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Jiong Jin - IEEE Xplore Author Profile

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With the intelligence and automation of industrial Internet of Things, a new collaborative Cloud-Fog Automation paradigm has emerged. The emergence of federated learning (FL) has further enhanced the capabilities of Cloud-Fog Automation, making it possible to develop more secure and versatile collaborative industrial models. However, FL faces various security risks. More importantly, the security ...Show More
The Internet of Robotic Things (IoRT) merges the capabilities of robotics with the connectivity and computing power of Internet of Things (IoT) technologies, enabling seamless data collection, processing, and exchange. This integration enhances robotic systems with greater intelligence, mobility, and autonomy, unlocking significant potential across various applications, including sustainable agric...Show More
Flocking control, as an essential approach for survivable navigation of multirobot systems, has been widely applied in fields, such as logistics, service delivery, and search and rescue. However, realistic environments are typically complex, dynamic, and even aggressive, posing considerable threats to the safety of flocking robots. In this article, based on deep reinforcement learning, an Asymmetr...Show More
Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named Learning...Show More
The Internet of Robotic Things (IoRT) has experienced rapid growth and garnered increased attention in recent years. Applications (Apps) play a crucial role in IoRT, as they provide users with an intuitive interface to access and operate services. However, as user demands increase, Apps become more complex, leading to longer operation sequences and more vulnerabilities. The existing testing method...Show More
Efficient data transmission scheduling within vehicular environments poses a significant challenge due to the high mobility of such networks. Contemporary research predominantly centers on crafting cooperative scheduling algorithms tailored for vehicular networks. Notwithstanding, the intricacies of orchestrating scheduling in vehicular social networks both effectively and efficiently remain a for...Show More
Neighborhood area networks (NANs), which connect all communication devices between substations and smart meters, constitute the fundamental last-mile infrastructure for controlling electricity distribution networks. In the absence of a mature NAN infrastructure, selecting communication technologies to meet scaling smart grid application requirements becomes challenging. This article presents a map...Show More
Satellite computing has emerged as a promising technology for next-generation wireless networks. This innovative technology provides data processing capabilities, which facilitates the widespread implementation of artificial intelligence (AI)-based applications, especially for image processing tasks involving deep neural network (DNN). With the limited computing resources of an individual satellit...Show More
Effective last-mile delivery is pivotal in smart logistics system. While existing delivery network architectures, such as Drone-as-a-Service (DaaS), are capable of enhancing order delivery effectiveness, they often fall short in provisioning diverse delivery services. Furthermore, DaaS-based last-mile delivery systems face challenges from limited payload capacity and range. In this paper, we propo...Show More
By melding the capabilities of robotics with the agility of edge computing, Robotic Edge System (RES) exemplifies the next generation of Internet Intelligent Service Systems, delivering incredible efficiency and adaptability across diverse real world applications. Inevitably, RES is susceptible to mechanical disruptions of robots, particularly when some tasks are assigned to faulty ones, leading t...Show More
With the rapid development of artificial intelligence (AI) and the Internet of Things (IoT), intelligent information services have showcased unprecedented capabilities in acquiring and analysing information. The conventional task processing platforms rely on centralised Cloud processing, which encounters challenges in infrastructure-less environments with unstable or disrupted electrical grids and...Show More
With the exponential advancement of beyond fifth/sixth generation (B5G/6G) mobile systems, coupled with the powerful capabilities of the Internet of Things (IoT) driven by artificial intelligence (AI), intelligent information services have become ubiquitous in various environments including the modern smart city. However, providing these intelligent services in infrastructure-less environments, su...Show More
Real-time services and the efficient use of resources in space-air-ground integrated network (SAGIN) environments are complex. It is challenging to enhance the operational efficiency of applications within SAGINs, particularly focusing on edge devices and UAVs that process and analyze data collected from IoT sensors in remote environments. This complexity poses unique issues for application perfor...Show More
With the rapid development of smart cities, the collection of vehicle trajectory data through sensors has increased significantly. While many studies have utilized calibrated physical car-following models (CFM) and machine learning techniques for trajectory prediction, these approaches often falter in complex, dynamic traffic scenarios. Addressing this gap, this paper introduces PS-TrajGAIL, a gen...Show More
Knowledge graph embedding (KGE) that maps entities and relations into vector representations is essential for downstream applications. Conventional KGE methods require high-dimensional representations to learn the complex structure of knowledge graph, but lead to oversized model parameters. Recent advances reduce parameters by low-dimensional entity representations, while developing techniques (e....Show More
Robotic push-grasping in densely cluttered environments presents significant challenges due to unbalanced synergy and redundancy between both actions, leading to decreased grasp efficiency. In this paper, a novel double-critic deep reinforcement learning framework is introduced to optimize the push-grasping synergy for robotic manipulation in such environments, aiming to significantly reduce pre-g...Show More
Presently, resource-constrained devices in manufacturing Internet of Things (MIoT), such as sensors and radio frequency identification (RFID) devices, collect a large amount of privacy-sensitive data. However, weak passwords and vulnerable encryption capabilities in MIoT have often become loopholes of security risks. To this end, this article proposes a novel secure data-sharing scheme based on th...Show More
Space-Air-Ground Integrated Network (SAGIN) has recently emerged as a viable solution for reliable transmission, high data rates, and seamless connectivity with extensive coverage. However, the characteristics of the computation and communication devices located at various levels of SAGIN make application placement within such environments a challenging task. Real-time service expectations and res...Show More
The varying working conditions of rolling bearings lead to data distribution shifts, which presents an obstacle to intelligent fault diagnosis. To alleviate the performance degradation, unsupervised domain adaptation (DA) is used to address the issue where the test set has a different distribution from the training set. Most existing intelligent transfer fault diagnosis methods align features by u...Show More
Incorporating edge and cloud computing with robotics provides extended options for robots to perform real-time sensing and actuation operations in various cyber–physical systems (CPSs), including smart farms. Such systems are prone to uncertain failures triggered by mechanical disruptions. Consequently, the overall system performance degrades, primarily when location-specific tasks are already ass...Show More
The Industry 4.0 digital transformation envisages future industrial systems to be fully automated, including the control, upgrade, and configuration processes of a large number of heterogeneous wired/wireless interconnected devices in Industrial Internet of Things environments. Most of the industrial automation systems today are based on the traditional International Society of Automation (ISA)-95...Show More
Driven by digital twin (DT) technology, the industrial Internet of Things (IIoT) is expanding to open up new frontiers in industrial applications. However, traditional DT modeling approaches require synchronizing massive amounts of data, resulting in high communications overhead and privacy vulnerability. To address this problem, this article proposes a novel DT architecture for IIoT, where the DT...Show More
To address pattern classification problems without involving complex computations, a Hash-Based Convolutional Deep-Thinking Pattern Classifier (HCDTPC) has been developed. This classifier draws inspiration from the human visual system and human thinking logic. The architecture consists of five layers, each serving a distinct purpose. In the initial convolutional layer, informative and distinct fea...Show More
The changes in rotating speed and load of mechanical systems cause the varying working conditions of gearboxes or bearings, so deep learning-based fault diagnosis models face inconsistent distribution of training and test sets, resulting in a great reduction in diagnosis accuracy. To solve this problem, various effective transfer learning-based fault diagnosis methods are proposed in the literatur...Show More
At Presently, it is still a great challenge to achieve online classification of traffic flows due to the highly varying network environments, e.g., unpredictable new traffic classes, network noise, and congestion. Traditional classification methods work well in stable network environments, but may not exhibit their performance in dynamic environments. To address online classification issues, a gra...Show More