In today's industrial Internet of Things systems, functional safety communication protocols are widely adopted to transmit safety protocol data unit (SPDU). While the guessing random additive noise decoding (GRAND) algorithm can improve the reliability of cyclic redundancy check (CRC)-coded SPDU, the decoding complexity of long SPDU remains too high for practical deployment. To address this, we extend the GRAND-based joint error detection and correction (JEDeC) strategy to long SPDU and propose a JEDeC-based packet fragmentation/assembling mechanism that fragments a long SPDU into multiple short SPDUs for parallel correction of erroneous bits. We clarify the decoder input settings and channel model used in this work. We show that the proposed approach achieves tractable decoding complexity and latency: for an assembled SPDU length of 1024 bits, fragment length of 64 bits, CRC signature length of 32 bits, and maximum error-correction capability of 4 bits, under a representative bit error rate (BER)$P_{e}=10^{-3}$, the BER is reduced from$10^{-3}$to$1.49\times 10^{-8}$, the packet error rate from$6.46\times 10^{-1}$to$8.11\times 10^{-7}$, and the residual error probability from$6.36\times 10^{-11}$to$3.42\times 10^{-16}$, with an average of$2.86\times 10^{3}$guessing attempts per assembled SPDU. These results indicate that the fragmentation/assembling mechanism can substantially improve safety-communication dependability with implementable cost.

Developing robust Alzheimer's Disease (AD) classification models necessitates extensive training data, but aggregating multi-center medical data poses privacy risks. Although Federated Learning (FL) and Swarm Learning (SL) allow training generic models without data sharing, their performance is limited by variations in AD pathology features and sample class imbalances across centers. To address this issue, we propose a novel Harmonized Swarm Learning framework with Guided Optimization (HSGO) to enhance multi-center collaboration while preserving data privacy. Our framework employs a class-balanced loss function to train a robust generic model and guides the optimization of personalized models towards the generic model, eliminating extra AD pathology feature extraction steps. Furthermore, we design a dynamic feature similarity storage mechanism to facilitate personalized training. Experiments performed under two different multi-center data partitioning scenarios demonstrate that HSGO achieves competitive performance when compared with five baseline methods. Additionally, Layer-wise Relevance Propagation (LRP) analysis indicates that HSGO may help identify potential key brain regions in AD by integrating local and global features compared to traditional SL.

Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires agents to navigate with lowlevel actions following natural language instructions in 3D environments. Most existing approaches utilize observation features from the current step to represent the viewpoint. However, these representations often conflate redundant and essential information for navigation, introducing ambiguity into the agent's action prediction. To address the problem of inadequate representation, we propose a Knowledge-andHistory Aware Visual Representation for Continuous Vision-and-Language Navigation (VLN-KHVR). The proposed approach constructs enriched visual representations tailored to navigation instructions, enhancing agents' navigation performance. Specifically, VLN-KHVR extracts image features from the current observation, retrieves relevant knowledge in the knowledge base, and obtains the history of the navigation episode. Subsequently, the knowledge and history features are filtered to eliminate the information irrelevant to navigation instruction. These refined features are integrated with the instruction for further interaction. Finally, the aggregated features are used to guide navigation. Our model outperforms previous methods on the VLN-CE benchmark, demonstrating the effectiveness of the proposed method.

Partial Differential Equations (PDEs) are fundamental for modeling physical systems, yet solving them in a generic and efficient manner using machine learning-based approaches remains challenging due to limited multi-input and multi-scale generalization capabilities, as well as high computational costs. This paper proposes the Multi-input and Multi-scale Efficient Transformer (MMET), a novel framework designed to address the above challenges. MMET decouples mesh and query points as two sequences and feeds them into the encoder and decoder, respectively, and uses a Gated Condition Embedding (GCE) layer to embed input variables or functions with varying dimensions, enabling effective solutions for multi-scale and multi-input problems. Additionally, a Hilbert curve-based reserialization and patch embedding mechanism decrease the input length. This significantly reduces the computational cost when dealing with large-scale geometric models. These innovations enable efficient representations and support multi-scale resolution queries for large-scale and multi-input PDE problems. Experimental evaluations on diverse benchmarks spanning different physical fields demonstrate that MMET outperforms SOTA methods in both accuracy and computational efficiency. This work highlights the potential of MMET as a robust and scalable solution for real-time PDE solving in engineering and physics-based applications, paving the way for future explorations into pre-trained large-scale models in specific domains. This work is open-sourced at https://github.com/YichenLuo-0/MMET.

As traditional analog industrial automation transitions toward digitalization, the efficient deployment of control systems becomes increasingly critical. Open programmable logic controllers (OpenPLC) and the Modbus communication protocol, leveraging the widespread adoption of containerization technologies, are gradually migrating to native cloud architectures. This paper investigates the deployment of OpenPLC in local environments, Docker containers, and Kubernetes clusters, evaluating several key performance indicators, including resource consumption, Modbus communication response time, throughput, multi-user handling, stability, and fault recovery. By designing experimental platforms tailored to different environments and conducting validation across multiple platforms, the experimental results indicate that, although local deployment performs better in terms of low latency and resource consumption, deployments on the Kubernetes platform exhibit significant advantages in fault recovery, throughput, and system stability. In contrast, Docker-based deployments demonstrate more balanced performance, but lag behind Kubernetes in scalability and fault tolerance. Based on the experimental findings, this study provides valuable insights for selecting the optimal deployment strategy for industrial automation systems, taking into account OpenPLC, containerization technologies, performance requirements, and the integration of cloud-fog automation for enhanced flexibility and scalability.

The integration of EtherCAT and TSN has been proposed to enhance performance of EtherCAT networks in industrial automation. EtherCAT over TSN transforms a traditional EtherCAT ring into multiple shorter rings interconnected via TSN switches, enabling concurrent data transmission across segments and reducing cycle time. However, the use of multiple segments introduces contention for the master in the return path, potentially leading to scheduling failures and an increase in cycle time. We study the impact of network topology, i.e., the number of segments and slave node distribution, on schedulability, and formulate the topology optimization problem for the converged network based on schedulability analysis. We evaluate our methodology and optimal solution using SMT and Integer Programming (LIP) solvers, respectively. Numerical results demonstrate the effectiveness of our method, and our solution outperforms baselines.

This article presents a Kubernetes-based database benchmarking framework for Cloud-Fog Automation (CFA) in industrial systems, integrating Locust (dynamic load simulation) and Prometheus (resource monitoring) to evaluate four databases under industrial workloads: write-heavy , query-intensive (real-time analytics), and 6:4 read-write hybrid (control-logic scenarios). Kubernetes, as a representative of cloud-native technologies, is a crucial support for industrial CPS, but the database performance for industrial data management is yet to be fully determined. In 1k-concurrent-user tests (emulating distributed CFA edge nodes), ReductStore delivered 649.63 req/s throughput with <6 ms latency, ideal for fog-level real-time control, while OpenGauss achieved P99 latency <1 ms (critical for PLC synchronization) at 4.69 Gi memory cost—quantifying trade-offs for resource-constrained fog deployments. The framework demonstrates Kubernetes’ role in elastic cloud-fog orchestration, aligning with CFA’s industrial demands: InfluxDB suits massive IIoT data aggregation, while OpenGauss optimizes mission-critical latency. Our results bridge cloud-native scalability with deterministic industrial performance, enabling cost-efficient DBMS selection for smart factories.

The convergence of Controller Area Network with Flexible Data-Rate (CAN-FD) and Time-Sensitive Networking (TSN) presents a critical pathway to enable deterministic cross-domain communication in next-generation intelligent vehicles. However, different transmission mechanisms are raising significant challenges in maintaining low jitter and guaranteed latency. This paper proposes a low jitter framework based on a CANFD-TSN gateway to address these limitations through three key innovations: 1) A CQF-based gateway architecture integrating cyclic queuing with deadline-aware traffic scheduling, 2) An ILP model optimizing queue switching cycles, and 3) A bidirectional phase alignment mechanism that compensates asymmetric queuing delays through gateway timestamp synchronization, achieving microsecond-level jitter suppression. Extensive OMNeT++ simulations demonstrate the framework’s effectiveness, 18% higher scheduling success rates compared to conventional methods (RCSF, SPs, EDF) under 160-flow scenarios, while reducing end-to-end jitter by 42% through alignment time compensation.

As industrial equipment becomes increasingly complex and intelligent, fault diagnosis (FD) technology has emerged as a critical means to ensure system reliability and safety, spurring the development of various real-time online monitoring techniques. Traditional fault diagnosis methods primarily rely on single data sources and specific algorithms, which makes it challenging to effectively integrate the multimodal data captured by diverse sensors and often overlooks the vital role of human expertise in the diagnostic process. By leveraging a universal fault diagnosis framework that combines Large Language Models (LLMs) with multimodal data, existing methods can be seamlessly integrated. LLMs possess powerful capabilities in natural language understanding, knowledge integration, and reasoning, enabling them to analyze text, images, signals, and other types of multimodal information to facilitate zero-shot and few-shot knowledge reasoning and fault diagnosis. This paper systematically reviews the development of LLM- and multimodal data-based fault diagnosis technologies, outlines key techniques such as data-driven processing, feature extraction, and feature fusion within LLM frameworks, and analyzes the technological advancements fostered by these methods. It also summarizes the advantages and limitations of this approach in fault diagnosis and health state assessment, and offers an outlook on the future trends and challenges of applying LLMs in multimodal fault diagnosis. The aim is to provide technical guidance for researchers and engineers, thereby accelerating the innovation and application of intelligent fault diagnosis technologies.

Cloud-Fog Automation (CFA) represents a fundamental paradigm to facilitate flexible deployment modalities of industrial applications. Adopting B5G/6G technologies, software-defined wireless CFA separates wireless networking and computing functions from proprietary hardware, thereby significantly improving the flexibility and scalability of industrial automation. Despite its advantages, optimizing the end-to-end performance in software-defined wireless CFA is technically challenging, due to computing resource fluctuations and the uncertainties imposed on communication. To tackle these challenges, this study presents a Reinforcement Learning (RL)-based latency optimization scheme for software-defined wireless CFA. Jointly considering the wireless channel conditions and computing resource constraints, it adaptively allocates B5G/6G Radio Access Network (RAN) resources for improved performance on end-to-end (E2E) latency. For validation, a full-stack software-defined CFA testbed was implemented utilizing open-source projects, e.g., srsRAN and Open5GS. The numerical data indicate that the proposed scheme surpasses the benchmark, simultaneously achieving lower end-to-end latency and improved performance stability.

Deep Reinforcement Learning (DRL) has achieved remarkable success in various fields by leveraging neural networks. However, applying DRL to control complex robot systems faces challenges, such as hard to converge and robust control to accommodate different environments. In this paper, we propose IDM-TD3, a new DRL framework which introduces an Inverse Dynamic Model into TD3 as the output map of the actor network to decouple the kinematics and dynamics of the control system. This decoupled configuration not only permits online fine-tuning of the IDM within target robot environment for a better control performence, but also facilitates the seamless transference of experiential knowledge across agents with akin kinematic features. Experimental results show that without pretraining, the convergence performance of the proposed method is comparable to that of our baseline algorithm TD3. If the kinematic network is pretrained using expert policies (even from environments with different dynamic parameters), we achieve much better convergence than TD3 and its combination with behavioral cloning. Moreover, by fine-tuning the IDM, our method exhibits robust control even in environments with distinct dynamic differences. This shows the promising application of our IDM-TD3 in many fields, particularly in addressing the generalization problem or harnessing pre-existing experiences for the training of nascent agents.

Ad Hoc networks, as a flexible type of wireless sensor network, find wide applications in disaster relief, and industrial scenarios. In industrial applications, there is a growing demand for deterministic communication to support time-critical business flows. However, previous research mainly focused on aspects like routing and re-routing, and few studies have addressed the issue of ensuring determinism. This paper proposes a novel Time-Triggered Ad Hoc (TTA) network framework. It uses a Time Division Multiple Access (TDMA)-based time-triggered transmission mechanism to achieve self-organized, deterministic communication. The framework includes time synchronization and offline scheduling to optimize transmission performance. Experimental results show that the TTA framework outperforms traditional methods in terms of network capacity, latency, and jitter, demonstrating its effectiveness in solving the determinism problem in Ad Hoc networks.

Virtual Programmable Logic Controllers (vPLCs), as a newborn technology, are becoming increasingly important in modern industrial automation due to their flexibility and scalability. There is lack of researches on data synchronization and redundancy mechanisms for vPLCs, limiting applications of vPLCs in critical industrial scenarios. This paper designs and implements a data synchronization and redundancy mechanism between vPLCs based on heartbeat detection to enhance the reliability of vPLC systems. The mechanism continuously monitors for failures and synchronizes data between vPLCs to ensure seamless control task takeover in the event of a failure. Experimental results demonstrate the mechanism’s high effectiveness in fault detection and recovery, achieving a redundancy switchover time that meets industrial application requirements.

Time-sensitive networking (TSN) is an important research direction for the transformation and upgrading of industrial internet infrastructure. In future industrial sites, TSN and traditional industrial networks will coexist in the same network, and this integration will be inevitable. Ensuring reliable and deterministic transmission of data flows in the converged network of Profinet and TSN will be a key research topic. This paper presents a compatible way for the Cyclic Queuing and Forwarding (CQF) queuing model of TSN and the Isochronous Real-Time (IRT) communication of Profinet. Firstly, we propose a Delay Reservation Mechanism based on CQF (DRM-CQF). This mechanism achieves reliable and deterministic transmission by delaying the sending time of cross-domain data flows in the Profinet and reserving transmission opportunities for cross-domain data flows in TSN. Secondly, we construct a mathematical optimization model based on DRM-CQF to schedule data flows in the converged network to seek the optimal schedule. Experimental results show that DRM-CQF can ensure the reliable transmission of cross-domain data flows in the Profinet and TSN converged network, and the end-to-end average delay is reduced by 49% compared with other CQF scheduling methods.

With the rapid development of the industrial internet of things and automation control systems, supervisory control and data acquisition (SCADA) systems have been widely adopted in industrial manufacturing due to their flexibility and scalability. The cloud-fog automation (CFA) paradigm is emerging to address higher real-time and computing demands in complex industrial environments. To fully leverage the efficiency, flexibility, and scalability of Kubernetes, an open-source container orchestration platform Kubernetes in managing containerized applications, this article investigates methods for deploying SCADA systems on the Kubernetes platform. This approach aims to capitalize on Kubernetes’ benefits, such as automated deployment, elastic scaling, and high availability, to optimize resource management and enhance system performance. To validate the proposed solution, we employs testing tools such as wrk and tc, along with monitoring tools like Prometheus and Grafana, to conduct a comprehensive evaluation of Kubernetes’ advantages in various scenarios. We focus on three key aspects: reliability, scalability, and security. The results demonstrate that Kubernetes can significantly improve the scalability, fault recovery capabilities, and stability of SCADA systems.

The growing frequency of medical emergencies, such as cardiac arrest and stroke, highlights the need for timely first aid interventions. Traditional emergency medical services (EMS) face challenges like delayed response times, resource disparities, and high-risk environments, often leading to inadequate care. Recent advancements in robotics, artificial intelligence, and cloud computing have enabled the development of first aid robots, which can provide automated, efficient medical assistance in critical situations. This paper explores the motivation for first aid robots, reviews the state of the art, and identifies key challenges. Finally, we propose a technical framework for potential implementation. Despite having promising potential to enhance emergency medical services and preserve lives, further research and multidisciplinary collaboration are needed for their effective deployment.

In modern smart factories, functional safety protocols are widely used to guarantee the reliable transmission of Safety Protocol Data Unit (SPDU). Using our constructed WirelessHP physical layer protocol and universal software radio peripherals (USRP) as the hardware platform, this paper proposes to improve the reliability of SPDU transmission by adopting the Joint Error Detection and Correction (JEDeC) strategy. Through actual experiments, the performance of JEDeC for decoding CRC-coded SPDUs is investigated in real industrial environments. As a preliminary study, our results demonstrate that the Bit Error Rate (BER) and Packet Error Rate (PER) of SPDU transmission are significantly improved compared to traditional CRC error detection mechanism. We also identify and discuss the challenging issue of high decoding complexity for future research.

Guest Editorial: Co-Design of Communication, Computing, and Control in Industrial Cyber-Physical Systems—Part I

Autonomous Industrial Cyber-Physical Systems (ICPS) represent a future vision where industrial systems achieve full autonomy, integrating physical processes seamlessly with communication, computing and control technologies while holistically embedding intelligence. Cloud-Fog Automation is a new digitalized industrial automation reference architecture that has been recently proposed. This architecture is a fundamental paradigm shift from the traditional International Society of Automation (ISA)-95 model to accelerate the convergence and synergy of communication, computing, and control towards a fully autonomous ICPS. With the deployment of new wireless technologies to enable almost-deterministic ultra-reliable low-latency communications, a joint design of optimal control and computing has become increasingly important in modern ICPS. It is also imperative that system-wide cyber-physical security are critically enforced. Despite recent advancements in the field, there are still significant research gaps and open technical challenges. Therefore, a deliberate rethink in co-designing and synergizing communications, computing, and control (which we term “3C co-design”) is required. In this paper, we position Cloud-Fog Automation with 3C co-design as the new paradigm to realize the vision of autonomous ICPS. We articulate the state-of-the-art and future directions in the field, and specifically discuss how goal-oriented communication, virtualization-empowered computing, and Quality of Service (QoS)-aware control can drive Cloud-Fog Automation towards a fully autonomous ICPS, while accounting for system-wide cyber-physical security.

Guest Editorial: Co-Design of Communication, Computing, and Control in Industrial Cyber-Physical Systems—Part II

The development of wireless communication has indeed promoted cloud-fog automation in the industry. It also introduces new issues of reliability and latency for control systems. This study sought to investigate the impacts of the commonly used industrial wireless network on the control performance parameters using a ball-and-beam (BB) time-critical balancing control system. An internal model control-inspired latency-aware wireless control framework (IMC-LA) is presented and employed in the BB system to handle the time delays and instability-creating elements introduced by wireless communication. A preliminary control assessment of the BB system under two new-generation wireless technologies, Wi-Fi 6 and 5G, was delivered. The correlation between network performance and control performance was analyzed statistically compared to the wired Ethernet condition. Test results show a dramatic decrease in position error after utilizing the proposed latency-aware wireless control framework. This study provides practical insights into the potential impacts of industrial wireless networks on control systems with a workable latency-aware wireless control approach. The methodology presented in this work has the potential to expedite the adoption of wireless communication in time-critical control. Note to Practitioners—Many factory automation processes experience undesirable latencies when transitioning from classic architecture to cloud automation architecture, particularly with the implementation of wireless communication. This paper aims to address the potential instability problem introduced by practical wireless networks and proposes a latency-aware control framework using the concept of internal model control. Meanwhile, this work also provides a way for practical operators to explore the relationship between critical parameters of communication networks and control performance parameters. The purpose of this paper is not to judge which wireless technology is better, instead we only want to demonstrate the effectiveness of the proposed latency-aware control in improving control performance under various wireless scenarios. The proposed framework is verified using the BB system for the commonly used cascaded PID control under two advanced wireless networks, 5G and Wi-Fi 6. It is also applicable to other industrial wireless networks with millisecond-class latency in the other regulatory control cases. The proposed IMC-LA wireless control framework reduces the significant effort and cost associated with constructing and tuning the controller in a real control system.

In real-world industrial scenarios, fault detection faces the widely recognized challenge of data imbalance, which not only refers to the scarcity of fault data but also includes the imbalance in healthy data. This article is concerned with imbalanced open-set fault detection (IOSFD), a practical yet challenging scenario in industrial applications where multiple healthy operating conditions and multiple fault types are imbalanced. In this article, we propose a new contrastive conditional adversarial autoencoder for IOSFD. It constructs an end-to-end unified model based on multi-class known healthy and faulty data to address the reliance of traditional methods on fault samples, while optimizing with class-specific weighted forces to ensure equal attention to imbalanced known classes. Input and feature reconstruction conditioned on operating modes are utilized to learn a compact decision plane and achieve both unknown fault detection and known data classification. Significantly, we formulate the optimization objective of conditional reconstruction based on contrastive learning and introduce adversarial training to further enhance the model’s performance. The effectiveness of the proposed method is validated through real-world pipeline leak detection and Tennessee-Eastman multi-fault detection.

In modern Industrial Internet of Things (IIoT) ecosystems, functional safety protocols are increasingly utilized to transmit safety protocol data unit (SPDU). Integrating the universal guessing random additive noise decoding (GRAND) algorithm with cyclic redundancy check (CRC)-coded SPDU can minimize SPDU retransmissions. However, it introduces residual error probability (REP) degradation that requires careful consideration. Using the IEC 61784-3 Standard and CRC assumptions, we derive a closed-form REP evaluation formula specific to SPDU length and maximum error correction capability. Our analysis reveals that, under the worst industrial conditions and for an SPDU length of 128 bits, the REP performance degrades by approximately 2:2 × 102times when the CRC signature length is 24 bits and up to one bit is guessing decoded. This degradation becomes more pronounced with increased error correction capability. To address this, we propose to compensate the REP degradation by adopting longer CRC signature. This paper provides a theoretical framework for adopting the GRAND algorithm in functional safety communication, setting a foundation for enhancing reliability in IIoT applications..

The advancement of large models has initiated a transformation in the field of time-series forecasting. Both the repurposing of existing large models and the development of large models tailored for time-series analysis have exhibited impressive performance. In industrial applications, challenges, such as limited data availability and constrained computational resources, render the first approach viable. However, it is important to note that this approach is still in its infancy and lacks both a thorough technical analysis and a unified effective framework. Meanwhile, as large models become a mainstream artificial intelligence paradigm, it is urgent to discuss typical industrial scenarios, such as how automated systems can transition from intelligent to collaborative operation and maintenance. In light of this premise, this article endeavors to advance a generalized technical framework for large model-driven time-series forecasting, under which existing methods can be subsumed. Then, within this overarching technical paradigm, the technical advancements facilitated by diverse methods will be systematically elucidated and analyzed, along with a comparative evaluation conducted across seven benchmark datasets. Concluding this analysis, the implementation pathway for the industrial automation system is delineated that integrates operator action commands to forecast post-action trends to assess action correctness in advance. Finally, the challenges and future directions of large model-based time-series forecasting are outlined.

Fiber-reinforced composites (FRCs) are widely utilized across various sectors, due to their outstanding mechanical properties. The arrangement of fibers within these composites considerably influences their mechanical behavior. However, the state-of-the-art techniques on fiber orientation measurement are plagued by issues such as discontinuous boundaries and the imprecise measurement of finely oriented fibers. To this end, this work introduces a pioneering margin-aware cylindrical deep hough network (MAC-DHN) to solve these problems. The cylindrical Hough architecture, which acknowledges the$\pi$-periodicity of fiber orientations, is specifically crafted to address the problem of discontinuous boundaries. Furthermore, we design an innovative sample-wise reweighting strategy for the cross-entropy loss that enhances the differentiation between finely oriented fibers. This strategy lessens the loss related to samples with minimal prediction probability margins between the correct classification and the adjacent fine-grained categories. To comprehensively examine the fiber orientation measurement techniques in FRCs, a new dataset named FrCs orientation measurement dataset (FCOM) has been built, and the proposed method has been rigorously assessed on this dataset. Experimental results indicate that the proposed method surpasses existing techniques in terms of$F\!-\!\text{measure}$and mean absolute error (MAE), with respective scores of 0.981 and 0.219.

The increasing prevalence of electric vehicles de-mands efficient and sustainable management of end-of-life lithium-ion batteries. This paper examines the use of Pretrained Foundation Models and Cloud-Fog Automation to improve robotic disassembly of these batteries. We evaluate the performance of two Vision Transformer Models, in tasks involving deformed, rusty, contaminated, and worn batteries. Our proposed architecture, utilizing cloud and fog computing, balances performance with resource efficiency, providing a scalable solution for electric vehicles battery recycling.

Industrial automation systems are pivotal in enhancing the digitization and intelligence of the industrial sector. In recent years, wireless communication technologies (such as B5G/6G) and Cloud Fog Automation (CFA) have revolutionized the industrial sector, providing unparalleled flexibility and scalability. However, the prevailing CFA architecture predominantly concentrates on network and system levels, often neglecting the optimization of the physical layer communication process. To achieve the optimal system-level performance of wireless CFA, this study introduces CFA-OpenRAN, an integrated architecture encompassing communication, computing, and control. By leveraging the open design, standardized interfaces, and software-defined paradigm of O-RAN, CFA-OpenRAN facilitates cross-domain optimization of communication, computing, and industrial control in wireless CFA. In the end, an application scenario of joint resource allocation and the corresponding experiment setup was developed.

Abstract For the hard guessing random additive noise decoding Markov order (GRAND-MO) algorithm, it is crucial to develop an efficient noise error patterns (NEPs) generator to facilitate its application in bursty channels. This paper proposes a practical hardware realization by generating the NEPs in a sequential manner. Based on classification of the four types of NEPs, we propose to iteratively calculate the “1" and the “0" permutations in the same time. Then, the novel “0" permutation regularization and bit flipping techniques are employed, through which the generation of the four types of NEPs is uniformed at the same way. Moreover, the proposed NEPs generator can generate all NEPs by using the “1" burst parameters, and is suitable for the guessing decoding of any linear block codes. Built on field programmable gate array (FPGA) implementation and comparison with existing benchmark, we show the proposed NEPs generator is a power-efficient architecture for realization. This work presents a new solution for the hardware implementation of the NEPs generator in GRAND-MO.

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 model, with some recently transitioned to Cloud Automation systems. Latest developments in network connectivity technologies, artificial intelligence, and Cloud/Fog computing technologies have motivated us to rethink the ISA-95 model. In this article, we propose a vision that aims to migrate most of the computational and automation tasks closer to the ground, which we term the collaborative “Cloud-Fog Automation” paradigm. We perform a comprehensive survey of the state-of-the-art and formulate the three pillars of this vision: Deterministic connectivity, deterministic connected intelligence, and deterministic networked computing. In each of these pillars, we review their latency and reliability, security, and functional safety requirements and challenges. Finally, we articulate and highlight key future research directions to realize this vision.

As security issues facing the industrial Internet of Things (IIoT) continue to emerge, industrial organizations are working to further improve the security system. Zero trust (ZT) is seen as the future of industrial security, with a rising voice, but currently, no concrete implementation technique is available. In this article, we start with the requirements of ZT security and attempt to design a ZT technical framework applicable to wireless IIoT. Specifically, a three-step ZT security framework is proposed that builds on the benefits of physical-layer security to enhance ZT in IIoT. Security zone formation is done first, which then facilitates a trusted environment for subsequent device authentication and cryptographic negotiation. By integrating physical-layer security, several promising techniques, including artificial noise, physical fingerprint, and key distribution, are well designed to accomplish the proposed framework. Our analysis reveals that the proposed framework and the designed particular implementation techniques are feasible to enhance ZT security in wireless IIoT.

Physical layer authentication (PLA) is a promising approach to ensure wireless network security. However, existing PLA algorithms require channel state information calibration and model training at each location, which limits the device's mobile ranges. To enable zero training deployment in uncalibrated scenarios, we propose a transferable PLA (TPLA) algorithm. It exploits the channel time-varying patterns as the scenario-independent features to avoid PLA failure in uncalibrated scenarios. To extract transferable time-varying patterns from dynamic industrial mobile environments, a neural network feature extractor is designed by a multibranch parallel architecture with multiscale channel receptive fields. Furthermore, the global and component features are fused by the model-level and decision-level fusion methods to accommodate different transferability and computational cost requirements. In experiments, TPLA achieves below 2.5% authentication error in a new scenario, which proves that TPLA is an important step toward zero training deployment of the PLA algorithm.

Channel-state information (CSI)-based physical-layer authentication (PLA) has gained significant attention. However, in industrial mobile scenarios, the time-varying channels and changing device locations limit the reliability of CSI-based PLA algorithms. Furthermore, the performance of existing PLA algorithms degrades sharply at uncalibrated locations. To improve the reliability and robustness of authentication, we propose a new spatiotemporal gradient-based-PLA (STG-PLA) algorithm enhanced by CSI-to-image transformation. We first extract correlation and scattering features to depict the multidimensional channel properties, including selectivity and dispersion. We then convert several individual CSI sequences to a CSI-image. Therefore, the spatiotemporal correlation gradient of the CSI-sequences is reflected in one CSI-image. Both simulations and experiments show that STG-PLA reduces the authentication error rate from$>10{\%}$(in existing studies) to$< 1{\%}$, which signifies considerable progress toward the practical applicability. Furthermore, with no model retraining, STG-PLA exhibits the robust performance when the device moves to uncalibrated locations.

Vocoder-based speech synthesis has become a promising technique to accommodate the demands of high-quality speech analysis, manipulation, and synthesis. However, most existing works focus on how to synthesize normal human voice with high signal-to-noise ratio, neglecting individuals' pathological voice disorder in speech interaction. In this work, we propose a non-linear voice repair vocoder for pathological vowels and sentences, which takes the pathological speech as input and generates high-quality repaired speech. Our approach is specifically designed to enhance the speech quality and intelligibility for individuals with voice disorders. We employ amplitude modulated-frequency modulated (AM-FM) and Teager energy operation techniques to enhance the quality of pitch and spectral envelope. To tackle the instability and fracture problem of pitch, we present spectral tracking algorithm, which not only avoids dramatic change in the edge of voice, but also reduces the errors of half-pitch. Furthermore, we design a spectral reconstruction algorithm, which can effectively rebuild the spectral structure by energy operation to accomplish spectral envelope repair. The proposed PVR-Vocoder shows exceptional performance in pathological voice intelligibility enhancement according to various quality measures including objective indicators, subjective evaluation, and spectrum observations.

The cumulative distribution function (CDF) of a non-central$\chi ^{2}$-distributed random variable (RV) is often used when measuring the outage probability of communication systems. For ultra-reliable low-latency communication (URLLC), it is important but mathematically challenging to determine the outage threshold for an extremely small outage target. This motivates us to investigate lower bounds of the outage threshold, and it is found that the one derived from the Chernoff inequality (named Cher-LB) is the most effective lower bound. This finding is associated with three rigorously established properties of the Cher-LB with respect to the mean, variance, reliability requirement, and degrees of freedom of the non-central$\chi ^{2}$-distributed RV. The Cher-LB is then employed to predict the beamforming gain in URLLC for both conventional multi-antenna systems (i.e., MIMO) under first-order Markov time-varying channel and reconfigurable intellgent surface (RIS) systems. It is exhibited that, with the proposed Cher-LB, the pessimistic prediction of the beamforming gain is made sufficiently accurate for guaranteed reliability as well as the transmit-energy efficiency.

We consider a broadband over-the-air computation empowered model aggregation approach for wireless federated learning (FL) systems and propose to leverage an intelligent reflecting surface (IRS) to combat wireless fading and noise. We first investigate the conventional node-selection based framework, where a few edge nodes are dropped in model aggregation to control the aggregation error. We analyze the performance of this node-selection based framework and derive an upper bound on its performance loss, which is shown to be related to the selected edge nodes. Then, we seek to minimize the mean-squared error (MSE) between the desired global gradient parameters and the actually received ones by optimizing the selected edge nodes, their transmit equalization coefficients, the IRS phase shifts, and the receive factors of the cloud server. By resorting to the matrix lifting technique and difference-of-convex programming, we successfully transform the formulated optimization problem into a convex one and solve it using off-the-shelf solvers. To improve learning performance, we further propose a weight-selection based FL framework. In such a framework, we assign each edge node a proper weight coefficient in model aggregation instead of discarding any of them to reduce the aggregation error, i.e., amplitude alignment of the received local gradient parameters from different edge nodes is not required.We also analyze the performance of this weight-selection based framework and derive an upper bound on its performance loss, followed by minimizing the MSE via optimizing the weight coefficients of the edge nodes, their transmit equalization coefficients, the IRS phase shifts, and the receive factors of the cloud server. Furthermore, we use the MNIST dataset for simulations to evaluate the performance of both node-selection and weight-selection based FL frameworks.

Internet of Things (IoT) enabled technology has rapidly and efficiently facilitate healthcare diagnose and treatment with low-cost and lightweight devices. Big data generated from IoT offers valuable and crucial information to guide decision-making, improve patient outcomes, and decrease healthcare costs, etc. The workshop is aiming to provide an opportunity for researchers and practitioners from both academia and industry to present the state-of-the-art research and applications in utilizing IoT and big data technology for healthcare by presenting efficient scientific and engineering solutions, addressing the needs and challenges for integration with new technologies, and providing visions for future research and development.

Secret key generation in physical layer security exploits the unpredictable random nature of wireless channels. The millimeter-wave (mmWave) channels have limited multipath and channel randomness in static environments. In this paper, for mmWave secret key generation of physical layer security, we use a reconfigurable intelligent surface (RIS) to induce randomness directly in wireless environments, without adding complexity to transceivers. We consider RIS to have continuous individual phase shifts (CIPS) and derive the RIS-assisted reflection channel distribution with its parameters. Then, we propose continuous group phase shifts (CGPS) to increase the randomness specifically at legal parties. Since the continuous phase shifts are expensive to implement, we analyze discrete individual phase shifts (DIPS) and derive the corresponding channel distribution, which is dependent on the quantization bit. We then derive the secret key rate (SKR) to evaluate the randomness performance. With the simulation results verifying the analytical results, this work explains the mathematical principles and lays a foundation for future mmWave evaluation and optimization of artificial channel randomness.

The Industrial Internet of Things (IIoTs) plays an important role in various industrial applications, which require multiple time-critical networks to be deployed in the same region. The limited communication resources inevitably incur network coexistence problems. For scenarios where coexisting networks cannot coordinate effectively, the centralized or partial-information-based decentralized resource allocation methods cannot be implemented. To address this concern, we propose a Cooperation-Free Reinforcement Learning (CF-RL) algorithm for the fully distributed resource allocation problem in coexisting IIoT systems. Each network adopts the proposed algorithm to minimize collisions through a trial-and-error approach without any information interaction. To resist the influence of environmental dynamics, each coexisting network learns the state transition probability of the resource block instead of the resource block's position. Moreover, to potentially ensure the overall system performance, each network additionally considers the period offset in the initialization phase and action selection phase, so that the coexisting networks have different preferences for different state transitions. We conduct extensive simulations to verify the convergence performance. Evaluation results show that the CF-RL algorithm almost achieves (more than 99.88%) the effect of centralized resource allocation and has obvious superiorities over other cooperation-free algorithms in terms of the convergence rate, the number of collisions, and the resource utilization ratio.

As many robot applications become more reliant on wireless communications, wireless network latency and reliability have a growing impact on robot control. This article proposes a network hardware-in-the-loop (N-HiL) simulation framework to evaluate the impacts of wireless on robot control more efficiently and accurately, and then improve the design by employing correlation analysis between communication and control performances. The N-HiL method provides communication and robot developers with more trustworthy network conditions, while the huge efforts and costs of building and testing the entire physical robot system in real life are eliminated. These benefits are showcased in two representative latency-sensitive applications: 1) safe multirobot coordination for mobile robots, and 2) human-motion-based teleoperation for manipulators. Moreover, we deliver a preliminary assessment of two new-generation wireless technologies, the Wi-Fi6 and 5G, for those applications, which has demonstrated the effectiveness of the N-HiL method as well as the attractiveness of the wireless technologies.

More and more robot automation applications have changed to wireless communication, and network performance has a growing impact on robotic systems. This study proposes a hardware-in-the-loop (HiL) simulation methodology for connecting the simulated robot platform to real network devices. This project seeks to provide robotic engineers and researchers with the capability to experiment without heavily modifying the original controller and get more realistic test results that correlate with actual network conditions. We deployed this HiL simulation system in two common cases for wireless-network-controlled robotic applications: (1) safe multi-robot coordination for mobile robots, and (2) human-motion-based teleoperation for manipulators. The HiL simulation system is deployed and tested under various network conditions in all circumstances. The experiment results are analyzed and compared with the previous simulation methods, demonstrating that the proposed HiL simulation methodology can identify a more reliable communication impact on robot systems.

We consider a broadband over-the-air computation empowered model aggregation scheme for federated learning (FL) in Industrial Internet of Things systems. Due to fading and communication noise, the received global gradient parameters inevitably become inaccurate, leading to a notable decrease of the learning performance. Instead of discarding any edge nodes to reduce the aggregation error, we propose to assign each of them a proper weight coefficient in the model aggregation procedures, i.e., amplitude alignment of the received local gradient parameters from different edge nodes is not required in this paper. We derive an upper bound on the performance loss of the proposed FL scheme, which is shown to be related to the weight coefficients of edge nodes and the mean-squared error (MSE) between the desired global gradient parameters and the actually received ones. Then, we derive a closed-form expression for MSE and use it as the objective function to formulate an optimization problem with respect to the edge nodes’ transmit equalization coefficients, their weight coefficients, and the receive scalars of the cloud server. We transform the formulated optimization problem into a convex one and solve it optimally using CVX. Last, we leverage the popular MNIST dataset and conduct experiments to evaluate the prediction accuracy of the proposed FL scheme. Simulation results demonstrate its superior performances.

The introduction of information and communication technologies makes network environments increasingly open, leaving smart-grid control systems incredibly vulnerable to malicious attacks. False data injection (FDI) attacks stealthily tamper with measurement data, resulting in erroneous decisions made by the control center that greatly influence the normal operation of the power system. By taking advantage of real-time data acquisition with edge computing, in this article, we propose a scheme based on classification of predicted residuals (CPRs) for the FDI attack detection. The CPR scheme first predicts the acquired measurement data at the edge of the sensing network via developing an accurate prediction model. Followed the novel real-time classification method under the edge devices supporting, it classifies the predicted residuals independent of the false data to enhance the detection accuracy. Through these two steps, the detection rate of FDI attacks is greatly improved. The proposed scheme is validated in a real microgrid testbed. Experimental results show that the CPR scheme performs well in detecting FDI attacks and remains sensitive in injection attack probability and magnitude. The detection scheme even has effectiveness at low injection attack probability and magnitude (5% and 0.018 per thousand, respectively). Furthermore, it also proves that the proposed scheme has applicability in high real-time requirements at the edge of smart grids.

Advanced industrial communication systems are fundamental pillars of the ongoing digital transformation of industrial systems. Initiatives like the Industry 4.0 and the Industrial Internet Consortium aim at increasing the overall production efficiency leveraging on fast, reliable, and deterministic communication technologies. The improvements of information and communication technologies (ICT), driven by the tremendous influence of the consumer market, result in positive relapse for the industrial ICT, allowing an ever-increasing amount of data and information to be gathered from systems, machines, and devices. On the other hand, the availability of low-cost computational capabilities permitted to easily process these “Big Data,” generating on-the-fly trend forecasts for the management systems, which, in turn, allow to issue actions for controlling the manufacturing processes in real time. The ensemble of all these technologies is generally addressed with the term industrial Internet of Things (IIoT).

Edge computing (EC) is an essential component of large-scale intelligent manufacturing systems for Industry 4.0, which promises to provide a preprocessing platform for the massive data generated by the terminals and guarantee lower delay and more security compared to directly processing data in cloud computing. Nevertheless, access authentication is a crucial security issue of current EC systems, and, thus, this article presents a solution to enhance the access classification accuracy by exploiting the physical layer information. Our method employs a weighted voting scheme for channel state information based authentication using a single sample which includes sample segmentation, grouping, and weighted voting and finally achieves the fast and low complexity secure-access requirement of the EC system without increasing the individual devices’ sample size and computational complexity. Experimental results utilizing public datasets and field-measured datasets demonstrate that the proposed weighted voting method has higher accuracy and robustness than existing methods.

Impulse interference is an important cause of transmission failure in the industry environments targeted by the Wireless High Performance (WirelessHP). As interleavers are commonly used to improve the reliability on the Orthogonal Frequency Division Multiplexing (OFDM) symbol level for long packet transmission, this paper considers the feasibility of applying short-packet bit interleaving to enhance the impulse/burst interference resisting capability on both OFDM symbol and frame level. Using the Universal Software Radio Peripherals (USRP) and PC hardware platform, the Packet Error Rate (PER) performance of interleaved coded short-packet transmission with Convolutional Codes (CC), Reed-Solomon (RS) codes, and RS+CC concatenated codes are tested and analyzed. The IEEE 1613 standard is applied for impulse interference generation, and extensive PER tests of CC$(1/2)$and RS$(31,21)+$CC$(1/2)$concatenated codes are conducted. We prove the effectiveness of bit interleaved coded short-packet transmission in real factory environments with practical experiments. Moreover, we investigate how PER performance depends on the interleavers, codes and impulse interference power and frequency.

In this paper, we consider a millimeter wave multiple-input single-output tracking system, where the time-varying angle of departure (AoD) is assumed to change following a discrete state Markov process. Depending on whether the associated AoD transition function is available or not, we propose two different training beam sequence design approaches. Specifically, in the case when the AoD transition function is available, we leverage the maximum a posteriori criterion to estimate the updated AoD in each beam tracking period. Since it is infeasible to derive an explicit expression for the resultant estimation error rate, we turn to its upper bound, which possesses a closed-form expression and is therefore used as the objective function to optimize the training beam sequence. Considering the complicated objective function and the unit modulus constraints imposed by the analog phase shifters, we resort to a particle swarm algorithm to solve the formulated optimization problem. In the case when the AoD transition function is unavailable, we turn to the maximum likelihood criterion for AoD estimation. To cope with the unknown AoD transition function, we reformulate the beam tracking problem as a partially observable Markov decision process problem and develop an actor-critic reinforcement learning framework to obtain an efficient training beam sequence design. Numerical results demonstrate superiorities of the proposed training beam sequence design approaches for both two cases.

In wireless high-performance communications (WirelessHP) target industrial applications, impulse interference is an important source that may cause burst errors in a transmitted packet. By concatenating interleaver with channel coding, this paper investigates the improvement of reliability for short packets transmission in WirelessHP. Based on our constructed hardware platform for WirelessHP protocols, the packets error rate (PER) of interleaved coded packets transmission for convolutional codes (CC) is tested with detailed analysis. Through practical experiments, we shown that interleavers can improve the PER performance in factory environments, the interleaver structure and code rate are also important factors affecting the improvement.

Industrial wireless networks (IWNs) systems require high performance and high security in critical manufacturing processes. However, the wireless environment suffers from low performances and fragile security due to lack of physical connection. In this paper, by taking advantages of reconfigurable intelligent surface (RIS) to enhance the physical layer (PHY) security of IWNs thus to resist eavesdropping attacks, where RIS reflects the incident signal at a certain phase shift to assist the legitimate transmission partners to transfer secret information without eavesdroppers' channel state information(CSI). In this way, the reflection of radio waves is actively controlled to overcome the negative effects of natural wireless propagation. The average secrecy capacity of the legal users is maximized by optimizing the phase shift of the RIS, which provides a cost-effective high security transmission solution for the IWNs. The widely experiments proof the effectiveness of proposed scheme.

Turbo codes are a family of near Shannon limit error correction coding schemes that usually are adopted for wireless data transmission. To reduce the power dissipation of a long-term evolution (LTE) advanced turbo decoder, in this paper, we propose a reverse calculation based low memory turbo decoder architecture by partitioning the trellis diagram and simplifying the max* operator. The designed forward state metrics calculation architecture is merged with two classical decoding schemes. Through field programmable gate array (FPGA) hardware implementation, the state metrics cache (SMC) capacity is reduced by 65%, the power dissipation of the reverse calculation architecture is significantly reduced for all tested clock frequencies, and the decoding performance is not affected as compared with classical decoding schemes. The proposed reverse calculation architecture is an effective technique to achieve better decoding performance for power-constrained applications.

In this article, a scheme to detect both clone and Sybil attacks by using channel-based machine learning is proposed. To identify malicious attacks, channel responses between sensor peers have been explored as a form of fingerprints with spatial and temporal uniqueness. Moreover, the machine-learning-based method is applied to provide a more accurate authentication rate. Specifically, by combining with edge devices, we apply a threshold detection method based on channel differences to provide offline training sample sets with labels for the machine learning algorithm, which avoids manually generating labels. Therefore, our proposed scheme is lightweight for resource constrained industrial wireless devices, since only an online-decision making is required. Extensive simulations and experiments were conducted in real industrial environments. Both results show that the authentication accuracy rate of our strategy with an appropriate threshold can achieve 84% without manual labeling.

Wireless communication is evolving to support critical control in automation systems. The fifth-generation (5G) mobile network air interface New Radio adopts a scalable numerology and mini-slot transmission for short packets that make it potentially suitable for critical control systems. The reliable minimum cycle time is an important indicator for industrial communication techniques but has not yet been investigated within 5G. To address such a question, this article considers 5G-based industrial networks and uses the delay optimization based on data-driven channel characterization (CCDO) approach to propose a method to evaluate the reliable minimum cycle time of 5G. Numerical results in three representative industrial environments indicate that following the CCDO approach, 5G-based industrial networks can achieve, in real-world scenario, millisecond-level minimum cycle time to support several hundred nodes with reliability higher than 99.9999%.

Industrial wireless cyber-physical systems are vulnerable to malicious node attacks, for example, clone node attack. The existing clone detection schemes are either based on upper layer observations or physical layer channel state information. The schemes based on upper layer observations are vulnerable to defamation while the schemes based on channel state information perform better against defamation, but are badly affected by channel conditions. This article applies physical layer reputation and back propagation neural network to clone detection, aiming at improving the detection accuracy. The proposed scheme accumulates the physical layer reputations by channel state information and input them to the neural network. The cloud server performs attack detection by group detection first. If a certain group is classified as attacked, the corresponding edge processor will perform attack tracing to identify the specific clone nodes. During the attack tracing stage, multiple reputations of each node is adopted for a comprehensive inspection. Extensive experiments are conducted on the Universal Software Radio Peripheral platform. The numerical results show that the proposed scheme significantly improves the detection accuracy.

With the fourth revolution of healthcare, i.e., Healthcare 4.0, collaborative robotics is spilling out from traditional manufacturing and will blend into human living or working environments to deliver care services, especially telehealthcare. Because of the frequent and seamless interaction between robots and care recipients, it poses several challenges that require careful consideration: 1) the ability of the human to collaborate with the robots in a natural manner; and 2) the safety of the human collaborating with the robot. In this regard, we have proposed a proximity sensing solution based on the self-capacitive technology to provide an extended sense of touch for collaborative robots, allowing approach and contact measurement to enhance safe and natural human-robot collaboration. The modular design of our solution enables it to scale up to form a large-area sensing system. The sensing solution is proposed to work in two operation modes: the interaction mode and the safety mode. In the interaction mode, utilizing the ability of the sensor to localize the point of action, gesture command is used for robot manipulation. In the safety mode, the sensor enables the robot to actively avoid obstacles.

Wireless communication is gaining popularity in the industry for its simple deployment, mobility, and low cost. Ultralow latency and high reliability requirements of mission-critical industrial applications are highly demanding for wireless communication, and the indoor industrial environment is hostile to wireless communication due to the richness of reflection and obstacles. Assessing the effect of the industrial environment on the reliability and latency of wireless communication is a crucial task, yet it is challenging to accurately model the wireless channel in various industrial sites. In this article, based on the comprehensive channel measurement results from the National Institute of Standards and Technology at 2.245 and 5.4 GHz, we quantify the reliability degradation of wireless communication in multipath fading channels. A delay optimization based on the channel characterization is then proposed to minimize packet transmission times of a cyclic prefix orthogonal frequency division multiplexing system under a reliability constraint at the physical layer. When the transmission bandwidth is abundant and the payload is short, the minimum transmission time is found to be restricted by the optimal cyclic prefix duration, which is correlated with the communication distance. Results further reveal that using relays may, in some cases, reduce end-to-end latency in industrial sites, as achievable minimum transmission time significantly decreases at short communication ranges.

To meet the extremely low latency constraints of industrial wireless control in critical applications, the wireless high-performance scheme (WirelessHP) has been introduced as a promising solution. The proposed design showed great improvements in terms of latency, but its performance in terms of reliability have not been fully tested yet. While traditional wireless systems achieve high reliability through packet retransmissions, this would impair the latency, and an approach based on channel coding is preferable in industrial applications. In this paper, a set of packet error rate (PER) tests is performed by applying concatenated Reed Solomon and convolutional codes to the WirelessHP physical layer, using a demonstrator based on a universal software radio peripheral platform. The effectiveness of channel coding to achieve 10-7level PER without retransmissions is shown in typical laboratory and factory environments.

Individuals, such as voice-related professionals, elderly people and smokers, are increasingly suffering from voice disorder, which implies the importance of pathological voice repair. Previous work on pathological voice repair only concerned about sustained vowel /a/, but multiple vowels repair is still challenging due to the unstable extraction of pitch and the unsatisfactory reconstruction of formant. In this paper, a multiple vowels repair based on pitch extraction and Line Spectrum Pair feature for voice disorder is proposed, which broadened the research subjects of voice repair from only single vowel /a/ to multiple vowels /a/, /i/ and /u/ and achieved the repair of these vowels successfully. Considering deep neural network as a classifier, a voice recognition is performed to classify the normal and pathological voices. Wavelet Transform and Hilbert-Huang Transform are applied for pitch extraction. Based on Line Spectrum Pair (LSP) feature, the formant is reconstructed. The final repaired voice is obtained by synthesizing the pitch and the formant. The proposed method is validated on Saarbrücken Voice Database (SVD) database. The achieved improvements of three metrics, Segmental Signal-to-Noise Ratio, LSP distance measure and Mel cepstral distance measure, are respectively 45.87%, 50.37% and 15.56%. Besides, an intuitive analysis based on spectrogram has been done and a prominent repair effect has been achieved.

Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed.

The eleven papers presented in this special issue provide a snapshot of the latest advances in the field of enabling technologies in health engineering and health informatics for the new revolution of Healthcare 4.0, hoping to further enable, drive and accelerate the research, development, and application of key technologies into healthcare systems.

With the paradigm shift from hospital-centric healthcare to home-centric healthcare in Healthcare 4.0, healthcare robotics has become one of the fastest growing fields of robotics. The combination of robot capabilities with human intelligence, for example, telerobotics for home care, is gradually showing promising potentials. In this paper, the Home-TeleBot system, a generalized IoT-enabled telerobotic architecture designed to support home-centric healthcare system, is proposed. In particular, the implementation of it is realized by integrating human-motion-capture subsystem with robot-control subsystem. The dual-arm cooperative robot, YuMi, imitates human motion captured by a set of wearable inertial motion capture devices to complete tasks. The proposed approach using workspace mapping and path planning of robot manipulators, facilitates telerobot to execute tasks in a natural and human-like way. Based on the constant of proportionality calculated by comparing the human original workspace with the robot original workspace, the workspace mapping is achieved by making assumptions of the distance between end-effectors (human hands, robot's grippers) and shoulders. Additionally, robot manipulators' path is planned by setting virtual obstacles to constrain robot motion, which aims to improve the performance of robot's human-like motion. As a specific example of application, we apply the proposed architecture to a fetching task based on dual-arm motion capture and mapping for telerobotics in home care.

An innovative feature of the 5th Generation mobile network (5G) is to consider industrial applications as use cases for which its new radio access, 5G New Radio, aims to provide ultra low latency and ultra high reliability performance. These requirements are fulfilled by minimizing standard performance indicators such as end-to-end latency and packet error rate. However, industrial control applications typically require periodic exchange of small data, where the ability of networks to support short and deterministic cycle times is the main key performance indicator. This paper proposes a methodology to evaluate the achievable cycle time of an industrial network deployed over the 5G New Radio specifications. Numerical results shows that 5G can achieve millisecond level cycle time with network size of several hundred, which is promising for many factory automation applications.

In Industrial Internet of Things (IIoTs), data generated during manufacturing are collected by sensors and need be processed timely. The direction of data transmissions from sensors to processing centers (fog nodes) is often called uplink transmission. In this paper, the cases with single and multiple distributed fog nodes, which are also referred to as centralized and distributed models, are studied. Two distributed schemes based on batched sparse (BATS) codes are proposed separately for the uplink of these two models. The expected rank and the recovery probability of the information from sensors at fog node(s) are derived. Comparison results show that by using the proposed BATS-based schemes, improved transmission reliability can be achieved compared to the XOR-based network coding (NC) scheme.

This paper considers an Industrial Internet of Thing (IIoT) system with a source monitoring a dynamic process with randomly generated status updates. The status updates are sent to an designated destination in a real-time manner over an unreliable link. The source is subject to a practical constraint of limited average transmission power. Thus, the system should carefully schedule when to transmit a fresh status update or retransmit the stale one. To characterize the performance of timely status update, we adopt a recent concept, Age of Information (AoI), as the performance metric. We aim to minimize the long-term average AoI under the limited average transmission power at the source, by formulating a constrained Markov Decision Process (CMDP) problem. To address the formulated CMDP, we recast it into an unconstrained Markov Decision Process (MDP) through Lagrangian relaxation. We prove the existence of optimal stationary policy of the original CMDP, which is a randomized mixture of two deterministic stationary policies of the unconstrained MDP. We also explore the characteristics of the problem to reduce the action space of each state to significantly reduce the computation complexity. We further prove the threshold structure of the optimal deterministic policy for the unconstrained MDP. Simulation results show the proposed optimal policy achieves lower average AoI compared with random policy, especially when the system suffers from stricter resource constraint. Besides, the influence of status generation probability and transmission failure rate on optimal policy and the resultant average AoI as well as the impact of average transmission power on the minimal average AoI are unveiled.

Evolution of smart sensing technologies provide an increasingly utilization for IoT-enabled healthcare. In the context of the aging population, the demand for elderly-assistant robots is increasing. At the same time, more and more attention has been paid to the more intuitive way of remote human-robot interaction. In this article, we present the design, implementation, and evaluation of a telerobotic-assisted healthcare system with the ability to achieve the remote elderly assistant and healthcare application. In this work, a remote operation interface using wearable inertial motion capture suit is proposed to control the YuMi robot remotely. The motion capture subsystem and the robot control subsystem are all based on robot operation system (ROS) to carry out the distributed design and integration. The robot arm is controlled by the position and orientation data of the operator's hand acquired by the motion capture suit. Furthermore, the robot's gripper is controlled by the finger bending signal acquired by a data glove. The achievement and performance of the introduced system was verified by experiments.

As one of the key enabling technologies of emerging smart societies and industries (i.e., industry 4.0), the Internet of Things (IoT) has evolved significantly in both the technologies and applications. It is estimated that more than 25 billion devices will be connected by wireless IoT networks by 2020. In addition to ubiquitous connectivity, many envisioned applications of the IoT, such as industrial automation, vehicle-to-everything (V2X) networks, smart grids, and remote surgery, will have stringent transmission latency and reliability requirements, which may not be supported by the existing systems. Thus, there is an urgent need for rethinking the entire communication protocol stack for wireless IoT networks.

As one of the key enabling technologies of emerging smart societies and industries (i.e., industry 4.0), the Internet of Things (IoT) has evolved significantly in both technologies and applications. It is estimated that more than 25 billion devices will be connected by wireless IoT networks by 2020. In addition to ubiquitous connectivity, many envisioned applications of IoT, such as industrial automation, vehicle-to-everything (V2X) networks, smart grids, and remote surgery, will have stringent transmission latency and reliability requirements, which may not be supported by existing systems. Thus, there is an urgent need for rethinking the entire communication protocol stack for wireless IoT networks. In this tutorial paper, we review the various application scenarios, fundamental performance limits, and potential technical solutions for high-reliability and low-latency (HRLL) wireless IoT networks. We discuss physical, MAC (medium access control), and network layers of wireless IoT networks, which all have significant impacts on latency and reliability. For the physical layer, we discuss the fundamental information-theoretic limits for HRLL communications, and then we also introduce a frame structure and preamble design for HRLL communications. Then practical channel codes with finite block length are reviewed. For the MAC layer, we first discuss optimized spectrum and power resource management schemes and then recently proposed grant-free schemes are discussed. For the network layer, we discuss the optimized network structure (traffic dispersion and network densification), the optimal traffic allocation schemes and network coding schemes to minimize latency.

Ultra-high reliable and low-latency communication (URLLC) is envisaged to support emerging applications with strict latency and reliability requirements. Critical industrial control is among the most important URLLC applications where the stringent requirements make the deployment of wireless networks critical, especially as far as latency is concerned. Since the amount of data exchanged in critical industrial communications is generally small, an effective way to reduce the latency is to minimize the packet's synchronization overhead, starting from the physical layer (PHY). This paper proposes to use a short one-symbol PHY preamble for critical wireless industrial communications, reducing significantly the transmission latency with respect to other wireless standards. Dedicated packet detection and synchronization algorithms are discussed, analyzed, and tuned to ensure that the required reliability level is achieved with such extremely short preamble. Theoretical analysis, simulations, and experiments show that detection error rates smaller than 10-6can be achieved with the proposed preamble while minimizing the latencies.

While the current generation of mobile and fixed communication networks has been standardized for mobile broadband services, the next generation is driven by the vision of the Internet of Things and mission-critical communication services requiring latency in the order of milliseconds or submilliseconds. However, these new stringent requirements have a large technical impact on the design of all layers of the communication protocol stack. The cross-layer interactions are complex due to the multiple design principles and technologies that contribute to the layers' design and fundamental performance limitations. We will be able to develop low-latency networks only if we address the problem of these complex interactions from the new point of view of submilliseconds latency. In this paper, we propose a holistic analysis and classification of the main design principles and enabling technologies that will make it possible to deploy low-latency wireless communication networks. We argue that these design principles and enabling technologies must be carefully orchestrated to meet the stringent requirements and to manage the inherent tradeoffs between low latency and traditional performance metrics. We also review currently ongoing standardization activities in prominent standards associations, and discuss open problems for future research.

Wireless networks are ever more deployed in the industrial control scenario, thanks to the numerous benefits they can bring, especially in terms of costs and flexibility. However, some critical fields of application, such as motion control, power systems automation, or power electronics control, to mention some, have extremely tight requirements in terms of timeliness, reliability, and determinism, which nowadays can only be satisfied by wired communication networks. Indeed, the available industrial wireless solutions are far from offering adequate performance levels, especially in the timing budget, due to the native limitations of their physical (PHY) layers. In this paper, an innovative approach for high-performance industrial wireless networks [wireless high performance (WirelessHP)] is presented, based on a substantial redesign of the lower layers of the industrial wireless protocol stack, with the aim of supporting the requirements of critical industrial control applications. The required levels of timeliness, reliability, and determinism are first derived through a comprehensive survey that looks at real-world application scenarios as well as at the performance of wired networks for industrial control, such as real-time Ethernet networks. The design of a new solution, which is able to satisfy these targets, is then discussed in detail, introducing a low-latency PHY layer that aims at reducing the transmission time of short packets to 1 μs, or even less. The feasibility of the proposed solution is presented through an experimental demonstrator based on software-defined radios, while its performance bounds are computed through theoretical analyses. Finally, future activities in the context of WirelessHP are widely discussed, providing an overview of the directions that will have to be addressed, particularly in the design of the upper layers.

Information is the key element in modern factory automation and process control systems, and one of the most difficult tasks is to provide, distribute, and properly process it. Information transfer and processing in this scenario depend to a large extent on appropriate communication systems, usually referred to as “industrial networks.” Significance, content, and properties of information may, of course, vary within a given application context. For instance, a distributed control system typically handles sensor and actuator information with stringent real-time requirements, whereas production planning needs to cope with huge amounts of data, such as those concerned with customer orders or bills of material, with more relaxed timing. The industrial networks deployed at the various hierarchical levels of automation systems thus have to meet different requirements, to ensure that timely and reliable data flows are maintained among the different components they connect, such as field devices, controllers, human–machine interface (HMI) systems, cloud computers, manufacturing execution systems (MES), and so on[1],[2]. According to the specific needs of the numerous application fields such as manufacturing, electrical power distribution, motion control, environmental monitoring, and chemical processes, to mention but a few[3],[4], industrial networks may have diverse architectures, traffic types, and performance.

Wireless industrial cyber-physical systems are increasingly popular in critical manufacturing processes. These kinds of systems, besides high performance, require strong security and are constrained by low computational capabilities. Physical layer authentication (PHY-AUC) is a promising solution to meet these requirements. However, the existing threshold-based PHY-AUC methods only perform ideally in stationary scenarios. To improve the performance of PHY-AUC in mobile scenarios, this article proposes a novel threshold-free PHY-AUC method based on machine learning (ML), which replaces the traditional threshold-based decision-making with more adaptive classification based on ML. This article adopts channel matrices estimated by the wireless nodes as the authentication input and investigates the optimal dimension of the channel matrices to further improve the authentication accuracy without increasing too much computational burden. Extensive simulations are conducted based on a real industrial dataset, with the aim of tuning the authentication performance, then further field validations are performed in an industrial factory. The results from both the simulations and validations show that the proposed method significantly improves the authentication accuracy.

The emergence of artificial intelligence (AI), empowered by robust computing infrastructure and abundance of data, maintains potential for radical transformation of human society, essentially a third phase in evolution. Numerous research endeavor, policy development, and thought-leadership are presently in progress aimed at discovering data-driven intelligent decision-making solutions for smart cities, smart grids, smart homes, and informed citizens as well as addressing potential risks posed by AI workplace automation. Joining this broad effort, this Special Section contributes six research articles that consolidate recent developments in AI for industrial informatics.

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.

Industrial applications pose the most stringent requirements to the underlying communication networks. Wireless industrial communication is gaining popularity due to its cost-effectiveness, flexibility and capability of functioning in harsh environments. However, to replace the wired counterpart in the most critical applications performing real-time control and monitoring, the according requirements in terms of latency, reliability and determinism must be met, the latency among which is found to be the bottleneck. To improve the latency performance of wireless communication, modification or new techniques should be developed. Moreover, medium access control (MAC) design should be based on valid timing parameters, as to achieve ultra-low latency, the timing parameters are pushed to the limits. In this paper, we consider to provide fundamental timing constraints as valid inputs for MAC design for wireless high performance network. We start from determining the fundamental constraints as well as the affecting factor in the timing perspective, and then we review and analyze some state-of-art wireless implementations in terms of the timing indexes. Based on the investigation and analysis, realistic timing constraints of different algorithms, hardware, mechanisms are presented, and three main directions for MAC design in wireless high performance network are outlined.

Physical layer authentication based on channel state information is an effective solution to preventing spoofing attacks in wireless communications by comparing the channel impulse responses. Existing theoretical analyses and experiments have proved the feasibility and efficiency in labs or offices. However, the environment of industrial wireless communication is significantly different. This paper applies physical layer authentication based on channel state information to measurements from four different industrial wireless communication scenarios, including indoor, outdoor, moving, and stationary scenarios. The analysis of the results allows to derive meaningful insights on the applicability of such a method to industrial wireless communications.

We analyze the delay performance of traffic dispersion in millimeter-wave (mm-wave) communications, where Nakagami-m fading channel is considered. To apply (min, +)-algebra network calculus in wireless communications, we develop a closed-form expression based on moment generating function (MGF), which characterizes the stochastic service process by staying in the bit domain, rather than transferring to the SNR domain. Subsequently, for traffic dispersion with mm-wave, we derive probabilistic delay bounds and effective capacity based on the obtained MGF of the cumulative service process. Besides, the impacts of several factors, e.g., the number of independent path, system gain (including antenna gain and adopted radio frequency) or Nakagami parameter, on the delay performance are studied. We not only comprehensively study the delay performance of traffic dispersion with mm-wave, but also demonstrate the feasibility and tractability of performance analysis.

The need for energy efficiency in wireless communication is prevalent in all areas, but to an even greater extent in low-power and lossy networks that rely on resource-constrained devices. This paper seeks to address the problem of modeling the battery lifetime of a duty-cycled node, participating in a wireless sensor network that is typically used in smart home and building applications. Modeling in MATLAB and experimentation with prototype testing are employed to predict and validate the battery lifetime. Various scenarios including sleepy end devices in a wireless sensor network are modeled and validated. They range from variable wake-up frequency and packet payload transmission to increasing network contention with the addition of network load. A comprehensive analysis of the main factors contributing to wasteful energy usage is provided. It can be concluded that the model can estimate the battery lifetime under different testing scenarios with an error rate less than 5%.

Motivated by the proliferation of wireless building automation systems (BAS) and increasing security-awareness among BAS operators, in this paper, we propose a taxonomy for the security assessment of BASs. We apply the proposed taxonomy to Thread, an emerging native IP-based protocol for BAS. Our analysis reveals a number of potential weaknesses in the design of Thread. We propose potential solutions for mitigating several identified weaknesses and discuss their efficacy. We also provide suggestions for improvements in future versions of the standard. Overall, our analysis shows that Thread has a well-designed security control for the targeted use case, making it a promising candidate for communication in next generation BASs.

In an industrial automation system, one of the most important parts is control loop. Fog computing is a potential solution for industrial control in time-critical applications as it provides distributed computing services closer to the connected devices. However, a huge amount of data exchanging among fog nodes causes high communication load, which constrains the overall response time from fog nodes to actuators. In this paper, we consider the erasure environment, batched sparse (BATS) codes are applied to the Map and the Data Shuffling stages of distributed fog computing process to reduce both the communication and the computation loads. The communication loads of the uncoded, the coded, and the proposed BATS-based schemes over erasure channels are calculated, respectively. Numerical results show that the BATS-based scheme can reduce the communication and the computation loads simultaneously, and furthermore reduce the overall response time from fog nodes to the actuators.

The aging of population has been recognized as one of the top grand global challenges. Age-related diseases such as neurodegenerative diseases, cardiovascular and cerebrovascular diseases, and psychological diseases have become the primary killers of human and consume the major portion of healthcare resources due to the long course of disease and large patient base. Powered by the technologies originated from manufacturing industries driven by the fourth revolution of industry (Industry 4.0), the fourth revolution in healthcare technologies (Healthcare 4.0) is also happening as envisioned by Pang et al. [item 1) in the Appendix]. In the Healthcare 4.0, vast amount of cyber and physical systems (CPS) are closely combined through the Internet of Things (IoT), intelligent sensing, big data analytics, artificial intelligence, cloud computing, automatic control, and autonomous execution and robotics to create not only digitalized healthcare products and technologies but also digitalized healthcare services and enterprises. Driven by these mega trends, the Health Engineering (i.e., the applications of engineering principles and convenience approaches to solve problems in health) is emerging as a new interdisciplinary field of research and development. This convergence research model provides a blueprint for addressing society’s most pressing health challenges and leads to a revolutionized healthcare system that enables the participation of all people for the early prediction and prevention of diseases, so that preemptive treatment can be delivered to realize personalized, precision, pervasive, and patient-centralized healthcare,

Greater demands are being placed on the access bandwidth, stability, and delay of network because of the quickening rhythm of life and work, especially in mobile scenario. In order to obtain a stable network with low latency and high bandwidth in mobile scenario, taking advantage of the wireless heterogeneous network in parallel is a good choice. Nowadays, people are increasingly concerned about the network quality under the mobile scenario. Some scholars have done the relevant measurements. However, all of those measurements mainly investigate part of the network parameters or part of mobile scenarios. In this paper, we make the following contributions. Firstly, in high‐speed mobile scenario, the wireless network qualities of different vendors are measured synthetically. Secondly, we analyze the benefits of taking advantage of the different vendors. Thirdly, we deploy the replication link mechanism in high‐speed mobile scenario and propose an algorithm to remove the duplicate packet in high‐speed mobile scenario. And the algorithm can also be used in another multipath schedule algorithm to improve the reliability.

Identifying different floors in multistory buildings is a very important task for precise indoor localization in industrial and commercial applications. The accuracy from existing studies is rather low, especially in multistory buildings with irregular structures such as hollow areas, which is common in various industrial and commercial sites. As a better solution, this paper proposes a hybrid floor identification (HYFI) algorithm, which exploits wireless access point (AP) distribution and barometric pressure information. It first extracts the distribution probability of APs scanned in different floors from offline training fingerprints and adopts Bayesian classification to accurately identify floor in well-partitioned zones without hollow areas. The floor information obtained from wireless AP distribution is then used to initialize and calibrate barometric pressure-based floor identification to compensate variable environmental effects. Extensive experiments confirm that the HYFI approach significantly outperforms purely wireless fingerprinting-based or purely barometric pressure-based floor identification approaches. In our field tests in multistory facilities with irregular hollow areas, it can identify the floor level with more than 96.1% accuracy.

A sensor data acquisition system is an essential part of an industrial automation control system. However, the variety of sensor producer causes the difficulty of protocol's unity. The mechanism which requires the acquisition boards to be powered off when the number or types or manufacturer of sensors is changed is unreasonable. Meanwhile, traditional sensor reading cycle depends more on the embedded program skills. In this paper, to solve these problems, a new method is proposed to design a reconfigurable data acquisition system for industrial sensors, in which field-programmable gate array (FPGA) is adopted as the core controller. We use both dynamic system reconfiguration and static system reconfiguration in our design. Performance of the proposed system is verified in practical application of an automated production system of poly carboxylic acid water reducing agent.

Owing to the semantic ambiguities, it has hindered the promotion of IEC 61499 in the field of industrial automation. In order to solve the thorny problem, this paper proposes an implementation scheme for performing formal modeling and simulation verification of semantics of functional block networks. Based on the synchrony hypothesis, the formal execution model is defined according to the fixed point semantics assuming that the behavior of a component functional block is monotonic. Subsequently, through specifying the evaluation of function blocks (FBs) as a process of solving the least-fixed point problem and transforming the network topology into a directed graph, a connectivity attenuation-based algorithm is put forward to ascertain the optimal scheduling policy of FBs with the minimum overhead. Finally, by conducting the experiment for an industrial application, the feasibility and validity of the presented implementation scheme is proved.

Industrial applications aimed at real-time control and monitoring of cyber-physical systems pose significant challenges to the underlying communication networks in terms of determinism, low latency, and high reliability. The migration of these networks from wired to wireless could bring several benefits in terms of cost reduction and simplification of design, but currently available wireless techniques cannot cope with the stringent requirements of the most critical applications. In this paper, we consider the problem of designing a high-performance wireless network for industrial control, targeting at Gbps data rates and 10-μs-level cycle time. To this aim, we start from analyzing the required performance and deployment scenarios, then we take a look at the most advanced standards and emerging trends that may be applicable. Building on this investigation, we outline the main directions for the development of a wireless high-performance system.

The next generations of industrial control systems will require high-performance wireless networks (named WirelessHP) able to provide extremely low latency, ultrahigh reliability, and high data rates. The current strategy toward the realization of industrial wireless networks relies on adopting the bottom layers of general purpose wireless standards and customizing only the upper layers. In this paper, a new bottom-up approach is proposed through the realization of a WirelessHP physical layer specifically targeted at reducing the communication latency through the minimization of packet transmission time. Theoretical analysis shows that the proposed design allows a substantial reduction in packet transmission time, down to 1 μs, with respect to the general purpose IEEE 802.11 physical layer. The design is validated by an experimental demonstrator, which shows that reliable communications up to 20 m range can be established with the proposed physical layer.

A smart environment system should automatically control the devices according to the sensing information and users' requirements so as to keep the environmental elements (e.g., temperature, light) within the desired range. System control with minimum power is one key issue in such a system. In this paper, we propose a multi-dimension model for system control. In this model, each environmental element is abstracted into a dimension, such that a service with conditions and targets can be formulated as a multi-dimensional service space, and a smart environment with many services may map to a comprehensive multi-dimensional service space through space computation. Based on this model, we propose a minimum power adjustment algorithm for energy-efficient scheduling in smart environment, which transforms the optimal control problem into the problem of the shortest weighted distance of point-to-polygonal in multi-dimensional space. Theoretical analysis and experimental results show that the proposed model is effective and efficient in energy-efficient system control. It is important to point out that the proposed algorithms are scalable when the number of dimensions or services increases.

Although home automation (HA) systems in the wired domain are widely accepted by consumers, in today's industry, the mega trend is steering HA systems along a wireless way. Theoretically, wireless solutions are able to provide HA systems with more flexibility and thus reducing engineering costs. In practice, however, deploying wireless HA systems actually requires more costs and efforts due to the lack of versatile software tools to support the whole engineering process. This paper defines and evaluates the engineering workflow and architecture for home automation systems. The proposed architecture is studied and implemented based on web technologies and graphical configuration environments, with the aim of reducing workloads of HA engineers at every stage. A prototype has been implemented to demonstrate the technical feasibility of the proposed architecture.

This paper presents a feasibility study of implementing parts of a distributed function block application as TinyOS modules running on Wireless Sensors as a part of Wireless Sensor Network. The paper first briefly describes underlying technologies and gives motivation for implementation of function blocks in TinyOS. The paper then presents implementation details about TinyOS realization of the one of the function block, which is a part of bigger distributed control application with the help of distributed function block application.

In various industrial automation applications, positioning enables location awareness which can be exploited in applications such as robotic precision control, amongst others. This paper discusses positioning for industrial automation applications. Examples of applications from an infrastructure perspective are presented, and the integration of high precision positioning technology into existing network infrastructure for such applications is discussed, as well as challenges involved. An impulse-radio ultra-wideband 802.15.4a based system for high accuracy/precision positioning is presented, which shows promising results, therefore motivating the use of UWB as a high accuracy and precision technology for positioning applications in future industrial automation systems.

A sensor interface device is essential for sensor data collection of industrial wireless sensor networks (WSN) in IoT environments. However, the current connect number, sampling rate, and signal types of sensors are generally restricted by the device. Meanwhile, in the Internet of Things (IoT) environment, each sensor connected to the device is required to write complicated and cumbersome data collection program code. In this paper, to solve these problems, a new method is proposed to design a reconfigurable smart sensor interface for industrial WSN in IoT environment, in which complex programmable logic device (CPLD) is adopted as the core controller. Thus, it can read data in parallel and in real time with high speed on multiple different sensor data. The standard of IEEE1451.2 intelligent sensor interface specification is adopted for this design. It comprehensively stipulates the smart sensor hardware and software design framework and relevant interface protocol to realize the intelligent acquisition for common sensors. A new solution is provided for the traditional sensor data acquisitions. The device is combined with the newest CPLD programmable technology and the standard of IEEE1451.2 intelligent sensor specification. Performance of the proposed system is verified and good effects are achieved in practical application of IoT to water environment monitoring.

The Internet of Things (IoT) application market (IAM) is supposed to be an effective approach for service distribution in the era of IoT. To protect the privacy and security of users, a systematic mechanism to determine the trustworthiness of the applications in the IAM is demanded. In this paper, an interactive trust model (ITM) is proposed based on interaction between application market and end users. In this model, application trustworthiness (AT) is quantitatively evaluated by the similarity between the application's behavior and the behavior expected by the user. In particular, by using the evaluation vector and feedback vector feature of application in the marketplace and behavior of applications on end devices can be exchanged in mathematical form to establish the connection between market and users. Behavior-based detecting agent on a users' device gives strong evidence about what applications have done to your privacy and security issues. Indicators derived by this model are presented in the market along with the application, and it helps users to more efficiently select the most appropriate application from the market.

Industrial wireless sensor network (IWSN) is a key enabling technology for the Internet-of-things (IoT). IWSN acts as one of the fundamental elements of the IoT infrastructure to bridge the physical sensors and actuators in field and backbone systems in the Internet. For deterministic performances, all mainstream IWSN standards utilize the slotted media access control (MAC) where the communication is allocated based on the superframe that comprises a number of slots in either contention-based access or contention-free access modes. In this paper, the planning of the superframe structure of the slotted MAC is investigated by two means: 1) a mathematical model of the MAC access latency based on the queue theory; and 2) an easy-to-use software tool based on packet-level simulation. The mathematical model gives an overall estimation of the average MAC access latency of the whole network. The software tool gives the exact latency of each packet and then can derive the optimal superframe structure of the network. The two means are validated correspondingly. With the methods proposed in this paper, IWSN designers can minimize the MAC access latency while satisfying the requirements at different generating rates of packet, number of nodes in the network, and packet buffer length of each node.

In-home healthcare services based on the Internet-of-Things (IoT) have great business potential; however, a comprehensive platform is still missing. In this paper, an intelligent home-based platform, the iHome Health-IoT, is proposed and implemented. In particular, the platform involves an open-platform-based intelligent medicine box (iMedBox) with enhanced connectivity and interchangeability for the integration of devices and services; intelligent pharmaceutical packaging (iMedPack) with communication capability enabled by passive radio-frequency identification (RFID) and actuation capability enabled by functional materials; and a flexible and wearable bio-medical sensor device (Bio-Patch) enabled by the state-of-the-art inkjet printing technology and system-on-chip. The proposed platform seamlessly fuses IoT devices (e.g., wearable sensors and intelligent medicine packages) with in-home healthcare services (e.g., telemedicine) for an improved user experience and service efficiency. The feasibility of the implemented iHome Health-IoT platform has been proven in field trials.

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