The study of the intricate flight patterns and behaviors of swarm insects, such as drosophilas, has long been a subject of interest in both the biological and computational realms. Tracking drosophilas is an essential and indispensable method for researching drosophilas’ behaviors. Still, it remains a challenging task due to the highly dynamic nature of these drosophilas and their partial occlusion in multi-target environments. To address these challenges, particularly in environments where multiple targets (drosophilas) interact and overlap, we have developed a long-term Trajectory 3D Position and Orientation Tracking Method (Long 3D-POT) that combines deep learning with particle filtering. Our approach employs a detection model based on an improved Mask-RCNN to accurately detect the position and state of drosophilas from frames, even when they are partially occluded. Following detection, improved particle filtering is used to predict and update the motion of the drosophilas. To further enhance accuracy, we have introduced a prediction module based on the self-attention backbone that predicts the drosophila’s next state and updates the particles’ weights accordingly. Compared with previous methods by Ameni, Cheng, and Wang, our method has demonstrated a higher degree of accuracy and robustness in tracking the long-term trajectories of drosophilas, even those that are partially occluded. Specifically, Ameni employs the Interacting Multiple Model (IMM) combined with the Global Nearest Neighbor (GNN) assignment algorithm, primarily designed for tracking larger, more predictable targets like aircraft, which tends to perform poorly with small, fast-moving objects like drosophilas. The method by Cheng then integrates particle filtering with LSTM networks to predict particle weights, enhancing trajectory prediction under kinetic uncertainties. Wang’s approach builds on Cheng’s by incorporating an estimation of the orientation of drosophilas in order to refine tracking further. Compared with those methods, our method performs with higher accuracy on detection, which increases by more than 10% on the F1 Score, and tracks more long-term trajectories, showing stability.

In recent years, microservices, as an emerging technology in software development, have been favored by developers due to their lightweight and low-coupling features, and have been rapidly applied to the Internet of Things (IoT) and Internet of Vehicles (IoV), etc. Microservices deployed in each unit of the IoV use wireless links to transmit data, which exposes a larger attack surface, and it is precisely because of these features that the secure and efficient placement of microservices in the environment poses a serious challenge. Improving the security of all nodes in an IoV can significantly increase the service provider’s operational costs and can create security resource redundancy issues. As the application of reinforcement learning matures, it is enabling faster convergence of algorithms by designing agents, and it performs well in large-scale data environments. Inspired by this, this paper firstly models the placement network and placement behavior abstractly and sets security constraints. The environment information is fully extracted, and an asynchronous reinforcement-learning-based algorithm is designed to improve the effect of microservice placement and reduce the security redundancy based on ensuring the security requirements of microservices. The experimental results show that the algorithm proposed in this paper has good results in terms of the fit of the security index with user requirements and request acceptance rate.