mixed reality-based remote inspection. However, safe and secure navigation of UAVs is a key requisite for their integration in the airspace. The GPS spoofing is one of the major security threats to remotely and autonomously controlled UAVs. In this paper, we propose a machine learning-based, mobile network-assisted UAV monitoring and control system that allows live monitoring of UAVs' locations and intelligent detection of spoofed positions. We introduce the Convolutional Neural Network (CNN) in the edge UAV Flight Controller (UFC) to locate a UAV and detect any GPS spoofing by comparing differences between the theoretical path loss computed by UFC and the corresponding path loss reported by the connected base station (BS). To reduce the detection latency as well as to increase the detection accuracy, transfer learning is leveraged to transfer the CNN knowledge between edge servers when the UAV handovers from one BS to another. The performance evaluation shows that the proposed solution can successfully detect spoofed GPS positions with an accuracy rate above 88% using only one BS.

By placing edge service functions in proximity to IoT facilities, edge computing can satisfy various IoT applications’ resource and latency requirements. Sensing-data-driven IoT applications are prevalent in IoT systems, and their task processing relies on sensing data from sensors. Therefore, to ensure the Quality of Service (QoS) of such applications in an edge-enabled IoT system, dedicated caching functions (CFs) are required to cache necessary sensing data. This article considers an edge-enabled IoT system and investigates the joint caching and computing service placement (JCCSP) problem for sensing-data-driven IoT applications. Then, deep reinforcement learning (DRL) is exploited to address the problem since it can adapt to a heterogeneous system with limited prior knowledge. In the proposed DRL-based approaches, a policy network based on the encoder–decoder model is constructed to address the issue of varying sizes of JCCSP states and actions caused by different numbers of CFs related to applications. Then, an on-policy REINFORCE-based method is adopted to train the policy network. After that an off-policy training method based on the twin-delayed (TD) deep deterministic policy gradient (DDPG) is proposed to enhance the training efficiency and experience utilization. In the proposed DDPG-based method, a weight-averaged twin-$Q$-delayed (WATQD) algorithm is introduced to reduce the bias of$Q$-value estimation. Simulation results show that our proposed DRL-based JCCSP approaches can achieve converged performance that is significantly superior to benchmarks. Moreover, compared with the original TD method, the proposed WATQD method can significantly improve the training stability.

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.

As important network infrastructures to support data storage and service delivery for worldwide users, cloud data centers are facing great threaten by frequent disasters around the world and thus the survivability of cloud data centers becomes a critical issue. Since both data and service evacuations are desired at the same time under a real disaster scenario, this paper studies a joint design of them to fight against disasters. We consider a disaster that can present an early warning time before it really affects cloud data centers, and by exploiting the intrinsic interplay between data and service evacuations and efficiently utilizing the early warning time we propose a joint data and service evacuation scheme for emergency protection. We first formulate the joint design as two optimal Integer Linear Program (ILP) models. Notice that the protection process is highly time-sensitive due to the early warning time constraint, two time-efficient heuristics are then designed by carefully selecting evacuated services and candidate evacuation nodes to achieve a better sharing of network resources between data backup and service migration. Extensive numerical results demonstrate the efficiency of the proposed scheme on improving survivability of data and services in cloud data centers. With a set of given resource and early warning time constraints, this work can guide data center operators to achieve a tradeoff between data backup and service migration.

This paper investigates the fundamental connection steering issue in cellular-enabled Unmanned Aerial Vehicles (UAVs), whereby a UAV steers the cellular connection across multiple Mobile Network Operators (MNOs) for ensuring enhanced Quality-of-Service (QoS). We first formulate the issue as an optimization problem for minimizing the maximum outage probability. This is a nonlinear and nonconvex problem that is generally difficult to be solved. To this end, we propose a new approach for solving the optimization problem based on Deep Reinforcement Learning (DRL), considering two important reinforcement learning algorithms (i.e., Deep Q-Learning (DQN) and Advantage Actor Critic (A2C)). Simulation results show that under the proposed approach, the UAVs can make optimal decisions to select the most suitable connection with MNOs for achieving the minimization of the maximum outage probability. Furthermore, the results also show that in our new approach, the A2C-based algorithm is better than the DQN-based one, especially when the number of MNOs increases, while the DQN-based algorithm can be executed in a shorter time.

The integration of cellular networks and unmanned aerial vehicle (UAV) swarm communications is expected to be a promising technology to provide ubiquitous network connectivity for various UAV assisted Internet of Things (IoT) applications. To support these IoT applications with stringent requirement of rate performance, this paper explores the maximum sum rate performance for the cellular-connected UAV swarm communications. The sum rate maximization can be formulated as a nonlinear and nonconvex optimization problem with the constraints of transmit power of UAVs, elevation angle, azimuth angle and height of antenna array equipped at base station (BS). According to the Karush–Kuhn–Tucker (KKT) optimality conditions and the standard interference function, we propose an iterative algorithm to solve the problem, wherein the problem is transformed into a concave optimization problem by utilizing the rate approximation and logarithmic transformations. The iterative algorithm is proved to converge to a global solution for the approximated concave optimization problem. Finally, simulation results are provided to indicate the effect of some important system parameters on the sum rate performance in the system.

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 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.

Unmanned aerial vehicle (UAV) swarm connected to millimeter wave (mmWave) cellular networks is emerging as a new promising solution to provide ubiquitous high-speed and long distance wireless communication services for supporting various applications. To satisfy different quality of service (QoS) requirements in future large-scale applications of such networks, this article investigates the rate performance, fairness and their tradeoff in the networks with directional antennas in terms of sum-rate maximization, fairness index maximization, max-min fair rate and proportional fairness. We first consider a more realistic mmWave 3D directional antenna array model for UAVs and base station (BS), where the antenna gain depends on the radiation angle of the antenna array. Based on this antenna array model, we formulate the performance, fairness and their tradeoff as four constrained optimization problems, and propose corresponding iterative algorithm to solve these problems by jointly optimizing elevation angle, azimuth angle and height of antenna array at BS in the downlink transmission scenario. Furthermore, we also explore them in uplink transmission scenario, where the interference issue among links is carefully considered. Finally, according to the sum rate, minimum rate and fairness index under each optimization problem, numerical results are provided to illustrate the impacts of network parameters on the performance, fairness and their tradeoff, and also to reveal new findings under both downlink and uplink transmission scenarios, respectively.

In indoor environment, passive human detection and localisation are important enabling technologies for elder healthcare, emergence rescue and target tracking applications. Recently, the fine‐grained channel state information (CSI) of Wi‐Fi was adopted for indoor localisation due to the low‐cost Wi‐Fi network interface card and available firmware modifications for CSI extraction. However, due to multipath fading and spatial‐temporal dynamics of wireless channel, stable CSI extraction is a challenging task to achieve reliable CSI fingerprint matching. In this study, the sensitivity of CSI is first analysed and stable CSI fingerprints can be obtained by reducing the variance from interference and white noise. The stable CSI fingerprints are then classified by quadratic discriminant analysis to achieve location matching. Extensive experiments have been conducted to justify the system performance. The results reveal that the proposed indoor passive localisation system outperforms passive CSI‐MIMO system in terms of performance.