Non-orthogonal multiple access (NOMA) enables multiple terminal devices to simultaneously share wireless resources, providing efficient computing offloading services for wireless devices in networks that integrate unmanned aerial vehicles (UAVs) with mobile edge computing (MEC). However, the broadcast characteristics of UAV line-of-sight (LoS) communication introduce serious security issues for NOMA-based UAV-MEC systems, especially when facing an aerial warden. To address this issue, we propose a covert communication scheme for NOMA-based UAV-MEC systems against an aerial warden, where the aerial warden monitors the task offloading behavior of terminal devices. In the proposed scheme, the average computing capacity is maximized by jointly optimizing the UAV trajectory and system resources while ensuring the covert performance requirements. Firstly, considering the terminal devices have a fixed number of computing tasks, a block coordinate descent (BCD)-based algorithm is proposed, which decomposes the non-convex original problem into several subproblems and solves them iteratively. Secondly, considering the case of dynamic tasks arrival at terminal devices, we propose a double-deep Q-learning (DDQN)-based algorithm, where the optimal strategy for trajectory planning and resource allocation is obtained. Simulation results demonstrate that the proposed scheme using two algorithms outperform their respective baselines.

Unmanned aerial vehicle (UAV)-assisted federated edge learning (FEL) has emerged as a promising paradigm for privacy-preserving data processing in resource-constrained environments. However, the reliance on open wireless communication inherently exposes the system to eavesdroppers, who can eavesdrop and exploit shared model updates to reconstruct sensitive data, posing serious threats to the privacy and security. To address this challenge, we propose a privacy-aware UAV-assisted FEL framework that integrates adaptive local differential privacy (DP) into the model upload process, where user-specific noise is injected into local updates to prevent eavesdroppers from reconstructing sensitive data. To further enhance security and privacy performance, an indicator named value of privacy and security (VoPS) is designed to characterize the combined connection between training cost and privacy leakage. Furthermore, limited system resources including bandwidth allocation, user CPU frequency, DP noise scale, and UAV CPU frequency are collaboratively optimized under considering leakage threshold and heterogeneous computing constraints. Then, a deep deterministic policy gradient (DDPG)-based resource collaboration and secure aggregation scheme is proposed to solve the problem, in which the continuous optimization strategy is intelligently generated through the interaction between the agent and the dynamic privacy-aware UAV-assisted FEL system. Simulation results validate the effectiveness of the proposed scheme in enhancing the security and privacy performance of the system.

Effective provision of integrated sensing, communication, and computation (ISCC) services in future networks will inevitably increase their operational complexity. The distinct requirements of diverse tasks for tailored ISCC devices further exacerbate the challenge of adaptively allocating constrained resources among concurrent tasks. To address these difficulties, a task-specific joint resource orchestration scheme is proposed in this paper to enhance the effectiveness of ISCC operation and heterogeneous tasks completion. Specifically, the completion of concurrent heterogeneous tasks by different devices relies on the task-specific sharing of limited resource among sensing, real-time data computing and delay-tolerant data processing. Consequently, a value of multi-task completion (VoC) indicator is designed to connect and balance among the diverse demands from concurrent tasks, including computing rate, time delay, and sensing performance. The VoC is then maximized by collaborative optimization of multi-dimensional resources, including transmit beamformer, local and offloading CPU-cycle frequency, data factor assignment and computation capacity. To solve this challenging optimization problem with the lack of close-form solution and coupling of multi-variables, we first transform it into an equivalent form that is tractable to handle. Next, the problem is decomposed into several subproblems, which can be approximately solved by iterative updates. Simulation results demonstrate the performance enhancement of the proposed scheme is superior to the benchmarks.

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) can provide flexible computing service for terminal-devices (TDs). However, malicious active aerial eavesdroppers can perform air-to-ground eavesdropping and air-to-air attacking, which makes TDs’ tasks offloading computation more vulnerable, posing significantly secure threats to UAV-enabled MEC. To overcome this challenge, we aim to design collaborative communication and computation schemes for the secure UAV-enabled MEC system, where an active aerial eavesdropper is capable of wiretapping the tasks information offloaded from TDs and transmitting attack signals to the legitimate network. The total weighted energy consumption of the system is minimized via optimizing time allocation, transmit power, local and offloading computation bits, as well as UAV trajectory. First, considering the given number of computational tasks of TDs, a block coordinate descent (BCD)-based scheme is proposed to decompose the original multi-variables-coupling and close-form-lacking problem into several tractable subproblems that can be addressed by iterations. Next, considering that there are dynamic and random tasks arriving to TDs’ original tasks, a deep reinforcement learning (DRL)-based scheme is proposed to maintain the stability of tasks, where the solution of computation, communication and trajectory optimization is intelligently obtained by adopting double-deep Q-learning (DDQN). Simulation results demonstrate that the proposed schemes outperform the respective benchmarks for secure UAV-enabled MEC against active aerial eavesdropping.

The vigorous development of maritime networks and the explosive growth of maritime sampling data put forward more and more high demands on the computing and communication capability of maritime equipment. Unmanned aerial vehicle (UAV), as the mobile relay device guarantees the capability by transferring part of the computing tasks of maritime equipment to carrying mobile edge computing (MEC) servers on land. However, the transmitting data of the UAV's communication channel can be easily intercepted due to the line of sight (LoS) feature, which brings the secure data transmission issue. To solve this issue, we propose a secure data transmission scheme in the UAV-relay assisted maritime MEC system. Specifically, a malicious UAV attempts to intercept the transmission data while another UAV helps forward the offloading computational data to the maritime surface users. A ground jammer transmits jamming signals with the object to block data intercepting. To maximize the users' minimum secure calculation capacity, we jointly optimize the transmit power of users and the relay UAV, the time slot allocation factor, and the UAV flight trajectory with block coordinate descent (BCD) and successive convex approximation (SCA) techniques. Numerical findings demonstrate that the proposed scheme can effectively improve the secure calculation capability of the system compared with four benchmark schemes.

The integration of mobile edge computing (MEC) and unmanned aerial vehicles (UAVs) has created new opportunities for efficient data processing and calculating services within the Internet of Things. However, the presence of the active eavesdropper brings serious vulnerabilities to the security calculation of terminal users (TUs), which can eavesdrop on TUs’ confidential content and compromise the quality of offloading calculation. In this paper, we propose an efficient energy consumption minimization scheme for the considered secure UAV-enabled MEC network including an active UAV eavesdropper. While ensuring security calculation for all TUs’ data, the network’s weighted energy consumption is achieved through trajectory and resource optimization, including time, local calculation and offloading calculation allocation. Due to the coupling of multi-variables and the non-convexity of the constraints, the problem is highly challenging to solve directly. To address this, an auxiliary variable is introduced to transform the problem into a more tractable form. The optimizing solution is then obtained through iterative updates, allowing for the convergence towards an optimizing solution. Simulation results show that the proposed scheme exhibits superior performance of reducing the network’s energy consumption compared to the benchmark scheme.

Unmanned aerial vehicle-aided (UAV-aided) mobile edge computing (MEC) network can greatly reduce the data growth pressure of Internet of Things (IoT) and expand the wireless communication coverage. However, there is a risk of eavesdropping on the offloading information of terminal users (TUs) because of UAV light-of-sight (LoS) transmission. In this paper, we propose a Dinkelbach-guided deep reinforcement learning (DRL) scheme for secure communication in the UAV-aided MEC network. Specifically, the security calculating efficiency of the network is maximized by optimizing offloading decision and resource allocation under the condition of the data queue stability and minimum calculating requirement. The problem is intractable due to the fractional structure and binary constraint. Firstly, we deal with the fractional structure by taking advantage of Dinkelbach optimization. Then, offloading decision is generated based on DRL and the resource is allocated by successive convex approximation (SCA). Simulation results show that the proposed Dinkelbach-guided DRL scheme efficiently improves the security calculating efficiency of the network.

Non-orthogonal multiple access (NOMA) allows multiple users to share link resource for higher spectrum efficiency. It can be applied to unmanned aerial vehicle (UAV) and mobile edge computing (MEC) networks to provide convenient offloading computing service for ground users (GUs) with large-scale access. However, due to the line-of-sight (LoS) of UAV transmission, the information can be easily eavesdropped in NOMA-based UAV-MEC networks. In this paper, we propose a secure communication scheme for the NOMA-based UAV-MEC system towards a flying eavesdropper. In the proposed scheme, the average security computation capacity of the system is maximized while guaranteeing a minimum security computation requirement for each GU. Due to the uncertainty of the eavesdropper’s position, the coupling of multi-variables and the non-convexity of the problem, we first study the worst security situation through mathematical derivation. Then, the problem is solved by utilizing successive convex approximation (SCA) and block coordinate descent (BCD) methods with respect to channel coefficient, transmit power, central processing unit (CPU) computation frequency, local computation and UAV trajectory. Simulation results show that the proposed scheme is superior to the benchmarks in terms of the system security computation performance.

With the maneuverability and mobility control of unmanned aerial vehicle (UAV), carrying mobile edge computing (MEC) servers on UAVs is able to effectively alleviate the explosive growth of data traffic pressure. However, UAV adopts line-of-sight transmission which has broadcasting characteristics. Malicious eavesdroppers can easily take advantage of the characteristics to eavesdrop information during the UAV edge computing. Therefore, the security of the UAV-MEC systems is a challenging problem. This article proposes a secure communication scheme for the dual-UAV-MEC system. In the proposed scheme, UAV server assists ground users in calculating the offloading tasks. In order to reduce the eavesdropping of offloading information by UAV eavesdropper, jammer sends interference signals on the ground. We aim to maximize the user's minimum secure calculation capacity by optimizing resources and trajectory of the UAV server. We first transform the optimization problem into a tractable form through mathematical methods and use successive convex approximation and block coordinate descent algorithms to solve it in an iterative manner. The final numerical results show that, compared with the benchmark schemes, the method proposed in this article effectively increases the secure calculation capacity of the system.