Facilitating the collecting and sharing of heterogeneous scientific data among researchers is a key approach to unlocking the full value of data. For data platforms to gain widespread adoption, they must satisfy two criteria simultaneously: (1) allow non-technical researchers to submit personalized data autonomously, and (2) comply with the FAIR principles — Findable, Accessible, Interoperable, and Reusable. To meet these requirements, we propose the Dynamic Container (DC) paradigm, which enables heterogeneous data collection and transforms the data schema designers from database experts to non-technical end users. We further introduce a data representation model that decomposes schemas into familiar components for end users and supports dynamical schema construction through an intuitive drag-and-drop interaction method. We have folded these ideas into a system and evaluated the usability through user studies. The results indicate that our system makes it more usable, efficient and less error-prone for end users to customize schemas.

Multi-agent reinforcement learning (MARL) requires effective coordination among multiple decision-making agents to achieve joint goals. Approaches based on a global value function face the curse of dimensionality, while fully decomposed centralized training with decentralized execution (CTDE) methods often suffer from relative overgeneralization. Coordination graphs mitigate this issue but typically fail to capture dynamic collaboration patterns that evolve over time and across tasks. We propose Dynamic Deep Factor Graphs (DDFG), a value decomposition algorithm that represents the global value via factor graphs and learns graph structures on the fly through a graph-generation policy, adapting to evolving inter-agent relations. We provide a theoretical upper bound on the approximation error of high-order decompositions and reveal how the maximum order $D$D trades off accuracy against computation, offering guidance for balancing performance and cost. Using max-sum for inference, DDFG efficiently derives joint policies. Experiments on higher-order predator-prey and SMAC show consistent gains over strong value-decomposition baselines, demonstrating improved sample efficiency and robustness in complex settings.

Recent advancements in reinforcement learning (RL) have driven progress across various domains; however, RL algorithms often struggle in complex multiagent environments due to challenges such as instability, low sample efficiency, and the curse of dimensionality. Hierarchical RL (HRL) provides a structured framework for decomposing complex tasks into more manageable subtasks, making it a promising approach for multiagent systems. In this article, we introduce a novel hierarchical architecture that autonomously generates effective subgoals without explicit constraints, thereby enhancing both training stability and adaptability. To further improve sample efficiency and adaptability, we propose a dynamic goal-generation strategy that adjusts subgoals in response to environmental changes. Additionally, we address the critical challenge of credit assignment in multiagent settings by integrating our hierarchical architecture with a modified QMIX network, thereby facilitating more effective strategy coordination. Extensive comparative experiments against state-of-the-art RL algorithms demonstrate that our approach achieves superior convergence speed and overall performance in multiagent environments. These results validate the effectiveness and flexibility of our method in handling complex coordination tasks. The implementation is publicly available at https://github.com/SICC-Group/GMAH

In this paper, we introduce a novel cooperative target tracking algorithm, namely the quantum-inspired belief propagation, aimed at rectifying the limitations observed in existing localization algorithms employed in multi-target cooperative tracking scenarios. Leveraging the principles of quantum superposition, our algorithm seeks to alleviate the uncertainty inherent in message fusion within belief propagation frameworks, thereby enhancing the accuracy and stability of multi-target cooperative localization. The utilization of the quantum Monte Carlo method facilitates the simulation of the message distribution process, with quantum particles embodying the superposition of multiple states concurrently. This approach effectively addresses the intractable integrations encountered in message updating on factor graphs, rendering the algorithm agnostic to the number of particles involved. Moreover, quantum unitary transformations and quantum black-box operations are deployed to encode factor graph function nodes for the propagation of quantum messages. This innovation surmounts the challenge posed by traditional factor graph function nodes’ inability to process quantum messages. Experimental findings corroborated the superiority of the proposed algorithm in terms of accuracy and robustness.

The rapid expansion of the Internet of Things (IoT) has brought forth new challenges and opportunities in securely managing and sharing vast amounts of data generated by connected devices. Blockchain technology, with its decentralization, tamper-resistance, and traceability, offers a promising framework for IoT data sharing but struggles to safeguard smart contracts and sensitive data. Integrating trusted execution environments (TEEs) with blockchain addresses these concerns, enabling secure execution and communication via remote attestation. However, existing remote attestation methods face challenges, including incompatibility across heterogeneous TEEs, inefficiency under frequent authentication, and vulnerability to DoS attacks. To tackle these, we propose a blockchain-based unified remote attestation scheme for IoT. Our three-tier blockchain architecture—comprising a certificate authority (CA) channel, an authoritative channel, and a business channel—separates authentication, attestation, and operations while ensuring auditability. An abstraction layer supports heterogeneous TEEs, and an authoritative blockchain stores authentication reports, enabling secure, frequent attestations. Additionally, a distributed CA system enhances resilience to DoS attacks. Experimental results validate our scheme’s efficiency and security, offering a robust solution for IoT data sharing.

In recent years, swarm robot systems have garnered increasing attention, both in the industry and academia. These collaborative systems demand effective solutions for data storage, sharing, and security to unlock their full potential. To address these needs, this paper introduces a comprehensive distributed storage and computation framework based on blockchain and federated learning technology. The framework enables real-time collaborative data storage and computation, ensuring the security and reliability of collective intelligence systems. For data storage, we combine blockchain and dynamic containers to achieve secure and efficient storage of diverse robot data. To facilitate secure data utilization and sharing among robots, we present a federated learning-based collaborative computation approach. It allows robots to exchange model parameters while safeguarding data security, providing a versatile collaborative computation framework for collective systems. To validate the security and resilience of our framework, we present a practical scenario involving multi-agent collaborative localization. We conduct a thorough evaluation of the performance and security of this collaborative localization system, offering valuable insights for researchers in the field of swarm robotics.

Accurate and real-time location tracking is vital for various applications in public safety and the military, particularly in search and rescue missions. Traditional filtering localization algorithms are more effective in linear environments and require precise initial estimates and system noise for optimal results. In complex and unreliable environments, these algorithms often yield poor localization results. To address these issues, this paper proposes a multi-agent collaborative localization algorithm based on reinforcement learning compensation filtering to tackle localization problems in complex environments and improve the robustness and accuracy of the localization algorithm. Specifically, this paper introduces a value decomposition-based reinforcement learning network for filtering compensation to reduce overall localization error and address the credit allocation problem in multi-agent reinforcement learning. This approach reduces the system’s positioning errors and addresses credit allocation issues common in multi-agent reinforcement learning.

In unknown emergency environments, agents use range sensors for localization and mapping, making optimal decisions among numerous uncertain candidate target locations. Exploration graphs can significantly reduce state space dimensionality. By employing deep reinforcement learning models with graph neural networks to predict actions, the agents can autonomously explore unknown environments with varying scales of landmarks. However, the opacity of strategy learning and prediction complicates understanding temporal and spatial correlations of state transitions and state-action relationships. To address this, we propose a multidimensional visualization framework to analyze high-dimensional state-action vector correlations. A post-hoc explanation tool based on feature attribution was designed to create importance sub-graphs, describing causal effects among nodes and explaining the agent’s decision-making. The framework’s effectiveness was validated using correlation analysis, node uncertainty entropy reduction, and navigation performance metrics.

Vision-based autonomous navigation is pivotal in mobile robot technology, involving autonomous movement, path planning, obstacle avoidance, and target reaching in unknown environments. Reinforcement Learning (RL) offers a trial-and-error-driven learning method for autonomous navigation. However, training RL models directly on physical robots is time-intensive and poses potential risks. Simulators accelerate RL training efficiency and reduce costs, but they typically provide only approximate models of robot dynamics and their interactions with the environment. This leads to a simulation-to-reality gap (Sim2Real Gap), where strategies trained in simulation under-perform in real-world applications, sometimes resulting in task failures. To address the issue of reducing the Sim2Real Gap in visual sampling training, this paper establishes a bridging plugin between the simulator and ROS, enabling the subscription to interaction data between the ROS-based robot and its real-world environment. We propose a Sim2Real domain adaptation method based on CycleGAN, which generates effective visual observations for autonomous navigation learning. The experimental results demonstrate that this domain adaptation-based method, by utilizing minimal real-world data, significantly reduces the Sim2Real Gap compared to approaches relying solely on simulation data, achieving a performance improvement of 62.38%.

Material data forms the foundation of the Industrial Internet of Things (IIoT). The rapid advancement of big data technology has opened up new opportunities for material research and development, ushering in the era of data-driven paradigms. As the cornerstone for material genetic engineering technology, the material big data platform is expanding its data scale and facing an increasing demand for sharing in light of the continuous progress and widespread application of big data technology. However, this development also poses security challenges, including the risks of data leakage and tampering. To address these challenges, this article focuses on the National Materials Genetic Engineering Discrete Data Exchange Platform (MGED). It leverages blockchain technology to design a secure material big-data storage and sharing architecture, S-MBDA, ensuring the security and reliability of the material’s big data platform. Additionally, a verifiable retrieval scheme based on a two-layer index structure of bitmap and MPT tree is proposed to enhance the efficiency of blockchain-based retrieval. This scheme aims to guarantee the integrity of retrieval data while achieving efficient and accurate searches across heterogeneous data sources. Through integrating blockchain technology and adopting a novel retrieval scheme, the article presents a comprehensive approach to secure material data storage, sharing, and retrieval. The proposed architecture and scheme address the critical security concerns associated with material big data platforms and contribute to the efficient and accurate retrieval of heterogeneous data.

Collaborative target tracking is an essential task in positioning systems, particularly in environments characterized by high dynamics, multi-source heterogeneous data, and interactive multi-agent scenarios. The challenge in such networks lies in the direct utilization of multi-source heterogeneous data as feature input for models. Additionally, the presence of high-dynamic time series data complicates the extraction of dependencies by the models. To address these issues, we introduce a novel approach that integrates a factor graph-based data fusion method with a graph neural network. This combination is designed to uncover potential dependencies between time series data and positional information within dynamic networks. Furthermore, we employ a self-attention mechanism, enabling distance-agnostic autonomous selection of complex network features. This innovation allows the model to achieve enhanced accuracy performance while simultaneously reducing computational costs. We validated our approach through simulation experiments. The results demonstrated the method’s effectiveness in fusing and selecting multi-source heterogeneous information within collaborative networks. It also excelled in identifying potential relationships between feature information and positional data, showcasing the robustness and applicability of our proposed solution in challenging collaborative target tracking environments.

In modern navigation and positioning systems, accurate location information is crucial for ensuring system performance and user experience. Particularly, in scenarios involving the use of multiple agents such as robots and drones for rescue operations in unknown complex environments, accurate localization is fundamental for subsequent actions. However, traditional filtering-based localization algorithms may exhibit suboptimal performance and are sensitive to initial estimates and system noise. To address these issues, this paper proposes a multi-agent collaborative localization algorithm based on reinforcement learning compensation filtering to tackle localization problems in complex environments and improve the robustness and accuracy. Specifically, this paper introduces a value decomposition-based reinforcement learning network for filtering compensation to reduce overall localization error and address the credit allocation problem in multi-agent reinforcement learning. The main contributions of this paper are as follows: Firstly, a local localization estimation method based on reinforcement learning compensation Extended Kalman Filter (EKF) is proposed, which further corrects the results of the EKF algorithm and eliminates initial estimation errors. Secondly, a global collaborative localization estimation algorithm (MARL_CF) based on credit allocation in multi-agent reinforcement learning is proposed, which maximizes the reduction of overall localization error through information sharing and global optimization. Finally, the effectiveness of the proposed algorithms is validated through both numerical simulation and physical experiments. The results demonstrate that the proposed MARL_CF significantly improve the accuracy and robustness of localization in complex environments.

With the advent of the era of data-driven material R&D, more and more countries have begun to build material Big Data sharing platforms to support the design and R&D of new materials. In the application process of material Big Data sharing platforms, storage and retrieval are the basis of resource mining and analysis. However, achieving efficient storage and recovery is not accessible due to the multimodality, isomerization, discrete and other characteristics of material data. At the same time, due to the lack of security mechanisms, how to ensure the integrity and reliability of the original data is also a significant problem faced by researchers. Given these issues, this paper proposes a blockchain-based secure storage and efficient retrieval scheme. Introducing the Improved Merkle Tree (MMT) structure into the block, the transaction data on the chain and the original data in the off-chain cloud are mapped through the material data template. Experimental results show that our proposed MMT structure has no significant impact on the block creation efficiency while improving the retrieval efficiency. At the same time, MMT is superior to state-of-the-art retrieval methods in terms of efficiency, especially regarding range retrieval. The method proposed in this paper is more suitable for the application needs of the material Big Data sharing platform, and the retrieval efficiency has also been significantly improved.

Real-time high-accuracy localization has a wide range of applications in scenarios, such as pedestrian navigation, emergency rescue, and vehicle networks. In these conditions, the measurement models are often nonlinear, and traditional Kalman and particle filters cannot provide long-time high-precision location-based services. To this end, we propose a Gaussian condensation filter (GCF) algorithm that can achieve high-accuracy localization in a harsh environment. However, aiming at the degradation of sampling points in target tracking based on the GCF, this article proposes a GCF algorithm based on particle flow which transfers the sample points satisfying the prior distribution of the target state to the posterior distribution, thereby improving the practical accuracy of the target-tracking algorithm. Further, to enhance the information fusion in the cooperative network, we propose a multitarget cooperative tracking algorithm to accomplish spatially constrained timing filtering of state information for improving the error correction of the target nodes on timing estimation. Numerical simulations are conducted to determine the effectiveness of our proposed algorithms. Compared with the GCF, its positioning accuracy is improved to 44.6%. Compared with the Gaussian condensation algorithm based on particle flow (PF), the practical accuracy of the GCF algorithm based on cooperative constrained PF in multitarget tracking is improved to 58.1%.

Abrupt-motion tracking is challenging due to the target’s unpredictable action. Although particle filter is suitable for target tracking of nonlinear non-Gaussian systems, it suffers from the problems of particle impoverishment and sample-size dependency. Inspired by quantum mechanics that one quantum bit could represent a superposition of two states, this paper proposes a diversity-preserving quantum-enhanced particle filter (DQPF). Firstly, we quantized the motion modes of particles into a superposition of several modes of motion, resulting in a quantum particle set that retains diversity. Aiming for the abruption of target motion, we propagate the quantum particles during the prediction stage. The quantum particles will already be in these possible positions even if abruption occurs, which addresses the abrupt-motion issue and reduces the tracking delay. Benefitting from quantum mechanics, the proposed particle filter has better precision and stability with fewer particles than the general particle filter. Compared to state-of-the-art, numerical experimental results demonstrate that the proposed DQPF has higher accuracy and stability under the same conditions, displaying superior performance to traditional modified particle filter methods.

Cooperative localization is essential for many Internet of Things (IoT)-related applications in harsh environments. Generally, the inertial navigation system is self-contained and adopted as the basis of a cooperative tracking system, but it still faces the problem of accumulated errors and cannot provide long-term, high-precision positioning. The particle filter (PF) is widely used to fuse multiple information to inhibit accumulative errors. However, particle degradation and impoverishment remain unsolved. This article proposed an IMU/time-of-arrival (TOA) fusion-based tracking method, namely, uncertainty-constrained belief propagation (UCBP). We address particle degradation and impoverishment by introducing uncertainty-constrained optimization into belief propagation (BP). An uncertainty-constrained resampling (UCR) method is applied to quantify the uncertainty in cooperative systems. Hierarchical resampling is realized to solve the particle impoverishment issue. Meanwhile, particle degradation is resolved through constrained resampling while ensuring the diversity of particles. Furthermore, we illustrated the factor graph (FG) structure of UCBP to mitigate the accumulation of errors through message fusion over the graph. Compared with the state-of-the-art methods, our proposed UCBP algorithm has better precision and robustness without introducing much time overhead.

Real-time high-precision navigation has many applications, such as pedestrian navigation, emergency rescue, and vehicle networks. In practice, the measurement models are often nonlinear, and sequential Bayesian filters, such as Kalman and particle filter, suffer from accumulative errors, which cannot provide long-time high-precision services for localization. To solve arbitrary noise distribution, this article proposes a Gaussian condensation filter (GCF) algorithm to achieve high-precision localization in a non-Gaussian noise environment. To this end, we proposed an error-ellipse resampling (EER)-based GCF (EER-GCF), which establishes error ellipses with different confidence probabilities and implements a resampling algorithm based on the sampling points’ geometrical positions. Furthermore, a cooperative EER-based GCF (CEER-GCF) is proposed to enhance information fusion in the multitarget network. This study accomplishes cooperative tracking based on spatial–temporal constraints to enhance error correction. The experimental results show that CEER-GCF can effectively eliminate the accumulative error and optimize state estimation, which outperforms state of the arts, such as unscented Kalman filter and particle filter.

Human motion tracking (HMT) has been a research focus in the last decades. In this paper, we propose an IMU/TOA-fusion-based platform to solve this problem. Firstly, Time-of-arrival (TOA)-based distance ranging method is considered to compensate for the drifting errors and accumulation introduced by inertial sensors. Secondly, a geometrical kinematic model and maximum correntropy criterion (MCC)-based Kalman filter method are proposed to fuse the multiple information. The open-source hardware and software are detailed in this paper for real-time human motion capture and reconstruction applications. Experiment results show that our proposed hardware can be easily equipped for total body motion reconstruction with a considerable enhancement of the wear-ability and comfort. Furthermore, the main achievements have been presented with a performance comparison between the proposed platform and state-of-the-art commercial ones. Above all, our proposed platform can significantly suppress the accumulative error and drifting problem of conventional inertial systems. More importantly, it realizes the open-source software and hardware, thus it has promising prospects for wearable human motion tracking applications.

Ultra-Wideband (UWB) technology has great potential for the cooperation and navigation among near ground mobile robots in GPS-denied environments. In this paper, an efficient two-segment UWB radio channel model is proposed with considering the multi-path condition in very near-ground environments and different surface roughness. We conducted field measurements to collect channel information, with both transmitter and receiver antennas placed at different heights above the ground: 0cm-20cm. Signal frequency was chosen at 4.3GHz with bandwidth of 1GHz. Three ground coverings were tested in common scenarios: brick, grass and robber fields. The proposed model has enhanced accuracy achieved by careful assessment of dominant propagation mechanisms in each segment, such as diffraction loss due to obstruction of the first Fresnel zone and higher-order waves produced by ground roughness. It is realized that antenna height and distance are the most influential geometric parameters to affect the path loss model. Once the antenna height is known, there exists a breakpoint distance in UWB propagation, which separates two segmentation using the different path-loss mechanism. Different surface types can cause different signal attenuation. Monte Carlo simulations are used to investigate the effects of antenna height, distance, ground surface type on mobile robots swarm communication to find out the antenna height is also a dominant factor on connectivity and the average number of neighbors. Within a certain range, the higher the antenna height and the closer the communication distance, the better the communication performance will usually be. Cramér-Rao lower bound(CRLB) of path loss estimator based on the proposed model is derived to show the relationship between CRLB with height and distance.

Near ground localization is a special type of localization scenario that is widely considered in both academic and industry. A proper way to describe time-of-arrival (TOA) ranging error for near ground localization applications has been in a strong demand since early 2010s. In this paper, we proposed a mathematical model for near ground TOA ranging error based on empirical measurement. According to investigation and analysis on the empirical data, the TOA ranging error can be modeled as a Gaussian random variable and its mean and variance depends on both the antenna height and the distance between reference node and target node. To further emphasize the value of this model, Cramer-Rao Lower Bound for two dimensional (2D) near ground localization has been calculate using the proposed error model as a typical case study. It is shown that the near ground TOA ranging error model is essential for algorithm design and system performance evaluation.