Industrial Internet Of Things Devices Research Articles

Radio fingerprinting for anomaly detection using federated learning in LoRa-enabled Industrial Internet of Things

Long Range (LoRa) communications are gaining popularity in the Industrial Internet of Things (IIoT) domain due to their large coverage and high energy efficiency. However, LoRa-enabled IIoT networks are susceptible to cyberattacks mainly due to their wide transmission window and freely operated frequency band. This has led to several categories of cyberattacks. However, existing anomaly detection systems are inefficient in detecting particularly impersonation attacks due to the dense deployment, heterogeneous IIoT devices and manufacturers involved.In this work, we introduce Hawk, a distributed anomaly detection system for detecting compromised devices in LoRa-enabled IIoT. Hawk first measures a device-type specific physical layer feature, Carrier Frequency Offset (CFO) and then leverages the CFO for fingerprinting the device, and consequently detecting anomalous deviations in the device’s CFO behavior, potentially caused by adversaries. To aggregate the device-type specific CFO behavior profile efficiently, Hawk uses federated learning, a distributed machine learning approach. To the best of our knowledge, Hawk is the first to utilise a federated learning method for anomaly-based intrusion detection in LoRa-enabled IIoT. We perform extensive experiments on a real-world dataset collected using 60 LoRa devices, primarily to assess the effectiveness of Hawk against emerging new and unknown attacks. The results show that Hawk improves the detection accuracy by more than 8% compared to the state-of-the-art solutions. Additionally, Hawk reduces the storage overhead by more than 40%, and exhibits significant robustness against cyberattack.

A sustainable Ethereum merge-based Big-Data gathering and dissemination in IIoT System

The Industrial Internet of Things (IIoT) has captured the attention of various smart farming industries in recent years by connecting physical objects of smart farms over the internet so that they can be discovered and queried globally using Enterprise Application Software (EAS). This continuous transmission and processing poses a significant challenge in dealing with the massive amount of Big-Data being gathered and disseminated. As more applications use cloud platforms to increase storage capacity by storing Big-Data on an untrusted cloud server, data security precautions should be taken. However, with the rapid increase in the number of IIoT devices and EAS end-users and their diverse categories, the existing device authentication mechanisms in IIoT suffer from single factor authentication and poor adaptability. In light of the security requirements in IIoT Big-Data sharing using untrusted cloud servers, we present in this paper a sustainable Ethereum merge-based Big-Data gathering and dissemination in IIoT system. Extensive theoretical and simulation results validate data gathering with a business incentive mechanism with a proper data propagation threshold is an important parameter for determining equilibrium and analysing dynamic properties of IIoT system, which directly affects data processing speed and its final state. The Ethereum merge mechanism controls data dissemination instances in both the industry and EAS through classification and integrity verification using distributed authentication devices. Proof-of-stake is used for this purpose, which uses randomly selected validators to confirm transactions and add new ledgers to consortium blockchains to transfer data using partial secrets, making it fast, secure, and sustainable. Our proposed scheme outperforms other state-of-the-art schemes in simulation over Hyperledger Fabric with improved security measures.

Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

Cognitive Radio Networks (CRNs) have become a successful platform in recent years for a diverse range of future systems, in particularly, industrial internet of things (IIoT) applications. In order to provide an efficient connection among IIoT devices, CRNs enhance spectrum utilization by using licensed spectrum. However, the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel selection. Specifically, the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User (PU) activity and create a robust routing path. This study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT domain. Thus, a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method, namely, Channel Availability Probability. Moreover, a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation technique. This protocol combines lower layer (Physical layer and Data Link layer) sensing that is derived from the channel estimation model. Also, it periodically updates and stores the routing table for optimal route decision-making. Moreover, in order to achieve higher throughput and lower delay, a new routing metric is presented. To evaluate the performance of the proposed protocol, network simulations have been conducted and also compared to the widely used routing protocols, as a benchmark. The simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio (with an improved margin of around 5–20% approximately) under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks (MCRNs). Moreover, the cross-layer routing protocol successfully achieves high routing performance in finding a robust route, selecting the high channel stability, and reducing the probability of PU interference for continued communication.

Optimizing Federated Learning With Deep Reinforcement Learning for Digital Twin Empowered Industrial IoT

The accelerated development of the Industrial Internet of Things (IIoT) is catalyzing the digitalization of industrial production to achieve Industry 4.0. In this article, we propose a novel digital twin (DT) empowered IIoT (DTEI) architecture, in which DTs capture the properties of industrial devices for real-time processing and intelligent decision making. To alleviate data transmission burden and privacy leakage, we aim to optimize federated learning (FL) to construct the DTEI model. Specifically, to cope with the heterogeneity of IIoT devices, we develop the DTEI-assisted deep reinforcement learning method for the selection process of IIoT devices in FL, especially for selecting IIoT devices with high utility values. Furthermore, we propose an asynchronous FL scheme to address the discrete effects caused by heterogeneous IIoT devices. Experimental results show that our proposed scheme features faster convergence and higher training accuracy compared to the benchmark.

Lyapunov-Guided Delay-Aware Energy Efficient Offloading in IIoT-MEC Systems

With the increasingly humanized and intelligent operation of Industrial Internet of Things (IIoT) systems in Industry 5.0, delay-sensitive and compute-intensive (DSCI) devices have proliferated, and their demand for low latency and low power consumption has become more and more eager. In order to extend the battery life and improve the quality of user experience, we can offload DSCI-type workloads to mobile edge computing (MEC) servers for processing. However, offloading massive amounts of tasks will incur higher energy consumption, which is a severe test for the limited battery capacity of devices. In addition, the delay caused by frequent communication between IIoT devices and MEC cannot be ignored. In this article, we first formulate the stochastic computation offloading problem to minimize long-term energy consumption. Then, we construct a virtual queue using perturbed Lyapunov optimization techniques to transform the problem of guaranteeing task deadlines into a stable control problem for the virtual queue. Based on this, a novel delay-aware energy-efficient (DAEE) online offloading algorithm is proposed, which can adaptively offload more tasks when the network quality is good. Meanwhile, it delays transmission in the case of poor connectivity but ensures that the deadline is not violated. Moreover, we theoretically demonstrated that DAEE can enable the system to achieve an energy-delay tradeoff, and analyzed the feasibility of constructing virtual queues to assist the actual queue offloading tasks. Finally, simulation results show that DAEE performs well in minimizing energy consumption and maintaining low latency, especially for DSCI-type tasks.

Application of internet of things-based efficient security solution for industrial

Recent years have seen a rise in demonstrations of the Industrial Internet of Things (IIoT). Creating secure Internet of Things (IIoT) applications has become a major challenge. Because of the constant demand for additional functionality, such as foresight, the number of connected devices in the enterprise facility is continually growing. Certifying a smaller number of signatories provides stronger authentication methods and prohibits trustworthy third parties from being publicly certified among the available encryption instruments. Certain network criteria must be met by the virtual network function (VNFs) used in this arrangement. As a result of this output, the final service may be delivered at the lowest possible cost, including processing time and network delay. According to the results of this study, our solution is better suited to simplifying trust management in IIoT devices. These findings reveal that our proposed confidence model may withstand behavioural shifts in hostile IIoT environments. The experimental results show our proposed method BCEPP has a high computational cost of 95.3%, a low latency ratio of 28.5%, and increased data transmitting rate of 94.1%, enhanced security rate of 98.6%, packet reception ratio of 96.1%, user satisfaction index of 94.5%, probability ratio of 33.8% when compared to other methods.

Boosting Accuracy of Differentially Private Federated Learning in Industrial IoT With Sparse Responses

Empowered by 5G, it has been extensively explored by existing works on the deployment of differentially private federated learning (DPFL) in the Industrial Internet of Things (IIoT). Through federated learning, decentralized IIoT devices can collaboratively train a machine learning model by merely exchanging model gradients with a parameter server (PS) for multiple global iterations. Differentially private (DP) mechanisms will be incorporated by IIoT devices (also called clients) to prevent the leakage of privacy due to the exposure of gradients because original gradients will be distorted DP noises. Yet, learning with distorted gradients can seriously deteriorate model accuracy, making DPFL unusable in reality. To address this problem, we propose a novel DPFL with sparse responses (DPFL-SR) algorithm, which applies the sparse vector technique to reduce the privacy budget consumption in each global iteration. Specifically, DPFL-SR evaluates the value of each gradient, and only distorts and uploads significant gradients to the PS because significant gradients are more essential for model training. Since insignificant gradients are not disclosed, the reserved privacy budget can be used to return significant gradients for more iterations so that DPFL-SR can achieve higher model accuracy without lowering the privacy protection level. Extensive experiments are conducted with the MNIST and Fashion-MNIST datasets to demonstrate the practicability and superiority of DPFL-SR in IIoT systems.

HMACCE: Establishing Authenticated and Confidential Channel From Historical Data for Industrial Internet of Things

Industrial Internet of Things (IIoT) is a new paradigm for building intelligent industrial control systems, and how to establish a secure channel in IIoT for machine-to-machine (M2M) communication is a critical problem because the devices in IIoT suffer from various attacks and may leak confidential information. Traditional authenticated and confidential channel establishment (ACCE) protocols neither apply for resource-constrained IIoT devices nor satisfy leakage resilience. In this paper, we introduce a new security notion: historical data based multi-factor ACCE (HMACCE) to address this issue and propose two HMACCE protocols. Our HMACCE protocols use three authentication factors, i.e., a symmetric secret key, historical data, and a set of secret tags associated with the historical data, to establish a secure communication channel between the client and the server. The key idea is to use the secret key managed by an IIoT edge device to quickly verify the relationship between the historical data and its associated tags stored on the server. Our HMACCE has the following remarkable features. First, it is lightweight and tailored for resource-constrained IIoT devices. Second, it is bounded historical tag leakage resilience, which means that if a small portion of the secret tags is leaked to an adversary, it will not affect its security with an overwhelming probability. Moreover, as a security enhancement service, our HMACCE can be easily integrated with legacy IIoT devices by running simple authenticated key exchange protocols.

Distributed Resource Distribution and Offloading for Resource-Agnostic Microservices in Industrial IoT

Due to increase in real-time mobile applications and Industrial Internet-of-Things (IIoT) devices, the edge computing paradigm provides a systematic and eccentric platform for real-time Internet-of-Things applications. Though the paradigm provides an effective infrastructure, however the resource requirements of IIoT devices change radically with time, which is described as a resource-agnostic property. Therefore, the estimation of resource requirements of IIoT devices is a critical and resilient assignment. In addition, it requires an extensive amount of resources to process the data traffic flows and microservice offloading. Hence, we present RAISE, a novel resource-agnostic microservice offloading scheme for mobile IIoT devices. RAISE efficiently estimates the resource-agnostic nature of IIoT devices to maximize their resource utilization in the network. Based on the estimated resource requirement, we propose a resource-agnostic microservice offloading scheme to maximize the success rate. Extensive experiments show that RAISE provides better performance in terms of network throughput and Quality-of-Service (QoS) than the other existing methods, SDTO and DTOS, in terms of cost and reliability.

A deep transfer learning approach for IoT/IIoT cyber attack detection using telemetry data

The rise of internet connectivity across the globe increases the count of IoT (internet of things)/IIoT (industrial internet of things) devices exponentially. The objects/devices which are connected to the internet are always prone to malicious attacks at various levels, such as physical, network, fog, and applications, which exist in the IoT architecture. Many researchers have addressed this issue and designed their own solutions based on machine and deep learning techniques. It is undeniable that deep learning outperforms machine learning (ML), but it necessitates a massive amount of datasets with appropriate labels. In this work, the deep transfer learning (TL) technique has been adapted for gated recurrent unit (GRU). Each model is trained using a dataset that belongs to one source IoT device (source domain), and this trained model is used to classify the malicious traffic in another dataset that belongs to some other IoT device (target domain). This approach is used for binary classification. These transfer learning models have been evaluated using an IoT/IIoT telemetry dataset called ToN IoT which comprises the sensor data generated from the seven different types of IoT devices. The highest accuracy achieved by IoT garage door was upto 99.76% as a source domain by fixing IoT thermostat as target domain. These models were also evaluated using some more metrics such as precision, recall, F1-measure, training time and testing time. By implementing transfer learning based GRU model, the accuracy of the model is improved from 69.20% to 99.76%. Moreover, to prove the efficiency of the proposed model, it is compared with state of art deep learning model and its results were analyzed in a detailed manner.

Efficient Anonymous Authentication Based on Physically Unclonable Function in Industrial Internet of Things

Owing to the open Industrial Internet of Things (IIoT) environment, information interacting between devices and servers is transmitted over the public channel, which may lead to privacy breach of the device identity. Furthermore, communication entities are not fully trusted, and they may maliciously disclose the device identity information. Therefore, the anonymity of devices must be guaranteed. In addition, IIoT is resource-constrained, and complex algorithms are unsuitable for the IIoT system. Several researchers have attempted to design anonymous authentication schemes. The one-authentication-multiple-access approach allows devices to access server resources multiple times after a single authentication, and its authentication overhead is independent of the number of accesses. This can reduce the computational burden for devices that need to access the server frequently. However, existing anonymous authentication schemes do not support multiple accesses after one authentication, and still suffer from privacy issues and low efficiency for devices that need frequent access to the server. To address these issues, we propose a new anonymous authentication scheme that uses group signature technology to ensure device anonymity and uses Merkle hash tree technology to achieve multiple accesses after one authentication, thereby greatly reducing the authentication overhead of IIoT devices. Then, we validate the security of the scheme using the random oracle model and the BAN logic. Finally, compared with other related schemes, the experimental results show that our proposed scheme is more efficient and practical for resource-constrained IIoTs than other schemes.

Разработка архитектуры комплекса систем промышленного Интернета вещей на основе интеллектуальных датчиков и сенсоров

In IIoT (Industrial Internet of Things) systems designed for enterprise management in real time, it is required to perform operational and intelligent processing of Big Data and issue a control signal to the actuators in a predictable time (on the order of several milliseconds). The high speeds of Big Data continuously generated by sensors of the industrial Internet of Things system make it difficult to obtain a control effect at a predictable time. The purpose of the study is to develop the architecture of a complex of IIoT systems to obtain a control effect at a predictable time in real time. The central issue of the task is the high-speed processing of structured data at the place of their occurrence to solve the contradiction between a large number of continuously generated necessary data and the need to process them at a predictable time. The decomposition of the IIoT system into separate IIoT systems according to the structures of the data used by them, followed by synthesis into a single complex of enterprise IIoT systems, is applied. The developed architecture of the IIoT system complex makes it possible to effectively implement distributed management of production processes in a predictable time, perform operational and intelligent processing of huge amounts of data of various formats continuously generated by industrial facilities. The complex of IIoT systems consists of separate systems of the industrial Internet of Things, each of which has its own structure of transmitted data and is implemented on the basis of a multi-level bus, which provides a high data transfer rate in a structured form, the ability to attach to the bus any IIoT device and any program used, including the Big Data system to identify hidden patterns in the work of the enterprise. The proposed solution of the architecture of the IIoT system complex based on intelligent sensors and touchsensors allows for effective management of enterprise equipment and technological process operations in real time with the immediate use of the new patterns found in the continuously incoming new data. The solution can be used by developers of industrial Internet of Things systems for the effective launch and implementation of projects, for the development and commissioning of IIoT systems.

SDN-Assisted Mobile Edge Computing for Collaborative Computation Offloading in Industrial Internet of Things

Mobile edge computing (MEC) can provision augmented computational capacity in proximity so as to better support Industrial Internet of Things (IIoT). Tasks from the IIoT devices can be outsourced and executed at the accessible computational access point (CAP). This computing paradigm enables the computing resources much closer to the IIoT devices, and thus satisfy the stringent latency requirement of the IIoT tasks. However, existing works in MEC that focus on task offloading and resource allocation seldom consider the load balancing issue. Therefore, load balance aware task offloading strategies for IIoT devices in MEC are urgently needed. In this article, software-defined network (SDN) technology is adopted to address this issue, since the rule-based forwarding policy in SDN can help determine the most suitable offloading path and CAP for undertaking the computation. To this end, we formulate an optimization problem to minimize the response latency in the proposed SDN-assisted MEC architecture. A greedy algorithm is put forward to obtain the approximate optimal solution in polynomial time. Simulation has been carried out to evaluate the performance of the proposed approach. The simulation results reveal that our approach outstands other approaches in terms of the response latency.

RNS-Based Adaptive Compression Scheme for the Block Data in the Blockchain for IIoT

The Industrial Internet of Things (IIoT) is the essential component of Industry 4.0. Blockchain is a promising technology for secure data sharing and trustable cooperation between IIoT devices. However, the ever-growing transaction records make it difficult for the storage-limited IIoT devices to join the blockchain network. In this article, an adaptive compression scheme is proposed to decrease the storage volume on each node. In the scheme, the block body is compressed by representing the included transactions as their remainders stored in the distributed nodes. The original transaction could be recovered based on the Chinese remainder theorem. In particular, each node adapts its compression ratio according to its storage resource. The nodes storing more data have advantages in transaction recovery, introducing an incentive mechanism for efficient storage utilization. The theoretical analysis and simulation results show that the proposed scheme can achieve a high compression ratio with good service availability. The proposed scheme dramatically lowers the threshold for IIoT devices to join the blockchain network, which is important for the large-scale application of blockchain in Industry 4.0.

An Anonymous and Outsourcing-Supported Multiauthority Access Control Scheme With Revocation for Edge-Enabled IIoT System

With the application of the Internet of Things and intelligent technologies in industrial systems, the manufacturing efficiency and product quality have been improved, leading to the development of the Industrial Internet of Things (IIoT). Uploading private data to the cloud may lead to data leakage without security protection. Thus, attribute-based encryption (ABE) is widely used to ensure data security and implement data access control, and some multiauthority ABE schemes are proposed to meet the requirement of attributes from different authorities, such as factory and government, which is more suitable for the IIoT system. However, the current multiauthority ABE schemes are with privacy leakage problems on attributes and low-efficiency issues. To address these problems, we propose a multiauthority ABE scheme, which protects users’ privacy by anonymizing attributes in authentication, reduces the computing burden of IIoT devices by adapting online/offline technique and outsourcing decryption to edge devices, and realizes effective attribute revocation. A formal security proof is presented that our scheme is replayable chosen ciphertext attack secure. Finally, we implement the proposed scheme, and experimental results show that our scheme is more efficient than the existing schemes.

An ensemble deep federated learning cyber-threat hunting model for Industrial Internet of Things

Industrial Internet of Things (IIoT) is an emerging technology with prompt evolution in diverse applications, including critical infrastructure. While the increasing number of IIoT devices in today’s critical infrastructure enhances their efficiency and reliability, it also increases their vulnerability towards cyber-attacks. Ambient Intelligence (AmI), including machine learning techniques, is a way to handle such challenges with minimizing the human role. Although using machine learning-based techniques is increased in some applications these days, they are not widely used in IIoT environments due to the privacy issues of transferring all the data into a single machine to train the models. This paper proposes an ensemble-based deep federated learning cyber-threat hunting model to hunt the attack samples without data sharing. The proposed hunting model consists of two parallel federated-based components, one analyzes the IIoT status based on the normal situation of the network, and the other analyzes it with considering the threat situation. This model used an ensemble of classifiers to make the final decision. The proposed cyber-threat hunting model is evaluated using two test cases and compared with some works in the literature and outperformed them in the f1-score metric. Moreover, evaluations show that the proposed model acts stable in facing different numbers of clients, and its training time is faster than the centralized models with the same computational complexity.

Efficient and Privacy-Preserving Blockchain-Based Multifactor Device Authentication Protocol for Cross-Domain IIoT

Industrial Internet of Things (IIoT) has emerged as a prospective technology that improves the productivity and automation level for industrial applications. Devices from cooperative IIoT domains will communicate and collaborate on the increasingly complicated manufacturing tasks. To secure cross-domain device collaborations, we propose combining the blockchain with multifactor authentication. Because the multifactor authentication conforms to IIoT devices’ operation modes and brings higher security levels, and the blockchain technology contributes to building trust among different domains. However, this combined usage still has limitations in terms of the potential loss of factor attack, the storage overhead on the blockchain, and the contradiction between efficiency and privacy preservation. Motivated by these facts, in this article, we develop a privacy-preserving blockchain-based multifactor device authentication protocol for cross-domain IIoT. Specifically, multiple factors are additionally encoded by the hardware fingerprint into random numbers, before being transformed into key materials. The blockchain only stores each domain’s dynamic accumulator, which accumulates derived key materials for devices, thereby reducing the overhead. Moreover, the on-chain accumulator is leveraged to efficiently verify the unlinkable identities of cross-domain IIoT devices. The security of our protocol is formally proved, and the security features and functionalities are, respectively, discussed. A proof-of-concept prototype was implemented to prove the efficiency and reliability. The comparison results indicate that the on-chain storage is greatly reduced. Finally, the smart contract’s performance was evaluated to show scalability.

Improving efficiency and security of IIoT communications using in-network validation of server certificate

The use of advanced communications and smart mechanisms in industry is growing rapidly, making cybersecurity a critical aspect. Currently, most industrial communication protocols rely on the Transport Layer Security (TLS) protocol to build their secure version, providing confidentiality, integrity and authentication. In the case of UDP-based communications, frequently used in Industrial Internet of Things (IIoT) scenarios, the counterpart of TLS is Datagram Transport Layer Security (DTLS), which includes some mechanisms to deal with the high unreliability of the transport layer. However, the (D)TLS handshake is a heavy process, specially for resource-deprived IIoT devices and frequently, security is sacrificed in favour of performance. More specifically, the validation of digital certificates is an expensive process from the time and resource consumption point of view. For this reason, digital certificates are not always properly validated by IIoT devices, including the verification of their revocation status; and when it is done, it introduces an important delay in the communications. In this context, this paper presents the design and implementation of an in-network server certificate validation system that offloads this task from the constrained IIoT devices to a resource-richer network element, leveraging data plane programming (DPP). This approach enhances security as it guarantees that a comprehensive server certificate verification is always performed. Additionally, it increases performance as resource-expensive tasks are moved from IIoT devices to a resource-richer network element. Results show that the proposed solution reduces DTLS handshake times by 50–60 %. Furthermore, CPU use in IIoT devices is also reduced, resulting in an energy saving of about 40 % in such devices.

EBAKE-SE: A novel ECC-based authenticated key exchange between industrial IoT devices using secure element

Industrial IoT (IIoT) aims to enhance services provided by various industries, such as manufacturing and product processing. IIoT suffers from various challenges, and security is one of the key challenge among those challenges. Authentication and access control are two notable challenges for any IIoT based industrial deployment. Any IoT based Industry 4.0 enterprise designs networks between hundreds of tiny devices such as sensors, actuators, fog devices and gateways. Thus, articulating a secure authentication protocol between sensing devices or a sensing device and user devices is an essential step in IoT security. In this paper, first, we present cryptanalysis for the certificate-based scheme proposed for a similar environment by Das et al. and prove that their scheme is vulnerable to various traditional attacks such as device anonymity, MITM, and DoS. We then put forward an inter-device authentication scheme using an ECC (Elliptic Curve Cryptography) that is highly secure and lightweight compared to other existing schemes for a similar environment. Furthermore, we set forth a formal security analysis using the random oracle-based ROR model and informal security analysis over the Doleve-Yao channel. In this paper, we present comparison of the proposed scheme with existing schemes based on communication cost, computation cost and security index to prove that the proposed EBAKE-SE is highly efficient, reliable, and trustworthy compared to other existing schemes for an inter-device authentication. At long last, we present an implementation for the proposed EBAKE-SE using MQTT protocol.

Correlation Aware Scheduling for Edge-Enabled Industrial Internet of Things

Industrial Internet of Things (IIoT) has attracted increasing attention for improving the efficiency of manufacturing. Plenty of computation-intensive and latency-sensitive applications are required by IIoT networks, which pose significant challenges for the computation capacities of IIoT networks. To address these challenges, Edge-enabled Industrial Internet of Things (E-IIoT) emerges. Edge devices located at the edge of IIoT networks enlarge computation capacities of IIoT networks and improve their efficiency accordingly. How to schedule computation resources wisely is a major problem in E-IIoT networks. Since IIoT devices in an E-IIoT network monitor the industrial site collaboratively, tasks for processing sensory data collected by them are correlated accordingly. That means, scheduling highly correlated tasks to be processed at the same device can improve computation efficiency. Inspired by this fact, we propose a correlation aware scheduling (CAS) algorithm for E-IIoT networks in this article. In specific, computation model decision and processing order decision are made by considering computation resources of devices and correlations among tasks in the algorithm to minimize latency of E-IIoT networks. The NP-hardness of correlation aware latency minimization scheduling problem in E-IIoT networks is first proved. Theoretical analysis on approximation ratio of the CAS algorithm is provided, and simulation results demonstrate the effectiveness of the proposed algorithm in reducing latency.