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.

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.

In this study, we investigate the use of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) mounted on energy-efficient uncrewed aerial vehicles (UAVs) to support satellite Internet of Things (IoT) communications served by low-Earth orbit (LEO) satellites. First, we propose a STAR-RIS-equipped UAV framework termed integrated STAR-RIS and UAV (ISRU). Then, we aim to optimize energy efficiency by jointly adjusting the UAV’s flight path, STAR-RIS phase-shifts, and power allocation among IoT devices, all while maintaining equitable user fairness level. However, solving this problem presents considerable challenges due to the nonconvexity and NP-hardness properties of the objective function and constraints. To address, our work introduces a Dinkelbach-based alternating optimization (AO) procedure termed integrated trajectory, phase-shift, and power allocation (ITPP). Our simulation results show that the integration of ISRU and ITPP can achieve 67% higher sum-rates than non-ISRU schemes and save up to 40% more energy than unoptimized trajectory schemes.

Accurate estimation and detection, along with phase shift optimization, are vital for implementing reconfigurable intelligent surface (RIS)-enabled multi-antenna systems in highly disruptive industrial IoT environments. Motivated by the remarkable capabilities of deep learning (DL) techniques, this paper introduces a pioneering approach to address challenges in channel estimation, channel correlation prediction, and symbol detection for industrial IoT. We develop an optimization framework for large-scale IoT deployments to maximize the signal-to-interference-plus-noise ratio (SINR) while minimizing transmit power. We also propose a transformer-based channel correlation predictor for IoT devices, which enables adaptive pilot retransmissions and reduces training overhead through a co-design approach that integrates communication, computation, and control. Extensive simulations under realistic, time-varying industrial IoT channel conditions demonstrate the superiority of our DL-driven approach, achieving significant improvements in detection accuracy and SINR.

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..

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.

Driven by digital twin (DT) technology, the industrial Internet of Things (IIoT) is expanding to open up new frontiers in industrial applications. However, traditional DT modeling approaches require synchronizing massive amounts of data, resulting in high communications overhead and privacy vulnerability. To address this problem, this article proposes a novel DT architecture for IIoT, where the DT can showcase the real-time operating status of the industrial environment. Swarm learning (SL) is an emerging decentralized federated learning (FL) technique that eliminates the need of a centralized server. We present a novel credibility-weighted SL scheme to construct the DT models, which improves data security while ensuring the fairness of participants as opposed to conventional FL. In addition, we develop a DT-assisted deep reinforcement learning algorithm for simultaneously optimizing the system reliability and energy consumption of IIoT. Simulation comparisons demonstrate that the proposed scheme outperforms some state-of-the-art benchmarks in terms of both reliability and energy consumption.

A revolution in advanced manufacturing has been driven by digital technology in the fourth industrial revolution, also known as Industry 4.0, and has resulted in a substantial increase in profits for the industry. In a new paradigm of Industry 5.0, advanced manufacturing will step further and be capable of offering customized products and a better user experience. A number of key enabling technologies are expected to play crucial roles in assisting Industry 5.0 in meeting higher demands of data acquisition and processing, communications, and collaborative robots in the advanced manufacturing process. The aim of this survey is to provide novel insights into advanced manufacturing in Industry 5.0 by summarizing the latest progress of key enabling technologies, such as artificial intelligence of things (AIoT), beyond 5G communications, and collaborative robotics. Finally, key directions for future research to enable this vision to become a reality, such as the industrial metaverse, are outlined.

The booming of the industrial Internet of Things (IIoT) brings an exponential increase in industrial devices, calling for more flexible and low-cost communications. The fifth generation and beyond (B5G) communication technologies provide a dedicated solution by supporting two industry-targeted technologies: Massive machine-type communications (mMTC) and ultra reliable low-latency communications (URLLC). In this article, we design a B5G-aided quality test system in advanced manufacturing, where various sensors are connected to the base station (BS) and send contextual information via mMTC. The BS and quality test machine transmit short length commands and small size feedback to each other, respectively, via URLLC. We formulate a long-term optimization problem to improve the product qualification rate by maximizing the expected average reward with limited testing capacity and changing configurations. To address this problem, we develop a novel context-aware combinatorial quality test (CC-QT) algorithm based on bandit learning (BL), which integrates contextual information to predict the product quality, and a combinatorial method to decrease the complexity of the BL process. Furthermore, we derive a performance upper bound of the proposed CC-QT and analyze its computational complexity. Experimental results illustrate the performance of CC-QT and substantiate its superiority over the existing algorithms.

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 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.

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.

The papers in this special section focus on industrial cyber-physical systems (CPS), with an emphasis on computing and communications applications. CPS systems are defined by integrating computation and communication facilities on the one hand and the monitoring and control of physical processes on the other hand. Industrial cyber–physical systems (ICPS) refer to the science and art of designing and using cyber–physical systems for industrial and process control applications, for example in smart factories, smart energy grids, smart transportation systems, smart cities, and several other areas. Many of these applications are time- and mission-critical and hence often require low latency and high reliability. In parallel, there has been a strong growth in integrating intelligence, often in the form of machine/deep learning, into applications, which also leads to vastly increasing computational requirements. These innovations then will be integrated into complex systems, which need to be properly engineered to become safe, reliable, trustworthy, and secure while at the same time being cost-efficient.

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.

Industrial wireless sensor and actuator networks (IWSANs) offer significant advantages to industrial automation. However, high-reliability demands and hard communication deadlines pose challenges to its practical applications. To achieve this goal, flooding is considered as a promising approach due to multipath diversity and simplicity. In this paper, an enhanced version of REALFLOW, a flooding-based routing protocol for IWSANs is presented. Compared to the original REALFLOW, network management and network stability are improved. REALFLOW is compared with four other flooding protocols via simulations. The simulation results show that REALFLOW has better performance in terms of reliability and consecutive transmission errors when considering deadlines. Compared with normal flooding, REALFLOW achieves comparable reliability performance with decreased redundancy. Measurements from a prototype implementation conducted in an industrial manufacturing workshop reveal that high-reliability and low-application failure rates can be achieved, giving more confidence in providing reliable wireless sensing and actuating for industrial automation.

Applying Industrial Wireless Sensor and Actuator Networks (IWSANs) in the industrial automation is a growing trend due to flexibility, mobility and low cost. According to the current standards, such asWirelessHART and ISA100.11a, multi-channel TDMA transmission is included for reliable and deterministic communication. In this paper, we clarify the dependence of TDMA scheduling for sensors and actuators and point out the low correlation between the scheduling delay and the overall quality of control, and focus on reducing jitter in scheduling for improving quality of control and system stability. We propose a scheduling algorithm, aiming for lowing jitter and compare it with two traditional real-time scheduling schemes. Our simulation results exhibit significantly lower jitters by applying our scheduling policy than those two traditional scheduling schemes.

In this work we propose an Adaptive Forward Error Correction (AFEC) algorithm for best effort Wireless Sensor Networks. The switching model is described in terms of a finite-state Markov model and it is based on the channel behavior, observed via Packet Delivery Ratio in the recent past. We compare the performance of AFEC with static FEC, as well as uncoded transmissions. The results demonstrate a gain in PDR achieved by introducing FEC coding in uncoded IEEE 802.15.4 transmissions, as well as the advantages over static FEC schemes, namely increased throughput and reduced energy consumption. The proposed solution is IEEE 802.15.4-compliant and requires no additional feedback channels.

High reliability and real-time performance are main research challenges in Industrial Wireless Sensor Networks (IWSNs). Existing routing protocols applied in IWSNs are either overcomplicated or fail to fulfill the stringent requirements. In this paper, we propose a reliable and flexible Received Signal Strength-based routing scheme. Our proposed solution can achieve a seamless transition in the event of topology change and can be applied in different industrial environments. The simulation results show that our solution outperforms conventional routing protocols in both reliability and latency. Furthermore, the result also proves that the changes of the network topology have no impact on data transmissions of other nodes by our scheme, whereas conventional routing protocols are shown to fail to recover the network in a short time. Finally, due to dynamic weighting mechanism, the proposed scheme is verified to achieve significantly higher reliability in scenarios with obstacles and avoid installation troubles, compared to location-based flooding scheme. Thus, our proposed scheme is considered to be more suitable for IWSNs than other routing protocols.

In this paper we address the issues of timeliness and transmission reliability of existing industrial communication standards. We combine a Forward Error Correction coding scheme on the Medium Access Control layer with a lightweight routing protocol to form an IEEE 802.15.4-conformable solution, which can be implemented into already existing hardware without violating the standard. After laying the theoretical foundations, we conduct a performance evaluation of the proposed solution. The results show a substantial gain in reliability and reduced latency, compared to the uncoded transmissions, as well as common Wireless Sensor Network routing protocols.