Industrial Internet Of Things Data Research Articles

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.

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.

Intrusion Detection of Industrial Internet-of-Things Based on Reconstructed Graph Neural Networks

Industrial Internet-of-Things (IIoT) are highly vulnerable to cyber-attacks due to their open deployment in unattended environments. Intrusion detection is an efficient solution to improve security. However, because the labeled samples are difficult to obtain, and the sample categories are imbalanced in real applications, it is difficult to obtain a reliable model. In this paper, a general framework for intrusion detection is proposed based on graph neural network technologies. In detail, a network embedding feature representation is proposed to deal with the high dimensional, redundant but categories imbalanced and rare labeled data in IIoT. To avoid the influence caused by the inaccurate network structure, a network constructor with refinement regularization is designed to amend it. At last, the network embedding representation weights and network constructor are trained together. The high accuracy and robust properties of the proposed method were verified by conducting intrusion detection tasks based on public datasets. Compared with several state-of-art algorithms, the proposed framework outperforms these methods in many evaluation metrics. In addition, a hard-in-the-loop platform is designed to test the performance in real environments. The results show that the method can not only identify different attacks but also distinguish between cyber-attacks and physical failures.

A secured industrial wireless iot sensor network enabled quick transmission of data with a prototype study

The Wireless IoT sensor network (IWSN) has significant potential in industrial settings, but to fully realize its benefits, a robust and scalable computer system is required to handle the continuous influx of data from various applications. In this research study, we propose an IoT sensor-cloud architecture that integrates WSN with cloud technology, providing a unique data analytics framework for highly secure analysis of sensor data. The proposed architecture emphasizes effective interoperability mechanisms in the cloud, and provides an IPv6 extensible enterprise WSN design and simulation technique. To demonstrate the effectiveness of our proposed architecture, we track the pH, resistivity, and dissolved oxygen levels of industrial effluents that are discharged into water sources. We use AT instructions in conjunction with the HTTP GET technique to gather and upload detector data to the ThingSpeak cloud through a GPRS internet connection, enabling real-time online monitoring and control using IoT functionality. The proposed architecture uses a distributed approach to handle high volumes of incoming data from the IoT sensors, storing the data in a scalable and accessible way for analysis. Real-time analysis is performed using a combination of batch and stream processing frameworks and machine learning algorithms, and the results are visualized using a web-based dashboard that provides real-time updates on key metrics and allows users to explore the data in different ways. Security is a top priority in our proposed architecture, and we use encryption technologies such as SSL/TLS and access control mechanisms such as OAuth2 to ensure the secure transmission and storage of sensitive industrial IoT data. The architecture is designed to be scalable and adaptable to handle a wide range of IoT use cases in industrial settings. The proposed IoT sensor-cloud architecture provides a robust and scalable solution for the collection, analysis, and exchange of significant amounts of IoT sensor information, enabling real-time monitoring and control of critical environmental parameters in industrial settings.

METHODOLOGY AND ARCHITECTURE FOR THE IMPLEMENTATION OF TPM 4.0 IN SMALL AND MEDIUM-SIZED COMPANIES

Total Productive Maintenance (TPM) is one of the main maintenance management strategies based on the principles of Lean Manufacturing (LM). The emergence of the Industry 4.0 (I4.0) paradigm has promoted the massive digitization of several functional areas, with predictive maintenance standing out as an ideal field for the application of IIoT (Industrial Internet of Things) sensorization and data analytics. However, TPM encompasses many more techniques and the scientific literature shows that the integration of I4.0 with TPM needs further development to understand how technologies can support continuous improvement. This paper explores the relationship between TPM and I4.0 tools. It proposes a roadmap for its implementation in SMEs to achieve a Cyber-Physical TPM 4.0 system. The proposal is being validated by means of a practical case study in an industrial SME, with no previous experience in TPM, and which is pursuing the digital transformation of its processes. Keywords: SME, TPM, Industry 4.0, Cyber-Physical

Edge-Based IIoT Malware Detection for Mobile Devices With Offloading

The advent of 5G brought new opportunities to leapfrog beyond current industrial Internet of Things (IoT). However, the ever growing IoT has also attracted adversaries to develop new malware attacks against various IoT applications. Although deep learning-based methods are expected to combat the sophisticated malwares by exploring the latent attack patterns, such detection can be hardly supported by battery-powered end devices, like Andriod-based smartphones. Edge computing enables near-real-time analysis of IoT data by migrating AI-enabled computation-intensive tasks from resource-constrained IoT devices to nearby edge servers, However, due to varying channel conditions and the demanding latency requirements of malware detection, it is challenging to coordinate the computing task offloading among multiple users. By leveraging the computation capacity and the proximity benefits of edge computing, we propose a hierarchical security framework for IoT malware detection. Considering the complexity of AI-enabled malware detection task, we provide a delay-aware computational offloading strategy with minimum delay. Specifically, we construct a coordinated representation learning model, named by Two Stream Attention-Caps (TSA-Caps) to capture the latent behavioral patterns of evolving malware attacks. Experimental results show that our system consistently outperforms the state-of-the-art systems in detection performance on four benchmark datasets.

Federated Learning for Distributed IIoT Intrusion Detection Using Transfer Approaches

Considering that low-cost and resource-constrained sensors coupled inherently could be vulnerable to growing numbers of intrusion threats, industrial Internet-of-Things (IIoT) systems are faced with severe security concerns. Data sharing for building high-performance intrusion detection models is also prohibited due to the sensitivity, privacy, and high value of IIoT data. This paper presents an anomaly-based intrusion detection system with federated learning for privacy-preserving machine learning in future IIoT networks. To tackle the urgent issue of training local models with non-independent and identically distributed (non-IID) data, we adopt instance-based transfer learning at local. Furthermore, to boost the performance of this system for IIoT intrusion detection, we propose a rank aggregation algorithm with a weighted voting approach. The proposed system achieves superior detection performance with 95.97% and 73.70% accuracy for AdaBoost and Random Forest respectively, outperforming the baseline models by 12.72% and 14.8%.

Artificial immunity based distributed and fast anomaly detection for Industrial Internet of Things

Recent years have witnessed an increased attack surface of the Industrial Internet of Things (IIoT), as the deep convergence of the Internet of Things (IoT) and other information and communications technologies (ICTs). However, the massive geographically-dispersed IoT devices and the big spatiotemporal data they generated, both pose extreme challenges to traditional centralized and cumbersome anomaly detection systems (ADSs). To meet this gap, in this paper we propose a novel artificial immunity based distributed and fast anomaly detection system, coined AIm-ADS. Specifically, first we develop an artificial immune system based data representation and distributed preprocessing framework, to characterize and prepare the hyper-dimensional IIoT data for anomaly detection (i.e., distinguishing between selfs and nonselfs). Second, we design a new negative selection based hyper-dimensional anomaly detector generation model along with a point-hyperspace (PH) tree based self-detector indexing mechanism, for creating anomaly detectors and indexing self-detectors in a hyperspace. Last, we craft a PH tree based fast anomaly detection method to identify anomalous behaviors of cyberattacks on IIoT. Extensive experiments on three publicly available and widely used real-world datasets (i.e., SDS, SWaT and WADI), demonstrate that the proposed AIm-ADS outperforms existing artificial immunity based and deep learning based anomaly detection studies, in terms of both the detection rate and detection time.

HiRAM: A hierarchical risk assessment model and its implementation for an industrial Internet of Things in the cloud

AbstractThe trend towards intelligent control processes has introduced the Internet of Things (IoT) and cloud computing technologies to factories. IoT devices can sense data and send it to a cloud for further analysis in a factory. Consequently, the quantity of such valuable data flowing in an industrial cyber‐physical system has gradually increased. Tailoring a risk assessment system for Industrial IoT (IIoT) is essential, particularly for a cloud platform that handles the IIoT data flow. In this study, we leverage analytic hierarchy processes (AHPs) and propose Hierarchical Risk Assessment Model (HiRAM) for an IIoT cloud platform. The proposed model allows the platform to self‐evaluate its security status. Furthermore, a modular and responsive Risk Assessment System based on HiRAM, called HiRAM‐RAS, is realized and evaluated in a real‐world IIoT cloud platform. We deploy HiRAM‐RAS to a sample application and introduce the practical deployment procedures. We then estimate the practicality of the HiRAM‐RAS by injecting different degrees of errors and launching Distributed denial‐of‐service (DDoS) attacks. The results demonstrate the changes in integrity and availability scores evaluated by HiRAM.

A Caching-Enabled Permissioned Blockchain Scheme for Industrial Internet of Things Based on Deep Reinforcement Learning

The integration of the industrial internet of things (IIoT) and blockchain has become a popular concept that provides IIoT with a trustworthy computing environment. Numerous IIoT nodes together form a decentralized network with rich location-aware computation resources, which can offer great data processing capabilities and low-latency services. However, we still face the challenges of how to efficiently process the massive IIoT data on resource-constrained IIoT nodes by blockchain smart contracts, as their storage capacity only allows them to store limited blockchain data. This work is aimed at improving the smart contract execution efficiency on these IIoT nodes by caching based on deep reinforcement learning. On the one hand, focusing on the characteristics of IIoT, the ledger structure, network architecture, and transaction flow are optimized. IIoT nodes are enabled to store and cache part of block data without affecting global data consistency. On the other hand, we formulated the blockchain caching problem as a Markov decision process and implemented a lightweight caching agent based on deep Q-learning. Proper features and a reward function are defined to minimize the execution delay of smart contracts. The extensive experimental results show that our proposed scheme can effectively reduce the data dissemination costs and smart contract execution delays of IIoT nodes that hold limited blockchain data.