Efficient heterogeneous signcryption scheme based on Edge Computing for Industrial Internet of Things

The Industrial Internet of Things (IIoT) is a crucial research field spawned by the Internet of Things (IoT). IIoT links all types of industrial equipment through the network; establishes data acquisition, exchange, and analysis systems; and optimizes processes and services, so as to reduce cost and enhance productivity. The introduction of edge computing in IIoT can significantly reduce the decision-making latency, save bandwidth resources, and to some extent, protect privacy. This paper outlines the research progress concerning edge computing in IIoT. First, the concepts of IIoT and edge computing are discussed, and subsequently, the research progress of edge computing is discussed and summarized in detail. Next, the future architecture from the perspective of edge computing in IIoT is proposed, and its technical progress in routing, task scheduling, data storage and analytics, security, and standardization is analyzed. Furthermore, we discuss the opportunities and challenges of edge computing in IIoT in terms of 5G-based edge communication, load balancing and data offloading, edge intelligence, as well as data sharing security. Finally, we introduce some typical application scenarios of edge computing in IIoT, such as prognostics and health management (PHM), smart grids, manufacturing coordination, intelligent connected vehicles (ICV), and smart logistics.

Industrial Internet of Things (IIoT) ensures reliable and efficient data exchanges among the industrial processes using Artificial Intelligence (AI) within the cyber-physical systems. In the IIoT ecosystem, devices of industrial applications communicate with each other with little human intervention. They need to act intelligently to safeguard the data confidentiality and devices' authenticity. The ability to gather, process, and store real-time data depends on the quality of data, network connectivity, and processing capabilities of these devices. Pervasive Edge Computing (PEC) is gaining popularity nowadays due to the resource limitations imposed on the sensor-embedded IIoT devices. PEC processes the gathered data at the network edge to reduce the response time for these devices. However, PEC faces numerous research challenges in terms of secured communication, network connectivity, and resource utilization of the edge servers. To address these challenges, we propose a secured and intelligent communication scheme for PEC in an IIoT-enabled infrastructure. In the proposed scheme, forged identities of adversaries, i.e., Sybil devices, are detected by IIoT devices and shared with edge servers to prevent upstream transmission of their malicious data. Upon Sybil attack detection, each edge server executes a parallel Artificial Bee Colony (pABC) algorithm to perform optimal network configuration of IIoT devices. Each edge server performs the job migration to their neighboring servers for load balancing and better network performance, based on their processing and storage capabilities. The experimental results justify the efficiency of our proposed scheme in terms of Sybil attack detection, the convergence curves of our pABC algorithm, delay, throughput, and control overhead of data communication using PEC for IIoT.

The evolution of the Industrial Internet of Things (IIoTs) greatly increases the volume of data generated by the connected IIoT devices. IIoT data are playing an increasingly important role in various industrial sectors. IIoT data sharing helps enterprises make better production decisions and respond to market changes timely. However, the distrust among IIoT entities and IIoT entities' distrust of data-sharing platforms may hinder the realization of data sharing. In this article, a decentralized IIoT data-sharing scheme based on blockchain and edge computing is proposed. A Proof of Storage and Transmission (PoST) consensus mechanism is proposed to meet data storage and transmission requirements of data owners in IIoT data-sharing networks. Based on the manufacture ties of data owners, shared data request probabilities are derived. The IIoT data sharing interactions between data owners and edge devices are modeled as a multiple-leader and multiple-follower Stackelberg game. The alternating direction method of multipliers (ADMMs) algorithm is used to obtain the optimal IIoT data sharing solutions in a distributed manner. Simulation results show that compared with the cooperative scheme, the total profit of edge devices is maximally increased by 59%, and the total utility of data owners is maximally increased by 52%.

The manufacturing industry is benefiting a lot since the Industrial Internet of Things (IIoT) deploys sensors and actuators to calibrate, audit, and analyze data to enhance efficiency and profitability, thereby automating systems in the industrial sector. The exponential growth of smart devices in the industrial sector requires real‐time connectivity, low latency, nearby storage, secure communication, and reliability. To meet this demand, fog computing is effectively used in IIoT applications. Fog computing solves most of the problems in the fourth industrial revolution, which is the interface between end‐user devices and cloud/web services. Fog also allows for local processing with minimal acceptable latency. This article focuses on how fog computing becomes part of IIoT and also explores the architecture of fog‐assisted IIoT applications. The taxonomy is built by classifying the existing literature to reveal the salient features of fog computing in IIoT. This article also discusses IIoT security solutions as well as research problems of fog‐assisted IIoT. This article also outlines the research challenges of fog‐assisted IIoT and also discusses the security solutions of IIoT.

In Industrial Internet of Things (IIoT), the certificate-based aggregate signature (CBAS) scheme was used to ensure privacy and security of IIoT, thus, the concrete construction of CBAS scheme without bilinear pairing has proposed, which was employed as an underlying building block to create an efficient data aggregation protocol. In this article, we first point out that the previous CBAS scheme cannot keep its claimed security. Particularly, a malicious KGC can compute a valid forgery signature, because it has the knowledge of the master secret key. After that, we discussed how specific forgery attacks could be made to against the existing CBAS schemes, which demonstrates that the previous related constructions cannot achieve its claimed security. In order to further provide a secure CBAS scheme for IIoT, we suggest a method to improve the CBAS scheme and propose a concrete construction of it. In the random oracle model, based on the hardness of discrete logarithm problem, the security of our new proposal can be proved. Compared with the previous CBAS schemes, our construction has better security, which is efficient with respect to computation and communication.

The Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) intersected in the realm of industry, transforming the industrial system to allow making real-time decisions at the edge. Latency issues, bandwidth consumption, and data security also pose a challenge to the traditional cloud-based system, and are of utmost priority in the industry where time is limited. Edge AI combines AI features with edge computing, moving the intelligence to the devices in the IIoT, allowing decisions to be made faster and more responsive to the context without depending on centralized infrastructures that might overwhelm the cloud. The paper summarizes the present-day developments in edge AI as a decision-making tool in IIoT and identifies the future directions. It discusses architectures, algorithms and applications that enable intelligent decision making at the network edge with particular focus on manufacturing, predictive maintenance, supply chain optimization and energy management. It is reviewed that facilitating technologies, such as lightweight deep learning models, federated learning, and hardware accelerators, and problems, such as scalability, interoperability, and cybersecurity are discussed. As depicted in the literature section, edge AI has been found to enhance efficiency of distributed industrial systems by reducing latencies, reliability, and independent decision-making. However, barriers such as the shortage of resources, failure to interface with current systems and standardized structures still persist. It is anticipated that future studies will be based on adaptive AI models and edge-cloud collaboration with application of 6G-enabled IIoT ecosystems. This paper summarizes the synthesis of state-of-the-art methods to inform the next generation of industrial automation and digital transformation powered by edge AI. It indicates the need of powerful, hardy, and scalable systems to open up the complete scope of opportunities that edge AI can introduce to decisions made by IIoT.

The industrial Internet of Things (IIoT) setting produces volumes of heterogeneous data that are interconnected, and anomaly detection is an essential part of the reliability, security, and efficiency of the system. The use of edge computing allows the analysis to be done nearer to data sources, which minimizes latency and reliance on cloud-based centralized data warehouses. The current anomaly detection solutions of IIoT usually use the traditional machine learning or shallow deep learning models, which cannot cope with the presence of high-dimensional data, low generalization, and large communication, resulting in poor accuracy and slow reaction. To solve these issues, this paper develops the Edge- IIoT Anomaly Framework (EIAF) that incorporates transfer learning with ResNet50 and an autoencoder that is deployed on the edge devices. ResNet50 will be utilized to extract highly detailed, transferable IIoT data stream feature representations, and an autoencoder will be utilized to recreate patterns successfully to detect anomaly indicators of outliers. This synergy improves the accuracy of the detection and reduces the use of resources. The proposed framework is especially appropriate in real-time monitoring of the industrial environment, where it is possible to detect faults before they occur and conduct proactive maintenance and prevent cyber-physical threats. Experimental results show that EIAF outperforms the current methods in accuracy on detecting the anomalies (by 97.2 percent), decreasing false positive rate (by 2 percent), and having a lower latency (178 ms), and a 94.5 percent reduction in resources consumption, which proves its suitability to serve as a solid and scalable solution to intelligent IIoT systems.

Hybrid platforms are based on the new technologies of the fourth industrial revolution such as IIoT and cloud computing, which allow local and remote operation. A hybrid system presents a low cost of operation and maintenance. This paper describes the development of a hybrid IIoT (Industrial Internet of Things) & Arduino IoT (Internet of Things) Cloud platform for the analysis of continuous industrial process data in real time through the cloud. This procedure offers a more stable and secure control through the new Cloud technology. The IIoT platform consists of industrial equipment and sensors that are controlled through Delta IIoT devices. An Arduino IoT Cloud with ESP32 module has the functionality of a router that allows sending data to the cloud, thereby achieving the control and monitoring of level and temperature variables of a continuous process. With this type of architecture, the industrial sector has great opportunities to improve its competitiveness by being able to analyze large amounts of data collected from sensors. In addition, the hybrid platform guarantees the visibility of the variables in real time from anywhere in the world where connectivity exists. The results are presented in graphs and are promising as there is no data loss in the cloud and data latency is almost negligible.KeywordsIndustrial Internet of Things (IIoT)Internet of Things (IoT)Industry 4.0Hybrid IIoT

With the fast development of industrial Internet of things (IIoT), a large amount of data is being generated continuously by different sources. Storing all the raw data in the IIoT devices locally is unwise considering that the end devices’ energy and storage spaces are strictly limited. In addition, the devices are unreliable and vulnerable to many threats because the networks may be deployed in remote and unattended areas. In this paper, we discuss the emerging challenges in the aspects of data processing, secure data storage, efficient data retrieval and dynamic data collection in IIoT. Then, we design a flexible and economical framework to solve the problems above by integrating the fog computing and cloud computing. Based on the time latency requirements, the collected data are processed and stored by the edge server or the cloud server. Specifically, all the raw data are first preprocessed by the edge server and then the time-sensitive data (e.g., control information) are used and stored locally. The non-time-sensitive data (e.g., monitored data) are transmitted to the cloud server to support data retrieval and mining in the future. A series of experiments and simulation are conducted to evaluate the performance of our scheme. The results illustrate that the proposed framework can greatly improve the efficiency and security of data storage and retrieval in IIoT.

Recent advancements of the Industrial Internet of Things (IIoT) have revolutionized modern urbanization and smart cities. While IIoT data contain rich events and objects of interest, processing a massive amount of IIoT data and making predictions in real-time are challenging. Recent advancements in artificial intelligence (AI) allow processing such a massive amount of IIoT data and generating insights for further decision-making processes. In this article, we propose several key aspects of AI-enabled IIoT data for smart city monitoring. First, we have combined a human-intelligence-enabled crowdsourcing application with that of an AI-enabled IIoT framework to capture events and objects from IIoT data in real time. Second, we have combined multiple AI algorithms that can run on distributed edge and cloud nodes to automatically categorize the captured events and objects and generate analytics, reports, and alerts from the IIoT data in real time. The results can be utilized in two scenarios. In the first scenario, the smart city authority can authenticate the AI-processed events and assign these events to the appropriate authority for managing the events. In the second scenario, the AI algorithms are allowed to interact with humans or IIoT for further processes. Finally, we will present the implementation details of the scenarios mentioned above and the test results. The test results show that the framework has the potential to be deployed within a smart city.

Enhanced Fault Diagnosis in IoT: Uniting Data Fusion with Deep Multi-Scale Fusion Neural Network

SummaryThrough the Industrial Internet of Things (IIoT), the manufacturing enterprises have significantly enhanced the production efficiency. The transmission of data in IIoT over public channels has brought about information leakage issues. Therefore, it is crucial to agree on a session key among the participants. In recent years, numerous authenticated key exchange (AKE) schemes have been designed. However, those AKE schemes which adopt the certificateless approach to tackle certificate management and key escrow may remain vulnerable to quantum attacks, and other AKE schemes which are based on lattice hard problem so as to achieve the post quantum security may incur key management issues. To simultaneously address the aforementioned challenges, we propose a lightweight certificateless lattice‐based authenticated key exchange (C2LAKE) scheme. Upon the hardness assumptions of the inhomogeneous small integer solution (ISIS) and computational bilateral inhomogeneous small integer solution (CBi‐ISIS) problems, the C2LAKE scheme has been demonstrated to be secure in the eCK model and the random oracle model (ROM). Better trade‐off among security and functionality features, and communication and computation costs make the C2LAKE scheme suitable and applicable in the IIoT environment.

Many different sectors are beginning to deploy cyber‐physical systems (CPS) and industrial Internet of Things (IIoT) devices, including wearables, sensors, and critical infrastructure. In order to ensure that the device's data remains confidential, it must be encrypted prior to transmission. However, obtaining authenticity and integrity in IIoT data is also crucial, and signcryption has been proposed as a solution to this challenge. An additional obstacle is how to search for signcrypted cloud data using plaintext keywords. In this paper, we suggest a plaintext‐checkable identity‐based signcryption for CPS towards IIoT (IBSC‐PCE), which enables plaintext searching of signcrypted cloud‐based data. We provide both confidentiality and integrity and also demonstrate the security of our approach under the q‐Diffie‐Hellman inversion problem (q‐DHIP). This problem has been shown to be secure in the random oracle model (ROM), which further reinforces the robustness of our approach. Compared to current state‐of‐the‐art methods, our proposed approach outperforms them.

Industrial Internet of Things (IIoT) brings together computers, devices, advanced analytics, and people in industries, such as transportation, oil plant, and power grid that leads to major efficiency and productivity gains for almost any industrial procedures. Due to the interconnection of devices in IIoT, communication security has become a critical issue to address in many emerging industry standards that require the authentication and key exchange procedure to be done to guarantee the authorized machine access (e.g., from users) and secure the data transmission between machines. To overcome the shortcoming (i.e., low entropy) of the memorable password in user authentication, it is rightfully recommended by industry standards (such as IEC-62443 family) to use multifactor authentication (MFA) for higher security levels. Notably, latency is one of the main sources of inefficiency when a device is communicating with other machines on IIoT. To mitigate latency, a smooth projective hash function (SPHF) built from well-studied standard assumptions is used to achieve a lowinteractivity multifactor authenticated key exchange protocol (MFAKE) because SPHF allows each party to prove to the others that he knows the right authentication factor(s). In this article, we are, therefore, motivated to build a new MFAKE named “secure remote multifactor (SRMF)” to achieve the humaninvolved “machine-to-machine” secure communication in IIoT. That is, SRMF leverages multiple user-centric authentication factors (such as password, biometric fingerprints, and PIN), and it can synergistically support multifactor registration (MFR), MFA, and multifactor key exchange (MFKE). Furthermore, to prevent authentication factors stored at the server exposing to attackers, the password-harden service (i.e., Pythia-PRF and USENIX'15) inspires us to develop a multifactor hardening service (MFHS) utilizing an oblivious pseudorandom function (OPRF). The balanced security of the proposed protocol is proved under the model of Bellare-Pointcheval-Rogaway (EUROCRYPTO'00) along with theoretical and experimental evaluations.

Through interconnecting machines, sensors, and devices, the Industrial Internet of Things (IIoT) has revolutionized industrial processes by allowing real-time data analysis and decision-making. The implementation of cloud, edge, and fog computing models has followed IIoT development, bringing unique security and privacy challenges in their wake. In this all-encompassing report, the potential risks, complications, and apprehensions of these models are analyzed with respect to IIoT integration. Examining cloud, edge, and fog computing models distinguishes and assesses their benefits when applied to IIoT. The paper analyzes the strengths offered by each model and addresses the risks associated with security and privacy, such as data infringement, malware, and denial-ofservice attacks. IIoT systems, in particular, pose significant challenges in providing protection. Cloud, edge, and fog computing models provide ample benefits but IIoT systems utilizing these models must have strong security and privacy policies in place. This paper highlights specific technical and organizational strategies to tackle potential challenges and risks. By effectively implementing these strategies, organizations can tap into the advantages of these models while ensuring the safety of their IIoT systems. Informed decisions can be made, and IIoT deployments can be safeguarded with the aid of the valuable resource given in this paper, which offers comprehensive analysis and recommendations. For those in the field of IIoT security, including practitioners and researchers, this paper is of great value.