Multi-drone single object tracking is a key technology in Internet of Things-based aerial sensing systems. However, existing methods often neglect both temporal continuity of the object across frames and spatial complementarity from multi-drone viewpoints, limiting their robustness in large-scale and dynamic IoT environments. To address these issues, this paper proposes a dynamic spatiotemporal information interaction framework for multi-drone single object tracking (DSTII-MDOT), where spatiotemporal information refers to the object’s feature evolution over time (temporal) and its complementary multi-view representations (spatial). First, a dynamic temporal feature aggregation (DTFA) module is proposed, which captures the object’s motion and appearance variations by exploiting frame-to-frame differences, thereby ensuring feature continuity and stable predictions across frames in long-term tracking. Next, a spatial alignment method for cross-modal (text-image) semantic alignment (CMSA) is proposed. This method ensures the semantic alignment between visual features and textual descriptions by leveraging the CLIP model and employs a multi-head cross-attention mechanism to capture the top-k regions of interest within the search area. This enhances cross-drone collaboration and reduces redundant communications in IoT networks. Finally, a wavelet frequency domain feature refinement (WFDFR) module is proposed to enhance the texture features of the template image, effectively solving the problem of blurred texture features or missing details in complex scenes. Experimental results on the MDOT dataset demonstrate that the proposed DSTII-MDSOT tracker surpasses existing advanced methods in both success rate and precision, validating the effectiveness and superiority of the proposed method. The code and models are available at https://github.com/JerryBryant24/DSTII-MDOT.

Federated learning coordinates model training in a distributed manner within Internet of Things (IoT) systems and ensures the privacy of local client data simultaneously. Nonetheless, traditional federated learning relies primarily on a unified global model and focuses on local feature extraction, failing to accommodate the diversity and personalized needs of clients in non-independent and identically distributed (non-IID) environments. To mitigate the decline in model accuracy posed by these challenges, we propose a discrepancy-aware federated learning method based on multi-branch feature fusion (FedDM). Firstly, we design a differential-aware aggregation strategy (DA), which adjusts the contribution of each client during model aggregation using Gaussian distribution statistics, to generate personalized local models. Next, we propose a multi-branch feature fusion mechanism (MFF) that integrates diverse feature representations through multi-scale pooling and feature enhancement, enabling the incorporation of features across both spatial and channel dimensions for a more holistic representation. Experimental results demonstrate that FedDM enhances model accuracy and robustness, while exhibiting adaptability when facing challenges posed by data distribution heterogeneity.

Vehicle trajectory prediction plays a crucial role in IoT-based intelligent transportation systems, which can effectively address key issues, such as driving safety and multivehicle collaboration. However, the sensitivity of trajectory data and the reluctance of data holders to share it constrain the prediction model’s ability to capture vehicle behavior patterns in different scenarios. To address the above problems, we propose a blockchain-enabled asynchronous federated proximal policy optimization framework (BE-AFPPO) for the trajectory prediction of self-driving vehicles. First, we propose a curiosity proximal policy optimization (C-PPO) algorithm. The method utilizes a driven exploration strategy to actively motivate the intelligent agent to explore the unknown state space. The avoidance policy model reaches a local optimum when processing trajectory data. In addition, we design historical gated recurrent unit (GRU) and future GRU as input layers. The target’s historical motion features and future trajectory features are extracted, respectively. Then, various data is received through asynchronous federated learning. This model can fully learn the vehicle’s behavior patterns in different scenarios, which improves prediction accuracy. Based on this, we develop a blockchain-based dynamic group practical Byzantine fault tolerance (DG-PBFT) consensus algorithm. This enhances the credibility and integrity of the data while enriching the sources of trajectory data. Finally, we perform the experiments on the publicly available dataset nuScenes. The results demonstrate that the proposed method improves the robustness and accuracy of trajectory prediction.

Accurate traffic flow prediction is crucial for alleviating traffic congestion and optimizing intelligent transportation systems. However, traffic flow is subject to uncertainties and exhibits complex spatial and temporal dependence and dynamic change characteristics. Moreover, many efforts rely on a single view, which makes it difficult to comprehensively capture multiple levels of spatial and temporal correlations, thus limiting the accuracy of predictions. Therefore, we propose the multi-view spatio-temporal dynamic graph convolution framework MVSTDG for more comprehensively exploring and fusing the multi-view spatio-temporal features. Firstly, we design a dual-path Time-Patch Convolution (TPConv) module to separately model short-term fluctuations and long-term periodic trends, enabling effective extraction of dynamic features at multiple temporal scales. Secondly, we construct a data-driven traffic pattern library to generate dynamic adjacency matrices and integrate them with static topologies view. An Adaptive Diffusion Graph Convolutional Network (ADGCN) is then employed to model both local and global spatial correlations. In addition, we design a cross-gated spatio-temporal fusion mechanism that adaptively adjusts the contribution of short-term and long-term information, enhances the interaction of spatio-temporal information, and improves the model’s adaptive capability under different time scales. The experimental results show that MVSTDG outperforms the state-of-the-art baselines in several evaluation metrics and demonstrates higher prediction accuracy and stability on the four real datasets.