Tracking mobile targets is a basic task for many applications, e.g., wildlife protection, area surveillance, battlefield reconnaissance, disaster rescue, etc. In these cases, multiple edge devices are usually deployed to collect images or videos from the target area. Two mainstream tracking methodologies have been proposed to recognize and track targets from such data sources cooperatively. The centralized strategies collect data from the edge devices and thereafter run mining and learning algorithms upon such data. On the contrary, the distributed strategies implement such algorithms in a distributed manner. However, these methods incur either high transmission costs or slow convergence speeds. To this end, this paper presents a novel cooperative tracking framework (i.e., FedTrack) based on adaptive federated learning. A dual reputation mechanism has been formulated, and subsequently, an adaptive node selection algorithm has been suggested to ascertain the nodes suitable for involvement in the training process. Furthermore, a strategy for selecting the aggregation node based on capability has been developed to enhance the efficiency of aggregation. As far as we know, FedTrack is the first federated learning framework for collaborative tracking. Experimental results demonstrate that FedTrack achieves comparable or even better accuracy than state-of-the-art methods, yet needs much fewer data transmission costs and much less time consumption.

With the widespread use of Internet of things(IoT), a large amount of data will be generated in the edge of network, which can facilitate a significant transformation in edge intelligent services by integrating with edge computing, 5G and artificial intelligence. However, since the intelligent edge services seriously rely on big data and computing resource, it challenges the traditional centralized data processing model. Data sharing is a promising way to tackle this problem, but some critical technical challenges still remain, such as fragile data privacy protection, inefficient data exchange and low quality of data fusion. To address these problems, a privacy-enhanced and intelligence-preserved data sharing system, name VFLChain, is proposed in this article. The proposed VFLChain is designed based on consortium blockchain and vertical federated learning, which can ensure trustworthy and secure data sharing without relying on any center platforms or third parties. Furthermore, a blockchain-assisted decentralized vertical federated learning is presented to adapt to the decentralized system and support privacy-preserved, intelligent and efficient edge data sharing, while improving quality of data through learning with different characteristic data samples. Then, a data sharing processing workflow in VFLChain is also described to demonstrated details of data sharing. The simulation experiments confirm that the proposed system and mechanism have good accuracy and stability, and guarantee an effective data sharing.

Taking COVID-19 as an example, we know that a pandemic can have a huge impact on normal human life and the economy. Meanwhile, the population flow between countries and regions is the main factor affecting the changes in a pandemic, which is determined by the airline network. Therefore, realizing the overall control of airports is an effective way to control a pandemic. However, this is restricted by the differences in prevention and control policies in different areas and privacy issues, such as how a patient’s personal data from a medical center cannot be effectively combined with their passenger personal data. This prevents more precise airport control decisions from being made. To address this, this paper designed a novel data-sharing framework (i.e., PPChain) based on blockchain and federated learning. The experiment uses a CPU i7-12800HX and uses Docker to simulate multiple virtual nodes. The model is deployed to run on an NVIDIA GeForce GTX 3090Ti GPU. The experiment shows that the relationship between a pandemic and aircraft transport can be effectively explored by PPChain without sharing raw data. This approach does not require centralized trust and improves the security of the sharing process. The scheme can help formulate more scientific and rational prevention and control policies for the control of airports. Additionally, it can use aerial data to predict pandemics more accurately.

A pandemic can have a huge impact on normal human life and the economy, taking COVID-19 as an example. While the population flow between countries and regions is the main factor affecting the change of a pandemic, exactly as the airline network. Therefore, realizing the overall control of airports is an effective way to control a pandemic. However, restricted to the differences in prevention and control policies in different areas and privacy issues, the patients’ personal data of the medical center cannot be effectively combined with the passengers’ personal data. This prevents more precise airport control decisions from being made. To the end, this paper designs a novel data sharing framework (i.e., PPChain) based on blockchain and federated learning. The experiment shows that the relationship between the epidemic and aircraft transport can be effectively explored by PPChain, without sharing raw data. This approach does not require centralized trust and improves the security of the shareing process.The scheme can help formulate more scientific and rational prevention and control policies on airports’ control. And it can use aerial data to predict pandemics more accurately.

Existing State-of-the-Art (SOTA) methods for situational awareness typically rely on high-bandwidth transmission of raw data or computationally intensive models, which are often impractical for resource-constrained edge devices in unstable communication environments. To address these limitations, this paper introduces a comprehensive framework for Regional Risk Monitoring utilizing unmanned swarm systems. We propose an innovative knowledge distillation approach (SIKD) that leverages both soft label dark knowledge and inter-layer relationships, enabling compressed models to run in real time on edge nodes while maintaining high accuracy. Furthermore, recognition results are fused using Bayesian inference to dynamically update the regional risk level. Experimental results demonstrate the feasibility of the proposed framework. Quantitatively, the proposed SIKD algorithm reduces the model parameters by 52.34% and computational complexity to 44.21% of the original model, achieving a 3× inference speedup on edge CPUs. Furthermore, it outperforms state-of-the-art baseline methods (e.g., DKD and IRG) in terms of convergence speed and classification accuracy, ensuring robust real-time risk monitoring.