The core task of target detection is to accurately identify and localize the object of interest from a multitude of interfering factors. This task is particularly difficult in UAV aerial images, where targets are often small and the background can be extremely complex. In response to these challenges, this study introduces an enhanced target detection algorithm for UAV aerial images based on the YOLOv7-tiny network. In order to enhance the convolution module in the backbone of the network, the Receptive Field Coordinate Attention Convolution (RFCAConv) in place of traditional convolution enhances feature extraction within critical image regions. Furthermore, the tiny target detection capability is effectively enhanced by incorporating a tiny object detection layer. Moreover, the newly introduced BSAM attention mechanism dynamically adjusts attention distribution, enabling precise target–background differentiation, particularly in cases of target similarity. Finally, the innovative inner-MPDIoU loss function replaces the CIoU, which enhances the sensitivity of the model to changes in aspect ratio and greatly improves the detection accuracy. Experimental results on the VisDrone2019 dataset reveal that relative to the YOLOv7-tiny model, the improved YOLOv7-tiny model improves precision (P), recall (R), and mean average precision (mAP) by 4.1%, 5.5%, and 6.5%, respectively, thus confirming the algorithm’s superiority over existing mainstream methods.

The development of Vehicle AD Hoc Networks (VANETs) has significantly enhanced the efficiency of intelligent transportation systems. Through real-time communication between vehicles and roadside units (RSUs), the immediate sharing of traffic information has been achieved. However, challenges such as network congestion, data privacy, and low computing efficiency still exist. Data privacy is at risk of leakage due to the sensitivity of vehicle information, especially in a resource-constrained vehicle environment, where computing efficiency becomes a bottleneck restricting the development of VANETs. To address these challenges, this paper proposes a certificateless aggregated signcryption scheme based on edge computing. This scheme integrates online/offline encryption (OOE) technology and a pseudonym mechanism. It not only solves the problem of key escrow, generating part of the private key through collaboration between the user and the Key Generation Center (KGC), but also uses pseudonyms to protect the real identities of the vehicle and RSU, effectively preventing privacy leakage. This scheme eliminates bilinear pairing operations, significantly improves efficiency, and supports conditional traceability and revocation of malicious vehicles while maintaining anonymity. The completeness analysis shows that under the assumptions of calculating the Diffie–Hellman (CDH) and elliptic curve discrete logarithm problem (ECDLP), this scheme can meet the requirements of IND-CCA2 confidentiality and EUF-CMA non-forgeability. The performance evaluation further confirmed that, compared with the existing schemes, this scheme performed well in both computing and communication costs and was highly suitable for the resource-constrained VANET environment.

With the boom in mobile internet services, computationally intensive applications such as virtual and augmented reality have emerged. Mobile edge computing (MEC) technology allows mobile devices to offload heavy computational tasks to edge servers, which are located at the edge of the network. This technique is considered an effective approach to help reduce the burden on devices and enable efficient task offloading. This paper addresses a dynamic real-time task-offloading problem within a stochastic multi-user MEC network, focusing on the long-term stability of system energy consumption and energy budget constraints. To solve this problem, a task-offloading strategy with long-term constraints is proposed, optimized through the construction of multiple queues to maintain users’ long-term quality of experience (QoE). The problem is decoupled using Lyapunov theory into a single time-slot problem, modeled as a Markov decision process (MDP). A deep reinforcement learning (DRL)-based LMADDPG algorithm is introduced to solve the task-offloading decision. Finally, Experiments are conducted under the constraints of a limited MEC energy budget and the need to maintain the long-term energy stability of the system. The results from simulation experiments demonstrate that the algorithm outperforms other baseline algorithms in terms of task-offloading decisions.