Industrial Internet Of Things Data Research Articles

Deep Multimodal Learning and Fusion Based Intelligent Fault Diagnosis Approach

Industrial Internet of Things (IoT) connecting society and industrial systems represents a tremendous and promising paradigm shift. With IoT, multimodal and heterogeneous data from industrial devices can be easily collected, and further analyzed to discover device maintenance and health related potential knowledge behind. IoT data-based fault diagnosis for industrial devices is very helpful to the sustainability and applicability of an IoT ecosystem. But how to efficiently use and fuse this multimodal heterogeneous data to realize intelligent fault diagnosis is still a challenge. In this paper, a novel Deep Multimodal Learning and Fusion (DMLF) based fault diagnosis method is proposed for addressing heterogeneous data from IoT environments where industrial devices coexist. First, a DMLF model is designed by combining a Convolution Neural Network (CNN) and Stacked Denoising Autoencoder (SDAE) together to capture more comprehensive fault knowledge and extract features from different modal data. Second, these multimodal features are seamlessly integrated at a fusion layer and the resulting fused features are further used to train a classifier for recognizing potential faults. Third, a two-stage training algorithm is proposed by combining supervised pre-training and fine-tuning to simplify the training process for deep structure models. A series of experiments are conducted over multimodal heterogeneous data from a gear device to verify our proposed fault diagnosis method. The experimental results show that our method outperforms the benchmarking ones in fault diagnosis accuracy.

Effective malware detection scheme based on classified behavior graph in IIoT

In Industrial Internet of Things(IIoT), secure transferring, computing and processing data are critical in developing automated environments, such as smart factories, smart airports and smart healthcare systems for high quality service. Therefore, how to make full use of the massive industrial data in IIoT while preventing malware intrusion and leaking out no privacy is a leading and promising work. In this paper, we focus on the research of malware detection and propose an architecture of a classified behavior graph-based intelligent detection model for malware attacks, which can not only avoid the high cost in graph matching but also achieve high malware detection accuracy. Experiments on the malware families Delf, Obfuscated, Small and Zlob, each malware family containing 880 samples, show that the highest accuracy TPR can reach up to 99.9%.

Применение потоковой кластеризации для идентификации гибридных темпоральных автоматов на аналоговых данных IIoT

Модели класса темпоральных автоматов предоставляют удобную абстракцию для решения задач диагностики и управления в системах промышленного Интернет вещей (IIoT). Для их применения необходимо выполнять идентификацию автомата путем пассивного обучения онлайн, используя только положительные образцы. Такой тип обучения возможен только для гибридных автоматов (HTA) с алгоритмом OTALA. Однако для обучения HTA необходима последовательность дискретных событий вместо непрерывных аналоговых временных рядов, обычно встречающихся в IIoT. A. von Birgelen, O. Niggemann и другие предпринимали попытки восполнить эту пробел за счет предварительной обработки наблюдений самоорганизационными картами (SOM) с преобразованием водораздела, однако полученные модели часто неэффективны в реальных условиях. В данной статье задача идентификации темпорального автомата по аналоговым данным IIoT решается с применением инкрементного алгоритма кластеризации на основе моделей (IMCF). IMCF - это потоковый алгоритм, который обрабатывает входные временные ряды в режиме онлайн и превращает их в последовательность дискретных состояний с четкими или нечеткими переходами между ними. Такие переходы далее используются как события, необходимые для идентификации HTA по алгоритму OTALA. Полученные модели экспериментально исследованы на примере системы IIoT, ранее представлявшей сложность для моделирования. Достигнутое повышение эффективности на 24,9–76,8 % демонстрирует, что дискретизация с помощью IMCF более информативна для идентификации HTA. Также обсуждаются более широкие перспективы применения HTA в системах IIoT и определены ограничения, остающиеся актуальными для подобных практических применений: дискретность переходов между состояниями HTA и отсутствие долговременной памяти переходов.

A Survey of Industrial Internet of Things Platforms for Establishing Centralized Data-Acquisition Middleware: Categorization, Experiment, and Challenges

The development of industrial Internet of Things (IIoT), big data, and artificial intelligence technologies is leading to a major change in the production system. The change is being propagated into the wave of transforming the existing system with a vertical structure into the corresponding horizontal platform or middleware. Accordingly, the way of acquiring IIoT data from an individual system is being altered to the way of being increasingly centralized through an integrated middleware of a scalable server or through a large platform. That said, middleware-based IIoT data acquisition must consider multiple factors, such as infrastructure (e.g., operation environment and network), protocol heterogeneity, interoperability (e.g., links with legacy systems), real-time, and security. This manuscript explains these five aspects in detail and provides a taxonomy of eighteen state-of-the-art IIoT data-acquisition middleware systems based on these aspects. To validate one of these aspects (network), we present our evaluation results at a real production site where IIoT data-acquisition loss rates are compared between wireless (long-term evolution) and wired networks. As a result, the wired communication can be more suitable for centralized IIoT data-acquisition middleware than wireless networks. Finally, we discuss several challenges in establishing the best IIoT data-acquisition middleware in a centralized way.

Dynamic Edge and Cloud Service Integration for Industrial IoT and Production Monitoring Applications of Industrial Cyber-Physical Systems

Industrial cyber-physical systems rely increasingly on data from Internet-of-Things (IoT) devices and other systems as continuously emerging use cases implement new intelligent features. Edge computing can be seen as an extension of the cloud in close physical proximity, in which some of the typical cloud computing loads are beneficial to run. This article studies data analytics application development for integration of industrial IoT data and composition of application services executed on edge and cloud. A solution is designed to support heterogeneous hardware and run-time platforms, and focuses on the service layer that enables flexible orchestration of data flows and dynamic service compositions. The unified model and system architecture implemented, using the open Arrowhead framework model, is verified through two representative industrial use cases.

Energy-Efficient Resource Allocation for Blockchain-Enabled Industrial Internet of Things With Deep Reinforcement Learning

Industrial Internet of Things (IIoT) has emerged with the developments of various communication technologies. In order to guarantee the security and privacy of massive IIoT data, blockchain is widely considered as a promising technology and applied into IIoT. However, there are still several issues in the existing blockchain-enabled IIoT: 1) unbearable energy consumption for computation tasks; 2) poor efficiency of consensus mechanism in blockchain; and 3) serious computation overhead of network systems. To handle the above issues and challenges, in this article, we integrate mobile-edge computing (MEC) into blockchain-enabled IIoT systems to promote the computation capability of IIoT devices and improve the efficiency of the consensus process. Meanwhile, the weighted system cost, including the energy consumption and the computation overhead, are jointly considered. Moreover, we propose an optimization framework for blockchain-enabled IIoT systems to decrease consumption, and formulate the proposed problem as a Markov decision process (MDP). The master controller, offloading decision, block size, and computing server can be dynamically selected and adjusted to optimize the devices energy allocation and reduce the weighted system cost. Accordingly, due to the high-dynamic and large-dimensional characteristics, deep reinforcement learning (DRL) is introduced to solve the formulated problem. Simulation results demonstrate that our proposed scheme can improve system performance significantly compared to other existing schemes.

Theodolite: Scalability Benchmarking of Distributed Stream Processing Engines in Microservice Architectures

Distributed stream processing engines are designed with a focus on scalability to process big data volumes in a continuous manner. We present the Theodolite method for benchmarking the scalability of distributed stream processing engines. Core of this method is the definition of use cases that microservices implementing stream processing have to fulfill. For each use case, our method identifies relevant workload dimensions that might affect the scalability of a use case. We propose to design one benchmark per use case and relevant workload dimension. We present a general benchmarking framework, which can be applied to execute the individual benchmarks for a given use case and workload dimension. Our framework executes an implementation of the use case's dataflow architecture for different workloads of the given dimension and various numbers of processing instances. This way, it identifies how resources demand evolves with increasing workloads. Within the scope of this paper, we present 4 identified use cases, derived from processing Industrial Internet of Things data, and 7 corresponding workload dimensions. We provide implementations of 4 benchmarks with Kafka Streams and Apache Flink as well as an implementation of our benchmarking framework to execute scalability benchmarks in cloud environments. We use both for evaluating the Theodolite method and for benchmarking Kafka Streams' and Flink's scalability for different deployment options.

Multibuffers Multiobjects Optimal Matching Scheme for Edge Devices in IIoT

Environments built from the edge-based Industrial Internet of Things (IIoT) are maelstroms of information that continuously flows between heterogeneous data objects, such as sensors and devices, and edge nodes. However, the explosive growth in the number of IIoT data objects connected to edge nodes generally results in significant computation and storage requirements, which exceed those of resource-constrained edge nodes. The problem mentioned above causes a major concern related to efficient memory usage and faster communication processing in data acquisition. Therefore, in this article, we propose a multibuffers multiobjects (MBMOs) architecture to support the parallel delivery of multiple data objects to multiple variable-length buffer blocks. Furthermore, a mathematical model is established, aiming to find the optimum buffer according to the size of each communication data packet. For the aforementioned matching problem, a spatiotemporal resource allocation algorithm is designed to maximize memory usage while minimizing communication processing time. We implement our MBMO in a monitoring system in which embedded programmable logic controllers (ePLCs) serve as the edge nodes. The analyses illustrate that memory usage and time efficiency are improved greatly with the utilization of MBMO.

A Sharding Scheme-Based Many-Objective Optimization Algorithm for Enhancing Security in Blockchain-Enabled Industrial Internet of Things

While the industrial Internet of Things (IIoT) can support efficient control of the physical world through large amounts of industrial data, data security has been a challenge due to various interconnections and accesses. Blockchain technology can support security and privacy preservation in IIoT data with its trusted and reliable security mechanism. Sharding technology can help improve the overall throughput and scalability of blockchain networks. However, the effectiveness of sharding is still challenging due to the uneven distribution of malicious nodes. By aiming to improve the performance of blockchain networks and reduce the possibility of malicious node aggregation, in this article, we propose a many-objective optimization algorithm based on the dynamic reward and penalty mechanism (MaOEA-DRP) to optimize the shard validation validity model. Then, an optimal blockchain sharding scheme is obtained. Compared with other state-of-the-art many-objective optimization algorithms, MaOEA-DRP performs better on the DTLZ test suite. The simulation results demonstrate that our proposed algorithm can significantly improve the throughput and validity of sharding for better security in the blockchain-enabled IIoT.

Auditable and Times limitable Secure Data Access Control for Cloud-based Industrial Internet of Things

Recently, the rapid development of Internet of things (IoT) and cloud computing technologies have greatly facilitated various industrial applications and Industrial IoT (IIoT). The widely deployed IIoT devices and large capacity of cloud significantly benefit for and bring convenience to various industrial sectors. However, there exist a large number of concerns about data security in IIoT, especially when a majority of sensitive IIoT data is shared in cloud. Although as one of the most promising technique, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) can provide fine-grained access control for IIoT data shared in cloud, there are still many drawbacks which impede the direct adoption of conventional CP-ABE. On the one hand, unlimited IIoT data access times may disable data access service of cloud and bring serious consequences. On the other hand, the access policies of ciphertexts usually consist of much sensitive information and cause privacy exposure. Moreover, the high computation overhead also extremely hinders resource-limited users in IIoT applications. To solve these problems, we propose TAHP-CP-ABE, a k-times and auditable hidden-policy CP-ABE scheme which is suitable for resource-limited users and privacy-aware access policies with data access times limitation in IIoT applications. Specifically, TAHP-CP-ABE preserves the privacy of access policies by hiding only attribute values and realizes limited access times as well as efficient IIoT ciphertexts decryption with decryption test and outsourced decryption. The security analysis and experimental results indicate that TAHP-CP-ABE is secure, efficient and practical.

A Collaboratively Developed Platform to Introduce Fundamentals of IoT and IIoT

Industrial Internet of Things (IIoT) is a core technology of Industry 4.0 capable of providing transformative data and insights. Successful implementation of IIoT requires knowledge bridging the Information Technology (IT) and Operational Technology (OT) domains. This work presents a collaborative effort between multiple industry partners and academia to provide a comprehensive, cost-effective orientation to IIoT scoped for an undergraduate engineering audience. The outcome is a scalable solution that allows for the presentation and development of core skills focused on IoT concepts, cloud, edge, on-premises, hybrid IIoT solutions, data fundamentals, monitoring, control, analysis, and data applications utilizing IIoT systems and devices. Presentations also focus on demonstrating the best methods for securely deploying IIoT technologies in hybrid architectures. In addition to serving as a pathway to safely prepare and upskill students before they engage with industrial-scale systems in the learning factory, the platform also serves as a foundation for a new undergraduate curriculum for preparing graduates for Industry 4.0 and the digital transformation of the manufacturing industry.

Benchmarking management techniques for massive IIoT time series in a fog architecture

Within the Industrial Internet of Things (IIoT) scenario, the online availability of a growing number of assets in factories enables the collection of vast amounts of data. Each asset produces time-series collections that must be handled with proper techniques while providing effective ingestion and retrieval performance in complex architectures, maintaining compliance with company and infrastructure boundaries. In this paper, we describe an experience in the management of massive time-series, conducted in a plant of Avio Aero. Firstly, we propose a fog-based architecture to ease the collection and analysis of these massive datasets. Then, we present the results of an empirical comparison of four DBMSs (PostgreSQL, Cassandra, MongoDB, and InfluxDB) in the ingestion and retrieval of gigabytes of real IIoT data. In particular, we tested different DBMS features under different types of queries. Results show that InfluxDB provides very good performance, but PostgreSQL can still be an interesting alternative.

Fairness-Based Packing of Industrial IoT Data in Permissioned Blockchains

In recent years, blockchain has been broadly applied to industrial Internet of Things (IIoT) due to its features of decentralization, transparency, and immutability. In existing permissioned blockchain based IIoT solutions, transactions submitted by IIoT devices are arbitrarily packed into blocks without considering their waiting times. Hence, there will be a high deviation of the transaction response times, which is known as the lack of fairness. Unfair permissioned blockchain decreases the quality of experience from the perspective of the IIoT devices. Moreover, some transactions can get timeouts if not responded for a long time. In this article, we propose Fair-Pack, the first fairness-based transaction packing algorithm for permissioned blockchain empowered IIoT systems. First, we gain the insight that fairness is positively related to the sum of waiting times of the selected transactions through theoretical analysis. Based on this insight, we transform the fairness problem into the subset sum problem, which is to find a valid subset from a given set with subset sum as large as possible. However, it is time consuming to solve the problem using a brute-force approach because there is an exponential number of subsets for a given set. To this end, we propose a heuristic and a min-heap-based optimal algorithm for different parameter settings. Finally, we analyze the time complexity of Fair-Pack and conduct extensive experiments. The results reveal that Fair-Pack is time-efficient and outperforms the existing algorithms significantly in terms of both fairness and average transaction response time.

VFL: A Verifiable Federated Learning With Privacy-Preserving for Big Data in Industrial IoT

Due to the strong analytical ability of big data, deep learning has been widely applied to model on the collected data in industrial Internet of Things (IoT). However, for privacy issues, traditional data-gathering centralized learning is not applicable to industrial scenarios sensitive to training sets, such as face recognition and medical systems. Recently, federated learning has received widespread attention, since it trains a model by only sharing gradients without accessing training sets. But existing research works reveal that the shared gradient still retains the sensitive information of the training set. Even worse, a malicious aggregation server may return forged aggregated gradients. In this article, we propose the VFL, a verifiable federated learning with privacy-preserving for big data in industrial IoT. Specifically, we use Lagrange interpolation to elaborately set interpolation points for verifying the correctness of the aggregated gradients. Compared with existing schemes, the verification overhead of VFL remains constant regardless of the number of participants. Moreover, we employ the blinding technology to protect the privacy of the privacy gradients. If no more than <inline-formula><tex-math notation="LaTeX">$\boldsymbol{n}$</tex-math></inline-formula>-2 of <inline-formula><tex-math notation="LaTeX">$\boldsymbol{n}$</tex-math></inline-formula> participants collude with the aggregation server, VFL could guarantee the encrypted gradients of other participants not being inverted. Experimental evaluations corroborate the practical performance of the presented VFL with high accuracy and efficiency.

Evaluating the Role of Big Data in IIOT-Industrial Internet of Things for Executing Ranks Using the Analytic Network Process Approach

Due to the enhancements of Internet of Things (IoT) and sensors deployments, the production of big data in Industrial Internet of Things (IIoT) is increased. The accessing and processing of big data become a challenging issue due to the limited storage space, computational time, networking, and IoT devices end. IoT and big data are well thought-out to be the key concepts when describing new information architecture projects. The techniques, tools, and methods that help to provide better solutions for IoT and big data can have an important role to play in the architecture of business. Different approaches are being practiced in the literature for evaluating the role of big data in IIoT. These techniques are not handling the situations when complexity of dependency arises among parameters of the alternatives. The proposed research uses the approach of Analytic Network Process (ANP) for evaluating the role of big data in IIoT. The results show that the proposed research works well for evaluating the role of big data in IIoT.

Lightweight Searchable Encryption Protocol for Industrial Internet of Things

Industrial Internet of Things (IoT) has suffered from insufficient identity authentication and dynamic network topology, thereby resulting in vulnerabilities to data confidentiality. Recently, the attribute-based encryption (ABE) schemes have been regarded as a solution to ensure data transmission security and the fine-grained sharing of encrypted IoT data. However, most of existing ABE schemes that bring tremendous computational cost are not suitable for resource-constrained IoT devices. Therefore, lightweight and efficient data sharing and searching schemes suitable for IoT applications are of great importance. To this end, In this article, we propose a light searchable ABE scheme (namely LSABE). Our scheme can significantly reduce the computing cost of IoT devices with the provision of multiple-keyword searching for data users. Meanwhile, we extend the LSABE scheme to multiauthority scenarios so as to effectively generate and manage the public/secret keys in the distributed IoT environment. Finally, the experimental results demonstrate that our schemes can significantly maintain computational efficiency and save the computational cost at IoT devices, compared to other existing schemes.

A Trustworthy Privacy Preserving Framework for Machine Learning in Industrial IoT Systems

Industrial Internet of Things (IIoT) is revolutionizing many leading industries such as energy, agriculture, mining, transportation, and healthcare. IIoT is a major driving force for Industry 4.0, which heavily utilizes machine learning (ML) to capitalize on the massive interconnection and large volumes of IIoT data. However, ML models that are trained on sensitive data tend to leak privacy to adversarial attacks, limiting its full potential in Industry 4.0. This article introduces a framework named PriModChain that enforces privacy and trustworthiness on IIoT data by amalgamating differential privacy, federated ML, Ethereum blockchain, and smart contracts. The feasibility of PriModChain in terms of privacy, security, reliability, safety, and resilience is evaluated using simulations developed in Python with socket programming on a general-purpose computer. We used Ganache_v2.0.1 local test network for the local experiments and Kovan test network for the public blockchain testing. We verify the proposed security protocol using Scyther_v1.1.3 protocol verifier.

A Robust Deep-Learning-Enabled Trust-Boundary Protection for Adversarial Industrial IoT Environment

In recent years, trust-boundary protection has become a challenging problem in Industrial Internet of Things (IIoT) environments. Trust boundaries separate IIoT processes and data stores in different groups based on user access privilege. Points where dataflow intersects with the trust boundary are becoming entry points for attackers. Attackers use various model skewing and intelligent techniques to generate adversarial/noisy examples that are indistinguishable from natural data. Many of the existing machine-learning (ML)-based approaches attempt to circumvent this problem. However, owing to an extremely large attack surface in the IIoT network, capturing a true distribution during training is difficult. The standard generative adversarial network (GAN) commonly generates adversarial examples for training using randomly sampled noise. However, the distribution of noisy inputs of GAN largely differs from actual distribution of data in IIoT networks and shows less robustness against adversarial attacks. Therefore, in this article, we propose a downsampler-encoder-based cooperative data generator that is trained using an algorithm to ensure better capture of the actual distribution of attack models for the large IIoT attack surface. The proposed downsampler-based data generator is alternatively updated and verified during training using a deep neural network discriminator to ensure robustness. This guarantees the performance of the generator against input sets with a high noise level at time of training and testing. Various experiments are conducted on a real IIoT testbed data set. Experimental results show that the proposed approach outperforms conventional deep learning and other ML techniques in terms of robustness against adversarial/noisy examples in the IIoT environment.

Intelligent Fault Diagnosis by Fusing Domain Adversarial Training and Maximum Mean Discrepancy via Ensemble Learning

Nowadays, the industrial Internet of Things (IIoT) has been successfully utilized in smart manufacturing. The massive amount of data in IIoT promote the development of deep learning-based health monitoring for industrial equipment. Since monitoring data for mechanical fault diagnosis collected on different working conditions or equipment have domain mismatch, models trained with training data may not work in practical applications. Therefore, it is essential to study fault diagnosis methods with domain adaptation ability. In this article, we propose an intelligent fault diagnosis method based on an improved domain adaptation method. Specifically, two feature extractors concerning feature space distance and domain mismatch are trained using maximum mean discrepancy and domain adversarial training respectively to enhance feature representation. Since separate classifiers are trained for feature extractors, ensemble learning is further utilized to obtain final results. Experimental results indicate that the proposed method is effective and applicable in diagnosing faults with domain mismatch.

Decision-Making Model for Securing IoT Devices in Smart Industries

The industrial Internet-of-Things (IIoT) is a powerful Internet of Things (IoT) application that enables industrial growth by ensuring transparent communication among the various entities of a company such as the manufacturing locations, design hubs, and packaging units. However, current industrial architectures are unable to efficiently deal with advanced security issues that come with this communication due to the distributed and expandable nature of IIoT networks. Furthermore, from a security perspective, malicious devices with the objective of modifying data from within the premises of the network pose a high risk for the IIoT. Therefore, introducing intelligent decision-making models to the IIoT can enhance our ability to examine any collected data in a more structured, efficient, and secure manner. In this article, we provide a decision-making model for securing IIoT data. The proposed model, based on the Technique for Order Preference by Similarity to the Ideal Solution, can provide secure information transmission and recording/storage using various communicating parameters. The degree of trust of the IoT devices is analyzed using these parameters. Simple additive weighting is integrated into the proposed model to remove inefficient and ill-structured parameters. The proposed model is validated using various spectrum sensing and security parameters against a baseline method for the IIoT. Simulation results show that the proposed model is approximately 85% more efficient in identifying malicious nodes and denial-of-service threats compared to the baseline method.