Industrial Internet Of Things Networks Research Articles

A Survey on AI-Empowered Softwarized Industrial IoT Networks

The future generation of mobile networks envision Artificial Intelligence (AI) and the Internet of Things (IoT) as key enabling technologies that will foster the emergence of sophisticated use cases, with the industrial sector being one to benefit the most. This survey reviews related works in this field, with a particular focus on the specific role of network softwarization. Furthermore, the survey delves into their context and trends, categorizing works into several types and comparing them based on their contribution to the advancement of the state of the art. Since our analysis yields a lack of integrated practical implementations and a potential desynchronization with current standards, we finalize our study with a summary of challenges and future research ideas.

A federated learning approach to network intrusion detection using residual networks in industrial IoT networks

AbstractThis paper introduces a sophisticated approach to network security, with a primary emphasis on utilizing deep learning for intrusion detection. In real-world scenarios, the high dimensionality of training data poses challenges for simple deep learning models and can lead to vanishing gradient issues with complex neural networks. Additionally, uploading network traffic data to a central server for training raises privacy concerns. To tackle these issues, the paper introduces a Residual Network (ResNet)-based deep learning model trained using a federated learning approach. The ResNet effectively tackles the vanishing gradient problem, while federated learning enables multiple Internet Service Providers (ISPs) or clients to engage in joint training without sharing their data with third parties. This approach enhances accuracy through collaborative learning while maintaining privacy. Experimental results on the X-IIoTID dataset indicate that the proposed model outperforms conventional deep learning and machine learning methods in terms of accuracy and other metrics used for evaluation. Specifically, the proposed methodology achieved 99.43% accuracy in a centralized environment and 99.16% accuracy in a federated environment.

Recurrent neural networks-guided vector-valued synchronized key exchange for secure and privacy-preserving communication in Industrial Internet of Things

This paper presents an innovative approach to the problem of key exchange in the Industrial Internet of Things (IIoT) implementation. Recurrent Neural Networks (RNNs) and vector-valued neural synchronization are the key components of the proposed secure transmission method. Drive-response methods are integrated to improve speed in important applications, meeting the continuous need for effective cryptographic key exchange between IIoT devices. Conventional algorithms face drawn-out assessment procedures that jeopardize neural synchronization concealment. This work examines how random input vector generation and synchronization issues in RNNs with drive-response are affected by proportional and non-proportional delays. Furthermore, it delves into an unexplored domain, investigating the synchronization of response-based RNN systems without delays and drive-response-based RNN systems with various proportional delays. It also suggests a simplified analysis of Artificial Neural Networks (ANNs) synchronization, organizing ANNs for session key switch-over using an RNN system. To achieve polynomial and non-polynomial synchronization in the proposed driver response systems, this method offers several benefits: it introduces a polynomial synchronization theory for RNNs to generate synchronized input vectors for ANN synchronization; it uses inequality assessment techniques and Lyapunov formulas to derive relevant control inputs and time-dependent conditions; it establishes the relationship between polynomial and non-polynomial synchronization; and it provides numerical examples demonstrating its effectiveness. It also builds a neural network for generating session keys throughout the IIoT network by aligning vector-valued ANNs in a reciprocal manner. This strategy outperforms previous approaches in the literature, validated through simulations, balances resilience against attacks with minimal computational load, resulting in more effective and resilient industrial applications.

A Comprehensive Survey of Security Challenges and Solutions of Industrial Internet of Things (IIoT) Networks

Despite the transformative potential of the Industrial Internet of Things (IIoT) in optimizing industrial operations, significant security and privacy challenges persist, underscoring the need for comprehensive solutions. This paper addresses this research gap by presenting a thorough survey of the security challenges and solutions in IIoT networks. Drawing from the extensive literature on IIoT, we identify and categorize key threats such as malware attacks, data breaches, and supply chain vulnerabilities. Additionally, we explore privacy concerns arising from the vast amounts of sensitive data collected and transmitted by IIoT devices. This analysis highlights the critical importance of encryption mechanisms, access controls, and authentication protocols as fundamental security measures. By synthesizing current research, this paper provides insights to guide the development of robust security strategies for IIoT deployments. Addressing these challenges is crucial to maintaining the reliability and integrity of industrial operations, and safeguarding against production disruptions, financial losses, and safety hazards. Through robust security measures and privacy-enhancing technologies, IIoT deployments can foster trust, compliance, and innovation in industrial settings, driving the evolution towards smart factories and connected enterprises. Keywords – Industrial Internet of Things, Cyber Security, Network Security, Threats.

Convnext-Eesnn: An effective deep learning based malware detection in edge based IIOT

A rising number of edge devices, like controllers, sensors, and robots, are crucial for Industrial Internet of Things (IIoT) networks for collecting data for communication, storage, and processing. The security of the IIoT could be compromised by any malicious or unusual behavior on the part of any of these devices. They may also make it possible for malicious software placed on end nodes to enter the network and perform unauthorized activities. Existing anomaly detection techniques are less effective due to the increasing diversity of the network and the complexity of cyberattacks. In addition, most strategies are ineffective for devices with limited resources. Therefore, this work presents an effective deep learning based Malware Detection framework to make the edge based IIoT network more secure. This multi-stage system begins with the Deep Convolutional Generative Adversarial Networks (DCGAN) based data augmentation method to overcome the issue of data imbalance. Next, a ConvNeXt-based method extracts the features from the input data. Finally, an optimized Enhanced Elman Spike Neural Network (EESNN) based deep learning is utilized for malware recognition and classification. Using two distinct datasets— MaleVis and Malimg— the generalizability of the suggested model is clearly demonstrated. With an accuracy of 99.24% for MaleVis and 99.31% for the Malimg dataset, the suggested strategy demonstrated excellent results and surpassed all other existing methods. It illustrates how the suggested strategy outperforms alternative models and offers numerous benefits.

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.

Maximizing throughput in NOMA-enable industrial IoT networks using digital twin and reinforcement learning

IntroductionIncreased deployment of heterogeneous and complex Industrial Internet of Things (IIoT) applications such as predictive maintenance and asset tracking places a substantial strain on the limited computational and communication resources. To cater to the rigorous demands of these applications, it is imperative to devise an adaptive online resource allocation method to enhance the efficiency of the current network operations. Multiaccess edge computing (MEC) and digital twins (DTs) are promising solutions that facilitate the realization of edge intelligence and find applications in various industrial applications. Yet, little is known about the advantage the two technologies offer to IIoT networks. ObjectiveThis study presents a joint optimization of offloading and resource allocation approach where MEC-server DT is created at the edge, and nonorthogonal multiple access (NOMA) communication is considered between IIoT devices and the industrial gateways (IGWs) for spectral efficiency. Our proposed framework is tailored to reduce mean task completion latency and enhance overall IIoT network throughput. MethodTo achieve our objective, we jointly optimize the computation resource allocation (RA), subchannel assignment (SA), and offloading decisions (OD). Given the inherent complexity of the problem, we further divide it into RA and SA/OD sub-problems. Employing Deep Reinforcement Learning (DRL), we have formulated a solution delineating the most efficient RA strategy and leveraged DT for optimal SA/OD strategies. ResultsSimulation results demonstrate the superior efficiency of our framework, realizing up to 92 % of the efficiency of the exhaustive search method while reducing computation and action decision time. ConclusionIn light of system dynamics considered for our work, the proposed framework perfomance showcase its robustness and potential application in real-world IIoT networks.

Enhancing Security and Reliability in Industrial IoT Networks through Machine Learning

Industrial Internet of Things (IIoT) networks are very important to modern manufacturing because they allow processes to be monitored, controlled, and improved in real time. IoT systems are linked to each other, which makes them vulnerable to cyberattacks, system breakdowns, and communication problems. This makes them less reliable and secure. To deal with these problems, we need advanced technologies that can find problems before they happen, reduce risks, and keep processes running smoothly. In this paper, we suggest a new way to make IIoT networks safer and more reliable by using machine learning methods together. Our system uses machine learning techniques to look at network traffic trends, spot strange behaviors, and find possible security holes in real time, using the huge amounts of data that IIoT devices produce. Our system can successfully find and stop a wide range of cyberattacks, such as hacking attempts, malware infections, and denial-of-service (DoS) attacks, because it is always learning from past data and changing to new threats. We also use machine learning models to predict when systems might fail or perform worse, so we can do preventative maintenance and keep downtime to a minimum.To prove that our method worked, we did a lot of tests using real-world IIoT datasets and checked how well our system worked by looking at how accurate it was at finding things, how often it gave false positives, and how fast it responded. These results show that our approach based on machine learning makes IIoT networks much safer and more reliable than standard rule-based approaches. In addition, our framework is strong against new and unknown threats, which shows that it could be used in a wide range of business settings.Overall, the paper research shows that machine learning has a lot of potential to make IIoT networks more reliable and to make sure that processes in industrial settings run smoothly and safely

Secure Computing for Fog-Enabled Industrial IoT.

Smart cities are powered by several new technologies to enhance connectivity between devices and develop a network of connected objects which can lead to many smart industrial applications. This network known as the Industrial Internet of Things (IIoT) consists of sensor nodes that have limited computing capacity and are sometimes not able to execute intricate industrial tasks within their stipulated time frame. For faster execution, these tasks are offloaded to nearby fog nodes. Internet access and the diverse nature of network types make IIoT nodes vulnerable and are under serious malicious attacks. Malicious attacks can cause anomalies in the IIoT network by overloading complex tasks, which can compromise the fog processing capabilities. This results in an increased delay of task computation for trustworthy nodes. To improve the task execution capability of the fog computing node, it is important to avoid complex offloaded tasks due to malicious attacks. However, even after avoiding the malicious tasks, if the offloaded tasks are too complex for the fog node to execute, then the fog nodes may struggle to process all legitimate tasks within their stipulated time frame. To address these challenges, the Trust-based Efficient Execution of Offloaded IIoT Trusted tasks (EEOIT) is proposed for fog nodes. EEOIT proposes a mechanism to detect malicious nodes as well as manage the allocation of computing resources so that IIoT tasks can be completed in the specified time frame. Simulation results demonstrate that EEOIT outperforms other techniques in the literature in an IIoT setting with different task densities. Another significant feature of the proposed EEOIT technique is that it enhances the computation of trustable tasks in the network. The results show that EEOIT entertains more legitimate nodes in executing their offloaded tasks with more executed data, with reduced time and with increased mean trust values as compared to other schemes.

Deep learning enabled intrusion detection system for Industrial IOT environment

The prevalence of security vulnerabilities in Internet of Things (IoT) applications poses a serious threat to enterprise systems, necessitating sophisticated and reliable defense solutions to counter emerging and evolving threats. For the Industrial Internet of Things (IIoT), stakeholders require trustworthy and sustainable systems that can prevent the loss of human life during critical operations. The impact of multi-variant persistent and sophisticated bot attacks on connected IIoTs is potentially catastrophic, and their detection presents a highly complex and critical challenge. Therefore, there is a pressing need for efficient and timely detection of IIoT botnet attacks. This research paper proposes a robust deep learning model named AttackNet for the detection and classification of different botnet attacks in IIoT based on adaptive based CNN-GRU model. The model is extensively evaluated using the latest dataset and standard performance evaluation metrics, demonstrating its capacity to protect IIoT networks against sophisticated cyber-attacks with a testing accuracy of 99.75%, a loss of 0.0063, precision and recall score of 99.75% and 99.74% respectively. Our proposed model demonstrates superior accuracy, particularly within the N_BaIoT dataset. It achieves an outstanding accuracy of 99.75% across ten classes, surpassing state-of-the-art techniques by a substantial margin ranging from 3.2% to 16.07%. Moreover, the proposed model outperforms state-of-the-art anomaly detection systems in IIoT based on a real-time IoT device dataset in terms of detecting and classifying botnet attacks accurately.

Belt rotation in pipe conveyors: Development of an overlap monitoring system using digital twins, industrial Internet of things, and autoregressive language models

Belt rotation is a critical failure mode in pipe conveyors. However, overlap position monitoring remains unexplored. Existing monitoring solutions are expensive and lack scalability, primarily relying on detecting angle variations beyond predefined thresholds. We developed a digital twin of a low-cost test rig for measuring belt rotation using 3D prototyping, infrared distance sensors, and a 3-axis accelerometer. We implemented a controller-responder industrial Internet of Things network using ESP-NOW protocol. Applying a minimum viable prompt, we fine-tuned an autoregressive language model for predicting the overlap position. We then developed a software application for cloud computing and local storage of measurements. Additionally, we developed a human–machine interface for future real-case applications. The bias and precision for the predicted angle (0.2294° ± 11.8822°), angular speed (0.1630 ± 0.7344 RPM), number of laps (0.0001336 ± 0.0448 laps), diameter (–0.1984 ± 6.3743 mm), and outer-edge side (100 % agreement) were suitable for the intended use.

Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning Model for IIoT Environment

Intrusion Detection in the Industrial Internet of Things (IIoT) concentrations on the security and safety of critical structures and industrial developments. IIoT extends IoT principles to industrial environments, but linked sensors and devices can be deployed for monitoring, automation, and control of manufacturing, energy, and other critical systems. Intrusion detection systems (IDS) in IoT drive to monitor network traffic, device behavior, and system anomalies for detecting and responding to security breaches. These IDS solutions exploit a range of systems comprising signature-based detection, anomaly detection, machine learning (ML), and behavioral analysis, for identifying suspicious actions like device tampering, unauthorized access, data exfiltration, and denial-of-service (DoS) attacks. This study presents an Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning (IID-SBODL) model for IIoT Environment. The IID-SBODL technique initially preprocesses the input data for compatibility. Next, the IID-SBODL technique applies Echo State Network (ESN) model for effectual recognition and classification of the intrusions. Finally, the SBO algorithm optimizes the configuration of the ESN, boosting its capability for precise identification of anomalies and significant security breaches within IIoT networks. By widespread simulation evaluation, the experimental results pointed out that the IID-SBODL technique reaches maximum detection rate and improves the security of the IIoT environment. Through comprehensive experimentation on both UNSW-NB15 and UCI SECOM datasets, the model exhibited exceptional performance, achieving an average accuracy of 99.55% and 98.87%, precision of 98.90% and 98.93%, recall of 98.87% and 98.80%, and F-score of 98.88% and 98.87% for the respective datasets. The IID-SBODL model contributes to the development of robust intrusion detection mechanisms for safeguarding critical industrial processes in the era of interconnected and smart IIoT environments.

Secure, light‐weight and dynamic PUF‐based mutual device authentication mechanism in industrial IoT networks

AbstractSecurity, a fundamental concern in the design and implementation of industrial Internet‐of‐Things (IIoT) networks, can be addressed through an effective authentication mechanism. Because the devices' battery, CPU, and memory are finite resources, any method designed for the IIoT must be resource sensitive. Our paper discussed a mechanism for device mutual authentication in IIoT networks that is adaptable, lightweight, safe, and efficient. In the proposed mechanism, the devices receive a primary secret value and physical unclonable function‐challenge/response pair (PUF‐CRPs) from the server and employ these values to authenticate one another. Later, IIoT devices requests server to update PUF‐CRPs for future communication We employed one‐way hash function, concatenation operation, XOR operation, and PUF‐CRPs to take into account the resource constraints of the devices. It has been established that the proposed mechanism is immune to well‐known attacks after it has undergone a formal evaluation for safety through BAN logic, and automatic validation through AVISPA and ProVerif tools. Comparative evaluation of the existing mechanisms and the proposed mechanism demonstrated that our proposed mechanism is superior to existing mechanisms, and its experimental study revealed that it uses 28% less energy. Our proposed mechanism is more secure and effective in terms of computing, communication, and storage overheads.

Revolutionizing intrusion detection in industrial IoT with distributed learning and deep generative techniques

In response to escalating cyber threats and privacy issues within the Industrial Internet of Things (IIoT), this research presents FedGenID, an advanced Federated Generative Intrusion Detection System, to safeguard IIoT networks. Our approach introduces a three-model framework: (1) a federated generative model, incorporating a Conditional Generative Adversarial Network (cGANs) for data augmentation, emphasizing only generator model updates to be shared among clients. This model uses a Wasserstein loss function with Gradient Penalty to amplify sample diversity, indicative of varying cyber threats. Concurrently, we address the issues of imbalanced and distributed data and deploy a data curation technique to align generated data within specific constraints. (2) A secondary model fine-tunes local Critics for enhanced resilience and detection of various adversarial attacks. (3) The third model focuses on precise cyber threat identification, leveraging augmented data for improved training under a synthetic federated learning schema, bolstering detection capability, especially against zero-day threats. Our evaluation of FedGenID, utilizing a novel industrial cybersecurity dataset, highlights its efficacy in non-IID, multi-class cyber threat detection and its resilience to adversarial attacks. Furthermore, we demonstrate how FedGenID can mitigate the negative impact of differential privacy-enhanced FL on model performance. The findings underscore FedGenID’s proficiency in detection accuracy, surpassing traditional FedID by 10% in the presence of zero-day attacks and high privacy regimes.

Joint Devices and IRSs Association for Terahertz Communications in Industrial IoT Networks

Joint Devices and IRSs Association for Terahertz Communications in Industrial IoT Networks

Proof-of-authority-based secure and efficient aggregation with differential privacy for federated learning in industrial IoT

The industrial internet of things (IIoT) uses connected devices and sensors to improve efficiency in industry, but increased reliance on these systems makes them prone to faults. To ensure the reliability of IIoT systems, robust fault detection is necessary, which can be provided by artificial intelligence (AI). The current state-of-the-art approach for AI in IIoT is mainly focused on centralized learning, which is inefficient due to its high communication cost. Federated learning (FL) addresses this limitation by enabling distributed training without exposing individual information. In order to provide secure and efficient FL for the IIoT environment, several research studies have investigated the use of blockchain networks. However, most of these studies ignore the processing time metric, which is crucial for IIoT networks. In this paper, we propose a secure and efficient parameter aggregation technique that enhances trust, security, and privacy for IIoT. We use a lightweight smart contract deployed with a proof-of-authority (PoA)-based blockchain in combination with a Gaussian differential privacy mechanism. We evaluate our proposed system using a real bearing fault dataset and demonstrate that it is able to provide a secure aggregation process with an accuracy of 94.00% and a processing time of 1.54 s, which is suitable for the IIoT environment with a private blockchain network.

Enhancing Industrial IoT Network Security through Blockchain Integration

In the rapidly evolving landscape of industrial ecosystems, Industrial IoT networks face increasing security challenges. Traditional security methods often struggle to protect these networks adequately, posing risks to data integrity, confidentiality, and access control. Our research introduces a methodology that leverages blockchain technology to enhance the security and trustworthiness of IoT networks. This approach starts with sensor nodes collecting and compressing data, followed by encryption using the ChaCha20-Poly1305 algorithm and transmission to local aggregators. A crucial element of our system is the private blockchain gateway, which processes and classifies data based on confidentiality levels, determining their storage in cloud servers or the Interplanetary File System for enhanced security. The system’s integrity and authenticity are further reinforced through the proof of authority consensus mechanism. This system employs Zero Knowledge Proof challenges for device authorization, optimizing data retrieval while maintaining a delicate balance between security and accessibility. Our methodology contributes to mitigating vulnerabilities in Industrial IoT networks and is part of a broader effort to advance the security and operational efficiency of these systems. It reflects an understanding of the diverse and evolving challenges in IoT security, emphasizing the need for continuous innovation and adaptation in this dynamic field.

Priority-aware scheduling method based on linguistic interval type 2 fuzzy logic systems for dense industrial IoT networks employing soft computing

The forthcoming era of industrialization is marked by intelligent production, with the manufacturing industry and the Industrial Internet of Things (IIoT) driving the creation and innovation of new concepts and systems. One innovative solution addressing the demands of industrial settings is the time-slotted channel hopping (TSCH) protocol, known for its ultra-low power consumption and reliable communication. However, the scheduling mechanism at the core of TSCH requires substantial time for every node. In this study, we propose a sustainable scheduling method for Industrial IoT networks called Linguistic Interval Type 2 Fuzzy Logic System (LIT2FLS) based Priority Aware TSCH (LIT2FLPAS). The suggested method aims to promote service diversification to extend battery life and reduce delay. The significance of each node is determined by the LIT2FLS method, which allocates the required number of slots based on priority and the volume of generated traffic. We compare our proposed LIT2FLPAS algorithm with two state-of-the-art scheduling functions, Scheduling Function-Zero (SF0) and Low Latency Scheduling Function (LLSF). The results demonstrate that LIT2FLPAS exhibits superior adaptability compared to SF0 and LLSF. It provides collision-free communications and significantly enhances the duty cycle by 8%, battery life by 30.32%, and packet loss ratio by 95% while reducing latency by 75.69% and collisions by 91%. These findings underscore the effectiveness of the LIT2FLPAS algorithm in addressing the specific challenges of industrial IoT networks, offering improved performance metrics and robust communication in dynamic industrial environments. The proposed method presents a promising solution for achieving efficient and reliable communication in the evolving landscape of intelligent industrial production.

Securing IIoT applications in 6G and beyond using adaptive ensemble learning and zero-touch multi-resource provisioning

The advent of Industry 5.0, driven by cutting-edge 6G technologies such as immersive cloud eXtended Reality (XR), Autonomous vehicles, holographic communication, and Digital Twin (DT), is set to trigger a substantial upswing in the deployment of the Industrial Internet of Things (IIoT) devices interconnected within networks. This expansion of IIoT devices will create a wider attack surface and increase the risk of data breaches, privacy violations, and system disruptions. Therefore, it is essential to design innovative mechanisms to ensure the reliability and security of these advanced 6G applications, as well as the IIoT devices that support them. In this context, we propose a novel Software-defined Networking (SDN)-based Ensemble Learning (EL) Framework for Secure IIoT applications in 6G and beyond, called AdaptSDN. The proposed framework leverages SDN technology to dynamically allocate network resources and deploy security measures on demand. It also leverages EL techniques to improve the intrusion detection system’s accuracy. By isolating IIoT devices into network slices, the framework limits the impact of attacks and reduces the potential for cascading failures. Digital twins are used for creating a virtual replica of the IIoT network, allowing for a real-time security threat detection. In particular, AdaptSDN includes three main modules: (1) A novel digital Twin (DT)-enabled data gathering and selection of informative features module to achieve two main objectives: reducing computational complexity and improving detection performance; (2) An SDN-based lightweight adaptive boosting module that uses an advanced boosting EL techniques to dynamically adjust weights and to effectively identify and respond to IIoT attacks in real-time; and (3) A zero-touch resources provisioning module that employs a non-cooperative game theory approach. This approach allows for automatically provisioning various resources in a network to efficiently mitigate network attacks; it enables SDN nodes to obtain the required virtual resources, i.e., storage, computing, and bandwidth, from the main orchestrator with respect to IIoT attack type. We carried out comprehensive experiments to assess the effectiveness of our proposed framework in detecting real-world IIoT attacks. The numerical results confirm that AdaptSDN has the potential to enable secure and reliable IIoT applications in 6G and beyond, meeting the stringent service requirements of the new emerging applications.

On Cyber-Physical Fault Resilience in Data Communication: A Case From A LoRaWAN Network Systems Design

Systems offering fault-resilient, energy-efficient, soft real-time data communication have wide applications in Industrial Internet-of-Things (IIoT). While there have been extensive studies for fault resilience in real-time embedded systems, investigations from cyber-physical systems (CPS) perspective are still much needed, as CPS faults occur not just from abnormal conditions in the software/hardware of the system, but also from the physical environment in which the system operates. At the same time, in addition to conventional fault tolerance strategies embedded in the software/hardware of the target system, CPS faults could be mitigated via some strategic systems re-configuration made available by the physical environment. This paper presents a design and implementation for CPS fault-resilient data communication, in the context of IIoT networks running LoRaWAN, a low-power wide-area networking standard. The proposed design combines collaborative IIoT end devices plus a network gateway piggybacked on a third-party cruising object that is part of the environment. With the focus on data communication, the study illustrates challenges and opportunities to address CPS fault resilience while meeting the needs for energy efficiency and communication timeliness that are common to IIoT systems. The implementation of the design is based on ChirpStack, a widely used open source framework for LoRaWAN. The results from experiment and simulation both show that the proposed scheme can tolerate limited errors of data communication while saving operating energy and maintaining timeliness of data communication to some extent.