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The phenomenal increase in the usage of Internet promotes the quality of trust in the scope of Internet of Things (IoT). Trust is beneficial in the provision of an effective, reliable, scalable, and trustworthy environment to users of the IoT network, where they can share their private information with each other on a secure communication platform. For successful communications among the Internet users, trust is an important factor to provide them with private infrastructures and secure environments, where exchanging data among devices becomes more easy and trustworthy. Therefore, trust management is a backbone for the successful and secure transmission of data among various nodes in a large-scale IoT network. To overcome the security issues, latency, and risk of malicious activities, a lightweight approach is proposed for those nodes in industrial IoT that cannot maintain security. LightTrust utilizes a centralized trust agent to generate and manage trust certificates that allow nodes to communicate for a specific time without performing trust computations. Trust agents also maintain a trust database to store the current trust degree for the aggregation/propagation purposes. Trust between two nodes is developed by direct observations in terms of compatibility, cooperativeness, and delivery ratio, whereas recommendations are used to develop trust in the context of indirect observations, i.e., experience or previous knowledge. The comparative simulations of the proposed and existing approaches are also performed whereby the results illustrate that the proposed approach efficiently maintains resilience and robust environments.

The fourth industrial revolution is paving the way for Industrial Internet of Things applications where large number of wireless nodes, equipped with sensors and actuators, monitor the production cycle of industrial goods. This paper proposes and analyses LoRaIN, a network architecture and MAC-layer protocol thought for on-demand monitoring of industrial machines. Our proprietary system is an energy-efficient, reliable and scalable solution, where the protocol is built on top of LoRa at 2.4 GHz. Indeed, the low-power characteristics of LoRa allow to reduce energy consumption, while Wireless Power Transfer is used to recharge batteries, avoiding periodic battery replacement. High reliability is obtained through the joint use of Frequency and Time Division Multiple Access. A dynamic LoRaIN scheduler manages the communication and recharging phases depending on the tasks assigned to the nodes, as well as the number of monitoring devices. Performance is measured in terms of network throughput, energy consumption and latency. Results demonstrate that the proposed solution is suitable for monitoring applications of industry machines.

Industrial Internet-of-Things (IIoT) applications require reliable and efficient wireless communication. Assuming dense wireless sensor networks (WSNs) operating in a harsh environment, a concept of a time-division multiple access (TDMA)-based WSN enriched with electronically steerable parasitic array radiator (ESPAR) antennas is proposed and examined in this work. The utilized antenna provides one omnidirectional and 12 directional radiation patterns that can be electronically switched by the sensor node. We introduce a relay discovery algorithm, which selects those sensor nodes with an ESPAR antenna capable to act as relay. The selection of the relay nodes is based on a certain link quality threshold that algorithm uses as input. The outcome is a reduction in the number of layers or hops with a guaranteed Quality of Service (QoS). To emphasize the physical aspect of the wireless propagation, we introduce the measured antenna radiation patterns and consider two different path-loss propagation models representing blockage-free and blockage-prone industrial environments. A number of network simulations were performed and signal-to-noise ratio (SNR) as a link quality measure was examined with respect to the network density and different measured radiation pattern settings. The main outcomes show a tradeoff between SNR per link and the percentage of nodes that can serve as relays. As a result, we propose network design guidelines that take under consideration the QoS range with respect to SNR together with an optimal number of antenna radiation patterns that should be selected as a tradeoff between latency, energy consumption, and reliability in a network.

The article examines the principles of developing wireless networks of autonomous gamma sensors in order to create systems for spatial environmental radiation monitoring. The main task of such systems is to control the level of gamma radiation in areas where potential sources of ionizing radiation are located. An autonomous gamma-ray spectrometer is used as a measuring sensor. The authors propose to apply measuring sensors based on a silicon photomultiplier to create autonomous wireless networks of the industrial Internet for radiation monitoring. To confirm the possibility of using this class of receivers as part of gamma spectrometers, the main structural elements of the system were modeled, and the experimental model of the gamma spectrometer was prototyped. The linearity and energy resolution of the experimental sample were also investigated. To test the model for constructing a gamma spectrometer, a CsI (Tl) scintillation crystal and a Sensl Array-60035-4P photomultiplier were used. The established range of recorded energies is in the range from 121 keV to 1332 keV, the relative energy resolution for the 137Cs peak is 11.07 %, the linearity of the transfer characteristic is 99.91 %. Based on this sensor, the architecture of an automated wireless system for monitoring the spatial distribution of gamma radiation has been developed. The results of the work allow the use of radiation monitoring systems in accordance with the requirements of Industry 4.0.

Industrial Internet of Thing (IIoT) systems are considered attractive ransomware targets because they operate critical services that affect human lives and have substantial operational costs. The major concern is with brownfield IIoT systems since they have legacy edge systems that are not fully prepared to integrate with IoT technologies. Various existing security solutions can detect and mitigate such attacks but are often ineffective due to the heterogeneous and distributed nature of the IIoT systems and their interoperability demands. Consequently, developing new detection solutions is essential. Therefore, this paper proposes a novel targeted ransomware detection model tailored for IIoT edge systems. It uses Asynchronous Peer-to-Peer Federated Learning (AP2PFL) and Deep Learning (DL) techniques as a targeted ransomware detection algorithm. The proposed model consists of two modules: 1) Data Purifying Module (DPM) aims to refine and reconstruct a valuable and robust representation of data based on Contractive Denoising Auto-Encoder (CDAE), and 2) Diagnostic and Decision Module (DDM) is used to identify targeted ransomware and its stages based on Deep Neural Network (DNN) and Batch Normalization (BN). The main strengths of this proposed model include: 1) each edge gateway’s modules work cooperatively with its neighbors in an asynchronous manner and without a third party, 2) it deals with both homogeneous and heterogeneous data, and 3) it is robust against evasion attacks. An exhaustive set of experiments on three datasets prove the high effectiveness of the proposed model in detecting targeted ransomware (known and unknown attacks) in brownfield IIoT and the superiority over the state-of-the-art models.

Time Division Multiple Access-based Medium Access Control protocols tend to be the choice for wireless networks that require deterministic delay guarantees, as is the case in many Industrial Internet of Things (IIoT) applications. As the optimal joint scheduling and routing problem for multi-hop wireless networks is NP-hard, heuristics are generally used for building schedules. However, heuristics normally result in sub-optimal schedules, which may result in packets missing their deadlines. In this paper, we present RECCE, a deep REinforcement learning method for joint routing and sCheduling in time-ConstrainEd networks with centralised control. During training, RECCE considers multiple routes and criteria for scheduling in any given time slot and channel in a multi-channel, multi-hop wireless network. This allows RECCE to explore and learn routes and schedules to deliver more packets within the deadline. Simulation results show that RECCE can reduce the number of packets missing the deadline by as much as 55% and increase schedulability by up to 30%, both relative to the best baseline heuristic. RECCE can deal well with dynamic network conditions, performing better than the best baseline heuristic in up to 74% of the scenarios in the training set and in up to 64% of scenarios not in the training set.

A new industrial production model based on digitalization, system interconnection, virtualization and data exploitation, has emerged. Upgrade of production processes towards this Industry 4.0 model is one of the critical challenges for the industrial sector and, consequently, the training of students and professionals has to address these new demands. To carry out this task, it is essential to develop educational tools that allow students to interact with real equipment that implements, in an integrated way, new enabling technologies, such as connectivity with standard protocols, storage and data processing in the cloud, machine learning, digital twins and industrial cybersecurity measures. For that reason, in this work, we present an educational environment on Industry 4.0 that incorporates these technologies reproducing realistic industrial conditions. This environment includes cutting-edge industrial control system technologies, such as an industrial firewall and a virtual private network (VPN) to strengthen cybersecurity, an Industrial Internet of Things (IIoT) gateway to transfer process information to the cloud, where it can be stored and analyzed, and a digital twin that virtually reproduces the system. A set of hands-on tasks for an introductory automation course have been proposed, so that students acquire a practical understanding of the enabling technologies of Industry 4.0 and of its function in a real automation. This course has been taught in a master’s degree and students have assessed its usefulness by means of an anonymous survey. The results of the educational experience have been useful both from the students’ and faculty’s viewpoint.

Currently, companies in the industrial sector are focusing their efforts on incorporating the advances contained in the Industry 4.0 model, to continue competing in an increasingly high-tech market. These advances, in addition to productivity, have a remarkable impact on the working environment of workers and on the measures adopted to maintain a healthy workspace. Thus, for example, there are projects to develop augmented reality technologies for maintenance and industrial training, advanced modelling tools for additive manufacturing, or Big Data analysis platforms for industrial data. However, the solutions designed are too specific to a particular industry problem or the platforms proposed are too generalist and not easily adaptable to the industries. This work seeks to provide a reference software architecture at the service of the connected industry that allows the provision of new capacities for process optimisation, predictive maintenance and real-time visualisation, integrating all the relevant information generated by the existing systems, incorporating new sources of data resulting from the digital society, and ensuring future compatibility with the new sources of information, solutions and Industrial Internet of Things (IIoT) devices that may be implemented.

Biosensors-based devices are transforming medical diagnosis of diseases and monitoring of patient signals. The development of smart and automated molecular diagnostic tools equipped with biomedical big data analysis, cloud computing and medical artificial intelligence can be an ideal approach for the detection and monitoring of diseases, precise therapy, and storage of data over the cloud for supportive decisions. This review focused on the use of machine learning approaches for the development of futuristic CRISPR-biosensors based on microchips and the use of Internet of Things for wireless transmission of signals over the cloud for support decision making. The present review also discussed the discovery of CRISPR, its usage as a gene editing tool, and the CRISPR-based biosensors with high sensitivity of Attomolar (10−18M), Femtomolar (10−15M) and Picomolar (10−12M) in comparison to conventional biosensors with sensitivity of nanomolar 10−9M and micromolar 10−3M. Additionally, the review also outlines limitations and open research issues in the current state of CRISPR-based biosensing applications.

Featured Application Design of hybrid wireless sensor networks for industrial environments, used for connecting sensor modules that capture information, enabling automated production lines and AI-driven assembly. With the deployment of data-driven assembly and production factories, challenges arise in sensor data acquisition and gathering. Different wireless technologies are currently used for transferring data, each with different advantages and constraints. In this paper, we present a hybrid network architecture for providing Quality of Service (QoS) in an industrial environment where guaranteed minimal data rates and maximal latency are of utmost importance for controlling devices and processes. The location of the access points (APs) is determined during the initial network-planning action, together with physical parameters such as frequency, transmit power, and modulation and coding schemes. Instead of performing network-planning just once before the network rollout, the network is monitored continuously by adding telemetry data to the frame header of all data streams, and the network is automatically reconfigured in real-time if the requirements are not met. By not using maximum transmit powers during the initial roll-out, more APs are needed, but coverage is guaranteed when new obstructions such as metallic racks or machinery are added. It is found that decreasing the transmit power by 6 dB gives the best trade-off between the number of required APs and network robustness. The proposed architecture is validated via simulations and via a proof-of-concept setup.

As Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices are becoming increasingly popular in the era of the Fourth Industrial Revolution, the orchestration and management of numerous fog devices encounter a scalability problem. In fog computing environments, to embrace various types of computation, cloud virtualization technology is widely used. With virtualization technology, IoT and IIoT tasks can be run on virtual machines or containers, which are able to migrate from one machine to another. However, efficient and scalable orchestration of migrations for mobile users and devices in fog computing environments is not an easy task. Naïve or unmanaged migrations may impinge on the reliability of cloud tasks. In this paper, we propose a scalable fog computing orchestration mechanism for reliable cloud task scheduling. The proposed scalable orchestration mechanism considers live migrations of virtual machines and containers for the edge servers to reduce both cloud task failures and suspended time when a device is disconnected due to mobility. The performance evaluation shows that our proposed fog computing orchestration is scalable while preserving the reliability of cloud tasks.

Cyber-security countermeasures are important for IIoT (industrial Internet of things) systems in which IT (information technology) and OT (operational technology) are integrated. The appropriate asset management is the key to creating strong security systems to protect from various cyber threats. However, the timely and coherent asset management methods used for conventional IT systems are difficult to be implemented for IIoT systems. This is because these systems are composed of various network protocols, various devices, and open technologies. Besides, it is necessary to guarantee reliable and real-time control and save CPU and memory usage for legacy OT devices. In this study, therefore, (1) we model various asset configurations for IIoT systems and design a data structure based on SCAP (Security Content Automation Protocol). (2) We design the functions to automatically acquire the detailed information from edge devices by “asset configuration management agent”, which ensures a low processing load. (3) We implement the proposed asset management system to real edge devices and evaluate the functions. Our contribution is to automate the asset management method that is valid for the cyber security countermeasures in the IIoT systems.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Industrial firms are under severe pressure to undertake digital transformation and leverage the Industrial Internet of Things (IIoT) and emerging technologies for the integration of industrial machines to share information on a real-time or near real-time basis. Though in recent years researchers have focused their attention on digital transformation, there is limited scholarly guidance for developing capabilities for such transformation. Drawing on dynamic capability theory and exploratory qualitative interviews with senior ‘elite’ executives from five of the world’s largest multinational firms, the study outlines a new conceptual framework for digital transformative capability development. The integrative framework demonstrates how the three core capabilities of digital sensing, digital seizing, and digital reconfiguring manifest through associated capabilities of Strategic Sensing, Rapid Prototyping, Organization Structure, Business Model Transformation, and Cultural/Mindset Transformation. Internal and external contingencies are proposed as moderators of the relationship between IIoT and emerging technologies, and digital transformative capability development. Collectively, the article makes the case for Digital Transformation Capability and sheds new light on the digital transformation process. Implications for theory and practice are highlighted, and limitations are discussed.

Large-scale deployments of the Internet of Things (IoT) are adopted for performance improvement and cost reduction in several application domains. The four main IoT application domains covered throughout this article are smart cities, smart transportation, smart healthcare, and smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor localization and received significant attention from the research community due to low-power, low-cost, and long-range communication. In addition, its signals can be used for communication and localization simultaneously. There are different proposed localization methods to obtain the IoT relative location. Each category of these proposed methods has pros and cons that make them useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated this work and provided the following contributions: (1) definition of the main requirements and limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey the most relevant methods used within the IoT ecosystem for improving GNSS-free localization accuracy, and (4) discussion covering the open challenges and future directions within the field. Some of the important open issues that have different requirements in different IoT systems include energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview of research works that have been published between 2018 to July 2021 and made available through the Google Scholar database.

The advance of digitalization in industry is making possible that connected products and processes help people, industrial plants and equipment to be more productive and efficient, and the results for operative processes should impact throughout the economy and the environment. Connected products and processes generate data that is being seen as a key source of competitive advantage, and the management and processing of that data is generating new challenges in the industrial environment. The article to be presented looks into the framework of the adoption of Artificial Intelligence and Machine Learning and its integration with IIoT (industrial Internet of Things) or IoT (Internet of Things) under industry 4.0, or smart manufacturing framework. This work is focused on the discussion around Artificial Intelligence/Machine Learning and IIoT/IoT as driver for Industrial Process optimization. The paper explore some related articles that were find relevant to start the discussion, and includes a bibliometric analysis of the key topics around Artificial Intelligence/Machine Learning as a value added solution for process optimization under Industry 4.0 or Smart Manufacturing paradigm. The main findings are related to the importance that the subject has acquired since 2013 in terms of published articles, and the complexity of the approach of the issue proposed by this work in the industrial environment., El avance de la digitalización en la industria está haciendo posible que los productos y procesos conectados colaboren con las personas, las plantas industriales y los equipos a ser más productivos y eficientes, y los resultados de los procesos operativos deberían impactar en toda la economía y el medio ambiente. Los productos y procesos conectados generan datos que se ven como una fuente clave de ventaja competitiva, la gestión y procesamiento de esos datos está generando nuevos desafíos en el entorno industrial. El artículo que se presentará analiza el marco de la adopción de la Inteligencia Artificial, y el Aprendizaje Automático, y su integración con IIoT o IoT bajo la industria 4.0, o el marco de fabricación inteligente. Este trabajo se centra en la discusión en torno a la Inteligencia Artificial/Aprendizaje Automático e IIoT/IoT como impulsor de la optimización de Procesos Industriales. Este trabajo explora algunos artículos relacionados que se consideraron relevantes para iniciar la discusión, e incluye un análisis bibliométrico de los temas clave en torno a la Inteligencia Artificial/Aprendizaje Automático como una solución de valor agregado para la optimización de procesos bajo el paradigma de Industria 4.0 o Manufactura Inteligente. Los principales hallazgos se relacionan con la importancia que ha adquirido el tema desde 2013 en cuanto a artículos publicados, y la complejidad del abordaje del tema propuesto por este trabajo en el ámbito industrial., Facultad de Informática

This paper is an extension of work originally presented in SoftCOM 2019 [1]. The novelty of this work reside in its focused improvement of our scheduling algorithm towards its usage on a real 5G infrastructure. Industrial IoT applications are often designed to run in a distributed way on the devices and controller computers with strict service requirements for the nodes and the links between them. 5G, especially in concomitance with Edge Computing, will provide the desired level of connectivity for these setups and it will permit to host application run-time components in edge clouds. However, allocation of the edge cloud resources for Industrial IoT (IIoT) applications, is still commonly solved by rudimentary scheduling techniques (i.e. simple strategies based on CPU usage and device readiness, employing very few dynamic information). Orchestrators inherited from the cloud computing, like Kubernetes, are not satisfying to the requirements of the aforementioned applications and are not optimized for the diversity of devices which are often also limited in capacity. This design is especially slow in reacting to the environmental changes. In such circumstances, in order to provide a proper solution using these tools, we propose to take the physical, operational and network parameters (thus the full context of the IIoT application) into consideration, along with the software states and orchestrate the applications dynamically.

Safety and security of Industrial Control Systems (ICS) applied in many critical infrastructures is essential. In these systems, hazards can be due either to system failure or cyber-attacks factors. Accurate hazard detection and reducing reconfiguration time after hazard is one of the most important objectives in these systems. One of the procedures that can reduce the reconfiguration time is determining the cause of hazards and, based on the aforementioned factors, adopting the best commands in reconfiguration time. However, it is difficult to differentiate between different types of hazard because their effects on the system can be similar. With the advent of IoT into ICS, known as IIoT, it has become possible to differentiate the hazards through the adoption of data from different IIoT sensors in the environment. In this article, we propose a risk management approach that identifies hazards based on the physical nature of these systems with the support from the IIoT. The identified hazards fall into four categories: stealthy attack, random attack, transient failure, and permanent failure. Then, the reconfiguration process is run based on the proposed differentiation, which provides a better performance and reconfiguration time. In the experimental section, a fluid storage system is simulated, showing 97% correct differentiation of hazards and reducing in 60% the lost time in the system recovery reconfiguration. This work is supported by the Spanish Ministry of Economy and Competitiveness and by the European Regional Development Fund under Contract RTI2018-094532-B-I00 (MINECO/FEDER).

Recently, Distributed Energy Resources (DERs) have been utilized with increasing frequency in Industrial Internet of Things (IIoT) to deal with energy and environmental challenges. IIoT with wireless communication technology, which is easy to be intercepted, often facing various attack. For the safety of the network, more complex algorithms need to be run on IIoT, but the action need more energy. In addition, in some application scenarios, the location where the packets were generated indicates that an event occurred. An attacker can find the sensor node through a backtracking attack, which is equivalent to reaching the place where the event occurred. In order to hide the location information of the event, it is necessary to protect source location privacy (SLP), which will also increase the energy consumption of IIoT. If only the traditional battery is used to power the nodes in IIoT, the lifetime of the system will be limited. When IIoT is deployed outdoors, it is often difficult to replace the battery. The existence of lakes make IIoT have coverage holes during deployment. In order to implement SLP and make the system work for a long time in the environment with deployment holes, we use DERs. Herein, we propose an SLP protection scheme based on phantom nodes, rings, and fake paths (PRFs) for IIoT. To increase the safety time of the network, the PRFs dynamically selects the phantom nodes. To adapt to a complex deployment environment, the ring can be flexibly deployed according to the terrain. The PRFs uses fake paths to confuse attackers. We integrate DERs technology into PRFs, such as using solar power modules, looking forward to extending the lifetime of the system. The experimental results proved that the PRFs could efficiently reduce backtracking attacks while maintaining a balance between security and network energy consumption of IIoT.

The Internet of Things (IoT) is rapidly becoming an integral component of the industrial market in areas such as automation and analytics, giving rise to what is termed as the Industrial IoT (IIoT). The IIoT promises innovative business models in various industrial domains by providing ubiquitous connectivity, efficient data analytics tools, and better decision support systems for a better market competitiveness. However, IIoT deployments are vulnerable to a variety of security threats at various levels of the connectivity and communications infrastructure. The complex nature of the IIoT infrastructure means that availability, confidentiality and integrity are difficult to guarantee, leading to a potential distrust in the network operations and concerns of loss of critical infrastructure, compromised safety of network end-users and privacy breaches on sensitive information. This work attempts to look at the requirements currently specified for a secure IIoT ecosystem in industry standards, such as Industrial Internet Consortium (IIC) and OpenFog Consortium, and to what extent current IIoT connectivity protocols and platforms hold up to the standards with regard to security and privacy. The paper also discusses possible future research directions to enhance the security, privacy and safety of the IIoT.

Based on digitalization and interconnectedness, Industry 4.0 causes a structural change in the value creation processes, and thus reinforces the transformation of business processes and business models. One way for companies to cope with this development and its associated challenges is to apply digital platforms in the value creation process. As the potential of digital platforms for industrial value creation can only be leveraged to its full extent with adequate implementation, this paper addresses the research question: “How are digital platforms best implemented in Industry 4.0 contexts?” Using a qualitative case study design, based on 32 semi-structured expert interviews, the study identifies different triggers and initiators, challenges, and respective countermeasures, as thematic core elements of implementation, and requirements for platforms. The research insights contribute to existing literature on Industry 4.0 and digital platforms. In addition, the paper discusses practical implications for industrial companies.

A number of industrial wireless technologies have emerged over the last decade, promising to replace the need for wires in a variety of use cases. Except for customized time division multiple access (TDMA)-based wireless technologies that can achieve ultralow latency over a very limited area, wireless communication generally has reliability and latency issues when it comes to industrial applications. Closed loop communication requires high reliability (over 99%), limited jitter and latency, which poses a challenge especially over a wide area measuring in hundreds of meters. Extended coverage is promised with the advent of sub-GHz technologies, one of them being IEEE 802.11ah which is the only one that offers sufficient data rate for frequent bidirectional communication. Thus, we evaluated IEEE 802.11ah for low-latency time-critical control loops. We propose the network setup for adjusting the network dynamics to that of control loops, enabling limited jitter and high reliability. We explore the scalability of IEEE 802.11ah network hosting both control loops and monitoring sensors that periodically transmit measurements. Assigning the control loop end-nodes to dedicated restricted access window (RAW) slot results in over 99.99% successful deliveries. Furthermore, interpacket delay is concentrated around the cycle-time in the following or preceding beacon interval in case the beacon interval is at least half the value of the shortest cycle-time. Adjusting the beacon interval to the fastest control loop in the network ensures latency requirements at the cost of maximum achievable throughput and energy consumption.

Eventually the industrial automation engineer is challenged to optimize a M2M communication process. For this, it is essential to observe the infrastructure offered by the company and the resources available in legacy equipment. Developing a simple, economically viable solution that makes use of open communication standards is highly recommended. Alternatives that do not require large investments gain a significant dimension; another important factor in this decision is that the chosen solution should be able to operate harmoniously with existing legacy systems. By doing so, the company will be in a position to gradually pursue the Industry 4.0 concept and to preserve the most investments already made. This paper aims to describe OPC UA technology, test it in a controlled environment in order to meet a real production demand and justify how and why its differentiated characteristics are recommended for Industry 4.0 applications. http://dx.doi.org/10.18226/23185279.v7iss2p40

Industrial Internet of Things (IIoT) technologies comprise sensors, devices, networks, and applications from the edge to the cloud. Recent advances in data communication and application using IIoT have streamlined predictive maintenance (PdM) for equipment maintenance and quality management in manufacturing processes. PdM is useful in fields such as device, facility, and total quality management. PdM based on cloud or edge computing has revolutionized smart manufacturing processes. To address quality management problems, herein, we develop a new calculation method that improves ensemble-learning algorithms with adaptive learning to make a boosted decision tree more intelligent. The algorithm predicts main PdM issues, such as product failure or unqualified manufacturing equipment, in advance, thus improving the machine-learning performance. Herein, semiconductor and blister packing machine data are used separately in manufacturing data analytics. The former data help in predicting yield failure in a semiconductor manufacturing process. The blister packing machine data are used to predict the product packaging quality. Experimental results indicate that the proposed method is accurate, with an area under a receiver operating characteristic curve exceeding 96%. Thus, the proposed method provides a practical approach for PDM in semiconductor manufacturing processes and blister packing machines.

The fourth industrial revolution, or Industry 4.0 (I4.0), makes use of wireless technologies together with other industrial Internet-of-Things (IIoT) technologies, cyber–physical systems (CPS), and edge computing to enable the optimization and the faster re-configuration of industrial production processes. As I4.0 deployments are ramping up, the practical integration of 5G wireless systems with existing industrial applications is being explored in both Industry and Academia, in order to find optimized strategies and to develop guidelines oriented towards ensuring the success of the industrial wireless digitalization process. This paper explores the challenges arisen from such integration between industrial systems and 5G wireless, and presents a framework applicable to achieve a structured and successful integration. The paper aims at describing the different aspects of the framework such as the application operational flow and its associated tools, developed based on analytical and experimental applied research methodologies. The applicability of the framework is illustrated by addressing the integration of 5G technology into a specific industrial use case: the control of autonomous mobile robots. The results indicate that 5G technology can be used for reliable fleet management control of autonomous mobile robots in industrial scenarios, and that 5G can support the migration of the on-board path planning intelligence to the edge-cloud.

Industry 4.0 heavily builds on massive deployment of Industrial Internet of Things (IIoT) devices to monitor every aspect of the manufacturing processes. Since the data gathered by these devices impact the output of critical processes, identity management and communications security are critical aspects, which commonly rely on the deployment of X.509 certificates. Nevertheless, the provisioning and management of individual certificates for a high number of IIoT devices involves important challenges. In this paper, we present a solution to improve the management of digital certificates in IIoT environments, which relies on partially delegating the certificate enrolment process to an edge server. However, in order to preserve end-to-end security, private keys are never delegated. Additionally, for the protection of the communications between the edge server and the IIoT devices, an approach based on Identity Based Cryptography is deployed. The proposed solution considers also the issuance of very short-lived certificates, which reduces the risk of using expired or compromised certificates, and avoids the necessity of implementing performance expensive protocols such as Online Certificate Status Protocol (OCSP). The proposed solution has been successfully tested as an efficient identity management solution for IIoT environments in a real industrial environment. This work was supported in part by the Spanish Ministry of Science and Innovation through the National Towards zeRo toUch nEtwork and services for beyond 5G (TRUE-5G) Project under Grant PID2019-108713RB-C53, in part by the European Commission through the Electronic Components and Systems for European Leadership-Joint Undertaking (ECSEL-JU) 2018 Program under the framework of key enabling technologies for safe and autonomous drones' applications (COMP4DRONES) Project under Grant 826610, with the national financing from France, Spain, Italy, The Netherlands, Austria, Czech, Belgium, and Latvia, in part by the Ayudas Cervera para Centros Tecnologicos Grant of the Spanish Centre for the Development of Industrial Technology (CDTI) through the Project EGIDA under Grant CER-20191012, and in part by the Basque Country Government through the Creating Trust in the Industrial Digital Transformation (TRUSTIND) ELKARTEK Program Project under Grant KK-2020/00054.

The certificate authority, a trusted entity, issues digital certificates which contain identity credentials to help Industrial Internet of Things (IIoT) devices to represent their authenticity in a secure means. The crucial challenge of a digital certificate is to how design a secure certification authority management system that can counteract cyberattacks on the IIoT network. Moreover, current IIoT systems are not capable of implementing complex mathematical operations due to their constrained power capacity and processing capability. This paper proposes an effective, secure symmetric cryptographic mechanism (ESSC) based on the certificate authority management and Elliptic Curve Diffie Hellman (ECDH) to share a digital certificate among IIoT devices. The proposed certificate authority is used to securely exchange the shared secret key and to resolve the problem of spoofing attacks that may be used to impersonate the identity of the certificate authority. Also, ESSC uses the shared secret key to encrypt the sensitive data during transmission through the insecure communication channel. This research studies the adversary model for ESSC on IIoT and analyzes the cybersecurity of ESSC in the random oracle model. The findings that result from the experiments show that ESSC outperforms the baseline in terms of communication, computation, and storage costs. ESSC thus provides an adequate lightweight digital certificate management and cryptographic scheme which can help in the detection and prevention of several cyberattacks that can harm IIoT networks.

The article is devoted to cybersecurity risk assessment of the dynamic device-to-device networks of a smart city. Analysis of the modern security threats at the IoT/IIoT, VANET, and WSN inter-device infrastructures demonstrates that the main concern is a set of network security threats targeted at the functional sustainability of smart urban infrastructure, the most common use case of smart networks. As a result of our study, systematization of the existing cybersecurity risk assessment methods has been provided. Expert-based risk assessment and active human participation cannot be provided for the huge, complex, and permanently changing digital environment of the smart city. The methods of scenario analysis and functional analysis are specific to industrial risk management and are hardly adaptable to solving cybersecurity tasks. The statistical risk evaluation methods force us to collect statistical data for the calculation of the security indicators for the self-organizing networks, and the accuracy of this method depends on the number of calculating iterations. In our work, we have proposed a new approach for cybersecurity risk management based on object typing, data mining, and quantitative risk assessment for the smart city infrastructure. The experimental study has shown us that the artificial neural network allows us to automatically, unambiguously, and reasonably assess the cyber risk for various object types in the dynamic digital infrastructures of the smart city.

Nowadays, the usage of the Industrial Internet of Things (IIoT) in practical applications has increased. The primary utilization is a fog cloud network, which offers different services, such as network and remote edges, at different places. Existing studies implemented the Service-Oriented Architecture (SOA) based on the fog-cloud network to run IIoT applications, such as e-healthcare, e-agriculture, renewable energy, etc. However, due to the applications' monolithic property, issues like failures, security, and cost factors occur, e.g. the failure of one service in SOA affects monolithic applications' performance in the system. With this motivation, this study suggests a microservice-based system to deal with the cost, security, and failure risks of IIoT applications in the fog-cloud system. The study improves the existing SOA systems for e-healthcare, e-agriculture, and renewable energy and minimizes the applications' overall cost. The performance evaluation shows that the devised systems outperform the existing SOA system in terms of failure, cost, and the deadline for all applications.

In today’s Industrial Internet of Things (IIoT) environment, where different systems interact with the physical world, the state proposed by the Industry 4.0 standards can lead to escalating vulnerabilities, especially when these systems receive data streams from multiple intermediaries, requiring multilevel security approaches, in addition to link encryption. At the same time taking into account the heterogeneity of the systems included in the IIoT ecosystem and the non-institutionalized interoperability in terms of hardware and software, serious issues arise as to how to secure these systems. In this framework, given that the protection of industrial equipment is a requirement inextricably linked to technological developments and the use of the IoT, it is important to identify the major vulnerabilities and the associated risks and threats and to suggest the most appropriate countermeasures. In this context, this study provides a description of the attacks against IIoT systems, as well as a thorough analysis of the solutions for these attacks, as they have been proposed in the most recent literature.

[EN] Internet of Things (IoT) is a new buzzword in information technology where real-world physical objects are made smart by integrating them with internet-enabled technologies. The things can sense information around them, communicate the sensed information over some protocol and employ the information to solve real-life problems. In IoT, several technologies are integrated under a common umbrella so that they can connect and exchange data over a network protocol. A huge amount of data is generated from diverse geographical locations with the consequent urge for fast aggregation of overall sensed information, leading to an increase in the need to store and process such data in a more efficient and effective manner. The traditional fields of embedded systems, WSN, real-time analytics, automation system, machine learning and others all contribute to enabling the IoT. This article is focused on discussing the various IoT technologies, protocols and their application and usage in our daily life. It also summarizes the current state-of-the-art IoT architecture in various spheres conventionally and all related terminologies that will give the forthcoming researchers a glimpse of IoT as a whole.

Within the big picture of the Internet of Things (IoT) a brand new paradigm has risen since few years ago in the context of industrial scenarios: the Industrial Internet of Things (IIoT), whose aim is to transplant all of the features, characteristics and scopes of the IoT into industrial settings. The latter ones might be subject to a wide range of environmental conditions from the point of view of both temperature and relative humidity. Therefore, whenever their monitoring is performed by means of devoted infrastructures, such extreme working conditions must be born in mind during the pertaining design phases. Mostly, industrial monitoring is put into effect by resorting to wireless sensor networks enabled by Low Power Wide Area Network (LPWAN) techonologies. This set of facilities includes an ample heterogeneity of standards and techniques. However, the Long Range (LoRa) modulation and the LoRa Wide Area Network (LoRaWAN) protocol extensively proved to be reliable and robust alternatives. Thus, in this paper the variations of hardware performances of a LoRaWAN sensor node, in terms of transmission capabilities, due to the changes of temperature and relative humidity, which are typical of industrial processes, are investigated. To this end, a measurement campaign within a controlled environment (i.e., a climatic chamber) was sorted out, whose results show a physiological performances drop that neither undermine the network reliability, nor damage the sensor node electronics.

The real and effective ground of all new concepts dedicated to the current advanced factories, as well as to the future digital ones, is close cooperativity of scattered applications in highly heterogeneous systems. Communication is the key enabling component, and all new approaches are inspired in practice to the demanding characteristics of industrial networks. These kinds of computer networks, together with new technologies derived from distant application fields, are the main technological means to accelerate the fast evolution of modern factory systems. Due to various communication requirements coming from the plurality of structures, components and application contexts, communication subsystems must be increasingly heterogeneous. Let us say clearly: this evolution cannot be stopped at this stage, no special universal solution is possible, and thinking about monogamous networking is a kind of dreamland. This paper is an analysis of the state of the art in the matter of heterogeneous networking in industry. It deeply investigates both wired and wireless technologies from the point of view of technological aspects and relevant key performance indicators, such as those related to dependability, and it contains a prospective estimation of future trends.

Despite standards such as International Standards Association (ISA-95) and Manufacturing Enterprise Solutions Association (MESA) International guiding operational process, asset automation and control for some time, the integration challenge remains for Industrial Internet of Things (IIoT) that the strategies of converging Information Technology (IT) and Operational Technology (OT) domains remain disparate. IT systems prioritize data governance and reliability to ensure the needs of enterprise users are met while OT systems prioritize human safety and asset reliability to ensure that the production process remain safe and effective. These differing priorities lead to greater complexities in the design of convergent systems necessary to progress Industrial Internet of Things, coined more recently as Industry 4.0. The gaps are between IoT computing layers and alignment to the ISA-95 automation layers plus the evolving constructs of Industry 4.0 and how in the future un/trusted data moves seamlessly between converged OT and IT domains. In doing so, the information governance links between technology, organizational processes, and people (TOP) across the ISA-95 and Manufacturing Operations Management (MOM) System stacks must be considered to provide true secure convergence of the OT and IT domains. This paper explores and builds upon the growing industrial manufacturing OT/IT/IoT convergence literature [1, 31], extending exploration of the role of TOP governance factors to RAMI4.0, suggesting a single OT/IT strategy for organizations to achieve Industry 4.0 initiatives. The case study research method provides insights into emerging industry and standards convergence practices. Applying these standards and aligning the interdependence between OT and IT strategies with TOP constructs, are explored in the context of achieving Industry 4.0 objectives robustly and resiliently through applying a holistic Data Infrastructure Platform (DIP) in process-centric organizations.

System integration is a crucial concept in the Industry 4.0 (I4.0) vision, where information processes supporting flexible production are digital. System integration paves the way for leveraging the Industrial Internet of Things, big data analysis, simulation, cloud computing, and augmented reality. The first step towards system integration is to examine the assets (machine software) ability to exchange information in an I4.0 setting. This paper aims to analyze challenges for asset interoperability by conducting asset integration in the University I4.0 laboratory (I4.0 lab). Conducting asset integration has been a part of building an Information Backbone (IB) as a minimum viable product in the I4.0 lab. An IB is a software infrastructure that involves integrating into various assets, e.g., warehouse, transport, and robotic systems, and providing communication among them. The pilot study reveals that the maturity of assets interoperability readiness are at very different levels, e.g. missing external interfaces, poor documentation, and varying technologies. These challenges need to be further addressed to collect architectural requirements for system integration, and establish a common vocabulary and understanding of I4.0 concepts.

With the rise of the Industrial Internet of Things (IIoT), there is an intense pressure on resource and performance optimization leveraging on existing technologies, such as Software Defined Networking (SDN), edge computing, and container orchestration. Industry 4.0 emphasizes the importance of lean and efficient operations for sustainable manufacturing. Achieving this goal would require engineers to consider all layers of the system, from hardware to software, and optimizing for resource efficiency at all levels. This emphasizes the need for container-based virtualization tools such as Docker and Kubernetes, offering Platform as a Service (PaaS), while simultaneously leveraging on edge technologies to reduce related latencies. For network management, SDN is poised to offer a cost-effective and dynamic scalability solution by customizing packet handling for various edge applications and services. In this paper, we investigate the energy and latency trade-offs involved in combining these technologies for industrial applications. As a use case, we emulate a 3D-drone-based monitoring system aimed at providing real-time visual monitoring of industrial automation. We compare a native implementation to a containerized implementation where video processing is orchestrated while streaming is handled by an external UE representing the IIoT device. We compare these two scenarios for energy utilization, latency, and responsiveness. Our test results show that only roughly 16 percent of the total power consumption happens on the mobile node when orchestrated. Virtualization adds up about 4.5 percent of the total power consumption while the latency difference between the two approaches becomes negligible after the streaming session is initialized.

The Transport Layer Security (TLS) 1.3 protocol supports a fast zero round-trip time (0-RTT) session resumption mechanism, enabling clients to send data in their first flight of messages. This protocol has been designed with Web infrastructure in mind, and requires these first messages to not change any state on the server side, as it is susceptible to replay attacks. This is disastrous for common IoT scenarios, where sensors often transmit state-changing data to servers. As bandwidth is a huge concern in the IoT, the field stands to benefit significantly from an efficient session resumption protocol that does not suffer from these limitations. Building on the observation that in IoT scenarios the set of clients is often bounded and fairly static, we propose rTLS (ratchet TLS), an efficient 0-RTT session resumption protocol that dramatically decreases bandwidth overhead, while adding forward secrecy and break-in resilience, and is not susceptible against replay attacks.

With declaring the highly transmissible COVID-19 as a pandemic, an unprecedented strain on healthcare infrastructures worldwide occurred. An enormous shortage in the personal protective equipment (PPE) and the spare parts (SP) for the mechanical ventilators ensued as a consequence of the failure of the centralized global supply chains. Additive manufacturing and Industrial Internet of Things (IIoT), as the pillars of Industry 4.0, arose as the robust noncentralized alternatives. When gathered and properly managed in the IIoT, 3D Printers (3DPs) can complement and support Healthcare 4.0 to face the current and future pandemics. Thus, this paper proposes a real-time green allocation and scheduling architecture designed and dedicated particularly for the large-scale distributed 3D printing tasks (3DPTs) of both PPE and SPs. Our proposed architecture comprises; a broker (B) and a cluster manager (CM). Dynamic status check for the 3DPs and admission control for 3DPTs are among the interconnected roles of CM. CM also performs task allocation and scheduling according to our proposed Online Ascending Load-Balancing Modified Best-Fit (OALMBF) allocation algorithm and Green Real-time Nesting Priority-Based Adaptive (GRNPA) scheduling algorithm. The performance of the proposed architecture was investigated under extremely high-load environments which resulted in a success ratio and a response rate of 99.9667% and 10.9665 seconds, respectively, for the 3000 3DPTs trial. These results proved the robustness and the scalability of our architecture that surpasses its state-of-the-art counterparts. Besides respecting the real-time requirements of the 3DPTs, the proposed architecture improves the utilization of the 3DPs and guarantees an even workload distribution.

IoT is often associated with low-power wireless, where best-effort communication is sufficient for many of its applications. Yet, in particular in Industrial IoT, strict quality of service (QoS) guarantees are required to enable deterministic real-time applications. Hence, wired communications are still predominant here, with Ethernet having become the main media for the industrial fieldbuses such as PROFINET, EtherCAT, or the emerging Field-Level Communications (FLC) of OPC UA. With the addition of Time-Sensitive Networking (TSN), an IEEE standards family for deterministic networking over Ethernet, Information Technology (IT) and Operational Technology (OT) can converge even at the shopfloor, as TSN-enabled Ethernet allows for several traffic types to co-exist in the same network. This paper proposes an extension to the W3C Web of Things (WoT) for semantics-enabled connectivity, which allows to cover application QoS requirements and network configuration within the W3C WoT abstractions. We contribute a QoS vocabulary for the W3C WoT Thing Description as well as design and open-source implementation of protocol bindings for OPC UA and NETCONF. Moreover, we discuss the challenges and pitfalls when designing new protocol bindings for W3C WoT.

The proliferation of the Internet of Things (IoT) caused new application needs to emerge as rapid response ability is missing in the current IoT end-devices. Therefore, Fog Computing has been proposed to be an edge component for the IoT networks as a remedy to this problem. In recent times, cyber-attacks are on the rise, especially towards infrastructure-less networks, such as IoT. Many botnet attack variants (Mirai, Torii, etc.) have shown that the tiny microdevices at the lower spectrum of the network are becoming a valued participant of a botnet, for further executing more sophisticated attacks against infrastructural networks. As such, the fog devices also need to be secured against cyber-attacks, not only software-wise, but also from hardware alterations and manipulations. Hence, this article first highlights the importance and benefits of fog computing for IoT networks, then investigates the means of providing hardware security to these devices with an enriched literature review, including but not limited to Hardware Security Module, Physically Unclonable Function, System on a Chip, and Tamper Resistant Memory.

The autonomic composition of Virtual Networks (VNs) and Service Function Chains (SFCs) based on application requirements is significant for complex environments. In this paper, we use graph transformation in order to compose an Extended Virtual Network (EVN) that is based on different requirements, such as locations, low latency, redundancy, and security functions. The EVN can represent physical environment devices and virtual application and network functions. We build a generic Virtual Network Embedding (VNE) framework for transforming an Application Request (AR) to an EVN. Subsequently, we define a set of transformations that reflect preliminary topological, performance, reliability, and security policies. These transformations update the entities and demands of the VN and add SFCs that include the required Virtual Network Functions (VNFs). Additionally, we propose a greedy proactive heuristic for path-independent embedding of the composed SFCs. This heuristic is appropriate for real complex environments, such as industrial networks. Furthermore, we present an Industrail Internet of Things (IIoT) use case that was inspired by Industry 4.0 concepts, in which EVNs for remote asset management are deployed over three levels, manufacturing halls and edge and cloud computing. We also implement the developed methods in Alevin and show exemplary mapping results from our use case. Finally, we evaluate the chain embedding heuristic while using a random topology that is typical for such a use case, and show that it can improve the admission ratio and resource utilization with minimal overhead.

International audience; In the last decade Internet of Things (IoT) grew up in an exponential behavior with applications requiring long range and low power wireless transmissions. Factory of the Future (FoF), also called Industry 4.0, aims to use IoT technologies to enhance productivity, therefore adding high reliability constraints. Several IoT standards were proposed and LoRa has emerged as a high potential candidate for a variety of applications. LoRa modulation is based on a chirp spread-spectrum technique and offers efficient transmission up to 50 kbps over several kilometers. The performance of LoRa in terms of symbol or bit error probability has been theoretically analyzed in few recent papers for a Gaussian channel. However, the industrial environment is often corrupted with impulsive non Gaussian noise generated by high-power equipment. In this paper, the impact of impulsive noise, modeled by the Middleton Class-A noise, is introduced and the robustness of a LoRa communication is studied. Compared to the Gaussian case, simulations show that impulsive noise may severely degrade system performance. This Signal-to-Noise Ratio loss can reach up to 10 dB, but increasing the spreading factor can reduce the noise impact.

International audience; The IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is the de facto routing protocol for Low Power and Lossy Networks (LLNs). It is a proactive and link-layer agnostic routing protocol standardized as RFC 6550 by the Internet Engineering Task Force (IETF). Based on the distance-vector technique, RPL builds a Destination Oriented Directed Acyclic Graph (DODAG) topology. To establish and maintain the routes, RPL uses DODAG Information Object (DIO) control packets, that are transmitted in broadcast, for RPL nodes to propagate the DODAG related information, while its transmission frequency depends on Trickle timer algorithm, i.e., the less stable the network the more DIOs are transmitted. Thus, when a new node intends to join the RPL DODAG, it listens for a DIO message from nearby nodes, which may take a very long time if the network is in a stable state. Therefore, RFC 6550 is equipped with the DODAG Informational Solicitation (DIS) message to solicit DIOs from nearby RPL nodes, similar to the Router Solicitation in IPv6 Neighbor Discovery. However, the solicitation procedure is not the most efficient one, since it resets the Trickle timers in the nodes that receive the DIS message and, thus, they transmit an unnecessarily large number of DIOs that congest the network and consume energy in the nodes. In this paper, we propose to augment RFC 6550, the RPL routing protocol, with additional DIS flags and options that allow a RPL node to better control how the nearby RPL nodes will respond to its solicitation for DIOs. Our performance evaluation in Contiki-NG & COOJA demonstrates that we can reduce the control packets in the network by up to 45.5%.

International audience; IEEE Std 802.15.4-2015 Time Slotted Channel Hopping (TSCH) is the de facto Medium Access Control (MAC) mechanism for industrial applications. It renders communications more resilient to interference by spreading them over the time (time slotted) and the frequency (channel hopping) domains. The 6TiSCH architecture bases itself on this new MAC layer to enable high reliability communication in Wireless Sensor Networks (WSNs). In particular, it manages the construction of a distributed communication schedule that continuously adapts to changes in the network. In this paper, we first provide a thorough description of the 6TiSCH architecture, the 6TiSCH Operation Sublayer (6top), and the Minimal Scheduling Function (MSF). We then study its behavior and reactivity from low to high traffic rates by employing the pythonbased 6TiSCH simulator. Our performance evaluation results demonstrate that the convergence pattern of MSF is the root cause of the majority of packet losses observed in the network. We also show that MSF is subject to over-provisioning of the network resources, especially in the case of varying traffic load.

Data storage in the Industrial Internet-of-Things scenario presents critical aspects related to the necessity of bringing storage devices closer to the point where data are captured. Concerns on storage temperature are to be considered especially when Solid State Drives (SSD) based on 3D NAND Flash technology are part of edge gateway architectures. Indeed, self-heating effects caused by oppressive storage demands combined with harsh environmental conditions call for proper handling at multiple abstraction levels to minimize severe performance slow downs and reliability threats. In this work, with the help of a SSD co-simulation environment that is stimulated within a realistic Industrial Internet-of-Things (IIoT) workload, we explore a methodology orthogonal to performance throttling that can be applied in synergy with the operating system of the host. Results evidenced that by leveraging on the SSD micro-architectural parameters of the queuing system it is possible to reduce the Input/Output operations Per Second (IOPS) penalty due to temperature protection mechanisms with minimum effort by the system. The methodology presented in this work opens further optimization tasks and algorithmic refinements for SSD and system designers not only in the IIoT market segment, but generally in all areas where storage power consumption is a concern.

International audience; Though the requirements are well defined and established, the adoption of radios for mission critical applications is lagging due to the fuzzy transmission characteristics of the wireless medium. To enable automation and safety applications, in particular for Machine-to-Machine traffic, strict reliability and availability demands must be met end-to-end. The Reliable and Available Wireless (RAW) Working Group (WG) aims specifically at enabling such guarantees by leveraging end-to-end Layer 3 services to compensate for the lossiness of the lower layers. In this letter, we present the Operation, Administration and Management (OAM) requirements to support industrial requirements. We detail first the operation challenge, the tools to verify that the network works properly, and to detect possibly faults. Then, we explain why we need to administrate the network to know in real time the state and the resources provisioned. Finally, we detail the mechanisms to make the network reliable and fault-tolerant.

Commissioning of a machine is still representing a very challenging operation and most steps are still executed manually by commissioning engineers. A future goal is to support the commissioning engineers and further automate the entire integration process of a newly installed system with a minimum of manual effort. This use case is known as plug-and-produce (PnP). In this contribution a concept of the Industrial Internet of Things is presented to improve the commissioning task for a pneumatic handling system. The system is based on a service-oriented architecture. Within this context, information models are developed to meet the requirements of PnP to provide relevant information via virtual representations, e.g. the asset administration shell, of the components to the commissioning process. Finally, a draft of the entire PnP process is shown, providing a general understanding of Industrial Internet of Things fluid power systems.

The Internet of Things (IoT) is turning into the following Internet-related insurgency. It enables billions of gadgets to be associated and speak with one another to share data that enhances the nature of our day by day lives. Then again, Cloud Computing gives on-request, advantageous and adaptable system which makes it conceivable to share computing assets; surely, this empowers dynamic information integration from different information sources. There are numerous issues hindering the effective implementation of both Cloud and IoT affecting the role of a chief information officer (CIO).The integration of Cloud Computing with the IoT is the best path on which to conquer these issues. The immense number of assets accessible on the Cloud can be to a great degree advantageous for the IoT, while the Cloud can acquire exposure to enhance its confinements with true protests in a more powerful and conveyed way. This paper gives an outline of the integration of the Cloud into the IoT by featuring the integration advantages and implementation challenges. Cloud computing has developed enormously throughout the years. Since the term appeared in mid 90s it was produced and being worked upon to make it a conceivable answer for business information stockpiling and availability issues. Expansive undertakings are progressively discovering cloud an affable arrangement even inside their stringent hierarchical approaches.Cloud has been such advanced that there is a surge of executing virtualization among CIOs. This has prompt a larger number of complexities than arrangements. The issues with cloud implementation are for the most part because of the scurry without legitimate investigation of one's circumstances and necessities previously. Following is a concise dialog on the difficulties looked by organizations amid executing cloud computing.This study focuses on the challenges faced by CIOs in a cloud and IOT based organization. This study will analyze the major challenges in cloud and IOT environments like security, privacy, performance, compliance, governance, portability, interoperability, lack of resources, cost management etc. As part of this study a survey was conducted on 400 plus IT and business leaders from various organizations from almost 30 plus countries and their responses are recorded and analyzed as part of this study

With the fast development of Industrial Internet of Things (IIoT), IIoT storage, which provides a lower cost, higher reliability of data, remote access services, and expandable storage space, is received much attention among enterprises and individual users. However, providing a secure IIoT storage service could be a challenging task for some situations, for example, how to search encrypted data in the IIoT. In this paper, we examine the security of a recent proposed certificateless searchable public key encryption scheme for IIoT environments by Ma et al. We find that their scheme is insecure against an off-line keyword guessing attack. Then, we propose an enhancement based on the scheme and prove the security of our enhancement with a formal model.

In the last century, the automotive industry has arguably transformed society, being one of the most complex, sophisticated, and technologically advanced industries, with innovations ranging from the hybrid, electric, and self-driving smart cars to the development of IoT-connected cars. Due to its complexity, it requires the involvement of many Industry 4.0 technologies, like robotics, advanced manufacturing systems, cyber-physical systems, or augmented reality. One of the latest technologies that can benefit the automotive industry is blockchain, which can enhance its data security, privacy, anonymity, traceability, accountability, integrity, robustness, transparency, trustworthiness, and authentication, as well as provide long-term sustainability and a higher operational efficiency to the whole industry. This review analyzes the great potential of applying blockchain technologies to the automotive industry emphasizing its cybersecurity features. Thus, the applicability of blockchain is evaluated after examining the state-of-the-art and devising the main stakeholders’ current challenges. Furthermore, the article describes the most relevant use cases, since the broad adoption of blockchain unlocks a wide area of short- and medium-term promising automotive applications that can create new business models and even disrupt the car-sharing economy as we know it. Finally, after strengths, weaknesses, opportunities, and threats analysis, some recommendations are enumerated with the aim of guiding researchers and companies in future cyber-resilient automotive industry developments.

Blockchain is experiencing rapid development and has the full potential of revolutionizing the IoT platform in the industrial field. In this paper, a light-weighted Blockchain-based platform for IIoT is presented to address security, trust, and island connection problem in the process of IIoT ecosystem construction. BPIIoT is comprised of the on-chain network and the off-chain network. All transactions are processed in the on-chain network such as including digital signature based on access control and programmable permission. The off-chain network deals with the storage, complex data processing, and other problems that blockchain cannot solve. The smart contract is utilized as the service contract of consumers and manufacture resources, providing on-demand manufacturing service. Two smart application cases, manufacturing equipment data sharing and maintenance service sharing from smart manufacturing, are implemented to explain the smart contract for equipment maintenance service and status data sharing service throughout maintenance, repair, and operation service network by the BPIIoT.