This paper investigates an unmanned aerial vehicle (UAV)-assisted multi-object offloading scheme for blockchain-enabled Vehicle-to-Everything (V2X) systems. Due to the presence of an eavesdropper (Eve), the system’s communication links may be insecure. This paper proposes deploying an intelligent reflecting surface (IRS) on the UAV to enhance the communication performance of mobile vehicles, improve system flexibility, and alleviate eavesdropping on communication links. The links for uploading task data from vehicles to a base station (BS) are protected by IRS-assisted physical layer security (PLS). Upon receiving task data, the computing resources provided by the edge computing servers (MEC) are allocated to vehicles for task execution. Existing blockchain-based computation offloading schemes typically focus on improving network performance, such as minimizing energy consumption or latency, while neglecting the Gas fees for computation offloading and the costs required for MEC computation, leading to an imbalance between service fees and resource allocation. This paper uses a utility-oriented computation offloading scheme to balance costs and resources. This paper proposes alternating phase optimization and power optimization to optimize the energy consumption, latency, and communication secrecy rate, thereby maximizing the weighted total utility of the system. Simulation results demonstrate a notable enhancement in the weighted total system utility and resource utilization, thereby corroborating the viability of our approach for practical applications.

In smart grids, real-time electricity data uploaded by smart meters may be analyzed by an attacker with other data analytics methods, which may expose users’ privacy. To ensure user privacy, differential privacy methods are often used to process data. However, these methods reduce the accuracy of the data results obtained by the center and lead to unavailability of the data. In this paper, we address this problem and propose a distributed differential privacy protection scheme. Two methods of data noise addition and data perturbation are fused and used in the protection scheme. Data accuracy is improved by optimizing the noise generation method. To address the problem of quantitatively balancing the users’ privacy needs with the central analytics needs, this paper describes the needs of both through mathematical definitions, i.e., data accuracy and data privacy, and proposes a privacy budget that balances data accuracy and privacy. The performance of the proposed scheme is evaluated using the typical power data, which proves the excellent performance.

In the realm of edge computing, the analog aggregation based Federated Learning Over the Air (FLOA) emerges as a promising technology, offering heightened communication efficiency and privacy provisions. This approach involves concurrent transmission and aggregation, where edge devices (workers) collectively upload their local updates to a Parameter Server (PS) through shared time-frequency resources. The PS then obtains averaged updates only but not the individual local ones, reducing latency and communication costs. However, this simultaneous process exposes FLOA to vulnerabilities, particularly Byzantine attacks. Addressing this concern, we introduce an innovative framework utilizing Unmanned Aerial Vehicles (UAVs) to assist in a heterogeneous FLOA. This framework mitigates the impact of Byzantine attacks while preserving the advantages of over-the-air computation for efficient federated learning. The UAVs engage in over-the-air computation, collecting gradients from local workers and aggregating them. Subsequently, the UAVs transmit these aggregated gradients to the PS in different time slots. Robust aggregation techniques are applied at the PS to combine updates from UAVs. To enhance the robustness of over-the-air transmissions from workers to UAVs, we propose a power control policy. The resilience of the proposed framework to attacks is demonstrated through the derived expected convergence rate, validated by experiments on real datasets.

Unmanned Aerial Vehicles (UAVs) are promising for their agile flight capabilities, allowing them to carry out tasks in various complex scenarios. The efficiency and accuracy of UAV operations significantly depend on high-precision positioning technology. However, the existing positioning techniques often struggle to achieve accurate position estimates in conditions of Non-Line-Of-Sight (NLOS). To address this challenge, we propose a novel high-precision UAV positioning method based on MultiLayer Perceptron (MLP) integrating Ultra-WideBand (UWB) and Inertial Measurement Unit (IMU) technologies, which can acquire centimeter-level high-precision location estimation. In the method, we simultaneously extract key features from channel impulse responses and state space of UAV for training an MLP model, which can not only reduce error of UWB signals from dynamically flying UAV to anchor in NLOS environments, but also adapt to the diverse environment settings. Specifically, we respectively apply the anchor node assisted position calibration method and cooperative positioning techniques to the dynamic flying UAVs for solving the issues of UWB signal being blocked and lost. We conduct extensive real-world experiments to demonstrate the effectiveness of our approach. The results show that the median positioning errors of UAV in hovering and flight are 6.3 cm and within 20 cm, respectively.