Digital twin (DT) has been widely adopted in intelligent transportation systems (ITS) to synchronize infrastructure and traffic-participant states with their cyberspace representations. It supports real-time monitoring, analysis, and innovative services at the network scale. However, system efficiency and scalability constraints remain to be addressed for DTs in high-mobility traffic scenarios. DT schemes must support frequent model updates, resulting in significant end-to-end latency for vehicle queries and increased communication overhead, which severely degrades system stability and reliability. To address these challenges, we propose a vision-based lightweight digital twin scheme, CV-LDT, for real-time semantic traffic-state representation from the physical space to the cyberspace. CV-LDT integrates monocular 3D detection with multi-source roadside infrastructure to construct an RSU-side semantic occupancy map, enabling geometry-aware and lane-level modeling beyond GPS-based point localization. A credibility-aware fusion mechanism supports consistent multi-view integration within an intersection, and an aggregation strategy supports cross-RSU synchronization at the cloud layer. Experiments on deployed RSUs with real-world data from the Yizhuang autonomous driving test zone in Beijing are complemented by large-scale simulations. The results demonstrate that CV-LDT achieves lower DT update latency and vehicle query latency, as well as reduced cloud communication overhead. Results from real-world scenarios confirm the practical effectiveness and scalability of CV-LDT.

Autonomous vehicles and intelligent transportation systems (ITS) are becoming a reality, demanding secure and reliable incident management. Blockchain has emerged as a promising solution for ensuring data integrity and traceability. However, integrating blockchain into V2X systems poses real-time processing system reliability challenges, as Roadside Units (RSUs) must simultaneously support latency-sensitive services and resource-intensive consensus protocols. These concurrent demands risk overloading RSUs and compromising system reliability, especially in safety-critical scenarios.In this paper, we propose a task-sharding mechanism for blockchain-integrated V2X incident management. Our method comprehensively shards both blockchain operations and V2X services, leveraging Software-Defined RSUs (SD-RSUs) to allocate resources to distinct service shards. Since RSUs handle varying data processing loads for tasks with different urgency levels, we introduce a QoS-driven role assignment algorithm that accounts for fluctuating workloads and service-specific latency constraints, taking into account practical deployment considerations.The proposed framework is validated using existing RSU deployments, real-world V2X incident data, and city-scale simulations. Results show significant improvements in processing efficiency, reduced latency, and reliable operation under load. These findings confirm the method’s practical potential for improving safety and resilience in large-scale ITS deployments.

Ensuring security and trustworthiness in large-scale Internet of Things (IoT) systems is critical, particularly with the increasing deployment of fog and edge computing (FEC) to support complex and distributed environments. While traditional security mechanisms effectively mitigate external threats, FEC-enabled IoT networks remain vulnerable to internal attacks that compromise system integrity and performance. This paper presents a unified, data-centric trust management framework featuring dynamic trust evaluation and propagation mechanisms. The framework establishes essential trust principles specifically designed for IoT environments, including dynamicity, context-dependence, fragility, objectivity, composability, and transitivity. It implements the principles through a layered trust model, assessing trust in entities based on their transmission behavior, data credibility, and historical records, thereby enabling a comprehensive and adaptive evaluation of trust. Additionally, we introduce the concept of community trust for edge nodes, which aggregates the trust of connected sensors using an optimized information entropy approach, ensuring robust inter-layer trust propagation. Simulations are conducted using a real-world dataset from an edge-based environmental monitoring system along the Wuyu Expressway in China. Results demonstrate that the proposed model significantly improves accuracy, reducing both false positive and false negative rates compared to existing methods. The proposed framework has been validated in real-world FEC-enabled IoT deployments, providing practical and interpretable trust metrics for industrial use. The quantitative analysis highlights the model's adaptability, sensitivity, and reliability against multiple attack scenarios, providing a practical solution for enhancing security and trust management in complex FEC-enabled IoT deployments.

Recent advancements in Cyber-Physical Systems (CPS) and Digital Twins (DT) offer significant benefits in smart cities and industrial automation. However, integrating these technologies with physical space introduces numerous security threats. Towards these challenges, we propose an edge-enabled data-centric dynamic trust evaluation model for perception nodes. This model integrates real-time metrics such as node interaction behaviors and quantified data accuracy with historical trust records, facilitating dynamic assessment of node reliability. Moreover, the model reduces the risk of misidentifying incidental anomalies as malicious nodes, thereby mitigating the impacts of inaccurate data and anomaly interaction behavior. Experimental validation using data from weather information system (RWIS) units along the Wuyu Highway in Shanxi Province demonstrates the model’s effectiveness in detecting anomalies like packet loss, replay attacks, data modification, and on-off attacks, ensuring system integrity.

Internet of Things (IoT) connects more and more ubiquitous physical objects into cyber space to enable the cyberphysical space convergence. The ubiquitous access, uniform identification and comprehensive cognition of physical objects have become the basic challenges of IoT-enabled cyber-physical convergence space. In order to solve these three problems, we propose an intelligent IoT service support platform in this paper. Firstly, we propose an edge computing-based open IoT system architecture to satisfy the real-time process analytics and bandwidth requirements demanded by ubiquitous access of large-scale IoT objects. Then the enabling technologies of edge computing-based object identification and resolution are proposed to solve the uniform identification and identity consistency problems of IoT objects accessing into cyber space. Thirdly, the enabling technologies of knowledge graph-based object description modeling and discovery system are also proposed to realize the comprehensive digital twins modeling of physical objects, and discover suitable objects to meet application requirements. Finally, combining the Industrial IoT (IIoT) application, an illustrative use case is introduced. The proposed approach can improve the capacities of physical objects to be accessed, recognized and cognized by cyber space so that smart IoT applications and edge services can be supported.

The Industrial Internet of Things (IIoT) enables the improvement of the productivity and intelligent level of factory. The procedure of product quality inspection has generally adopted machine intelligence algorithms instead of manual operation to improve efficiency. In this paper, we propose a product quality inspection system scheme based on software-defined edge intelligent controller (SD-EIC). By adopting the software definition and resource virtualization technologies, the hardware platform of SD-EIC is designed to support the real-time control tasks and non-real-time edge computing tasks at the same time. To this end, we propose the scheme and architecture of product quality inspection system based on SD-EIC. Multiple virtual controllers and virtual edge computing nodes are constructed on a set of SD-EIC hardware platform to realize the integrated deployment of the real-time control for terminal devices and the AI model reasoning of product defect recognition algorithm based on machine vision respectively. In addition, the management and control scheme of product quality inspection system based on industrial information model is proposed. By constructing the semantic-based digital twin information model of terminal device, the flexible adjustment and parameter configuration of terminal device are realized to meet the demands of flexible production and manufacturing. The proposed product quality inspection system solution can effectively improve the utilization of hardware resources and the efficiency of product quality inspection, and reduce the overall deployment cost of the system. It can flexibly adapt to product diversity and different industrial scenarios.

The Industrial Internet of Things (IIoT) enables the improvement of the productivity and intelligent level of factory. The procedure of product quality inspection has generally adopted machine intelligence algorithms instead of manual operation to improve efficiency. In this paper, we propose a product quality inspection system scheme based on software-defined edge intelligent controller (SD-EIC). By adopting the software definition and resource virtualization technologies, the hardware platform of SD-EIC is designed to support the real-time control tasks and non-real-time edge computing tasks at the same time. To this end, we propose the scheme and architecture of product quality inspection system based on SD-EIC. Multiple virtual controllers and virtual edge computing nodes are constructed on a set of SD-EIC hardware platform to realize the integrated deployment of the real-time control for terminal devices and the AI model reasoning of product defect recognition algorithm based on machine vision respectively. In addition, the management and control scheme of product quality inspection system based on industrial information model is proposed. By constructing the semantic based digital twin information model of terminal device, the flexible adjustment and parameter configuration of terminal device are realized to meet the demands of flexible production and manufacturing. The proposed product quality inspection system solution can effectively improve the utilization of hardware resources and the efficiency of product quality inspection, and reduce the overall deployment cost of the system. It can flexibly adapt to a variety of different industrial scenarios.

The Industrial Internet of Things (IIoT) enables interconnection and intelligent collaboration among basic industrial production factors which include human, machine, thing, method and environment. In the current IIoT applications, it is difficult for collaborative optimization and unified management and control of industrial production factors. Applications and industrial production factors are tightly coupled, so that many industrial software applications generally have the problems such as high degree of customization and difficult replication and promotion. In this paper, focusing on the most basic industrial production factors, we propose the solution and system architecture of software-defined Industrial Internet of Things (SD-IIoT) based on the ideas and technologies of software definition and Cyber-Physical System (CPS). The principle of SD-IIoT is introduced from the perspective of cyber-physical space mapping. On basis of the digital twin models of industrial production factors, the system architecture of SD-IIoT is designed, which decouples upper-level industrial applications from the underlying industrial production factors. Furthermore, the software definition mechanism based on industrial information model is proposed to abstract and describe industrial production factors with semantic technology to implement the virtualized modeling. The SD-IIoT paradigm can maximize the utilization of resources, and achieve the modular management, on-demand reusing, dynamic reconfigurability and efficient collaboration of industrial production factors, thus improving overall service capability of IIoT.

The Industrial Internet of Things (IIoT) enables intelligent interaction and automated collaboration among industrial production factors (i.e., human, machine, thing, method and environment) to improve productivity and intelligent level of factory. The industrial intelligent control system is the basis for realizing IIoT. It can enable industrial production with the abilities of autonomous decision-making and system autonomy. As an extension and expansion of the industrial cloud platform capabilities, edge computing can support industrial intelligent control with low-latency, high-reliability, and high-security edge intelligent services. Combined with the ideas and technologies of edge computing, software definition and Cyber-Physical System (CPS), we propose the solution and framework of software-defined industrial intelligent control (SDIIC) to realize intelligent control based on edge computing from two levels of software and hardware. At the software level, we propose the scheme of industrial intelligent control oriented software-defined edge computing (SDEC) platform to realize the intelligent and flexible management and autonomous coordination of edge devices. At the hardware level, the architecture and key technologies of the software-defined edge controller are proposed. The software-defined virtual controller with differentiated control and computing capabilities is implemented on the general standardized hardware resources. It can support both real-time industrial control and non-real-time edge computing task processing. The SDEC platform and the software-defined edge controller enable the SDIIC solution to realize industrial system autonomy and intelligent control.

Edge computing is a bridge for realizing the convergence between physical space and cyber space in the Internet of Things (IoT) paradigm. Large numbers of physical objects produce a huge amount of data that needs to be efficiently processed in the edge side. This situation urgently requires novel ideas and framework in the design and management of edge computing to improve and enhance its performance. In this article, we propose an approach and principle of software-defined edge computing (SDEC) from the perspective of cyber-physical mapping, where the ultimate goal is to achieve a highly automatic and intelligent edge computing system. The SDEC can also help realize flexible management and intelligent collaboration among various edge hardware resources and services by way of software. To this end, we design an SDEC-based open IoT system architecture which decouples upper level IoT applications from the underlying physical edge resources and builds dynamically reconfigurable smart edge services. The software-definition mechanism of the SDEC platform is proposed to introduce the detailed processes that the underlying physical devices are defined in the form of software. We also describe an illustrative application case about smart factory to present the practical effectiveness of the proposed scheme. Finally, we outline several challenges which are worthy of in-depth study and research. The SDEC paradigm can share, reuse, recombine, and reconfigure edge resources and services so that the overall service capability of the edge side can be improved.

The Internet of Things (IoT) connects more and more devices and supports an ever-growing diversity of applications. The heterogeneity of the cross-industry and cross-platform device resources is one of the main challenges to realize the unified management and information sharing, ultimately the large-scale uptake of the IoT. Inspired by software-defined networking, we propose the concept of software-defined device (SDD) and further elaborate its definition and operational mechanism from the perspective of cyber-physical mapping. Based on the device-as-a-software concept, we develop an open IoT system architecture which decouples upper-level applications from the underlying physical devices (Physical-D) through the SDD mechanism. A logically centralized controller is designed to conveniently manage Physical-D and flexibly provide the device discovery service and the device control interfaces for various application requests. We also describe an application use scenario which illustrates that the SDD-based system architecture can implement the unified management, sharing, reusing, recombining, and modular customization of device resources in multiple applications, and the ubiquitous IoT applications can be interconnected and intercommunicated on the shared Physical-D.

Edge computing is a bridge for realizing the convergence between physical space and cyber space. Large numbers of physical objects produce a huge amount of data that needs to be efficiently processed in the edge side. This situation urgently requires novel ideas and framework in the design and management of edge computing to improve and enhance its performance. In this paper, we propose an approach and principles of Software-Defined Edge Computing (SDEC) from the perspective of cyber-physical mapping, where the ultimate goal is to achieve a highly automatic and autonomous edge computing system. And SDEC can also help realize flexible management and intelligent collaboration among various edge hardware resources and services by way of software. To this end, we design a SDEC-based open edge computing system architecture which decouples upper-level applications from the underlying physical edge resources and builds dynamically reconfigurable smart edge services. This approach can share, reuse and recombine edge resources and services so that overall service capability of the edge side is improved. Finally, we outline several challenges which are worthy of in-depth study and research.

Smart home technology has gained more and more attention in recent years as a part of internet of things, by making living environments smarter. The technology consists of deploying different sensors and actuators and making devices intelligent inside homes. Unfortunately, the heterogeneity of devices makes sharing and reusing the collected data difficult, while the complex relation among the user, device, location, and observation within the smart home has emerged new challenges in device search. Firstly, we proposed an ontology-based smart home domain model (SHOM), secondly, we proposed a device search framework in the smart home (DSF-SH), in which rule-based reasoning, ontology-based query expansion, and Tongyici Cilin-based query expansion are combined in order to overcome the limitation of keyword-based search technique, interpret the meaning of user's queries and help upper applications find available devices by working as a middleware. Finally, a prototype is developed to demonstrate the feasibility of the DSF-SHe The results show that it significantly increases the recall, the precision, and the F-score as compare to the keyword-based search technique.

The identification and resolution technology are the prerequisite for realizing identity consistency of physical-cyber space mapping in the Internet of Things (IoT). Face, as a distinctive noncoded and unstructured identifier, has especial advantages in identification applications. With the increase of face identification based applications, the requirements for computation, communication, and storage capability are becoming higher and higher. To solve this problem, we propose a fog computing based face identification and resolution scheme. Face identifier is first generated by the identification system model to identify an individual. Then, a fog computing based resolution framework is proposed to efficiently resolve the individual's identity. Some computing overhead is offloaded from a cloud to network edge devices in order to improve processing efficiency and reduce network transmission. Finally, a prototype system based on local binary patterns (LBP) identifier is implemented to evaluate the scheme. Experimental results show that this scheme can effectively save bandwidth and improve efficiency of face identification and resolution.

In the Internet of Things scene, the wireless sensor network (WSN) is widely used to monitor and perceive various context environments. The efficient utilization of time division multiple access (TDMA) slot resource has attracted more and more attention, especially for applications with high network performance requirements, for example, vehicular networks. The characteristics of the WSN, which have limited battery volume and variable topology structure, restrict the development of the centralized time slot allocation algorithm. Moreover, the traditional distributed time slot allocation algorithm is helpless to reduce the energy consumption, even if the variable topology is solved to some extent. In this paper, we propose the distributed TDMA scheduling algorithm based on exponential backoff rule and energy-topology factor, namely EB-ET-distributed randomized (DRAND) algorithm. We analyze the typical DRAND time slot assignment algorithm and the distributed TDMA slot scheduling algorithm based on energy-topology factor, which is proposed in our another work. By introducing the idea of Lamport's bakery algorithm, the priority control algorithm based on exponential backoff rules and energy-topology factor are presented to appropriately adjust the priority of time slot allocation and greatly reduce the probabilities of message collision and time slot allocation failure. Then, we introduce the implementation processes of the EB-ET-DRAND scheduling algorithm in various different states. The time slot structure and frame formats of algorithm are designed in detail. Finally, we implement a mesh network simulation system to evaluate the performance of proposed scheme. The experimental results indicate that the EB-ET-DRAND scheduling algorithm greatly improves the performance of time slot allocation and reduces the message complexity, time complexity, and energy consumption.

Face identification and resolution technology is crucial to ensure the identity consistency of humans in physical space and cyber space. In the current Internet of Things (IoT) and big data situation, the increase of applications based on face identification and resolution raises the demands of computation, communication, and storage capabilities. Therefore, we have proposed the fog computing-based face identification and resolution framework to improve processing capacity and save the bandwidth. However, there are some security and privacy issues brought by the properties of fog computing-based framework. In this paper, we propose a security and privacy preservation scheme to solve the above issues. We give an outline of the fog computing-based face identification and resolution framework, and summarize the security and privacy issues. Then the authentication and session key agreement scheme, data encryption scheme, and data integrity checking scheme are proposed to solve the issues of confidentiality, integrity, and availability in the processes of face identification and face resolution. Finally, we implement a prototype system to evaluate the influence of security scheme on system performance. Meanwhile, we also evaluate and analyze the security properties of proposed scheme from the viewpoint of logical formal proof and the confidentiality, integrity, and availability (CIA) properties of information security. The results indicate that the proposed scheme can effectively meet the requirements for security and privacy preservation.

The emergence of Internet of Things (IoT) has enabled the interconnection and intercommunication among massive ubiquitous things, which caused an unprecedented generation of huge and heterogeneous amount of data, known as data explosions. On the other hand, although that cloud computing has served as an efficient way to process and store these data, however, challenges, such as the increasing demands of real time or latency-sensitive applications and the limitation of network bandwidth, still cannot be solved by using only cloud computing. Therefore, a new computing paradigm, known as fog computing, has been proposed as a complement to the cloud solution. Fog computing extends the cloud services to the edge of network, and makes computation, communication and storage closer to edge devices and end-users, which aims to enhance low-latency, mobility, network bandwidth, security and privacy. In this paper, we will overview and summarize fog computing model architecture, key technologies, applications, challenges and open issues. Firstly, we will present the hierarchical architecture of fog computing and its characteristics, and compare it with cloud computing and edge computing to emphasize the similarities and differences. Then, the key technologies like computing, communication and storage technologies, naming, resource management, security and privacy protection are introduced to present how to support its deployment and application in a detailed manner. Several application cases like health care, augmented reality, brain machine interface and gaming, smart environments and vehicular fog computing are also presented to further explain fog computing application scenarios. Finally, based on the observation, we propose some challenges and open issues which are worth further in-depth study and research in fog computing development.

The emergence of Internet of Things (IoT) has enabled the interconnection and intercommunication among massive ubiquitous things, which caused an unprecedented generation of huge and heterogeneous amount of data, known as data explosions. On the other hand, although that cloud computing has served as an efficient way to process and store these data, however, challenges, such as the increasing demands of real time or latency-sensitive applications and the limitation of network bandwidth, still cannot be solved by using only cloud computing. Therefore, a new computing paradigm, known as fog computing, has been proposed as a complement to the cloud solution. Fog computing extends the cloud services to the edge of network, and makes computation, communication and storage closer to edge devices and end-users, which aims to enhance low-latency, mobility, network bandwidth, security and privacy. In this paper, we will overview and summarize fog computing model architecture, key technologies, applications, challenges and open issues. Firstly, we will present the hierarchical architecture of fog computing and its characteristics, and compare it with cloud computing and edge computing to emphasize the similarities and differences. Then, the key technologies like computing, communication and storage technologies, naming, resource management, security and privacy protection are introduced to present how to support its deployment and application in a detailed manner. Several application cases like health care, augmented reality, brain machine interface and gaming, smart environments and vehicular fog computing are also presented to further explain fog computing application scenarios. Finally, based on the observation, we propose some challenges and open issues which are worth further in-depth study and research in fog computing development.