Robot skill reconfiguration often disrupts system continuity in dynamic flexible production systems, particularly in mixed-model assembly. To address this challenge, this article proposes behavior tree and action primitive (BTAP) enabled low-code/no-code program method for robot task execution, overcoming the limitations of traditional static programming for robot operation. First, the proposed method establishes a genes-inspired modular recombination mechanism, in which action primitives are structured like DNA-sequence components. Ontology Web Language is utilized to formally characterize the relational logic among action primitives, which helps form the primitive library. This library supports genes-inspired operations, such as crossover and mutation, for dynamic skill generation. Next, planning domain definition language-to-behavior tree (BT) conversion algorithm is developed to automatically transform high-level instructions into executable BTs, achieving automated mapping from task logic to robotic sequential behaviors. Then, parametric meta-learning approach is proposed, which integrates adaptive parameter space contraction with Bayesian optimization. This method narrows the search space through historical task feature retrieval and optimizes action parameters using Gaussian process surrogate models coupled with Expected Quantile Improvement function. Finally, the proposed BTAP method is verified on a multirobot motor assembly line. Results demonstrate rapid convergence to a 95.46% success rate within 15 training cycles for the TwoArmPegInsertion, while consistently maintaining contact forces and torque fluctuations well below the safety thresholds.

In practical engineering, the non-availability of fault samples is a serious impediment to the construction of high-precision fault diagnosis models. To overcome this challenge, a Principal Component Analysis - Time-Frequency - Residual Network (PCA-TF-ResNet) fault data generation network is proposed in this paper. The network integrates fault failure mechanisms and time-frequency features to generate fault data using normal data. The generated data closely resemble real fault data, thus supporting the training of large fault diagnosis models. First, the Principal Component Analysis (PCA) is applied to normal data to extract key time-frequency features, which are used as sensitive feature factors. Second, a loss function is designed for the generation network that combines sensitive feature indicators and the weighted fault mechanisms of different fault types to guide the generation of fault data. Third, fault data corresponding to two typical faults are generated from the normal data using the PCA-TF-ResNet fault data generation network. Subsequently, these generated data are used to train fault diagnosis models. Last, the performance of the proposed method is validated by experiments conducted under various fault diagnosis models. The results demonstrate that the generated fault data can effectively simulate real fault characteristics, exhibiting good diagnostic performance.

As a key smart manufacturing technology, the digital twin has consistently received attention for its potential applications in smart workshops. Although various digital twin software are available with their unique characteristics, they are often unable to simultaneously achieve real-time data access, physical device control, model customization, and function expansion in the complex and changing environment of smart workshops, making it difficult to meet the diverse needs of users. This paper presents a novel dual-mode scalable digital twin software--FactoryTwin, for smart workshops based on open-source technology. This software simulates smart workshops and enables real-time monitoring and online control of equipment in physical spaces. Moreover, the customizability of the software model and the scalability of its functions are ensured. The smart production line was utilized to verify the effectiveness of the software functions. This approach is expected to provide a reference for the development of digital twin software in the future.

Inadequate resource coordination and control can result in poor quality of experience (QoE) for user devices in heterogeneous edge-enabled cyber-physical systems. Unfortunately, in a cooperative edge network, existing studies have rarely jointly optimized communication, computing resources, and batch size for QoE guarantee when controlling task offloading. To this end, we investigate the problem of harnessing bandwidth, computation, and batch size for fair quality of experience (HARBOR) in a practical collaborative edge-AI environment, where UEs have different accuracy requirements of inference services and edge devices possess different batch processing capabilities. Specifically, we introduce the task completion efficiency as the task-completion-time-to-deadline ratio to quantify individual QoE. Then, we formulate the problem HARBOR as a mixed integer nonlinear programming with constraints of accuracy, bandwidth, computation, task hard deadlines and so on. The objective is to minimize the maximum task completion efficiency among all tasks to achieve task-level fairness. After providing the NP-hardness proof for HARBOR, we then devise an efficient scheme named e-HARBOR with a competitive ratio guarantee, to solve the decoupled sub-problems of HARBOR with calibrated long short-term memory network for resource prediction. Both testbed and simulation experiments evidently demonstrate that the proposed scheme works efficiently and scales well compared to baselines.

To address the issue in zero-shot fault diagnosis (ZSFD) where fault attribute definitions (FADs) rely heavily on manual design and the accuracy of FAD depends on the expertise of developers, this article embedded expert knowledge into deep learning network, proposed a ZSFD method based on depth correlation feature extraction network (DCFEN), and automatically constructed FAD. Taking advantage of the periodic characteristics of bearing fault signals and the advantages of correlation analysis operation (CAO) in periodic signal analysis, DCFEN extracts the periodic characteristics of input signals in multiple dimensions by integrating CAO with deep learning. In addition, a soft-threshold-based feature percolation mechanism and FAD evaluation function are designed to generate the attributes related to bearing faults. The experimental results show that the FADs established by DCFEN are accurate, and the fault diagnosis performance of the proposed ZSFD is superior to the existing methods in unseen scenarios.

In real industrial scenarios, fault data are characterized by class imbalance, a major challenge for data-driven intelligent fault diagnosis. This article proposes a novel three-level regularization framework that integrates data, models, and labels to diagnose the imbalanced fault. First, a signal to image (S2I) module is introduced, which converts 1-D signals into 2-D images to conduct research and reduce model development workload and model-specific dependencies. Then, a regularization framework is proposed consisting of three submodules, inner local feature regularization (ILFR), outer local feature regularization (OLFR), and class balance margin loss (CBML), improving the faulty health state recognition accuracy without degrading the normal health state recognition performance. Finally, adequate experiments are carried out on four mechanical fault datasets. The results show that under the extremely imbalanced conditions, the proposed framework can improve the accuracy of the baseline method by 28%, 38%, and 25% on the three datasets (including PU, JNU, and UoC), respectively. Moreover, the proposed framework outperforms the SOTA method on the CWRU dataset, which validates the effectiveness and superiority of the proposed framework.

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 paper proposes a novel secure data-sharing scheme based on the integration of blockchain and fusion of both real and fake data to address the data security requirements of resource-constrained MIoT devices. First, a computing resource collaboration architecture is designed, thereby enabling these devices to interface with multiple devices with full resource to implement flexible resource scheduling. Then, a resource-assistance mechanism is devised through blockchain-based smart contracts by utilizing the idle resources of full nodes to complement the computational tasks launched by resource-constrained nodes. In addition, polygon semantic rules are proposed to improve the security of private data. Subsequently, real data artifacts are generated by data tampering to achieve privacy cover, avoiding the consumption of computing resources in traditional encryption algorithms. Finally, the feasibility of the proposed scheme is verified on a customized candy production line. The experimental results validate that data protection using polygon semantic rules can prevent actual data from being peeped. Moreover, the results also indicate that the proposed method can obtain resource assistance from other nodes through the resource compensation mechanism.

With the development of artificial intelligence, machine vision technology based on deep learning is an effective way to improve production efficiency. Because of the rapid update of the automobile manufacturing industry and the large variety of products, the learning time and the number of learning samples of the deep learning model are limited, which brings great difficulties to the recognition of components. Therefore, considering the economic benefits of enterprises, this paper proposes an intelligent component recognition method appropriate for small datasets, aiming to explore an automatic system for component recognition suitable for industrial manufacturing environments. The method completes the generation of the dataset through the system architecture with the potential for automation and the image cropping method based on feature detection and then designs a deep learning network based on coarse‐fine‐grained feature fusion to generate an intelligent recognition model of components. Finally, the designed network achieves an accuracy of 95.11%, and compared with the traditional classical network on multiple datasets, the designed network has better performance. Thus, the proposed method can improve the production flexibility of the automobile manufacturing industry and improve equipment intelligence.

Since the underground transportation of coal mainly relies on the mine conveyor belt to complete, the mine conveyor belt with large pieces of coal will affect transportation safety. Therefore, to address the problem of real‐time monitoring of lump coal, the method Ghost‐ECA‐Bi FPN (GEB) YOLOv5 for lump coal in the process of mining conveyor belt transportation is proposed based on a lightweight neural network and multisource information fusion. First, the image preprocessing is performed by adaptive histogram equalization, which reduces the influence of coal dust, dust, and uneven lighting on target monitoring. Second, the redundancy of the convolution process is exploited, and a lightweight neural network GhostNet is introduced to optimize the feature extraction process. In addition, combined with the efficient channel attention mechanism, the 1D convolution enables local cross‐channel information interaction, which can solve the problem of imbalance between model complexity and performance. Finally, the feature information of the three stages is fused using a weighted bidirectional feature pyramid network to enhance the generalization ability of the model. The experimental results show that the improved GEB YOLOv5 algorithm has obvious advantages. In terms of model structure, the number of network layers reduces by 36.97%, and the number of model structure parameters and floating‐point operations reduce by 64.53% and 69.14%, respectively. Moreover, the model volume reduces from 92.7 M to 33.0 M. Regarding the monitoring performance, the precision and recall rates improve by 1.19% and 1.11%, respectively. Furthermore, the real‐time performance improves from 68.34 FPS to 110.70 FPS. It can be seen that the problem of the model performance against the model complexity is effectively solved in this experiment and the real‐time monitoring of lump coal is realized.

End-to-end intelligent diagnosis of rotating machinery under speed fluctuation and limited samples is challenging in industrial practice. The existing limited samples methods usually focus on the data distribution or learning strategy with particularity. Generative adversarial network (GAN) provides a data generation solution with portability in fault diagnosis with limited samples. However, GAN has problems with gradient vanishing, weak extraction of global features, and redundant training. This article proposes a dual-threshold attention-guided GAN (DTAGAN) to generate high-quality infrared thermal (IRT) images to assist fault diagnosis. First, Wasserstein distance and gradient penalty are combined to design loss function to avoid gradient vanishing. Second, attention-guided GAN is constructed to extract global thermal-correlation features of IRT images. Finally, dual-threshold training mechanism is developed to improve the generation quality and training efficiency. The comparative experiments show that DTAGAN is superior to comparison methods in fault diagnosis of rotor-bearing system under speed fluctuation and limited samples.

Although deep neural networks (DNNs) have been increasingly applied in industrial cyber physical systems (ICPSs), they are vulnerable to security attacks due to the tight interaction between cyber elements and physical elements. In this article, we aim to protect the core IP of DNNs, i.e., the model weights, against security attacks. Different from conventional approaches, a layerwise protection framework is proposed to ensure the confidentiality of DNN model weights during the inference procedure such that the security quality is maximized, while satisfying the latency constraint of the DNN task. Based on the layerwise execution characteristics of DNN tasks, the encrypted layer-related weights are decrypted and fed to the next layer of DNN in plaintext. CPU-field programmable gate array (FPGA) coscheduling is considered to accelerate the execution of confidentiality protection, where CPU is utilized to conduct the decryption of weights and FPGA is used to perform the layer execution of DNN. Considering to provide optimal confidential protection for each layer, the problem is transformed into a quality of security maximization problem subject to layerwise execution constraint and deadline constraint of the DNN application. Due to the problem being NP-hard, a fast approximation algorithm is proposed to obtain the near-optimal solution under given real-time and security constraints. Extensive experiments and a real-life ICPS application evaluate the efficiency of the proposed techniques.

The routing algorithm based on a single routing metric parameter is difficult to meet the Quality of Services (QoS) requirements of multi-source data flows in the software-defined factory heterogeneous network, resulting in link congestion and waste of network resources. To solve the problem, this paper proposes a QoS optimization method for software-defined factory heterogeneous networks based on the double deep Q network (DDQN). First, a QoS optimization architecture is proposed for software-defined factory heterogeneous networks, and the optimization function for network latency and load balancing is established. Then, the DDQN algorithm is used to obtain the optimal paths of multi-source data flows, and the optimal path is uniformly issued by the software-defined network controller. Experiments showed that the proposed algorithm has good convergence and generalizability. Compared with the existing algorithms, it outperforms in network latency, network jitter, and network throughput, improving network load balancing and routing efficiency. This research is able to provide a solution to the QoS optimization and dynamic traffic scheduling in smart heterogeneous networks and provide a technical reference point for realizing customized smart factories.

To increase the flexibility and the dynamism of communication networks, Software Defined Networking (SDN) has emerged as a challenging approach to decouple control and data planes, using a logically centralized controller able to manage the underlying network resources. However, traditional network solutions can not be always used in SDN. In this paper, we deal with routing issues in the setup of dynamic SDN spanning Fog/Edge and IoT systems for supporting the new generation of applications. In particular, we present a modified version of Dijkstra's routing algorithm that can optimize complex routing metrics and uses MapReduce to speed up the configuration of routers in a software-defined network. The system can optimize the packet routing accordingly to different parameters including, e.g., hops, latency, and energy efficiency policies. To show the effective benefits of the proposed solution, we performed evaluations on the revised MapReduce version of the Dijkstra routing algorithm considering a highly scalable network topology with thousands of virtual nodes.

Digital twin technology and machine learning are emerging technologies in recent years. Through digital twin technology, it is virtually possible to virtualize a product, process or service and the information interaction and co-evolution between physical and information world. Machine Learning (ML) can improve the cognitive, reasoning and decision-making abilities of the digital twin through knowledge extraction. The full life cycle management of complex equipment is considered the key to the intelligent transformation and upgrading of the modern manufacturing industry. The application of the above two technologies in the full life cycle management of complex equipment is going to make each stage of the life cycle more responsive, predictable and adaptable. In this study, we have proposed a full life cycle digital twin architecture for complex equipment. We have described four specific scenarios in which two typical machine learning algorithms based on deep reinforcement learning are applied which are further used to enhance digital twin in various stages of complex equipment. At the end of this study, we have summarized the application advantages of the combination of digital twin and machine learning while addressing future research direction in this domain.

The smart connected devices are the first choice of cybercriminals for spreading spy wares and different security attacks. The current security standards and protocols for Internet of Things (IoT) have failed in providing security to these devices. In addition, IoT market giants are producing nonsecure smart products in order to grab the open market. Furthermore, low resources of IoT devices, limits the traditional host-based protection solutions like anti-virus, IDS, IPS, etc. To overcome the resource constraintness and security barriers of smart devices, a network-level security architecture based on lightweight cryptographic parameters is required. Software-defined networking (SDN) is a new networking paradigm to overcome the control, management, and security issues in traditional networking. The SDN controller handles all the computation and complexities at the network level, rather than smart devices. In this research, we first present a new privacy-preserving security architecture for SDN-based smart homes. Subsequently, an anonymous lightweight authentication mechanism (ALAM) is designed based on the proposed security architecture core foundations. Furthermore, the security characteristics of the proposed protocol are formally analyzed using Burrows-Abadi-Needham (BAN) logic and ProVerif, followed by informal security analysis. Finally, performance evaluation and comparative analysis of the scheme is carried out.

The traditional production paradigm of large batch production does not offer flexibility toward satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multivariety and small-batch customized production modes. For this, artificial intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are: self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to the external needs, and extract the process knowledge, including business models, such as intelligent production, networked collaboration, and extended service models. This article focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, that is, machine learning, multiagent systems, Internet of Things, big data, and cloud-edge computing, are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.

The existing intelligent fault diagnosis methods of rotor-bearing system mainly focus on vibration analysis under steady operation, which has low adaptability to new scenes. In this article, a new framework for rotor-bearing system fault diagnosis under varying working conditions is proposed by using modified convolutional neural network (CNN) with transfer learning. First, infrared thermal images are collected and used to characterize the health condition of rotor-bearing system. Second, modified CNN is developed by introducing stochastic pooling and Leaky rectified linear unit to overcome the training problems in classical CNN. Finally, parameter transfer is used to enable the source modified CNN to adapt to the target domain, which solves the problem of limited available training data in the target domain. The proposed method is applied to analyze thermal images of rotor-bearing system collected under different working conditions. The results show that the proposed method outperforms other cutting edge methods in fault diagnosis of rotor-bearing system.

The development of Industry 4.0 has provided the possibility to meet frequent changes in product type and batches, a sharp decline in the delivery cycle, constraints of quality cost, and other relevant parameters of customized production mode. Intelligent manufacturing, as a core of Industry 4.0, represents a deep integration of new IT technologies, such as the industrial Internet of Things and service-oriented architecture, and manufacturing process. To realize intelligent manufacturing, this article introduces a cloud-assisted and edge-decision-making manufacturing architecture that contains a cloud and production edges. An intelligent production edge is designed to provide the traditional devices the abilities of data access and self-decision making. Besides, the proposed architecture is modeled as a multiagent system with the edge intelligence support, describing the agent-based reconfiguration mechanism from the three aspects, namely, agent interaction, agent behavior, and negotiation mechanism. The experimental results show that the reconfigurable method based on the proposed architecture can be used in the mixed-flow production scenario based on random orders, to improve the adaptability and robustness.

In the context of Industry 4.0, extensive deployment and application of advanced manufacturing equipment and various sensors is leading to a growing demand for data exchange between different devices. In smart factories, network transmission has multiprotocol features of wired/wireless communication, and different data flows have different real-time requirements. In this article, a heterogeneous network architecture based on software-defined network is proposed for realizing cross-network flexible forwarding of multisource manufacturing data and optimized utilization of network resources. Subsequently, the mechanism of cross-network fusion and scheduling (CNFS) is analyzed from the perspective of high dynamic characteristics and different delay requirements of data flows. Based on this analysis, a route-aware data flow dynamic reconstruction algorithm is proposed. The proposed algorithm improves the efficiency of manufacturing data cross-network fusion, especially for multivariety and small-batch intelligent manufacturing systems. Furthermore, for meeting the bandwidth requirements of different delay flows, a delay-sensitive network bandwidth scheduling algorithm is proposed. Finally, the effectiveness of the proposed CNFS mechanism is verified using a candy packaging intelligent production line prototype platform.

At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing.

Through the Industrial Internet of Things (IIoT), a smart factory has entered the booming period. However, as the number of nodes and network size become larger, the traditional IIoT architecture can no longer provide effective support for such enormous system. Therefore, we introduce the Blockchain architecture, which is an emerging scheme for constructing the distributed networks, to reshape the traditional IIoT architecture. First, the major problems of the traditional IIoT architecture are analyzed, and the existing improvements are summarized. Second, we introduce a security and privacy model to help design the Blockchain-based architecture. On this basis, we decompose and reorganize the original IIoT architecture to form a new multicenter partially decentralized architecture. Then, we introduce some relative security technologies to improve and optimize the new architecture. After that we design the data interaction process and the algorithms of the architecture. Finally, we use an automatic production platform to discuss the specific implementation. The experimental results show that the proposed architecture provides better security and privacy protection than the traditional architecture. Thus, the proposed architecture represents a significant improvement of the original architecture, which provides a new direction for the IIoT development.

Industry 4.0, which exploits cyber-physical systems and represents digital transformation of manufacturing, is deeply affecting healthcare as well as other traditional production sector. To accommodate the increasing demand of agility, flexibility, and low cost in healthcare sector, a data-driven reconfigurable production mode of Smart Factory for pharmaceutical manufacturing is proposed in this paper. The architecture of the Smart Factory is consisted of three primary layers, namely perception layer, deployment layer, and executing layer. A Manufacturing's Semantics Ontology based knowledgebase is introduced in the perception layer, which is responsible for plan scheduling of pharmaceutical production. The reconfigurable plans are generated from the production demand of drugs as well as the information statement of low-level machine resources. To further functionality reconfiguration and low-level controlling, the IEC 61499 standard is also introduced for functionality modeling and machine controlling. We verify the proposed method with an experiment of demand-based drug packing production, which reflects the feasibility and adequate flexibility of the proposed method.

The degradation of bearings plays a key role in the failures of industrial machinery. Prognosis of bearings is critical in adopting an optimal maintenance strategy to reduce the overall cost and to avoid unwanted downtime or even casualties by estimating the remaining useful life (RUL) of the bearings. Traditional data-driven approaches of RUL prediction rely heavily on manual feature extraction and selection using human expertise. This paper presents an innovative two-stage automated approach to estimate the RUL of bearings using deep neural networks (DNNs). A denoising autoencoder-based DNN is used to classify the acquired signals of the monitored bearings into different degradation stages. Representative features are extracted directly from the raw signal by training the DNN. Then, regression models based on shallow neural networks are constructed for each health stage. The final RUL result is obtained by smoothing the regression results from different models. The proposed approach has achieved satisfactory prediction performance for a real bearing degradation dataset with different working conditions.

In recent years, smart factory in the context of Industry 4.0 and industrial Internet of Things (IIoT) has become a hot topic for both academia and industry. In IIoT system, there is an increasing requirement for exchange of data with different delay flows among different smart devices. However, there are few studies on this topic. To overcome the limitations of traditional methods and address the problem, we seriously consider the incorporation of global centralized software defined network (SDN) and edge computing (EC) in IIoT with EC. We propose the adaptive transmission architecture with SDN and EC for IIoT. Then, according to data streams with different latency constrains, the requirements can be divided into two groups: 1) ordinary and 2) emergent stream. In the low-deadline situation, a coarse-grained transmission path algorithm provided by finding all paths that meet the time constrains in hierarchical Internet of Things (IoT). After that, by employing the path difference degree (PDD), an optimum routing path is selected considering the aggregation of time deadline, traffic load balances, and energy consumption. In the high-deadline situation, if the coarse-grained strategy is beyond the situation, a fine-grained scheme is adopted to establish an effective transmission path by an adaptive power method for getting low latency. Finally, the performance of proposed strategy is evaluated by simulation. The results demonstrate that the proposed scheme outperforms the related methods in terms of average time delay, goodput, throughput, PDD, and download time. Thus, the proposed method provides better solution for IIoT data transmission.

In the context of Industry 4.0, industrial robotics such as automated guided vehicles have drawn increased attention due to their automation capabilities and low cost. With the support of cognitive technologies for industrial Internet of Things (IoT), production processes can be significantly optimized and more intelligent manufacturing can be implemented for smart factories. In this paper, for advanced material handling, a cognitive industrial entity called context-aware cloud robotics (CACR) are introduced and analyzed. Compared with the one-time on-demand delivery, CACR is characterized by two features: (1) context-aware services and (2) effective load balancing. First, the system architecture, advantages, challenges, and applications for CACR are introduced. Then, fundamental functions for material handling are articulated, namely, decisionmaking mechanisms and cloud-enabled simultaneous localization and mapping. Finally, a CACR case study is performed to highlight its energy-efficient and cost-saving material handling capabilities. Simulations indicate the superiority of cognitive industrial IoT and show that using CACR for material handling can significantly improve energy efficiency and save cost.

Due to the development of modern information technology, the emergence of the fog computing enhances equipment computational power and provides new solutions for traditional industrial applications. Generally, it is impossible to establish a quantitative energy-aware model with a smart meter for load balancing and scheduling optimization in smart factory. With the focus on complex energy consumption problems of manufacturing clusters, this paper proposes an energy-aware load balancing and scheduling (ELBS) method based on fog computing. First, an energy consumption model related to the workload is established on the fog node, and an optimization function aiming at the load balancing of manufacturing cluster is formulated. Then, the improved particle swarm optimization algorithm is used to obtain an optimal solution, and the priority for achieving tasks is built toward the manufacturing cluster. Finally, a multiagent system is introduced to achieve the distributed scheduling of manufacturing cluster. The proposed ELBS method is verified by experiments with candy packing line, and experimental results showed that proposed method provides optimal scheduling and load balancing for the mixing work robots.

Industry 4.0 has become more popular due to recent developments in cyber-physical systems, big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a revolutionary change, and evolving applications, such as product lifecycle management, are becoming a reality. In this paper, we propose and implement a manufacturing big data solution for active preventive maintenance in manufacturing environments. First, we provide the system architecture that is used for active preventive maintenance. Then, we analyze the method used for collection of manufacturing big data according to the data characteristics. Subsequently, we perform data processing in the cloud, including the cloud layer architecture, the real-time active maintenance mechanism, and the offline prediction and analysis method. Finally, we analyze a prototype platform and implement experiments to compare the traditionally used method with the proposed active preventive maintenance method. The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.

To accommodate the trend toward mass customization launched by intelligent manufacturing, the paper proposes the adoption of model-integrated computing (MIC) paradigm in the motion control system development process for enhancing flexibility and robustness. Hierarchical structural and behavioral diversities in motion control system are considered during the implementation of MIC paradigm. For design-phase implementation, a motion-control-domain-specific modeling language is developed, and formal semantics are integrated. With regard to execution-phase implementation, a real-time runtime framework compliant with the IEC 61499 standard is proposed. Extensions of function block chain and priority-based event propagation are proposed. Dynamically extendable FB types library for motion control domain is constructed. A prototype three-axis motion control system is modeled using the proposed modelling language and is then deployed to the implemented framework to prove the feasibility of the adoption of the MIC paradigm in motion control domain.

Multimedia transmissions for IoT (Internet-of-Things) sensing has a high demand of route capacity and tight requirements of end-to-end delay. In this paper, we address the problems on how to guarantee delay-related QoS requirements and to balance the energy consumption, while using multipath routing to offer high transmission capability for IoT multimedia sensing. This motivates us to design a Multipath Planning for Single-Source based transmissions routing scheme, namely MPSS, which establishes desirable multiple route paths following B-spline trajectories based on geographical information of source and sink node, sending and receiving angles, and inter-path distance. We further utilize a factor of hop distance to reduce the cumulated error of each hop due to the density of nodes, and to guarantee the delay-related QoS requirements. A Multipath Planning for Multi-Source routing scheme is also designed, namely MPMS, to assign the angle scope according to the source node's priority and traffic. Experimental results show that MPSS can effectively generate well-patterned multiple spline-based routes, and the end-to-end delay is under control according to the delay QoS requirement, while the total energy consumption is minimized.

The multidisciplinary nature and time criticality of computing in Cyber-Physical Robot Systems (CPRS) makes it significantly different from traditional computer systems. This article attempts to create a usage-specific language called Cyber-Physical Robot Language (CPRL), which supports the CPRS design and implementation in an integrative and swift way. Multiview description and integration strategies as well as formal execution semantics for usage-specific simulation and verification are outlined. A graphic unified environment for CPRS modeling is supplied, in which several tools are integrated. A 6-DOF distributed robot system development in the environment is presented. The approach is an attempt to support CPRS design in an effective way, at the same time guaranteeing the system function and performance requirements.

With the urban development and enlargement, various regions such as residential zones and administrative districts now appear as parts of cities. People exhibit different mobility patterns in each region, which is closely relevant to region-wide functions. In this article, we propose a scheme to discover region-wide functions using large-scale Shanghai taxicab trajectories that capture enormous traces for more than 13,000 taxicabs over a period of about 3 years. We investigate these taxicab trajectories and conduct an extensive preliminary study. Then, we divide the city into disjointed regions using Voronoi decomposition. By incorporating people's pick-up and drop-off information, we refine the Voronoi partitioning results to identify region-wide functional areas. Finally, we study people's movement frequency on weekdays and weekends for every kind of urban functional regions. We also look into human mobility within or across the identified urban functional regions. Experimental results show that human movement is bounded with the function of urban regions, and more than 90% of people visit neighboring (less than 20km travel distance) functional regions with high probability.

Social networks and mobile networks are exposing human beings to a big data era. With the support of big data analytics, conventional intelligent transportation systems (ITS) are gradually changing into data-driven ITS (D2ITS). Along with traffic growth, D2ITS need to solve more real-life problems, including the issue of unlicensed taxis and their identification, which potentially disrupts the taxi business sector and endangers society safety. As a remedy to this issue, a smart model is proposed in this paper to identify unlicensed taxis. The proposed model consists of two submodel components, namely, candidate selection model and candidate refined model. The former is used to screen out a coarse-grained suspected unlicensed taxi candidate list. The list is taken as an input for the candidate refined model, which is based on machine learning to get a fine-grained list of suspected unlicensed taxis. The proposed model is evaluated using real-life data, and the obtained results are encouraging, demonstrating its efficiency and accuracy in identifying unlicensed taxis, helping governments to better regulate the traffic operation and reduce associated costs.

With the development of cloud computing and mobility, mobile cloud computing has emerged and become a focus of research. By the means of on-demand self-service and extendibility, it can offer the infrastructure, platform, and software services in a cloud to mobile users through the mobile network. Security and privacy are the key issues for mobile cloud computing applications, and still face some enormous challenges. In order to facilitate this emerging domain, we firstly in brief review the advantages and system model of mobile cloud computing, and then pay attention to the security and privacy in the mobile cloud computing. By deeply analyzing the security and privacy issues from three aspects: mobile terminal, mobile network and cloud, we give the current security and privacy approaches.

Recently, cyber-physical systems (CPSs) have emerged as a cutting edge technology for next-generation industrial applications, and are undergoing rapid development and inspiring numerous application domains. In this article, we propose a novel CPS application for energy management framework (EMF) toward autonomous electric vehicle (AEV) in smart grid. We first give a brief overview of related technologies, including open research issues of CPS, enabling wireless communication technologies for CPS, smart grid, AEV and its path planning, energy-efficient design for AEV, etc. Then we design EMF from the following aspects, such as AEV with wireless sensor networks (WSNs) navigation, smart grid communication architecture for EMF and AEV charging station. The proposed EMF is able to collect the real-time power consumption status and demand from AEV and charging stations. We also address EMF to overcome some issues, such as real-time traffic information. Subsequently, the energy-efficient design schemes for AEV are proposed and formulated from the point of view of path planning and event-based control technique. Finally, we outline the issues and challenges for EMF.