Industrial Internet Of Things Research Articles

Proxy re-encryption with plaintext checkable encryption for integrating digital twins into IIoT

Digital Twin (DT) technology has emerged as a robust mechanism for overseeing and improving the lifecycle processes of the Industrial Internet of Things (IIoT) by creating their virtual counterparts. The widespread employment of cloud computing is apparent in delivering computational and storage services for the DT within the IIoT environment. Ensuring the confidentiality of DT data is crucial since data owners, cloud service providers, and users operate across separate trust domains. Accordingly, public key encryption is a viable solution for managing access. However, traditional public key encryption encounters challenges in achieving effective search and secure sharing of encrypted DT data in the cloud. This paper introduces a proxy re-encryption scheme with plaintext checkable encryption for data access control (PRE-IBSC-PCE), which addresses the challenges associated with searching for and sharing encrypted digital twin data in the cloud. The PRE-IBSC-PCE achieves efficient data search and sharing while ensuring privacy, integrity, non-repudiation, and authenticity. Meanwhile, we prove that PRE-IBSC-PCE is secure under the decisional bilinear Diffie–Hellman assumption in the random oracle model. Lastly, when compared to similar schemes, the performance analysis of the PRE-IBSC-PCE is more efficient and suitable for DT applications in IIoT.

ATOM: Adaptive Task Offloading With Two-Stage Hybrid Matching in MEC-Enabled Industrial IoT

ATOM: Adaptive Task Offloading With Two-Stage Hybrid Matching in MEC-Enabled Industrial IoT

Blockchained Federated Learning for Internet of Things: A Comprehensive Survey

The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comprehensively reviews Blockchained Federated Learning (BlockFL) that joins the benefits of both Blockchain and Federated Learning to provide a secure and efficient solution for the demand. We compare the existing BlockFL models in four Internet-of-Things (IoT) application scenarios: Personal IoT (PIoT), Industrial IoT (IIoT), Internet of Vehicles (IoV), and Internet of Health Things (IoHT), with a focus on security and privacy, trust and reliability, efficiency, and data diversity. Our analysis shows that the features of decentralization and transparency make BlockFL a secure and effective solution for distributed model training, while the overhead and compatibility still need further study. It also reveals the unique challenges of each domain presents unique challenges, e.g., the requirement of accommodating dynamic environments in IoV and the high demands of identity and permission management in IoHT, in addition to some common challenges identified, such as privacy, resource constraints, and data heterogeneity. Furthermore, we examine the existing technologies that can benefit BlockFL, thereby helping researchers and practitioners to make informed decisions about the selection and development of BlockFL for various IoT application scenarios.

Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols

The Internet of Things (IoT) will bring about the next industrial revolution in Industry 4.0. The communication aspect of IoT devices is one of the most critical factors in choosing the device that is suitable for use. Thus far, the IoT physical layer communication challenges have been met with various communications protocols that provide varying strengths and weaknesses. This paper summarizes the network architectures of some of the most popular IoT wireless communications protocols. It also presents a comparative analysis of some of the critical features, including power consumption, coverage, data rate, security, cost, and quality of service (QoS). This comparative study shows that low-power wide area network (LPWAN)-based IoT protocols (LoRa, Sigfox, NB-IoT, LTE-M) are more suitable for future industrial applications because of their energy efficiency, high coverage, and cost efficiency. In addition, the study also presents an Industrial Internet of Things (IIoT) application perspective on the suitability of LPWAN protocols in a particular scenario and addresses some open issues that need to be researched. Thus, this study can assist in deciding the most suitable IoT communication protocol for an industrial and production field.

ZSGEEHCP: Zonal Stable Gateway Based Energy Efficient Heterogeneous Clustering Protocol For WSN- Based IoT.

In future most of the devices on the earth connected to the internet for making world of intelligent networks. The internet of things (IoT) and Industrial IoT play a very significant role for implementing such kind of smart networks. So, in that case, the transmission of packets of data sent to the base station from sensor nodes with minimum energy consumption in a homogeneous or heterogeneous environment to hike the stability of the network appears as a big challenge for IoT-based networks. But, such kind of problem can be overcome by using a proper clustering algorithm. In this work, different kinds of classical clustering algorithms are studied precisely. Most of the traditional clustering algorithm protocols presume that all the nodes have the same amount of energy, they are unable to fully exploit the presence of node heterogeneity and the same problem will be faced with IoT-based networks. In this specific piece of work, we recommend a gateway based heterogeneous energy-based clustering algorithm which is crucial for many wireless sensor networks along with IoT-based applications. In this concept geographical area is divided into three zone, Advanced, Intermediate and normal zone. Here, advance nodes send data through gateway to the base station while intermediate node use clustering algorithm for transferring data and normal nodes communicate data to the base station directly. ZSGHCP is driven by the mounded election protocol probabilities of each node elected as a cluster head depending on the rest of the energy. By simulation, we show that ZSGHCP invariably extends the stability time as compared to current clustering procedures. Finally, we investigate the sensitivity of our ZSGHCP protocol to network heterogeneity characteristics that capture energy imbalance, throughput, and no of alive nodes. We construct that ZSGHCP payout extends the durability region for excessive values of additional energy brought by extra higher-powered nodes.

Polling Mechanisms for Industrial IoT Applications in Long-Range Wide-Area Networks

LoRaWAN is a low-power wide-area network (LPWAN) technology that is well suited for industrial IoT (IIoT) applications. One of the challenges of using LoRaWAN for IIoT is the need to collect data from a large number of devices. Polling is a common way to collect data from devices, but it can be inefficient for LoRaWANs, which are designed for low data rates and long battery life. LoRaWAN devices operating in two specific modes can receive messages from a gateway even when they are not sending data themselves. This allows the gateway to send commands to devices at any time, without having to wait for them to check for messages. This paper proposes various polling mechanisms for industrial IoT applications in LoRaWANs and presents specific considerations for designing efficient polling mechanisms in the context of industrial IoT applications leveraging LoRaWAN technology.

3MSF: A Multi-Modal Adaptation of the 6TiSCH Minimal Scheduling Function for the Industrial IoT

Although wireless devices continuously gain communication capabilities, even state-of-the-art Industrial Internet of Things (IIoT) architectures, such as Internet Protocol version 6 over the Time-Slotted Channel Hopping (TSCH) mode of IEEE 802.15.4 (6TiSCH), continue to use network-wide, fixed link configurations. This presents a missed opportunity to (1) forego the need for rigorous manual setup of new deployments; and (2) provide full coverage of particularly heterogeneous and/or dynamic industrial sites. As such, we devised the Multi-Modal Minimal Scheduling Function (3MSF) for the TSCH link layer, which, combined with previous work on the routing layer, results in a 6TiSCH architecture able to dynamically exploit modern multi-modal hardware on a per-link basis through variable-duration timeslots, simultaneous transmission, and routing metric normalization. This paper describes, in great detail, its design and discusses the rationale behind every choice made. Finally, we evaluate three basic scenarios through simulations, showcasing both the functionality and flexibility of our 6TiSCH implementation.

An All-Inclusive Taxonomy and Critical Review of Blockchain-Assisted Authentication and Session Key Generation Protocols for IoT

Authentication and Session Key Generation Protocols (SKGPs) play an essential role in securing the communication channels of connected Internet of Things (IoT) devices. Recently, through blockchain integration, scholars have tried to enhance the security and applicability of SKGPs. In brief, blockchain is a distributed ledger technology that can provide interesting features such as immutability, transparency, and accountability without any need for the active participation of trusted parties. This survey presents a comprehensive critical review of blockchain-assisted authentication and SKGPs, suggested for different IoT domains, including Internet of Vehicles, Internet of Drones, and Industrial IoT. Our survey categorizes existing schemes based on several criteria, including IoT application domains, security aspects, and blockchain components. By presenting an unbiased critical review and taxonomy of protocols, we aim to clarify the key challenges. Our review will specifically indicate what properties authors gained or lost through the integration of blockchain. To our best knowledge, this survey is the only one that offers all prerequisites for interested readers in blockchain-integrated SKGPs, such as security features and attacks, attack models, verification tools, blockchain types, blockchain platforms, and consensus mechanisms. Further, our survey elaborates existing research gaps in blockchain-assisted SKGPs. In doing so, we aim to guide future research in this field and provide researchers with the essential information they require.

Improving edge AI for industrial IoT applications with distributed learning using consensus

Improving edge AI for industrial IoT applications with distributed learning using consensus

The Industrial Internet of Things (IIoT) and its roles in the Fourth Industrial Revolution: A review

The Industrial Internet of Things and Industry 4.0 are now two highly sought-after areas of research and development, attracting significant interest from both academic and industrial sectors. The two ideas, Industry 4.0 and IIoT, share significant similarities, with Industry 4.0 being seen as the use of IIoT specifically in the automation and manufacturing sectors. Within the framework of the present Industry 4.0 paradigm, many growth pathways have emerged, collectively leading to notable enhancements in terms of efficiency, flexibility, communication, adaptability, customization, and modularity in the industrial sector. The Industry 4.0 is rapidly evolving within the framework of the Industrial Internet of Things (IIoT), and the authors are recognizing the necessity for a comprehensive and in-depth overview of the many research areas that are currently expanding. The area will remain intriguing in the foreseeable future due to its significant potential for enhancing the existing industrial technologies. An exhaustive evaluation of the current systems in the automotive sector, emergency response, and chain management on IIoT has been conducted, revealing that IIoT has been widely adopted across several technological domains. Industry 4.0 is the term used to describe the present automation and data sharing trend in businesses. Presently, there is a dearth of agreement about the assessment of an organization's readiness for Industry 4.0. Industry 4.0 encompasses a diverse array of digital technologies that profoundly influence industrial enterprises. The literature on Industry 4.0 has had significant exponential growth during the previous decade. The results of our research confirm the idea of Industry 4.0 as a concept that goes beyond the Smart Manufacturing sector, hence opening up possibilities for collaboration with other interconnected disciplines.

Design and Implementation of Long Range Wide Area Networks for Future Industrial IoT Applications

aims: To design a Low Power Wide Area Network technology to provide long-range connectivity for IIoT applications. background: The evolution of Long Range Wide Area Networks ( LoRaWAN) is a potential candidate for next generation networks for managing the massive number of devices in the Industrial Internet of Things (IIoT). objective: To design a Low Power Wide Area Network technology to provide long-range connectivity for IIoT applications. method: In addition to implement LoRaWAN, this research work deploys the proposed LoRaWAN into the 5G communication technology to achieve the massive IIOT use cases. result: The deployment of this hybrid LORA network has demonstrated its long range, low power, stability, flexibility, and low deployment cost through extensive performance evaluation carried out. conclusion: This paper concludes the feasibility of deploying LoRaWAN technology for the future generation IIOT applications. other: No Applicable

A Comparative Study on Machine Learning Models for Fault Detection and Classification in Manufacturing Integration of IoT and Artificial Intelligence in Industry 4.0 management

The present study presents an examination of fault detection and classification in manufacturing that is possible due to IoT and Artificial Intelligence in the Industry 4.0 structure. The study is based on IoT and  compares three Artificial Intelligence algorithms such as Naive Bayes , Extreme Gradient Boosting , and k-Nearest Neighbors . The results displayed that in ten trials of the experiment achieving the accuracy from 0.82 to 0.89 . The interpretability tool allowed for the understanding of the models’ predictions. The consistent accuracy in both SHAP and LIME results, i.e. 0.80 and 0.89, suggest that both models performed effectively in model explanation. Thus, end users need to combine advanced machine learning algorithms with interpretable explanations to enhance fault detection and classification in manufacturing. This will consequently allow for better production processes and increased innovation in Industry 4.0 manufacturing.

An efficient cloud-integrated distributed deep neural network framework for IoT malware classification

The proliferation of interconnected devices in the Internet of Things (IoT) landscape has introduced significant security concerns. With the integration of android devices, the potential for attackers to exploit vulnerabilities becomes a crucial issue. Timely detection of malware attacks has emerged as a critical challenge, especially in industrial IoT (IIoT), to prevent permanent damage. The slow inference speed of the current algorithms causes limited performance and a significant delay in the detection and mitigation of malware, especially in large-scale environments such as IIoT. High resource utilization can limit the size of the model (number of parameters), which could be implemented in embedded devices, which causes lower accuracy of the model. Hence, we proposed a HierarchicalCloudDNN, a distributed malware detection framework that used modified state-of-the-art deep learning algorithms in a boosting ensemble architecture. The proposed method could scale efficiently from IIoT devices up to the edge and cloud to speed up malware detection, simultaneously reduce resource utilization, and maintain a controllable level of accuracy compared to rival methods in malware detection. The objective is to minimize both run-time and resource consumption while maintaining performance characteristics. The performance analysis of the HierarchicalCloudDNN method was evaluated using standard performance measures on the frequently used BIG 2015 dataset. Based on evaluations, the proposed approach has demonstrated an impressive accuracy rate of 98.90%, outperforming many state-of-the-art models in performance, response time, and resource utilization. These findings suggest a notable decrease in both false positives and false negatives while maintaining accuracy. The proposed distributed malware analysis framework is scalable, robust, and helpful for the timely detection of malicious activity, especially in geographically distributed environments such as the IIoT.

An end-to-end learning approach for enhancing intrusion detection in Industrial-Internet of Things

The Industrial-Internet of Things (I-IoT) stands out as one of the most dynamically evolving subfields within the expansive realm of the Internet of Things (IoT). Its exponential growth is reshaping industrial landscapes, bringing forth transformative innovations and advancements at an unprecedented pace, as the core of Industry 4.0. Among the formidable challenges faced by the Industrial-Internet of Things, cybersecurity stands out as a critical concern. Deep learning-based Intrusion Detection System (IDS) solutions showcase their steadfast ability to secure resource-limited, investigation-demanding, and complex I-IoT environments. However, their effectiveness hinges not only on the model but also on the dataset on which they are trained. While numerous literature studies delve into this field, existing proposed models often grapple with challenges. They are frequently trained on outdated, non-diverse datasets or lack specific features crucial for I-IoT networks. Recent efforts, thankfully, introduce more adequate datasets like Edge-IIoTset. Researchers leverage this extensive dataset to train models, focusing on detecting the 14 sophisticated attacks. These attacks predominantly target real I-IoT networks. Despite these efforts, none of the existing models proves entirely efficient. A review of literature solutions reveals that many models cannot detect all 15 classes in the dataset. Some are multi-staged or overly complex. In response to these challenges, this paper presents an End-to-End learning , non-complex CNN1D model tailored to the specific problem of detecting 14 sophisticated threats targeting I-IoT environments. Our proposed model demonstrated remarkable efficiency with an accuracy of 99.96%, successfully detecting all 15 classes in the Edge-IIoTset dataset with a minimal loss of 0.0011. Not only that, but our model was validated with k-fold cross-validation, demonstrating its efficiency in preserving the same performance on unseen data and its ability to be generalized for real-world I-IoT environments.

A robust and effective 3-factor authentication protocol for smart factory in IIoT

Smart factory, as an intelligent application of industrial internet of things (IIoT), significantly enhances the efficiency of industrial processes while also reducing resource waste. In a system where security is crucial, the user authentication mechanism is essentially designed to prevent a range of security issues, including production failures caused by illegal intrusions from hackers. To verify the user’s identity and ensure data be accessed with a secure session key, a large number of authentication protocols have been provided for IIoT. However, existing newly alternatives show deficiency either in terms of superior performance or robust security. To get a better balance of security and efficiency, this paper designs a robust and effective 3-factor user authentication protocol. Then this paper shows the detailed security analyses and automated verification by the Proverif tool, where the ProVerif tool used in this paper can detect whether the eCK adversary with stronger attack ability can break the protocol, while the general ProVerif can only detect threat of the Dolev-Yao adversary with weaker attack ability to the protocol security. Subsequently, this paper presents performance comparison which indicates that the proposed protocol can be superior to those newly alternatives. Especially, the comparison results show that the computation cost consumed in the proposed protocol can be reduced by 62.2% than the average cost of all six compared alternatives. Lastly, the evaluation results on the energy consumption and the network delay indicate the practicability of proposed protocol for smart factory.

A security-enhanced scheme for MQTT protocol based on domestic cryptographic algorithm

Recently, the rapid advancement of Industrial Internet of Things (IIoT) technology has led to the utilization of various communication protocols. Among these, the Message Queue Telemetry Transport (MQTT) protocol has become prevalent. Operating on a publish/subscribe model, MQTT’s popularity in IIoT because of simplicity, lightweight nature, and ease of implementation. However, traditional MQTT protocol standards have neglected critical security concerns during the communication process, such as ineffective identity authentication, weak data confidentiality, and a lack of access control functions. To address these challenges, this paper introduces a security-enhanced scheme for the MQTT protocol based on domestic cryptographic algorithms. The proposed method enforces mutual identity authentication between the MQTT Client and the MQTT Broker using digital certificates. It also implements access control through an Access Control List (ACL) and ensures end-to-end data security via the symmetric encryption algorithm SM4. Through comprehensive security analysis and experimental validation, this paper demonstrates that the enhanced scheme effectively rectifies the MQTT protocol’s security deficiencies, achieving these improvements with small overhead.

SIHQR model with time delay for worm spread analysis in IIoT-enabled PLC network

Industrial Internet of Things (IIoT) is a product of deep integration between Internet of Things (IoT) and industrial control networks. As an important component of IIoT, Programmable Logic Controller (PLC) has become a springboard for many hackers to disrupt industrial networks by spreading viruses. It’s known that worm can cause enormous loss. Therefore, revealing the rules of worm spread in PLC networks and exploring methods to inhibit worm spread are increasingly important. To this end, we propose a new worm spread model named SIHQR with time delay. Additionally, we establish a game model between susceptible and infected nodes in order to express the infection rate by several game parameters. Through analysis, we discuss the conditions for the emergence of three scenarios: disease-free equilibrium, endemic equilibrium and Hopf bifurcation. We study the stability at the equilibrium points and obtain an expression for a threshold value which determines whether the system exhibits endemic equilibrium or Hopf bifurcation. In the experimental section, we analyze the impact of initial values of node states and the magnitude of game parameters on worm spread in the three aforementioned scenarios. Finally, we propose a method to mitigate or even eliminate the Hopf bifurcation. The experimental results show that the proposed model has welldone performance.

Blockchain-Enabled Intelligent IoT Protocol for High-Performance and Secured Big Financial Data Transaction

In the recent era, the communication network with the support of many wireless technologies is giving benefits for remote access. Such a communication model increases the flexibility for data storage with the management of network resources efficiently with the integration of the Internet of Things (IoT). Although, the industrial Internet of Things (IIoT) has enabled the development of numerous machine learning-based solutions to provide real-time applications. However, most of the solutions are not prepared to cope with heterogeneous services and the huge amount of device data in the digital world. Furthermore, conducting financial transactions over the Internet raises several security issues. Such restrictions compromise the sensitive data of financial institutions and also degrade the trust of network users in the system. Thus, this article presents a secured blockchain model for high-performance computing in a big data environment, which aims to protect the business activities for financial interaction with intelligent services of software-defined network (SDN) architecture. First, to keep the security credentials, the SDN controller creates an association between the IoT devices and maintains local and global records. Second, the machine learning approach is explored using reliable and fault-tolerant methods to support the network scalability and extract the updated routing information for transmitting financial data. The proposed protocol also provides data integrity with a high level of network availability and copes with the financial security of big data by investigating cryptographic approaches. Our proposed protocol is tested using simulation, and various experiments are performed to show its efficacy in terms of network throughput, computing overhead, data delay, response time, and dropped packets as compared to tunicate swarm algorithm-based optimized routing mechanism (TORM) and RouteChain.

A Reinforcement Learning-Based Incentive Mechanism for Task Allocation Under Spatiotemporal Crowdsensing

With the development of the Industrial Internet of Things (IoT), the work of large-scale data collection makes spatiotemporal crowdsensing (SC) play an important role. Mobile devices equipped with sensors could act as workers to collect and process data for uploading. In the task allocation process, a fully static allocation fails to meet the needs of realistic conditions, while a completely dynamic allocation fails to achieve the desired results. Therefore, we assume a task-scheduled execution scenario that combines the above two conditions. In the pre-allocation process, an original time location constraints (ORTA) allocation algorithm is first proposed. Then it is optimized (OPTA) to fully utilize the remaining time of the workers and increase the matched number. In addition, the design of the incentive mechanism is an effective means to improve the task completion rate of the platform. To efficiently utilize the limited platform budget in the long run, a Q-learning-based algorithm is proposed to identify target inspire tasks and subsequently increase their reward to attract workers’ participation. Finally, comparison experiments are conducted on real datasets to verify the effectiveness of our algorithm. Furthermore, the experiments on a Raspberry Pi local terminal are conducted under a satellite-based environment.

Optimizing Tradeoff Between Learning Speed and Cost for Federated-Learning-Enabled Industrial IoT

Optimizing Tradeoff Between Learning Speed and Cost for Federated-Learning-Enabled Industrial IoT