Industrial Internet Of Things Data Research Articles

An effective method for anomaly detection in industrial Internet of Things using XGBoost and LSTM

In recent years, with the application of Internet of Things (IoT) and cloud technology in smart industrialization, Industrial Internet of Things (IIoT) has become an emerging hot topic. The increasing amount of data and device numbers in IIoT poses significant challenges to its security issues, making anomaly detection particularly important. Existing methods for anomaly detection in the IIoT often fall short when dealing with data imbalance, and the huge amount of IIoT data makes feature selection challenging and computationally intensive. In this paper, we propose an optimal deep learning model for anomaly detection in IIoT. Firstly, by setting different thresholds of eXtreme Gradient Boosting (XGBoost) for feature selection, features with importance above the given threshold are retained, while those below are ignored. Different thresholds yield different numbers of features. This approach not only secures effective features but also reduces the feature dimensionality, thereby decreasing the consumption of computational resources. Secondly, an optimized loss function is designed to study its impact on model performance in terms of handling imbalanced data, highly similar categories, and model training. We select the optimal threshold and loss function, which are part of our optimal model, by comparing metrics such as accuracy, precision, recall, False Alarm Rate (FAR), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision–Recall Curve (AUC-PR) values. Finally, combining the optimal threshold and loss function, we propose a model named MIX_LSTM for anomaly detection in IIoT. Experiments are conducted using the UNSW-NB15 and NSL-KDD datasets. The proposed MIX_LSTM model can achieve 0.084 FAR, 0.984 AUC-ROC, and 0.988 AUC-PR values in the binary anomaly detection experiment on the UNSW-NB15 dataset. In the NSL-KDD dataset, it can achieve 0.028 FAR, 0.967 AUC-ROC, and 0.962 AUC-PR values. By comparing the evaluation indicators, the model shows good performance in detecting abnormal attacks in the Industrial Internet of Things compared with traditional deep learning models, machine learning models and existing technologies.

A privacy-aware authentication and usage-controlled access protocol for IIoT decentralized data marketplace

Data is ubiquitous, powerful and valuable today. With vast instalments of Industrial Internet-of-Things (IIoT) infrastructure, data is in abundance albeit sitting in organizational silos. Data Marketplaces have emerged to allow monetization of data by trading it with interested buyers. While centralized marketplaces are common, they are controlled by few and are non-transparent. Decentralized data marketplaces allow the democratization of rates, trading terms and fine control to participants. However, in such a marketplace, ensuring privacy and security is crucial. Existing data exchange schemes depend on a trusted third party for key management during authentication and rely on a ‘one-time-off’ approach to authorization. This paper proposes a user-empowered, privacy-aware, authentication and usage-controlled access protocol for IIoT data marketplace. The proposed protocol leverages the concept of Self-Sovereign Identity (SSI) and is based on the standards of Decentralized Identifier (DID) and Verifiable Credential (VC). DIDs empower buyers and give them complete control over their identities. The buyers authenticate and prove claims to access data securely using VC. The proposed protocol also implements a dynamic user-revocation policy. Usage-controlled based access provides secure ongoing authorization during data exchange. A detailed performance and security analysis is provided to show its feasibility.

Dempster Combination Rule-Aided Multi-Sensor Decision-Level-Based Data Fusion in Industrial Internet of Things (IIoT) over Zero-Trust Security

Data fusion is a process of accommodating data from discrete sources to generate more relevant information with enhanced accuracy and compatibility rather than the data gathered and information produced by the identical or particular source. To achieve better accuracy in the information inferred by the Industrial Internet of Things (IIoT) system, data fusion is an emerging technology that takes data from different involved devices as input and generates more consistent and accurate information. To safeguard the IIoT data involved in communication foremost is to ensure the authenticity of the devices to block unauthorized access and thus enhance the data confidentiality and data integrity as well. Zero-trust security is thus employed in the IIoT infrastructure to meet the security requirements in question. The proposed approach, Dempster combination rule-aided multi-sensor decision-level-based data fusion (MS-DLDF) in Industrial Internet of Things (IIoT) over zero-trust security, breaks down the whole concept into phases – initially, the device authenticity is ensured before entering and accessing the network, next, the device data is then encoded to avoid data theft or breach from any unauthorized device, and lastly, the data fusion algorithm is executed to gather data from multiple sources as input and reach to more appropriate and relevant information as compared to the information inferred from a single source. The proposed MS-DLDF concludes with the outstanding results of the Dempster combination rule with the increased number of sensors in complex networks as compared to the single-source data fusion and Bayesian estimation data fusion with enhanced accuracy by 6%, minimal computation time by 15%, increased precision rate 6%, reduced false positive rate by 31%, and the amplified data confidentiality rate by 2%, respectively.

Interpreting Industrial IoT Data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

Interpreting Industrial IoT Data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

A GSO‐based multi‐objective technique for performance optimization of blockchain‐based industrial Internet of things

SummaryThe latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain‐based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain‐based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain‐based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision‐making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain‐based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations.

Secure multi-party computation with secret sharing for real-time data aggregation in IIoT

Real-time analytics in Industrial Internet-of-Things (IIoT) has received remarkable attention recently due to its capacity to prevent downtime and manage risks. However, the sensed data in IIoT is considered private. Thus, the sensed data of IIoT nodes cannot be transmitted and utilized directly in the cloud server due to the risk of privacy leakage. Data aggregation can effectively balance the availability of data with privacy concerns, making it particularly well-suited for IIoT systems. Although several privacy-preserving aggregation schemes in IIoT have been proposed, the majority of them can only support a single type of aggregation that limits the application scenarios of data aggregation. To address the problems mentioned above, a real-time aggregation analysis scheme for IIoT is proposed, which is constructed based on secure multi-party computation with secret sharing. Specifically, the multi-party computation with secret sharing is utilized to implement data aggregation process for IIoT that achieves multiple types of data aggregation. In addition, the secret sharing is utilized in the proposed scheme that can significantly improve the efficiency of the proposed scheme compared with similar schemes. Moreover, the proposed scheme does not require the involvement of a trusted authority in the data aggregation. Security and performance analyses show that the proposed scheme can enhance the security of the sensed data while effectively aggregating data for real-time analytics in IIoT.

Efficient plaintext checkable identity‐based signcryption in cyber‐physical systems towards IIoT

AbstractMany different sectors are beginning to deploy cyber‐physical systems (CPS) and industrial Internet of Things (IIoT) devices, including wearables, sensors, and critical infrastructure. In order to ensure that the device's data remains confidential, it must be encrypted prior to transmission. However, obtaining authenticity and integrity in IIoT data is also crucial, and signcryption has been proposed as a solution to this challenge. An additional obstacle is how to search for signcrypted cloud data using plaintext keywords. In this paper, we suggest a plaintext‐checkable identity‐based signcryption for CPS towards IIoT (IBSC‐PCE), which enables plaintext searching of signcrypted cloud‐based data. We provide both confidentiality and integrity and also demonstrate the security of our approach under the q‐Diffie‐Hellman inversion problem (q‐DHIP). This problem has been shown to be secure in the random oracle model (ROM), which further reinforces the robustness of our approach. Compared to current state‐of‐the‐art methods, our proposed approach outperforms them.

Balancing data privacy and sharing in IIoT: Introducing the GFL-LFF aggregation algorithm

As the Industrial Internet of Things (IIoT) continues to evolve, the need for effective data aggregation schemes that balance the imperatives of data sharing and individual data privacy becomes paramount. Traditional data aggregation methods often entail risks of sensitive information exposure during data transmission and storage. This paper proposes a novel Graphical Federated Learning-based Local Fennec Fox (GFL-LFF) algorithm to ensure secure aggregation by minimizing the revelation of individual data. In this study, a Graph Neural Network (GNN) with federated learning is employed to enhance privacy protection for IIoT's data aggregation scheme. A Local search-based fennec fox optimization is utilized for tuning the parameters of GNN. The GFL and LFF approaches are integrated to enhance the security of the data that can be shared through the IIoT system. The MNIST dataset, CIFAR-10 dataset, and LFW dataset were taken to conduct the experimentation results, and the evaluation measures, such as throughput, end-to-end delay, network lifetime, latency, and energy consumption, are utilized to evaluate the proposed GFL-LFF method, and these results are compared with other approaches. The GFL-LFF method achieved a throughput of 0.98 Mbps, an end-to-end delay of 1.2 s, a network lifetime of 5610 rounds, a latency of 1.6 s, and an energy consumption of 0.2 mJ. The experimental results illustrate the effectiveness of the proposed GFL-LFF method for IIoT's data aggregation scheme in privacy protection.

Robust Trajectory and Offloading for Energy-Efficient UAV Edge Computing in Industrial Internet of Things

Efficient data processing and computation are essential for the industrial Internet of things (IIoT) to empower various applications, which yet can be significantly bottlenecked by the limited energy capacity and computation capability of the IIoT nodes. In this paper, we employ an unmanned aerial vehicle (UAV) as an edge server to assist IIoT data processing, while considering the practical issue of UAV jittering. Specifically, we propose a joint design on trajectory and offloading strategies to minimize energy consumption due to local and edge computation, as well as data transmission. We particularly address the UAV jittering that induces Gaussian-distributed uncertainties associated with flying waypoints, resulting in probabilistic-form flying speed and data offloading constraints. We exploit the Bernstein-type inequality to reformulate the constraints in deterministic forms and decompose the energy minimization to solve for trajectory and offloading separately within an alternating optimization framework. The subproblems are then tackled with the successive convex approximation technique. Simulation results show that our proposal strictly guarantees robustness under uncertainties and effectively reduces energy consumption as compared with the baselines.

Edge Intelligence for Industrial IoT: Opportunities and Limitations

Industrial Internet of Things (IIoT) nowadays represents a fundamental technology for the Industry 4.0 initiatives for connecting devices, operators and processes so as to eventually fuel digital transformation. However, if based exclusively on Cloud Computing, the IIoT faces inevitable limitations and barriers. Therefore, in this paper we deal with the Edge Intelligence (EI), a novel paradigm at the confluence of Edge Computing and artificial Intelligence, and, particularly, we analyze its impact on the processing of the IIoT data in the proximity of the events of interest rather than on Cloud servers. Therefore, a general discussion on the most relevant benefits, limitations and open challenges of the IIoT-EI duo is proposed hereinafter and exemplified through an emblematic IIoT use case: this is related to safety-critical tasks based on video analytics and provides a full-fledged comparison between Cloud-and EI-based IIoT deployments (in terms of reliability, responsiveness, bandwidth usage, energy footprint and usability), finally outlining a general strategy for large-scale EI-aided IIoT systems engineering.

PpNNT: Multiparty Privacy-Preserving Neural Network Training System

By leveraging smart devices (e.g., industrial Internet of Things (IIoT)) and real-time data analytics, organizations such as production plants can benefit from increased productivity, reduced costs, enhanced self-monitoring, and autonomous decision-making. In such a setting, machine learning plays an important role in data analytics, but the use of conventional centralized machine learning solutions may raise uncomfortable concerns about data privacy. Hence, one can explore the use of federated learning. In this paper, we propose the <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> rivacy- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u> reserving deep <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> eural <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> etwork <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> raining (PpNNT), which is designed to support federated learning in the multi-party setting. To minimize the overall costs, we further design a hybrid architecture to fully maximize resource utilization. Our proposed design allows the PpNNT system to provide high security, efficiency, and scalability for IIoT data analytics, as evidenced by our theoretical security proof and experimental results on the CIFAR10 dataset.

SPOT-I: Similarity Preserved Optimal Transport for Industrial IoT Data Imputation

SPOT-I: Similarity Preserved Optimal Transport for Industrial IoT Data Imputation

Optimizing Industrial IoT Data Security Through Blockchain-Enabled Incentive-Driven Game Theoretic Approach for Data Sharing

Optimizing Industrial IoT Data Security Through Blockchain-Enabled Incentive-Driven Game Theoretic Approach for Data Sharing

Provably secure lightweight certificateless lattice‐based authenticated key exchange scheme for IIoT

SummaryThrough the Industrial Internet of Things (IIoT), the manufacturing enterprises have significantly enhanced the production efficiency. The transmission of data in IIoT over public channels has brought about information leakage issues. Therefore, it is crucial to agree on a session key among the participants. In recent years, numerous authenticated key exchange (AKE) schemes have been designed. However, those AKE schemes which adopt the certificateless approach to tackle certificate management and key escrow may remain vulnerable to quantum attacks, and other AKE schemes which are based on lattice hard problem so as to achieve the post quantum security may incur key management issues. To simultaneously address the aforementioned challenges, we propose a lightweight certificateless lattice‐based authenticated key exchange (C2LAKE) scheme. Upon the hardness assumptions of the inhomogeneous small integer solution (ISIS) and computational bilateral inhomogeneous small integer solution (CBi‐ISIS) problems, the C2LAKE scheme has been demonstrated to be secure in the eCK model and the random oracle model (ROM). Better trade‐off among security and functionality features, and communication and computation costs make the C2LAKE scheme suitable and applicable in the IIoT environment.

Blockchain-assisted verifiable certificate-based searchable encryption against untrusted cloud server for Industrial Internet of Things

The Industrial Internet of Things (IIoT) has brought practical application value to many industries, where significant amounts of IIoT data and resources are outsourced to cloud server (CS) via diverse networks for data fusion, monitoring, sharing, and calculation analysis. Considering privacy, there is a need to execute the encryption operation on the data before outsourcing, while how to retrieve the encrypted data from CS becomes a thorny issue. Furthermore, the untrusted CS in charge of storing and searching the ciphertexts may return incorrect or incomplete search results for some interest. Verifiable public key searchable encryption (VPKSE) provides the ability to encrypt data, retrieve ciphertext, and verify search results simultaneously. However, the malicious behavior of CS has not been sufficiently considered in most existing schemes, that is, their verifiability only ensures the correctness of search results, neglecting completeness. In this paper, the verifiability of VPKSE is re-examined, and three verifiability levels are defined detailedly. On this basis, a blockchain-assisted verifiable certificated-based searchable encryption (BVCBSE) scheme for IIoT is put forward. The integration of blockchain and cryptographic accumulator ensures that an untrusted CS must return correct and complete search results, achieving the highest level of verifiability. In addition, security analysis demonstrates that BVCBSE can resist keyword guessing attack. Performance evaluation illustrates that BVCBSE is efficient and practical.

Enabling the circular economy in the digital transformation era: evidence from an emerging country

PurposeRecently, the concept of the circular economy (CE) has witnessed significant momentum in academic and professional circles. However, there is a dearth of research that studies the enabling factors of the CE in the era of digital transformation. The existing research aimed to identify the impact of Industry 4.0 readiness on the CE in manufacturing firms operating in Jordan, as well as to identify the mediating role of the industrial Internet of things and big data analytics.Design/methodology/approachFor this work objectives, 380 questionnaires were analyzed. Convergent validity and discriminant validity tests were performed through partial least squares-structural equation modelling (PLS-SEM) in the Smart-PLS programme. Data reliability was confirmed. A bootstrapping technique was used to analyze the data and then hypothesis testing was performed.FindingsThe results indicate that Industry 4.0 readiness, industrial Internet of things (IIoT) and big data analytics positively enable CE, also the IIoT and big data analytics positively mediate the nexus between Industry 4.0 readiness and CE.Practical implicationsThis study promotes the idea of focusing on Industry 4.0 readiness to enhance CE in the Jordanian manufacturing sector and knowing the effect of IIoT and big data analytics in this relationship.Originality/valueThis research developed a theoretical model to understand how Industry 4.0 readiness might enhance the CE in manufacturing firms by invoking the IIoT and big data analytics as mediating constructs in the relationship between Industry 4.0 readiness and CE. This paper offers new theoretical and practical contributions that add value to industry 4.0 and CE literature by testing these constructs' mediation models in the manufacturing sector.

DMCLAEKS: Pairing-free designated-tester multi-recipient certificateless authenticated encryption with keyword search for concealing search patterns

Modern Industrial Internet of Things (IIoT) technologies, such as smart grids, 5G-enabled unmanned aerial vehicles (UAV), and supply chain 4.0, can be leveraged to promote intelligent management. Yet, IIoT systems create enormous volumes of data that must be outsourced to the cloud for storage and to provide end-users with search capabilities. The outsourcing of IIoT data to a third-party cloud service provider (CSP) creates a number of data leakage issues. Recently, Certificateless Authenticated Encryption with Keyword Search (CLAEKS) was proposed to address the issue of encrypted data querying in cloud-assisted IIoT. But so far, the majority of existing techniques depend on expensive bilinear pairing, and they are all single-receiver. In addition, these methods are extremely vulnerable to keyword guessing attacks and do not hide the search pattern, making it simple for an attacker to deduce the search content. In this paper, we present a rapid and trustworthy designed-tester multi-recipient CLAEKS (dMCLAEKS) protocol with the numerous advantages: (1) no expensive bilinear pairing procedures; (2) supporting for ciphertexts with multiple recipients; (3) defending against Inside Keyword Guess Attacks (IKGA); (4) incapability to determine search pattern. A comparative between our suggested scheme and other similar schemes is also offered to demonstrate that the proposed method is stronger overall.

Industrial data monetization: A blockchain-based industrial IoT data trading system

The Internet of Things (IoT) devices utilized in manufacturing industries produce vast volumes of valuable data that can revolutionize productivity and sustainability. While existing centralized data marketplaces facilitate data trading, they are often marred by trust issues, a single point of failure, and significant security and privacy vulnerabilities. Addressing these critical shortcomings, this paper introduces a blockchain-based industrial data trading system that not only ensures secure and transparent data trading but also offers advantages over conventional systems. Unlike traditional marketplaces, our proposed system emphasizes trustworthiness by mitigating third-party risks, enhancing data integrity through decentralized storage, and employing graph technology for efficient blockchain querying. Further, with integrated access control, our system elevates security standards. Preliminary evaluations reveal the system’s potential to offer a more secure, transparent, auditable, and trustworthy data trading environment, distinctly outpacing the capabilities of current marketplaces.

Decentralized IoT System Based on Blockchain and Homomorphic Technologies

Decentralization within IoT eliminates the need to use distributed networks within IoT to communicate only with servers that may face difficulties related to the internet, vulnerabilities, DDOS, or hijacks, merging blockchain with IoT converted the IoT system into a decentralized with many benefits and outcomes from this conversion. An encryption scheme homomorphic technique (HE) is a method that encrypts the cipher data without the need to decrypt it,paillier encryption method is used. This paper aims to propose a system that integrates Paillier cryptosystem homomorphic technology with IoT and lightweight blockchain technology to provide decentralization to the IoT environment and improve security. The proposed system results in improving the IoT device’s work environment by solving the main challenges of security using blockchain, privacy using homomorphism, and data volume using blockchain. The data set used to implement and evaluate the proposed system is industrial internet of things data The dataset used in this paper is generated via machine industry 4.0 Storage System status which represents the system failure and work status. this system is evaluated using standard metrics used to evaluate the blockchain effectiveness and time, resources consumed and shows better results in time and power consumption. Index Terms— internet of things, homomorphic, blockchain, IoT decentralization, IioT.

MapChain-D: A Distributed Blockchain for IIoT Data Storage and Communications

With the rapid growth of the Industrial Internet of Things (IIoT) devices, managing extensive volume of IIoT data becomes a significant challenge. While the conventional cloud storage approaches with centralised data centres suffer from high latency for large-scale IIoT data storage due to the increased communications and latency overheads, distributed storage frameworks such as blockchains have become promising solutions. In this paper, we design and analyse a dual-blockchain framework for secure and scalable distributed data management in large-scale IIoT networks. The proposed framework, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> , consists of a data chain that is mapped to an index chain to provide efficient data storage and lookup. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> is designed for practical IIoT applications with storage, latency, and communications constraints. Detailed data exchange protocols are presented for the data insertion and retrieval operations in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> . Based on these, theoretical analyses are provided on the space, time, and communications complexities of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> compared with conventional single-chain frameworks with local and distributed data storage. We implement our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> prototype using open-source LoRaWAN communications with multiple RPi and Arduino devices, Kademlia-based distributed hash table (DHT), and Ethereum-based blockchain with proof-of-authority (PoA) consensus. Experimental results from our prototype show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> is more suitable to be deployed on resource-constrained IIoT devices. We also highlight the scalability and flexibility of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> with different numbers of edge nodes in the system.