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The application of the Internet of Things (IoT) in industrial systems and other new technological advancements led to the development of the Industrial Internet of Things (IIoT). IIoT can help overcome the shortcomings of the conventional monitoring and control system while enabling enterprises to create a unified monitoring system to automate operations, provide a safe working environment, enforce compliance effectively, and regulate environmental issues. Given the advantages that IoT brings to the forefront, it makes sense that so many mining companies have raised their investment following the implementation of IoT-enabled solutions in their organisations. To increase safety, productivity, and environmental sustainability in mining operations, this article examines the current state of information technology in the mineral industry with an emphasis on the implications and challenges brought on by the technological diversity of various systems and devices used in those activities. The study contributes valuable insights into the integration of IoT technologies in the mining industry, highlighting its potential to improve safety, efficiency, and sustainability. The paper paves the way for future research and development efforts aimed at overcoming the challenges of adopting IIoT in mining operations by identifying gaps and proposing a comprehensive IoT architecture. The innovations of the study are encapsulated in its approach to detailing the application of IoT technologies in enhancing mining operations and the establishment of an overall IIoT architecture suitable for the general operations in the mining industry., Full text license: CC BY-NC-ND

The application of the Internet of Things (IoT) in industrial systems and other new technological advancements led to the development of the Industrial Internet of Things (IIoT). IIoT can help overcome the shortcomings of the conventional monitoring and control system while enabling enterprises to create a unified monitoring system to automate operations, provide a safe working environment, enforce compliance effectively, and regulate environmental issues. Given the advantages that IoT brings to the forefront, it makes sense that so many mining companies have raised their investment following the implementation of IoT-enabled solutions in their organisations. To increase safety, productivity, and environmental sustainability in mining operations, this article examines the current state of information technology in the mineral industry with an emphasis on the implications and challenges brought on by the technological diversity of various systems and devices used in those activities. The study contributes valuable insights into the integration of IoT technologies in the mining industry, highlighting its potential to improve safety, efficiency, and sustainability. The paper paves the way for future research and development efforts aimed at overcoming the challenges of adopting IIoT in mining operations by identifying gaps and proposing a comprehensive IoT architecture. The innovations of the study are encapsulated in its approach to detailing the application of IoT technologies in enhancing mining operations and the establishment of an overall IIoT architecture suitable for the general operations in the mining industry., Full text license: CC BY-NC-ND

Electronic waste (e-waste) contains toxic elements causing an important impact on environmental and human health. However, the presence of valuable metals, such as copper or gold, among others, make recycling a necessity for obtaining an alternative source of raw materials. Conventional metal recovery methods are environmentally unsound, prompting the exploration of greener alternatives like bioleaching, which utilizes the activity of microorganisms for a more sustainable recovery. However, the mechanisms involved in the process and the conditions to optimize the metabolic paths are still not completely known. Monitorization and automatization of the different stages composing the global process are crucial for advancing in the implementation of this novel technology at an industrial scale. For the first time, an open-source industrial IoT system is designed to enhance and regulate bioleaching by implementing real-time monitoring and control within the plant’s infrastructure. This system includes an Android app that displays real-time plant data from sensors and a robust server featuring a flexible application programming interface (API) for future applications. The app caters to specific needs such as remote sensor reading, actuator control, and real-time bioleaching alerts, ensuring secure access and proactive event management. By utilizing collected data, it minimizes downtime, equipment failures, and supply chain disruptions. The server maintains seamless communication with the plant controller, enabling efficient pump activation and sensor data transmission. A telegram bot demonstrates the API’s flexibility by forwarding plant alerts to users. During validation with concurrent remote user access, the application demonstrated its ability to prevent irreversible plant failures through an advanced alarm system. Ultimately, this IIoT system amplifies plant performance, safety, and efficiency by optimizing processes and decision-making capabilities. It emerges as a, Authors acknowledge the Spanish Government, through the project PID2020-117520RA-I00 for the financial support provided to perform this research. Authors also acknowledge Ramon Miquel for his contribution during the participation in the project., Peer Reviewed, Objectius de Desenvolupament Sostenible::13 - Acció per al Clima, Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura, Postprint (published version)

Massive Multiple-Input Multiple-Output (mMIMO) is one of the essential technologies introduced by the Fifth Generation (5G) of wireless communication systems. However, although mMIMO provides many benefits for wireless communications, it cannot ensure uniform wireless coverage and suffers from inter-cell interference inherent to the traditional cellular network paradigm. Therefore, industry and academia are working on the evolution from conventional Centralized mMIMO (CmMIMO) to Distributed mMIMO (DmMIMO) architectures for the Sixth Generation (6G) of wireless networks. Under this new paradigm, several Access Points (APs) are distributed in the coverage area, and all jointly cooperate to serve the active devices. Aiming at Machine-Type Communication (MTC) use cases, we compare the performance of CmMIMO and different DmMIMO deployments in an indoor industrial scenario considering regular and alarm traffic patterns for MTC. Our simulation results show that DmMIMO’s performance is often superior to CmMIMO. However, the traditional CmMIMO can outperform DmMIMO when the devices’ channels are highly correlated.

Broadband powerline communication is a technology developed mainly with consumer applications and bulk data transmission in mind. Typical use cases include file download, streaming, or last-mile internet access for residential buildings. Applications gaining momentum are smart metering and grid automation, where response time requirements are relatively moderate compared to industrial (real-time) control. This work investigates to which extent G.hn technology, with existing, commercial off-the-shelf components, can be used for real-time control applications. Maximum packet rate and latency statistics are investigated for different G.hn profiles and MAC algorithms. An elevator control system serves as an example application to define the latency and throughput requirements. The results show that G.hn is a feasible technology candidate for industrial IoT-type applications if certain boundary conditions can be ensured.

This article explores a solution using fog computing and Internet of Things technologies to address some of the drawbacks with cloud computing. We show the potential in the combination of scalable distributed systems technologies, for enabling a distributed cluster using typical lightweight IoT devices. Answering research questions related to the expected overhead, as well as how work and chunk size affect the computational time and scalability of the system. All in order to evaluate the performance and potential of this technology for Industry 5.0 and future industrial IoT applications. Our results show the great potential of the approach, but we also identify necessary improvements that needs to be made in order to achieve performance superiority over the traditional cloud approaches and for the technology to truly proliferate.

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

Nowadays, the flexible localization solution for various devices for work safety is one of the most demanding research questions. Notably, it is expected to provide an acceptable level of precision in different types of environments empowered by wearable technology and the Interent of Things (IoT) devices. Existing leading localization technologies are adapted for certain conditions, e.g., Wi-Fi, BLE, UWB are used for indoor areas and various GNSS-based ones for outdoor. This work focuses on investigating the LoRaWAN (868 MHz band) as a potential candidate to bridge this gap, being one of the most reliable and recognized communication technologies for the Industrial Internet of Things (IIoT). In the past, the research community had a lot of critics with respect to the applicability of LoRaWAN for localization, while the vision is facing a tremendous change over the past two years. The purpose of this work is to assess the feasibility of LoRaWAN as a localization solution for work safety applications in the industrial scenario from different angles. The work is based on two measurement campaigns conducted at the Brno University of Technology (BUT), Brno, Czech Republic, and University Politechnica in Bucharest (UPB), Bucharest, Romania. The campaigns cover both indoor and outdoor scenarios, provide the practical limitations of the positioning in standalone and k-NN powered localization systems. According to the results, LoRaWAN-based localization with relatively dense gateways deployment allows for achieving a meter-level accuracy, which may be suitable for the localization of workers.

Nowadays, the flexible localization solution for various devices for work safety is one of the most demanding research questions. Notably, it is expected to provide an acceptable level of precision in different types of environments empowered by wearable technology and the Interent of Things (IoT) devices. Existing leading localization technologies are adapted for certain conditions, e.g., Wi-Fi, BLE, UWB are used for indoor areas and various GNSS-based ones for outdoor. This work focuses on investigating the LoRaWAN (868 MHz band) as a potential candidate to bridge this gap, being one of the most reliable and recognized communication technologies for the Industrial Internet of Things (IIoT). In the past, the research community had a lot of critics with respect to the applicability of LoRaWAN for localization, while the vision is facing a tremendous change over the past two years. The purpose of this work is to assess the feasibility of LoRaWAN as a localization solution for work safety applications in the industrial scenario from different angles. The work is based on two measurement campaigns conducted at the Brno University of Technology (BUT), Brno, Czech Republic, and University Politechnica in Bucharest (UPB), Bucharest, Romania. The campaigns cover both indoor and outdoor scenarios, provide the practical limitations of the positioning in standalone and k-NN powered localization systems. According to the results, LoRaWAN-based localization with relatively dense gateways deployment allows for achieving a meter-level accuracy, which may be suitable for the localization of workers.

Nowadays, the flexible localization solution for various devices for work safety is one of the most demanding research questions. Notably, it is expected to provide an acceptable level of precision in different types of environments empowered by wearable technology and the Interent of Things (IoT) devices. Existing leading localization technologies are adapted for certain conditions, e.g., Wi-Fi, BLE, UWB are used for indoor areas and various GNSS-based ones for outdoor. This work focuses on investigating the LoRaWAN (868 MHz band) as a potential candidate to bridge this gap, being one of the most reliable and recognized communication technologies for the Industrial Internet of Things (IIoT). In the past, the research community had a lot of critics with respect to the applicability of LoRaWAN for localization, while the vision is facing a tremendous change over the past two years. The purpose of this work is to assess the feasibility of LoRaWAN as a localization solution for work safety applications in the industrial scenario from different angles. The work is based on two measurement campaigns conducted at the Brno University of Technology (BUT), Brno, Czech Republic, and University Politechnica in Bucharest (UPB), Bucharest, Romania. The campaigns cover both indoor and outdoor scenarios, provide the practical limitations of the positioning in standalone and k-NN powered localization systems. According to the results, LoRaWAN-based localization with relatively dense gateways deployment allows for achieving a meter-level accuracy, which may be suitable for the localization of workers.

The Industrial Internet of Things (IIoT) enables the integration of physical devices such as sensors and actuators into the virtual world of automation application systems via different communication protocols. Interoperability among the "things" appears to be one of the biggest conceptual and technological challenges in developing the IIoT framework. Typically, collaboration at the field device level is very limited. Instead, the decision-making process is usually propagated to higher levels with substantial computational resources. This centralized architecture has been widely deployed based on global cloud infrastructure. However, sending data over the cloud for analysis may bring about privacy and security threats. Besides, network latency could be another factor that reduces adaptability. In this article, we propose a decentralized approach that applies the concepts of local automation cloud. By using semantic technologies to achieve autonomicity, the approach enables real-time monitoring of the control systems within one local cloud and automates orchestration and configuration locally through adaptation based on semantic policies. The approach is deployed and tested on a chemical production use case in which business-level policies have been used for dynamical planning for suppliers and automatic detection of malfunctioning sensors with subsequent adaptation to continuing supply planning and production as smooth as possible., Validerad;2022;Nivå 2;2022-03-10 (johcin);Funder: EU ECSEL (737459 and 826452)

The Industrial Internet of Things (IIoT) enables the integration of physical devices such as sensors and actuators into the virtual world of automation application systems via different communication protocols. Interoperability among the "things" appears to be one of the biggest conceptual and technological challenges in developing the IIoT framework. Typically, collaboration at the field device level is very limited. Instead, the decision-making process is usually propagated to higher levels with substantial computational resources. This centralized architecture has been widely deployed based on global cloud infrastructure. However, sending data over the cloud for analysis may bring about privacy and security threats. Besides, network latency could be another factor that reduces adaptability. In this article, we propose a decentralized approach that applies the concepts of local automation cloud. By using semantic technologies to achieve autonomicity, the approach enables real-time monitoring of the control systems within one local cloud and automates orchestration and configuration locally through adaptation based on semantic policies. The approach is deployed and tested on a chemical production use case in which business-level policies have been used for dynamical planning for suppliers and automatic detection of malfunctioning sensors with subsequent adaptation to continuing supply planning and production as smooth as possible., Validerad;2022;Nivå 2;2022-03-10 (johcin);Funder: EU ECSEL (737459 and 826452)

Nowadays, the flexible localization solution for various devices for work safety is one of the most demanding research questions. Notably, it is expected to provide an acceptable level of precision in different types of environments empowered by wearable technology and the Interent of Things (IoT) devices. Existing leading localization technologies are adapted for certain conditions, e.g., Wi-Fi, BLE, UWB are used for indoor areas and various GNSS-based ones for outdoor. This work focuses on investigating the LoRaWAN (868 MHz band) as a potential candidate to bridge this gap, being one of the most reliable and recognized communication technologies for the Industrial Internet of Things (IIoT). In the past, the research community had a lot of critics with respect to the applicability of LoRaWAN for localization, while the vision is facing a tremendous change over the past two years. The purpose of this work is to assess the feasibility of LoRaWAN as a localization solution for work safety applications in the industrial scenario from different angles. The work is based on two measurement campaigns conducted at the Brno University of Technology (BUT), Brno, Czech Republic, and University Politechnica in Bucharest (UPB), Bucharest, Romania. The campaigns cover both indoor and outdoor scenarios, provide the practical limitations of the positioning in standalone and k-NN powered localization systems. According to the results, LoRaWAN-based localization with relatively dense gateways deployment allows for achieving a meter-level accuracy, which may be suitable for the localization of workers.

Wireless Time-Sensitive Networking (WTSN) has emerged as a promising technology for Industrial Internet of Things (IIoT) applications. To meet the latency requirements of WTSN, wireless local area network (WLAN) such as IEEE 802.11 protocol with the time division multiple access (TDMA) mechanism is shown to be a practical solution. In this paper, we propose the RT-WiFiQA protocol with two novel schemes to improve the latency and reliability performance: real-time quality of service (RT-QoS) and fine-grained aggregation (FGA) for TDMA-based 802.11 systems. The RT-QoS is designed to guarantee the quality-of-service requirements of different traffic and to support the FGA mechanism. The FGA mechanism aggregates frames for different stations to reduce the physical layer transmission overhead. The trade-off between the reliability and FGA packet size is analyzed with numerical results. Specifically, we derive a critical threshold such that the FGA can achieve higher reliability when the aggregated packet size is smaller than the critical threshold. Otherwise, the non-aggregation scheme outperforms the FGA scheme. Extensive experiments are conducted on the commercial off-the-shelf 802.11 interface. The experiment results show that compared with the existing TDMA-based 802.11 system, the developed RT-WiFiQA protocol can achieve deterministic bounded real-time latency and greatly improves the reliability performance., QC 20230228

Nowadays, the flexible localization solution for various devices for work safety is one of the most demanding research questions. Notably, it is expected to provide an acceptable level of precision in different types of environments empowered by wearable technology and the Interent of Things (IoT) devices. Existing leading localization technologies are adapted for certain conditions, e.g., Wi-Fi, BLE, UWB are used for indoor areas and various GNSS-based ones for outdoor. This work focuses on investigating the LoRaWAN (868 MHz band) as a potential candidate to bridge this gap, being one of the most reliable and recognized communication technologies for the Industrial Internet of Things (IIoT). In the past, the research community had a lot of critics with respect to the applicability of LoRaWAN for localization, while the vision is facing a tremendous change over the past two years. The purpose of this work is to assess the feasibility of LoRaWAN as a localization solution for work safety applications in the industrial scenario from different angles. The work is based on two measurement campaigns conducted at the Brno University of Technology (BUT), Brno, Czech Republic, and University Politechnica in Bucharest (UPB), Bucharest, Romania. The campaigns cover both indoor and outdoor scenarios, provide the practical limitations of the positioning in standalone and k-NN powered localization systems. According to the results, LoRaWAN-based localization with relatively dense gateways deployment allows for achieving a meter-level accuracy, which may be suitable for the localization of workers.

Nowadays, the flexible localization solution for various devices for work safety is one of the most demanding research questions. Notably, it is expected to provide an acceptable level of precision in different types of environments empowered by wearable technology and the Interent of Things (IoT) devices. Existing leading localization technologies are adapted for certain conditions, e.g., Wi-Fi, BLE, UWB are used for indoor areas and various GNSS-based ones for outdoor. This work focuses on investigating the LoRaWAN (868 MHz band) as a potential candidate to bridge this gap, being one of the most reliable and recognized communication technologies for the Industrial Internet of Things (IIoT). In the past, the research community had a lot of critics with respect to the applicability of LoRaWAN for localization, while the vision is facing a tremendous change over the past two years. The purpose of this work is to assess the feasibility of LoRaWAN as a localization solution for work safety applications in the industrial scenario from different angles. The work is based on two measurement campaigns conducted at the Brno University of Technology (BUT), Brno, Czech Republic, and University Politechnica in Bucharest (UPB), Bucharest, Romania. The campaigns cover both indoor and outdoor scenarios, provide the practical limitations of the positioning in standalone and k-NN powered localization systems. According to the results, LoRaWAN-based localization with relatively dense gateways deployment allows for achieving a meter-level accuracy, which may be suitable for the localization of workers.

Industrial Internet of Things (IIoT) is an emerging domain, converting common objects into connecting objects with ubiquitous internet access to automate industry. Due to different vendors, supporting different infrastructures, a set of communication protocols such as Zigbee, 6LowPAN, Wireless Fidelity (Wi-Fi), Hyper Text Transfer Protocol (HTTP), etc. are introduced for IIoT. Thus, a closed ecosystem for smart devices is created. Particularly, when two or more industrial IoT applications are developed using different application-layer protocols such as Constrained Application Protocol (CoAP), Advanced Message Queuing Protocol (AMQP), or MQ Telemetry Transport (MQTT), devices are called heterogeneous devices and interoperability becomes a major challenge. In the existing literature, device-level interoperability using different application-layer protocols is enhanced with the help of intermediators at the network layer which includes servers, brokers, or gateways/adapters to route communication. However, these intermediators lead to several other issues such as dependency on network layer components, load balancing, single point of failure, and scalability. Therefore, the interoperability issue needs to be addressed at the application layer using a device intermediator instead of utilizing network layer components. For this purpose, Model Driven Engineering (MDE) is selected because less attention is paid to IIoT interoperable solutions development using MDE. To bridge this gap, a Model Driven Architecture (MDA) based approach is proposed that reduces the processing time and effort to develop these IIoT interoperable systems. For this purpose, (i) a metamodel, (ii) a UML profile, and (iii) transformation rules are developed to make heterogenous application-layer protocols interoperable using devices as intermediator. The initial feasibility of the proposed solution is demonstrated through a real-world case study i.e., a smart city. Results show that a complete solut

In this work, we design an elastic open radio access network (O-RAN) slicing for the Industrial Internet of things (IIoT). Due to the rapid spread of IoT in the industrial use-cases such as safety and mobile robot communications, the IIoT landscape has been shifted from static manufacturing processes towards dynamic manufacturing workflows (e.g., Modular Production System). But unlike IoT, IIoT poses additional challenges such as severe communication environment, network-slice resource demand variations, and on-time information update from the IIoT devices during industrial production. First, we formulate the O-RAN slicing problem for on-time industrial monitoring and control where the objective is to minimize the cost of fresh information updates (i.e., age of information (AoI)) from the IIoT devices (i.e., sensors) with the device energy consumption and O-RAN slice isolation constraints. Second, we propose the intelligent O-RAN framework based on game theory and machine learning to mitigate the problem’s complexity. We propose a two-sided distributed matching game in the O-RAN control layer that captures the IIoT channel characteristics and the IIoT service priorities to create IIoT device and small cell base station (SBS) preference lists. We then employ an actor-critic model with a deep deterministic policy gradient (DDPG) in the O-RAN service management layer to solve the resource allocation problem for optimizing the network slice configuration policy under time-varying slicing demand. Furthermore, the proposed matching game within the actor-critic model training helps to enforce the long-term policy-based guidance for resource allocation that reflects the trends of all IIoT devices and SBSs satisfactions with the assignment. Finally, the simulation results show that the proposed solution enhances the performance gain for the IIoT services by serving an average of $50\%$

Wireless use cases in industrial internet-of-thing (IIoT) networks often require guaranteed data rates ranging from a few kilobits per second to a few gigabits per second. Supporting such a requirement in a single radio access technique is difficult, especially when bandwidth is limited. Although non-orthogonal multiple access (NOMA) can improve the system capacity by simultaneously serving multiple devices, its performance suffers from strong user interference. In this paper, we propose a Q-learning-based algorithm for handling many-to-many matching problems such as bandwidth partitioning, device assignment to sub-bands, interference-aware access mode selection (orthogonal multiple access (OMA), or NOMA), and power allocation to each device. The learning technique maximizes system throughput and spectral efficiency (SE) while maintaining quality-of-service (QoS) for a maximum number of devices. The simulation results show that the proposed technique can significantly increase overall system throughput and SE while meeting heterogeneous QoS criteria.

This work is a data descriptor paper for measurements related to various operational aspects of LoRaWAN communication technology collected in Brno, Czech Republic. This paper also provides data characterizing the long-term behavior of the LoRaWAN channel collected during the two-month measurement campaign. It covers two measurement locations, one at the university premises, and the second situated near the city center. The dataset’s primary goal is to provide the researchers lacking LoRaWAN devices with an opportunity to compare and analyze the information obtained from 303 different outdoor test locations transmitting to up to 20 gateways operating in the 868 MHz band in a varying metropolitan landscape. To collect the data, we developed a prototype equipped with a Microchip RN2483 Low-Power Wide-Area Network (LPWAN) LoRaWAN technology transceiver module for the field measurements. As an example of data utilization, we showed the Signal-to-noise Ratio (SNR) and Received Signal Strength Indicator (RSSI) in relation to the closest gateway distance.

This work is a data descriptor paper for measurements related to various operational aspects of LoRaWAN communication technology collected in Brno, Czech Republic. This paper also provides data characterizing the long-term behavior of the LoRaWAN channel collected during the two-month measurement campaign. It covers two measurement locations, one at the university premises, and the second situated near the city center. The dataset’s primary goal is to provide the researchers lacking LoRaWAN devices with an opportunity to compare and analyze the information obtained from 303 different outdoor test locations transmitting to up to 20 gateways operating in the 868 MHz band in a varying metropolitan landscape. To collect the data, we developed a prototype equipped with a Microchip RN2483 Low-Power Wide-Area Network (LPWAN) LoRaWAN technology transceiver module for the field measurements. As an example of data utilization, we showed the Signal-to-noise Ratio (SNR) and Received Signal Strength Indicator (RSSI) in relation to the closest gateway distance.

This work is a data descriptor paper for measurements related to various operational aspects of LoRaWAN communication technology collected in Brno, Czech Republic. This paper also provides data characterizing the long-term behavior of the LoRaWAN channel collected during the two-month measurement campaign. It covers two measurement locations, one at the university premises, and the second situated near the city center. The dataset’s primary goal is to provide the researchers lacking LoRaWAN devices with an opportunity to compare and analyze the information obtained from 303 different outdoor test locations transmitting to up to 20 gateways operating in the 868 MHz band in a varying metropolitan landscape. To collect the data, we developed a prototype equipped with a Microchip RN2483 Low-Power Wide-Area Network (LPWAN) LoRaWAN technology transceiver module for the field measurements. As an example of data utilization, we showed the Signal-to-noise Ratio (SNR) and Received Signal Strength Indicator (RSSI) in relation to the closest gateway distance.

Industry 4.0 will only become a reality through the convergence of Operational and Information Technologies (OT & IT), which use different computation and communication technologies. Cloud Computing cannot be used for OT involving industrial applications, since it cannot guarantee stringent non-functional requirements, e.g., dependability, trustworthiness and timeliness. Instead, a new computing paradigm, called Fog Computing, is envisioned as an architectural means to realize the IT/OT convergence. In this paper we propose a Fog Computing Platform (FCP) reference architecture targeting Industrial IoT applications. The FCP is based on: deterministic virtualization that reduces the effort required for safety and security assurance; middleware for supporting both critical control and dynamic Fog applications; deterministic networking and interoperability, using open standards such as IEEE 802.1 Time-Sensitive Networking (TSN) and OPC Unified Architecture (OPC UA); mechanisms for resource management and orchestration; and services for security, fault tolerance and distributed machine learning. We propose a methodology for the definition and the evaluation of the reference architecture. We use the Architecture Analysis Design Language (AADL) to model the FCP reference architecture, and a set of industrial use cases to evaluate its suitability for the Industrial IoT area.

The ubiquity foreseen by the Internet-of-Things (IoT) and powered by the 5G advancements has motivated research on wireless solutions for critical applications, in particular, Industrial IoT (IIoT). Nevertheless, there is little or no research on a unified design methodology for IIoT that tackles the conflicting wireless system performances of Power, Latency, and Reliability (PLR). Obtaining such a framework is vital for empowering further development and fair comparison in future IIoT designs. Thereby, this paper presents a novel design methodology to tackle PLR trade-off in IIoT Wireless Systems (IIoT-WS). This new methodology uses a meet-in-the-middle system approach to design an entire PLR RF system, and a custom Multiple-Criteria Decision Analysis (MCDA) to help decide the best design variables that are both resource-efficient and PLR-balanced for a given application. Finally, to quantify the methodology for critical wireless systems, a lab-demonstrator for automotive application is designed and its performance compared to other wireless systems standards.

The evolution of the current technological landscape has opened an emergent paradigm that enables interoperability between the digital and physical world, leading to a new generation of industrial systems. This new digitalization era marks the beginning of the fourth industrial revolution, usually referred to as "Industry 4.0". By employing recent technologies and concepts such as Industrial Internet of Things (IIoT), Cyber-physical Systems (CPS), Cloud-based technologies, , Service-oriented Architecture (SoA), and Artificial Intelligence (AI), the Industry 4.0 approach aims to address the dynamic evolution of contemporary requirements as well as improve the sustainability and efficiency in industrial production. While this new industrial paradigm facilitates the integration and collaboration among industrial components, it also introduces greater complexity to the industrial systems, thereby potentially increasing costs related to system development and maintenance. Specifically, significant engineering effort is dedicated to addressing the heterogeneity, interoperability and scalability of those integrated components. As a result, in order to mitigate those challenges, the self-adaptive solution appears as a potential approach to automate the management and supervision of the systems. Self-adaptation allows the system to adapt in the face of changes in its operating environment and in the system itself without human intervention. This thesis outlines the progress made towards self-adaptation in industrial production. It proposes an architectural design that enables dynamic adaptation for IIoT systems. Particularly, in order to facilitate the integration of heterogeneous and numerous physical components, the proposed approach shifts from tightly-coupled automation systems to loosely-coupled flexible information and communication infrastructure by employing service-oriented and decentralized technologies. Furthermore, the concept of Autonomic Computing (AC) is expl

The remaining useful life (RUL) estimations become one of the most essential aspects of predictive maintenance (PdM) in the era of industry 4.0. Predictive maintenance aims to minimize the downtime of machines or process, decreases maintenance costs, and increases the productivity of industries. The primary objective of this bibliometric paper is to understand the scope of literature available related to RUL prediction. Scopus database is used to perform the analysis of 1673 extracted scientific literature from the year 1985 to 2020. Based on available published documents, analysis is done on the year-wise publication data, document types, language-wise distribution of documents, funding sponsors, authors contributions, affiliations, document wise citations, etc. to give an in-depth view of the research trends in the area of RUL prediction. The paper also focuses on the available maintenance methods, predictive maintenance models, RUL models, deep learning algorithms for RUL prediction challenges and future directions in the RUL prediction area.

Purpose - As energy demand increases in tandem with an increasing climate crisis, the world runs towards renewable energy generation. Within the area of Industrial Internet of Things (IIoT) there are a multitude of opportunities that should be capitalized on, but this requires an integration of the connected systems of Information Technology (IT) and the governing systems of Operational Technology (OT). In the utility sector, this has proven very complex. Hence, the purpose of this paper is to explore the challenges between utility companies, IT providers, and OT providers in the ecosystem to identify activities to combat these challenges by developing a contingency framework. Thus, contributing to the development of Smart Grids (SG) within renewable energy generation. Method – To fulfill the purpose of this study, the partnership between the Swedish branch of a global technology company and a sizeable Swedish energy producer has been investigated. A qualitative single case study has been conducted with an inductive, explorative approach. Empirical data were collected from 22 interviews and 4 workshops from six different companies across five countries. The interviews and workshops were conducted in three different waves: 1) Explorative, 2) Investigatory, and 3) Validatory. The data were analyzed using thematic analysis. Findings – Findings from our data analysis have identified challenges and key activities in four main categories: 1) IT/OT Collaborative Challenges, 2) IT/OT Technical Challenges, 3) IT/OT Collaborative Activities, and 4) IT/OT Technical Activities. These findings are combined to form a contingency framework that emphasizes the activities to overcome industry challenges. Theoretical and Practical Implications – Our findings and framework expand on current literature in IIoT, SGs, and Innovation Ecosystems development by investigating the collaborative challenges and activities within IT/OT collaboration rather than specific technologies or ecosystem

Today, the edge-cloud computing paradigm starts to gain increasing popularity, aiming to enable short latency, fast decision-making and intelligence at the network edge, especially for industrial applications. The container-based virtualization technology has been put on the roadmap by the industry to implement edge-cloud computing infrastructures. Has the performance of the container-based edge-cloud computing stacks reached industry requirement? In this paper, from the industrial client perspective, we provide a performance evaluation methodology and apply it to the state-of-the-art containerization-based edge-cloud computing infrastructures. The influences of the message sending interval, payload, network bandwidth and concurrent devices on full stack latency are measured, and the processing capability of executing machine learning tasks are benchmarked. The results show that containerization on the edge does not introduce noticeable performance degradation in terms of communication, computing and intelligence capabilities, making it a promising technology for the edge-cloud computing paradigm. However, there is a large room for performance improvement between current implementation of the edge-cloud infrastructure and the demanding requirements anticipated by time-critical industrial applications. We also emphasize and showcase that partitioning of an industrial application into microservices throughout the whole stack can be considered during solution design. The proposed evaluation methodology can be a reference to users of edge-cloud computing as well as developers to get a client perspective overview of system performance., Funding: Swedish Innovation Agency, VinnovaVinnova

Today, the edge-cloud computing paradigm starts to gain increasing popularity, aiming to enable short latency, fast decision-making and intelligence at the network edge, especially for industrial applications. The container-based virtualization technology has been put on the roadmap by the industry to implement edge-cloud computing infrastructures. Has the performance of the container-based edge-cloud computing stacks reached industry requirement? In this paper, from the industrial client perspective, we provide a performance evaluation methodology and apply it to the state-of-the-art containerization-based edge-cloud computing infrastructures. The influences of the message sending interval, payload, network bandwidth and concurrent devices on full stack latency are measured, and the processing capability of executing machine learning tasks are benchmarked. The results show that containerization on the edge does not introduce noticeable performance degradation in terms of communication, computing and intelligence capabilities, making it a promising technology for the edge-cloud computing paradigm. However, there is a large room for performance improvement between current implementation of the edge-cloud infrastructure and the demanding requirements anticipated by time-critical industrial applications. We also emphasize and showcase that partitioning of an industrial application into microservices throughout the whole stack can be considered during solution design. The proposed evaluation methodology can be a reference to users of edge-cloud computing as well as developers to get a client perspective overview of system performance., Funding: Swedish Innovation Agency, VinnovaVinnova

This work studies a real-time environment monitoring scenario in the industrial Internet of things, where wireless sensors proactively collect environmental data and transmit it to the controller. We adopt the notion of risk-sensitivity in financial mathematics as the objective to jointly minimize the mean, variance, and other higher-order statistics of the network energy consumption subject to the constraints on the age of information (AoI) threshold violation probability and the AoI exceedances over a pre-defined threshold. We characterize the extreme AoI staleness using results in extreme value theory and propose a distributed power allocation approach by weaving in together principles of Lyapunov optimization and federated learning (FL). Simulation results demonstrate that the proposed FL-based distributed solution is on par with the centralized baseline while consuming 28.50% less system energy and outperforms the other baselines.

While information delivery in industrial Internet of things demands reliability and latency guarantees, the freshness of the controller’s available information, measured by the age of information (AoI), is paramount for high-performing industrial automation. The problem in this work is cast as a sensor’s transmit power minimization subject to the peak-Aol requirement and a probabilistic constraint on queuing latency. We further characterize the tail behavior of the latency by a generalized Pareto distribution (GPD) for solving the power allocation problem through Lyapunov optimization. As each sensor utilizes its own data to locally train the GPD model, we incorporate federated learning and propose a local-model selection approach which accounts for correlation among the sensor’s training data. Numerical results show the tradeoff between the transmit power, peak AoI, and delay’s tail distribution. Furthermore, we verify the superiority of the proposed correlation-aware approach for selecting the local models in federated learning over an existing baseline.

The Industrial Internet of Things (IIoT) and Industry 4.0 aim to streamline production processes and keep manufacturing viable and profitable. This presents enterprises with the opportunity to boost productivity while improving efficiency and safety and reducing costs. With heightened interest from both researchers and industry experts, IIoT has witnessed remarkable advances over the recent years thanks to developments in related technologies such as Industrial Wireless Networks (IWNs), Software-Defined Networking (SDN), cloud computing, and Multi Access Edge Computing (MEC). Despite the proven ability of these technologies to advance the course of IIoT and Industry 4.0, an equally important but less investigated problem is ensuring that the resources upon which these technologies depend are optimally allocated and efficiently utilized. This doctoral dissertation proposes a software-defined approach towards improving resource management and efficiency in IIoT systems. First, an SDN-based data offloading scheme is designed to coordinate data offloading for IIoT applications. This will enable constrained IIoT devices to relay their more demanding operations for energy and resource optimization. Second, a system model is developed to leverage the synergy between SDN, MEC, and containerization technologies in advancing IIoT applications for better resource management, more specifically for containerized edge microservices. Third, a novel SDN-enabled Resource Management (SDRM) scheme is developed based on Satisfiability Modulo Theory (SMT) constraint programming. With this scheme, SDRM will be able to automatically compute the optimal resource allocation for different IIoT network models and dynamically adjust assigned resources based on predefined constraints to ensure Service Level Agreements (SLAs). Lastly, the effects of collaborative edge-cloud computing for such SDN-based IIoT implementations are examined. The results from our implementation models demonst, Tiivistelmä Teollisen esineiden internetin (IIoT) ja Teollisuus 4.0:n tarkoituksena on virtaviivaistaa tuotantoprosesseja ja pitää valmistus kannattavana ja kannattavana. Tämä tarjoaa yrityksille mahdollisuuden lisätä tuottavuutta ja samalla parantaa tehokkuutta, turval- lisuutta ja vähentää kustannuksia. IIoT on osoittanut huomattavaa edistystä viime vuosina sekä tutkimuksen että teollisuuden lisääntyneen kiinnostuksen ansiosta, mikä on tapahtunut asiaan liittyvien teknologioiden, kuten teollisten langattomien verkko- jen (IWN), ohjelmisto-ohjattujen verkkojen (SDN), pilvipalvelujen ja reunalasken- nan (MEC) ansiosta. Huolimatta näiden teknologioiden todistetusta kyvystä edistää IIoT:n ja Industry 4.0:n kulkua, yhtä tärkeä mutta vähemmän tutkittu ongelma on varmistaa, että resurssit, joista nämä tekniikat ovat riippuvaisia, kohdennetaan opti- maalisesti ja käytetään tehokkaasti. Tässä väitöskirjassa ehdotetaan ohjelmistojen määrittelemää lähestymistapaa IIoT-järjestelmien resurssienhallinnan ja tehokkuuden parantamiseksi. Ensinnäkin SDN-pohjainen tietojen purkujärjestelmä on suunniteltu koordinoimaan tietojen purkua IIoT-sovelluksille. Näin rajoitetut IIoT-laitteet voivat välittää vaativampia toimintojaan energian ja resurssien optimointiin. Toiseksi ke- hitetään järjestelmämalli, joka hyödyntää SDN-, MEC- ja konttiteknologioiden välistä synergiaa IIoT-sovellusten edistämisessä resurssien hallinnan lisäämiseksi, erityisesti konttien reunamikropalveluissa. Kolmanneksi kehitetään uusi SDN-yhteensopiva resurssienhallintajärjestelmä (SDRM), joka perustuu SMT (Satisfiability Modulo The- ory) -rajoitusohjelmointiin. Tämän avulla SDRM pystyy automaattisesti laskemaan optimaalisen resurssien kohdistuksen eri IIoT-verkkomalleille ja säätämään dynaamisesti varattuja resursseja ennalta määritettyjen rajoitusten perusteella palvelutasosopimuksen (SLA) varmistamiseksi. Lopuksi tarkastellaan yhteistyöhön perustuvan reunapilvi- laskennan vaikutuksia tällaisiin SDN-pohjaisiin IIoT

This work is a data descriptor paper for measurements related to various operational aspects of LoRaWAN communication technology collected in Brno, Czech Republic. This paper also provides data characterizing the long-term behavior of the LoRaWAN channel collected during the two-month measurement campaign. It covers two measurement locations, one at the university premises, and the second situated near the city center. The dataset’s primary goal is to provide the researchers lacking LoRaWAN devices with an opportunity to compare and analyze the information obtained from 303 different outdoor test locations transmitting to up to 20 gateways operating in the 868 MHz band in a varying metropolitan landscape. To collect the data, we developed a prototype equipped with a Microchip RN2483 Low-Power Wide-Area Network (LPWAN) LoRaWAN technology transceiver module for the field measurements. As an example of data utilization, we showed the Signal-to-noise Ratio (SNR) and Received Signal Strength Indicator (RSSI) in relation to the closest gateway distance.

This work is a data descriptor paper for measurements related to various operational aspects of LoRaWAN communication technology collected in Brno, Czech Republic. This paper also provides data characterizing the long-term behavior of the LoRaWAN channel collected during the two-month measurement campaign. It covers two measurement locations, one at the university premises, and the second situated near the city center. The dataset’s primary goal is to provide the researchers lacking LoRaWAN devices with an opportunity to compare and analyze the information obtained from 303 different outdoor test locations transmitting to up to 20 gateways operating in the 868 MHz band in a varying metropolitan landscape. To collect the data, we developed a prototype equipped with a Microchip RN2483 Low-Power Wide-Area Network (LPWAN) LoRaWAN technology transceiver module for the field measurements. As an example of data utilization, we showed the Signal-to-noise Ratio (SNR) and Received Signal Strength Indicator (RSSI) in relation to the closest gateway distance.

This work is a data descriptor paper for measurements related to various operational aspects of LoRaWAN communication technology collected in Brno, Czech Republic. This paper also provides data characterizing the long-term behavior of the LoRaWAN channel collected during the two-month measurement campaign. It covers two measurement locations, one at the university premises, and the second situated near the city center. The dataset’s primary goal is to provide the researchers lacking LoRaWAN devices with an opportunity to compare and analyze the information obtained from 303 different outdoor test locations transmitting to up to 20 gateways operating in the 868 MHz band in a varying metropolitan landscape. To collect the data, we developed a prototype equipped with a Microchip RN2483 Low-Power Wide-Area Network (LPWAN) LoRaWAN technology transceiver module for the field measurements. As an example of data utilization, we showed the Signal-to-noise Ratio (SNR) and Received Signal Strength Indicator (RSSI) in relation to the closest gateway distance.

Today, the edge-cloud computing paradigm starts to gain increasing popularity, aiming to enable short latency, fast decision-making and intelligence at the network edge, especially for industrial applications. The container-based virtualization technology has been put on the roadmap by the industry to implement edge-cloud computing infrastructures. Has the performance of the container-based edge-cloud computing stacks reached industry requirement? In this paper, from the industrial client perspective, we provide a performance evaluation methodology and apply it to the state-of-the-art containerization-based edge-cloud computing infrastructures. The influences of the message sending interval, payload, network bandwidth and concurrent devices on full stack latency are measured, and the processing capability of executing machine learning tasks are benchmarked. The results show that containerization on the edge does not introduce noticeable performance degradation in terms of communication, computing and intelligence capabilities, making it a promising technology for the edge-cloud computing paradigm. However, there is a large room for performance improvement between current implementation of the edge-cloud infrastructure and the demanding requirements anticipated by time-critical industrial applications. We also emphasize and showcase that partitioning of an industrial application into microservices throughout the whole stack can be considered during solution design. The proposed evaluation methodology can be a reference to users of edge-cloud computing as well as developers to get a client perspective overview of system performance., Funding: Swedish Innovation Agency, VinnovaVinnova

Industry 4.0 will only become a reality through the convergence of Operational and Information Technologies (OT & IT), which use different computation and communication technologies. Cloud Computing cannot be used for OT involving industrial applications, since it cannot guarantee stringent non-functional requirements, e.g., dependability, trustworthiness and timeliness. Instead, a new computing paradigm, called Fog Computing, is envisioned as an architectural means to realize the IT/OT convergence. In this paper we propose a Fog Computing Platform (FCP) reference architecture targeting Industrial IoT applications. The FCP is based on: deterministic virtualization that reduces the effort required for safety and security assurance; middleware for supporting both critical control and dynamic Fog applications; deterministic networking and interoperability, using open standards such as IEEE 802.1 Time-Sensitive Networking (TSN) and OPC Unified Architecture (OPC UA); mechanisms for resource management and orchestration; and services for security, fault tolerance and distributed machine learning. We propose a methodology for the definition and the evaluation of the reference architecture. We use the Architecture Analysis Design Language (AADL) to model the FCP reference architecture, and a set of industrial use cases to evaluate its suitability for the Industrial IoT area.

The ubiquity foreseen by the Internet-of-Things (IoT) and powered by the 5G advancements has motivated research on wireless solutions for critical applications, in particular, Industrial IoT (IIoT). Nevertheless, there is little or no research on a unified design methodology for IIoT that tackles the conflicting wireless system performances of Power, Latency, and Reliability (PLR). Obtaining such a framework is vital for empowering further development and fair comparison in future IIoT designs. Thereby, this paper presents a novel design methodology to tackle PLR trade-off in IIoT Wireless Systems (IIoT-WS). This new methodology uses a meet-in-the-middle system approach to design an entire PLR RF system, and a custom Multiple-Criteria Decision Analysis (MCDA) to help decide the best design variables that are both resource-efficient and PLR-balanced for a given application. Finally, to quantify the methodology for critical wireless systems, a lab-demonstrator for automotive application is designed and its performance compared to other wireless systems standards.

In the perspective of the emerging Industrial Internet of things (IIoT), the 6TiSCH working group has been created with the main goal to integrate the capabilities of the IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) with the IPv6 protocol stack. In order to support time-critical applications in IIoT, reliable real-time communication is a key requirement. Specifically, aperiodic critical traffic, such as emergency alarms, must be reliably delivered to the DODAG root within strict deadline bounds to avoid system failure or safety-critical situations. Currently, there is no mechanism defined in the 6TiSCH architecture for timely and reliably handling of such traffic and its prioritization over the non-critical one. In this paper, we introduce REA-6TiSCH, a reliable emergency-aware communication scheme to support real-time communications of emergency alarms in 6TiSCH networks. In REA-6TiSCH, the aperiodic emergency traffic is opportunistically enabled to hijack transmission cells pre-assigned for the regular periodic traffic in the TSCH schedule. Moreover, we introduce a distributed optimization scheme to improve the probability that an emergency flow is delivered successfully within its deadline bound. To the best of our knowledge, this is the first approach to incorporate emergency alarms in 6TiSCH networks. We evaluate the performance of REA-6TiSCH through extensive simulations and the results show the effectiveness of our proposed method in handling emergency traffic compared to Orchestra scheme. Additionally, we discuss the applicability of REA-6TiSCH and provide guidelines for real implementation in 6TiSCH networks., NIIT

Purpose - As energy demand increases in tandem with an increasing climate crisis, the world runs towards renewable energy generation. Within the area of Industrial Internet of Things (IIoT) there are a multitude of opportunities that should be capitalized on, but this requires an integration of the connected systems of Information Technology (IT) and the governing systems of Operational Technology (OT). In the utility sector, this has proven very complex. Hence, the purpose of this paper is to explore the challenges between utility companies, IT providers, and OT providers in the ecosystem to identify activities to combat these challenges by developing a contingency framework. Thus, contributing to the development of Smart Grids (SG) within renewable energy generation. Method – To fulfill the purpose of this study, the partnership between the Swedish branch of a global technology company and a sizeable Swedish energy producer has been investigated. A qualitative single case study has been conducted with an inductive, explorative approach. Empirical data were collected from 22 interviews and 4 workshops from six different companies across five countries. The interviews and workshops were conducted in three different waves: 1) Explorative, 2) Investigatory, and 3) Validatory. The data were analyzed using thematic analysis. Findings – Findings from our data analysis have identified challenges and key activities in four main categories: 1) IT/OT Collaborative Challenges, 2) IT/OT Technical Challenges, 3) IT/OT Collaborative Activities, and 4) IT/OT Technical Activities. These findings are combined to form a contingency framework that emphasizes the activities to overcome industry challenges. Theoretical and Practical Implications – Our findings and framework expand on current literature in IIoT, SGs, and Innovation Ecosystems development by investigating the collaborative challenges and activities within IT/OT collaboration rather than specific technologies or ecosystem

Industry 4.0 will only become a reality through the convergence of Operational and Information Technologies (OT & IT), which use different computation and communication technologies. Cloud Computing cannot be used for OT involving industrial applications, since it cannot guarantee stringent non-functional requirements, e.g., dependability, trustworthiness and timeliness. Instead, a new computing paradigm, called Fog Computing, is envisioned as an architectural means to realize the IT/OT convergence. In this paper we propose a Fog Computing Platform (FCP) reference architecture targeting Industrial IoT applications. The FCP is based on: deterministic virtualization that reduces the effort required for safety and security assurance; middleware for supporting both critical control and dynamic Fog applications; deterministic networking and interoperability, using open standards such as IEEE 802.1 Time-Sensitive Networking (TSN) and OPC Unified Architecture (OPC UA); mechanisms for resource management and orchestration; and services for security, fault tolerance and distributed machine learning. We propose a methodology for the definition and the evaluation of the reference architecture. We use the Architecture Analysis Design Language (AADL) to model the FCP reference architecture, and a set of industrial use cases to evaluate its suitability for the Industrial IoT area.

The evolution of the current technological landscape has opened an emergent paradigm that enables interoperability between the digital and physical world, leading to a new generation of industrial systems. This new digitalization era marks the beginning of the fourth industrial revolution, usually referred to as "Industry 4.0". By employing recent technologies and concepts such as Industrial Internet of Things (IIoT), Cyber-physical Systems (CPS), Cloud-based technologies, , Service-oriented Architecture (SoA), and Artificial Intelligence (AI), the Industry 4.0 approach aims to address the dynamic evolution of contemporary requirements as well as improve the sustainability and efficiency in industrial production. While this new industrial paradigm facilitates the integration and collaboration among industrial components, it also introduces greater complexity to the industrial systems, thereby potentially increasing costs related to system development and maintenance. Specifically, significant engineering effort is dedicated to addressing the heterogeneity, interoperability and scalability of those integrated components. As a result, in order to mitigate those challenges, the self-adaptive solution appears as a potential approach to automate the management and supervision of the systems. Self-adaptation allows the system to adapt in the face of changes in its operating environment and in the system itself without human intervention. This thesis outlines the progress made towards self-adaptation in industrial production. It proposes an architectural design that enables dynamic adaptation for IIoT systems. Particularly, in order to facilitate the integration of heterogeneous and numerous physical components, the proposed approach shifts from tightly-coupled automation systems to loosely-coupled flexible information and communication infrastructure by employing service-oriented and decentralized technologies. Furthermore, the concept of Autonomic Computing (AC) is expl

Today, the edge-cloud computing paradigm starts to gain increasing popularity, aiming to enable short latency, fast decision-making and intelligence at the network edge, especially for industrial applications. The container-based virtualization technology has been put on the roadmap by the industry to implement edge-cloud computing infrastructures. Has the performance of the container-based edge-cloud computing stacks reached industry requirement? In this paper, from the industrial client perspective, we provide a performance evaluation methodology and apply it to the state-of-the-art containerization-based edge-cloud computing infrastructures. The influences of the message sending interval, payload, network bandwidth and concurrent devices on full stack latency are measured, and the processing capability of executing machine learning tasks are benchmarked. The results show that containerization on the edge does not introduce noticeable performance degradation in terms of communication, computing and intelligence capabilities, making it a promising technology for the edge-cloud computing paradigm. However, there is a large room for performance improvement between current implementation of the edge-cloud infrastructure and the demanding requirements anticipated by time-critical industrial applications. We also emphasize and showcase that partitioning of an industrial application into microservices throughout the whole stack can be considered during solution design. The proposed evaluation methodology can be a reference to users of edge-cloud computing as well as developers to get a client perspective overview of system performance., Funding: Swedish Innovation Agency, VinnovaVinnova

Today, the edge-cloud computing paradigm starts to gain increasing popularity, aiming to enable short latency, fast decision-making and intelligence at the network edge, especially for industrial applications. The container-based virtualization technology has been put on the roadmap by the industry to implement edge-cloud computing infrastructures. Has the performance of the container-based edge-cloud computing stacks reached industry requirement? In this paper, from the industrial client perspective, we provide a performance evaluation methodology and apply it to the state-of-the-art containerization-based edge-cloud computing infrastructures. The influences of the message sending interval, payload, network bandwidth and concurrent devices on full stack latency are measured, and the processing capability of executing machine learning tasks are benchmarked. The results show that containerization on the edge does not introduce noticeable performance degradation in terms of communication, computing and intelligence capabilities, making it a promising technology for the edge-cloud computing paradigm. However, there is a large room for performance improvement between current implementation of the edge-cloud infrastructure and the demanding requirements anticipated by time-critical industrial applications. We also emphasize and showcase that partitioning of an industrial application into microservices throughout the whole stack can be considered during solution design. The proposed evaluation methodology can be a reference to users of edge-cloud computing as well as developers to get a client perspective overview of system performance., Funding: Swedish Innovation Agency, VinnovaVinnova

Industrial Internet of things (IIoT) and machine-to-machine (M2M) connectivity are strictly related and represent the next step in the ever-evolving world of industrial communications. The lack of a single wireless technology capable of satisfying new requirements in terms of flexibility, scalability, and low-power consumption led to the proposal of many different protocols to support the plethora of brand new offered services. In this chapter, an overview of low-power wide-area network (LPWAN) technologies is provided. After a discussion about the common features of available LPWAN solutions, the focus will be moved to the LoRaWAN standard, which is attracting the most significant interest from both the industrial and academic domains. Finally, some considerations on the use of LPWAN solutions for industrial applications, and the need and availability of network simulation tools, will be sketched.

Wireless technologies for short-range communication play a central role in the massive diffusion of the Internet of Things (IoT) paradigm. Such communication solutions rely extensively on the availability of unlicensed spectrum in the form of bands for industrial, scientific, and medical (ISM) applications. While ISM bands greatly simplify network deployments by avoiding operator-related costs and facilitating worldwide applicability, they present the shortcoming of non-cooperative spectrum usage, which manifests in the form of radio interference. Interference and time-varying environments generate complex and dynamic scenarios for wireless network deployments, endangering communication performance. The problem becomes especially critical when the timeliness and reliability of the communication are subject to stringent requirements, which is the case for several industrial IoT (IIoT) applications. This work aims to enhance the reliability and performance of wireless communication in IoT networks by enriching the existing methods for radio-environment analysis. The central idea of this research is that a run-time analysis of the radio channel properties is a crucial element to ensure performance stability in unpredictable radio environments with potentially disruptive interference.An added challenge of this work comes from the hypothesis that such an analysis can be performed even with strongly resource-constrained platforms without hindering routine network functionalities. The employed methodology is heavily reliant on experimental validation, encompassing implementation on IoT radio devices and measurement campaigns. This thesis makes two principal scientific contributions. The first contribution is the design of a comprehensive collection of methods for the analysis of the radio environment, designed to operate entirely onboard on IoT radio platforms.The approaches encompass interference detection, classification, spectrum analysis, link-state analysis, and detec, Vid tidpunkten för disputationen var följande delarbete opublicerat: delarbete 7 inskickat.At the time of the doctoral defence the following paper was unpublished: paper 7 submitted.

The Routing Protocol for Low power and lossy networks (RPL) has been introduced as the de-facto routing protocol for the Industrial Internet of Things (IIoT). In heavy load scenarios, particular parent nodes are likely prone to congestion, which in turn degrades the network performance, in terms of packet delivery and delay. Moreover, there is no explicit strategy in RPL to prioritize the transmission of different traffic types in heterogeneous 6TiSCH networks, each according to its criticality. In this paper, we address the aforementioned issues by introducing a congestion control and service differentiation strategies to support heterogeneous 6TiSCH networks in IIoT applications. First, we introduce a congestion control mechanism to achieve load balancing under heavy traffic scenarios. The congestion is detected through monitoring and sharing the status of the queue backlog among neighbor nodes. We define a new routing metric that considers the queue occupancy when selecting the new parent node in congestion situations. In addition, we design a multi-queue model to provide prioritized data transmission for critical data over the non-critical ones. Each traffic type is placed in a separate queue and scheduled for transmission based on the assigned queue priority, where critical data are always transmitted first. The performance of the proposed work is evaluated through extensive simulations and compared with existing work to demonstrate its effectiveness. The results show that our proposal achieves improved packet delivery and low queue losses under heavy load scenarios, as well as improved delay performance of critical traffic.

The Routing Protocol for Low power and lossy networks (RPL) has been introduced as the de-facto routing protocol for the Industrial Internet of Things (IIoT). In heavy load scenarios, particular parent nodes are likely prone to congestion, which in turn degrades the network performance, in terms of packet delivery and delay. Moreover, there is no explicit strategy in RPL to prioritize the transmission of different traffic types in heterogeneous 6TiSCH networks, each according to its criticality. In this paper, we address the aforementioned issues by introducing a congestion control and service differentiation strategies to support heterogeneous 6TiSCH networks in IIoT applications. First, we introduce a congestion control mechanism to achieve load balancing under heavy traffic scenarios. The congestion is detected through monitoring and sharing the status of the queue backlog among neighbor nodes. We define a new routing metric that considers the queue occupancy when selecting the new parent node in congestion situations. In addition, we design a multi-queue model to provide prioritized data transmission for critical data over the non-critical ones. Each traffic type is placed in a separate queue and scheduled for transmission based on the assigned queue priority, where critical data are always transmitted first. The performance of the proposed work is evaluated through extensive simulations and compared with existing work to demonstrate its effectiveness. The results show that our proposal achieves improved packet delivery and low queue losses under heavy load scenarios, as well as improved delay performance of critical traffic.

Nowadays, no business activity escapes the fourth industrial revolution, called industry 4.0, which is characterized by digitalization of processes. The possibility of simultaneously having systems with greater interconnection, more information and greater flexibility, allows companies to have a clearer view of their processes and consequently improve their effectiveness and efficiency. The digital transformation can no longer be based simply on making the processes more efficient, but on creating more sustainable and profitable customer relationships, continuously aligning the value of the product with the changing customer requirements. Even though managing assets over the Internet is increasingly common, much effort is needed to identify the functionality that should be provided by these platforms to enhance existing asset management practices. The effort of IT vendors is focused on the development of IoT platforms, which allow, among other functions, to create a connection between machinery and digital systems, protect all devices and data against hacking or attacks, control operations and maintenance of equipment or perform different analyses of assets or systems. The aim of this paper is to understand the functionalities of the existing IAMP platforms, providing a system that evaluates these functionalities based on the business objectives from the point of view of asset management. This methodology allows maintenance managers guiding the evolution of the life cycle of their assets according to the business value conception. This makes this methodology especially suitable for supporting new challenging scenarios of maintenance management. In this paper we first talk about the structure of an IAMP, then how they integrate the asset management model and a summary of the features and modules that have the most known IAMP platforms. Finally, an evaluation system of IAMP platforms and a case study is presented based on their content for asset management.

Maintenance Management is a key pillar in companies, especially energy utilities, which have high investments in assets, and so for its proper contribution has to be integrated and aligned with other departments in order to conserve the asset value and guarantee the services. In this line, Intelligent Assets Management Platforms (IAMP) are defined as software platforms to collect and analyze data from industrial assets. They are based on the use of digital technologies in industry. Beside the fact that monitoring and managing assets over the internet is gaining ground, this paper states that the IAMPs should also support a much better balanced and more strategic view in existing asset management and concretely in maintenance management. The real transformation can be achieved if these platforms help to understand business priorities in work and investments. In this paper, we first discuss about the factors explaining IAMP growth, then we explain the importance of considering, well in advance, those managerial aspects of the problem, for proper investments and suitable digital transformation through the adoption and use of IAMPs. A case study in the energy sector is presented to map, or to identify, those platform modules and Apps providing important value-added features to existing asset management practices. Later, attention is paid to the methodology used to develop the Apps’ data models from a maintenance point of view. To illustrate this point, a methodology for the development of the asset criticality analysis process data model is proposed. Finally, the paper includes conclusions of the work and relevant literature to this research