Multi-access Edge Computing (MEC) enables users to handle resource-intensive and latency-sensitive tasks. However, the offloading behaviors, which are closely correlated with wireless channel conditions, can inadvertently reveal users' location information to untrustworthy MEC servers. Existing location privacy-aware task offloading (LPTO) mechanisms have not fully considered and comprehensively analyzed personalized location privacy protection requirements. To address this gap, this paper proposes a differential privacy (DP)-based personalized LPTO mechanism for MEC environments that jointly optimizes the perturbation region, privacy budget, and offloading rate while maximizing the offloading utility. We quantify personalized privacy requirements by incorporating task sensitivity, user privacy preference, and task priority. Then, we propose a two-timescale (2Ts) optimization framework to solve the complex personalized location privacy-aware task offloading optimization problem. Specifically, we optimize the perturbation region on a long timescale to align with long-term privacy requirements. In contrast, the offloading ratio and privacy budget are dynamically optimized on a short timescale based on instantaneous channel states and offloading workloads. Furthermore, we model the privacy-aware offloading problem as a Markov decision process (MDP) and develop a dual-agent deep reinforcement learning (DRL)-based personalized LPTO mechanism (DDPLM) to optimize strategies under dynamic MEC systems. Simulation results validate that the proposed DDPLM achieves personalized location privacy protection while reducing computational costs.

Satellite-aided airborne systems can enable data acquisition in remote areas for the intelligent transportation systems (ITS), leveraging the satellite coverage alongside the mobility and multifunctional capabilities of autonomous aerial vehicles (AAVs). However, due to the harsh environment, ensuring secure and timely task execution is complicated by uncertainties related to both channel conditions and eavesdropping threats. This paper proposes a two-stage optimization method to fully exploit AAVs’ flying, sensing, compressing, and transmitting capabilities for secure data acquisition under dual uncertainties. In the first stage, a deep reinforcement learning strategy is employed to optimize sensing and trajectory planning to explore the eavesdropping environment and balance computational and transmission demands. Building on the sensed information about the eavesdropping environment, the second stage focuses on minimizing task completion time through optimal resource allocation and hierarchical A* path planning, with the channel uncertainty addressed by incorporating an outage probability constraint. Simulations demonstrate that the proposed method can reduce data completion time by 35.3%, validating its effectiveness in uncertain environments.

The dependable transmission of data and the efficacy of wireless communication depend critically on the detection and classification of interference. Traditional classification algorithms may not yield the requisite precision in identifying and categorizing diverse types of interference, whereas deep learning (DL) algorithms necessitate high-quality data and training samples, which prove unfeasible in real-time communication scenarios. In addressing these challenges, we present a novel approach that utilizes the denoising diffusion probabilistic model (DDPM) for offline processing of collected signals before feature extraction and subsequently sending the signals into a predefined classifier. Our experimental analyses show that our approach delivers up to 91% accuracy without any prior information, outperforming both generative adversarial network (GAN)-based and other traditional DL algorithms, even with limited signal samples of only 5. More significantly, our approach underscores the feasibility of employing generative models in signal processing and achieves state-of-the-art performance on high-precision recognition in real-world communication scenarios.

Ultra-dense deployment of small cells can enhance capacity, extend coverage, and improve the spectrum and energy efficiency. However, intra-tier interference among different spectrum-sharing small cells is critical and needs to be well coordinated from a resource utilization perspective, such as the distributed power control. Especially in the ultra-dense small cell networks, the effects of intra-tier interference cannot be ignored, although the perceived interference from one small cell is infinitesimal for a specific small cell when designing a power control policy. It is difficult to make the model, analysis, and design of the distributed power control due to a huge number of small cells. In this paper, we formulate the power control of a generic small cell as a mean field game (MFG) with an interference dominator, where we derived the coupled Hamilton-Jacobi-Bellman (HJB) and the Fokker-Planck-Kolmogorov (FPK) equations. The presented MFG framework of power control can characterize the complex interference interaction and strategically rational decision-making of the generic and dominating players. Simulation results show the dominating effects of the interference dominator on the power control.

Cloud-supported Internet of Things (Cloud-IoT) has been broadly deployed in smart grid systems. The IoT front-ends are responsible for data acquisition and status supervision, while the substantial amount of data is stored and managed in the cloud server. Achieving data security and system efficiency in the data acquisition and transmission process are of great significance and challenging, because the power grid-related data is sensitive and in huge amount. In this paper, we present an efficient and secure data acquisition scheme based on ciphertext policy attribute-based encryption. Data acquired from the terminals will be partitioned into blocks and encrypted with its corresponding access subtree in sequence, thereby the data encryption and data transmission can be processed in parallel. Furthermore, we protect the information about the access tree with threshold secret sharing method, which can preserve the data privacy and integrity from users with the unauthorized sets of attributes. The formal analysis demonstrates that the proposed scheme can fulfill the security requirements of the Cloud-IoT in smart grid. The numerical analysis and experimental results indicate that our scheme can effectively reduce the time cost compared with other popular approaches.