Industrial Internet Of Things Communication Research Articles

A Novel Approach for the Enhancement of Security through Defining the Spatial Secrecy Outage Probability in the Industrial Internet of Things

The rapid development of the Industrial Internet of Things (IIoT) has brought enormous opportunities to the industrial environment, but has also posed significant challenges to network security, especially physical layer security, which is crucial for the reliable transmission of messages among interconnected devices and sensors. Focusing on enhancing the physical layer security in IIoT communication, this paper proposes a mechanism for the Spatial Secrecy Outage Probability (SSOP) to ensure the secure transmission of data. The SSOP algorithm assumes that the location of legitimate devices is known, while the eavesdropper devices have a random distribution of locations, forming the SSOP model related to the device locations from the perspective of insecure regions (ISRs) and secure regions (SRs), and the closed-form expression for its upper bound is derived. Subsequently, under the constraint of the SSOP conditions, we establish an optimization model with the objective of system throughput maximization based on the Spatial Secrecy Outage Probability (SRM-SSOP). By optimizing the transmission rate of legitimate channels or controlling the transmission power, the maximization of the system throughput can be achieved, thus reducing the consumption of communication resources. Through sophisticated modeling and simulation in MATLAB, we verified the accuracy of the definition and derived the upper bound of the SSOP. The experimental results also show that the SSOP algorithm gradually converges to a specific value as the number of antenna elements increases. In addition, we observed that, when the receiver device is aligned with the normal direction of the transmit antenna array, the SSOP reaches a minimum value. This performance is crucial for understanding and optimizing the security performance of IIoT communication systems.

A new three-factor authentication scheme using Chebyshev chaotic map for peer-to-peer Industrial Internet of Things

The Industrial Internet of Things (IIoT) is an emerging technology for smart manufacturing and productivity improvement. Due to the increasing complexity of manufacturing, devices with smart industrial sensors from several domains work together on the same job, raising serious security and privacy concerns about peer-to-peer (P2P) communication. Specifically, P2P secure communication should be focused on when it comes to high efficiency and security of information exchange between two industrial sensors. However, none of the existing schemes can resist physical attacks towards industrial sensors. To resolve the problem, we propose a new three-factor authentication and key agreement scheme named NTFAK for P2P IIoT communication using Physical Unclonable Functions (PUF) and Chebyshev chaotic mapping, where PUF is used to replace the smart card factor in the traditional schemes. The unclonable property of PUF and its ability to securely identify a device by challenge-response can provide safety to physical attacks. Besides, the use of the Chebyshev chaotic map also can provide high computational efficiency while ensuring better security. The proposed scheme enables secure communication between any two industrial sensors, which can resist physical attacks and offer a smaller key size, faster computation, and higher efficiency for IIoT. The security of our scheme is formally proved by the random oracle model, and security properties are discussed to show our scheme resists various attacks. Moreover, experiments were implemented to prove the efficiency of our scheme. Specifically, the results show that NTFAK can improve the computation and communication overheads by 36.6% and 17.5% at least, compared to existing schemes, while also ensure a minimum time reduction of 30.4% in delay time. In terms of security and functionality features, NTFAK satisfies the proposed 14 properties, outperforming existing solutions.

Two-Phase Industrial Control System Anomaly Detection Using Communication Patterns and Deep Learning

Several cases of Industrial Internet of Things (IIoT) attacks with zero-day vulnerabilities have been reported. To prevent these attacks, it is necessary to apply an abnormal behavior detection method; however, there are three main problems that make it hard. First, there are various industrial communication protocols. Instead of IT environments, many unstandardized protocols, which are usually defined by vendors, are used. Second, legacy devices are commonly used, not only EOS (End-of-service), but also EoL (End-of-Life). And last, the analysis of collected data is necessary for defining normal behavior. This behavior should be separately defined in each IIoT. Therefore, it is difficult to apply abnormal behavior detection in environments where economic and human investment is difficult. To solve these problems, we propose a deep learning based abnormal behavior detection technique that utilizes IIoT communication patterns. The proposed method uses a deep learning technique to train periodic data acquisition sequences, which is one of the common characteristics of IIoT. The trained model determined the sequence of packet is normal. The proposed technique can be applied without an additional analysis. The proposed method is expected to prevent security threats by proactively detecting cyberattacks. To verify the proposed method, a dataset was collected from the Korea Electric Power Control System. The model that defines normal behavior based on the application layer exhibits an accuracy of 79.6%. The other model, defining normal behavior based on the transport layer, has an accuracy of 80.9%. In these two models, most false positives and false negatives only occur when the abnormal packet is in a sequence.

ASAP-IIOT: An Anonymous Secure Authentication Protocol for Industrial Internet of Things.

With the increasing demand for a digital world, the Industrial Internet of Things (IIoT) is growing rapidly across various industries. In manufacturing, particularly in Industry 4.0, the IIoT assumes a vital role. It encompasses many devices such as sensing devices, application servers, users, and authentication servers within workshop settings. The security of the IIoT is a critical issue due to wireless networks' open and dynamic nature. Therefore, designing secure protocols among those devices is an essential aspect of IIoT security functionality and poses a significant challenge to the IIoT systems. In this paper, we propose a lightweight anonymous authentication protocol to preserve privacy for IIoT users, enabling secure IIoT communication. The protocol has been validated to demonstrate its comprehensive ability to overcome various vulnerabilities and prevent malicious attacks. Finally, the performance evaluation confirms that the proposed protocol is more effective and efficient than the existing alternatives.

OTFS-Based Joint Communication and Sensing for Future Industrial IoT

Effective wireless communications are increasingly important in maintaining the successful closed-loop operation of mission-critical Industrial Internet of Things (IIoT) applications. To meet the ever-increasing demands on better wireless communications for IIoT, we propose an orthogonal time-frequency space (OTFS) waveform-based joint communication and radio sensing (JCAS) scheme—an energy-efficient solution for not only reliable communications but also high-accuracy sensing. OTFS has been demonstrated to have higher reliability and energy efficiency than the currently popular IIoT communication waveforms. JCAS has also been highly recommended for IIoT, since it saves cost, power, and spectrum compared to having two separate radio frequency systems. Performing JCAS based on OTFS, however, can be hindered by a lack of effective OTFS sensing. This article is dedicated to filling this technology gap. We first design a series of echo preprocessing methods that successfully remove the impact of communication data symbols in the time-frequency domain, where major challenges, such as intercarrier and intersymbol interference and noise amplification, are addressed. Then, we provide a comprehensive analysis of the signal-to-interference-plus-noise ratio (SINR) for sensing and optimize a key parameter of the proposed method to maximize the SINR. The extensive simulations show that the proposed sensing method approaches the maximum-likelihood estimator with respect to the estimation accuracy of target parameters and manifests applicability to wide ranges of key system parameters. Notably, the complexity of the proposed method is only dominated by a 2-D Fourier transform.

A Multi-Entity Knowledge Joint Extraction Method of Communication Equipment Faults for Industrial IoT

The Industrial Internet of Things (IIoT) deploys massive communication devices for information collection and process control. Once it reaches failure, it will seriously affect the operation of the industrial system. This paper proposes a new method for multi-entity knowledge joint extraction (MEKJE) of IIoT communication equipment faults. This method constructs a multi-task tightly coupled model of fault entity and relationship extraction. We use it to implement word embedding and bidirectional semantic capture to generate computable text vectors. At the same time, a multi-entity segmentation method is proposed, which uses noise filtering to distinguish the multi-fault relationship of single corpus. We constructed a dataset of communication failures in power IIoT and conducted experiments. The experimental results show that the method performs best in tests with the Faulty Text dataset and the CLUENER dataset. In particular, the model achieves an F1 value of 78.6% in the evaluation of relationship extraction for multiple entities, and a significant improvement of 5–8% in its accuracy and recall. It enables effective mapping and accurate extraction of fault knowledge data.

Encryption with Image Steganography Based Data Hiding Technique in IIoT Environment

Rapid advancements of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) pose serious security issues by revealing secret data. Therefore, security data becomes a crucial issue in IIoT communication where secrecy needs to be guaranteed in real time. Practically, AI techniques can be utilized to design image steganographic techniques in IIoT. In addition, encryption techniques act as an important role to save the actual information generated from the IIoT devices to avoid unauthorized access. In order to accomplish secure data transmission in IIoT environment, this study presents novel encryption with image steganography based data hiding technique (EIS-DHT) for IIoT environment. The proposed EIS-DHT technique involves a new quantum black widow optimization (QBWO) to competently choose the pixel values for hiding secrete data in the cover image. In addition, the multi-level discrete wavelet transform (DWT) based transformation process takes place. Besides, the secret image is divided into three R, G, and B bands which are then individually encrypted using Blowfish, Twofish, and Lorenz Hyperchaotic System. At last, the stego image gets generated by placing the encrypted images into the optimum pixel locations of the cover image. In order to validate the enhanced data hiding performance of the EIS-DHT technique, a set of simulation analyses take place and the results are inspected interms of different measures. The experimental outcomes stated the supremacy of the EIS-DHT technique over the other existing techniques and ensure maximum security.

Study on abnormal data acquisition method of industrial internet of things communication based on node clustering

In order to overcome the problems of low efficiency and poor accuracy of traditional abnormal data capture, this paper proposes an industrial IoT communication abnormal data capture method based on node clustering. First, analyse the structure of Industrial Internet of Things (IIoT), collect communication data and extract the characteristics of industrial communication data; then, the node subordination function is constructed, and the abnormal data types are divided by common neighbour node analysis. Finally, the matching degree between features is extracted through node clustering calculation, and the matching degree is input into the classifier function and the communication exception data capture result is output. The results show that the malicious node capture rate of this method is always higher than 92%, the capture time is always less than 3.6 seconds and the effect of abnormal data capture of IoT communication is improved.

Study on abnormal data acquisition method of industrial Internet of things communication based on node clustering

Study on abnormal data acquisition method of industrial Internet of things communication based on node clustering

A Stable Data Transmission Method of Industrial Internet of Things Communication Based on Bat Algorithm

A Stable Data Transmission Method of Industrial Internet of Things Communication Based on Bat Algorithm

A stable data transmission method of industrial internet of things communication based on bat algorithm

A stable data transmission method of industrial internet of things communication based on bat algorithm

A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithms

AbstractWith the substantial industrial growth, the industrial internet of things (IIoT) and many IoT avenues have emerged. However, the existing industrial architectures are still inefficient to deal with advanced security issues due to the distributed and distensible nature of the network IIoT communication networks. Therefore, solutions for improving intelligent decision‐making actions to the IIoT are sorely necessary. Thus, in this paper, the main cybersecurity attacks are predicted by applying a deep learning model. The various security and integrity features such as the DoS, malevolent operation, data type probing, spying, scanning, intrusion detection, brute force, web attacks, and wrong setup is analysed and detected by a novel sparse evolutionary training (SET) based prediction model. To scrutinize the conduct of the proposed SET‐based prediction model, evaluation parameters, such as, precision, accuracy, recall, and F1 score are measured and compared to other state‐of‐the‐art algorithms, in which the proposed SET‐based model achieved an average accuracy of 0.99% for an average testing time of 2.29 ms. Results reveal that the proposed model improved the attack detection accuracy by an average of 6.25% when compared with the other state‐of‐the‐art machine learning models in a real scenario of IoT security in Industry 4.0.

Sparse Bayesian learning based channel estimation in FBMC/OQAM industrial IoT networks

The next generation of communication technology is accelerating the transformation of industrial internet of things (IIoT). Filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM), as a candidate wireless transmission technology for beyond fifth generation (5G), has been widely concerned by researchers. However, effective channel estimation (CE) in IIoT communication should be solved. In practice, wireless channels have block–sparse structures. For the conventional sparse channel model, the general sparse channel estimation methods do not take the potential block–sparse structure information into account. In this paper, we have investigated the sparse Bayesian learning (SBL) framework for sparse multipath CE in FBMC/OQAM communications. Block SBL (BSBL) algorithm is proposed to estimate the channel performance by exploiting the block–sparse structure of sparse multipath channel model. The BSBL method can improve the estimation performance by using the block correlation of the training matrix. Computer simulation results demonstrate the robustness of the BSBL CE approach in FBMC/OQAM systems, which can achieve lower mean square error (MSE) and bit error rate (BER) than traditional least squares (LS) method and classical compressive sensing methods. The state of art compressive sampling matching pursuit (CoSaMP) greedy algorithm with a prior knowledge of sparse degree can provide slightly better CE performance than BSBL algorithm, but the proposed method maintains robustness in practical channel scenario without the prior knowledge of sparse degree.

Research Trends and Performance of IIoT Communication Network‐Architectural Layers of Petrochemical Industry 4.0 for Coping with Circular Economy

In the present era, many Petrochemical Industries (PIs) are driven energetically due to IIoT (Industrial Internet of Things) Communication Networks/Architectural Layers (CNs/ALs), abbreviated as PI4.0‐CNs/ALs. PI4.0 fruitfully participated to achieve the Circular Economy (CE) by speeding the reutilization, recovery, and recycling of scrap materials by minimizing cost, unproductive operations, energy consumption, emission of flue gases, etc. Recently, it has been ascertained that the identification and measurement of Research Trends (RTs) of CNs‐ALs help the PI4.0 to build the future CE. In addressing the said research challenge, the objective of this research dossier is turned towards inculcating into future PI4.0 researchers the RTs of CNs/ALs of PI4.0, so that the researches can be organized over the very weak and moderately performing CNs‐ALs to hike the future CE. To materialize the RTs of PI4‐CNs/ALs, the authors conducted the Systematic Literature Survey (SLS) focusing on PI4.0‐CNs/ALs, i.e., Internet of Things (IoTs), Cyber Physical System (CPS), Virtual Reality (VR), Integration (I), Data Optimization (DO), Enterprise Resource Planning (ERP), Plant Control (PC), Data and Analytics (DA), Network (N), and Information and Data Management (IDM). The authors searched three hundred two research documents, wherein two hundred seventy‐five research manuscripts qualified as RQ2. Next, the authors collected the DOIs/URLs corresponding to each CN‐AL and explored the Sum of Digit Scoring System (SDSS) to summarize the DOIs/URLs of PI4.0‐CNs/ALs. The RTs of DO have been determined as excellent and stronger over 2007‐2017 than residue CNs/ALs. Eventually, the authors advised scholars to focus on the research areas of very weak and moderately weak performing CNs/ALs in order to attain future CE.

Physical Layer Authentication for Non-Coherent Massive SIMO-Enabled Industrial IoT Communications

Achieving ultra-reliable, low-latency and secure communications is essential for realizing the industrial Internet of Things (IIoT). Non-coherent massive multiple-input multiple-output (MIMO) is one of promising techniques to fulfill ultra-reliable and low-latency requirements. In addition, physical layer authentication (PLA) technology is particularly suitable for secure IIoT communications thanks to its low-latency attribute. A PLA method for non-coherent massive single-input multiple-output (SIMO) IIoT communication systems is proposed in this paper. This method realizes PLA by embedding an authentication signal (tag) into a message signal, referred to as "message-based tag embedding". It is different from traditional PLA methods utilizing uniform power tags. We design the optimal tag embedding and optimize the power allocation between the message and tag signals to characterize the trade-off between the message and tag error performance. Numerical results show that the proposed message-based tag embedding PLA method is more accurate than the traditional uniform tag embedding method which has an unavoidable tag error floor close to 10%.

Industrial internet of things over IEEE 802.15.4 TSCH networks: design and challenges

Using the internet of things technologies in manufacturing provides a promising opportunity to build powerful industrial systems and applications. The quest for mobility, flexibility, and low-energy consumption has created a strong push toward using low-power wireless solutions to enable the industrial internet of things (IIoT). This paper presents a survey of the emerging research concerning IIoT with a focus on the most promising solutions. It, first, outlines the main requirements to be addressed in order to build a powerful IIoT system. The paper, then, presents an overview accompanied by a comparative study of the most prevalent IIoT communication technologies. The study reveals the potential of the IEEE 802.15.4 standard with its time slotted channel hopping (TSCH) mode to lead the way through the IIoT, thanks to its latency, reliability and low-power characteristics along with the support of multi-hop communications. Based on this outcome, the paper provides an in-depth look at TSCH mechanisms and outlines the most challenging issues. Finally, the paper concludes the need to propose new solutions addressing such issues in order to make successful IIoT systems.

Industrial internet of things over IEEE 802.15.4 TSCH networks: design and challenges

Using the internet of things technologies in manufacturing provides a promising opportunity to build powerful industrial systems and applications. The quest for mobility, flexibility, and low-energy consumption has created a strong push toward using low-power wireless solutions to enable the industrial internet of things (IIoT). This paper presents a survey of the emerging research concerning IIoT with a focus on the most promising solutions. It, first, outlines the main requirements to be addressed in order to build a powerful IIoT system. The paper, then, presents an overview accompanied by a comparative study of the most prevalent IIoT communication technologies. The study reveals the potential of the IEEE 802.15.4 standard with its time slotted channel hopping (TSCH) mode to lead the way through the IIoT, thanks to its latency, reliability and low-power characteristics along with the support of multi-hop communications. Based on this outcome, the paper provides an in-depth look at TSCH mechanisms and outlines the most challenging issues. Finally, the paper concludes the need to propose new solutions addressing such issues in order to make successful IIoT systems.

A Robust ECC-Based Provable Secure Authentication Protocol With Privacy Preserving for Industrial Internet of Things

Wireless sensor networks (WSNs) play an important role in the industrial Internet of Things (IIoT) and have been widely used in many industrial fields to gather data of monitoring area. However, due to the open nature of wireless channel and resource-constrained feature of sensor nodes, how to guarantee that the sensitive sensor data can only be accessed by a valid user becomes a key challenge in IIoT environment. Some user authentication protocols for WSNs have been proposed to address this issue. However, previous works more or less have their own weaknesses, such as not providing user anonymity and other ideal functions or being vulnerable to some attacks. To provide secure communication for IIoT, a user authentication protocol scheme with privacy protection for IIoT has been proposed. The security of the proposed scheme is proved under a random oracle model, and other security discussions show that the proposed protocol is robust to various attacks. Furthermore, the comparison results with other related protocols and the simulation by NS-3 show that the proposed protocol is secure and efficient for IIoT.

Trust-Based Communication for the Industrial Internet of Things

Recently, the Industrial Internet of Things (IIoT) is attracting growing attention from both academia and industry. Meanwhile, trust-based communication is widely utilized in various systems. In this article, studying the performance of IIoT, we investigate trust-based communication for IIoT. In particular, devoting attention to sensor- cloud, which is a paradigm of IIoT, we propose three types of trust-based communication mechanisms for sensor-cloud. Furthermore, with numerical results, we show that trust-based communication can greatly enhance the performance of sensor-cloud. Eventually, open research issues with respect to trust-based communication for sensor-cloud are discussed.