The industrial domain is experiencing relatively a higher growth rate than other Internet-ofThings (IoT) market domains. Much as a lot is said about its technological capabilities andapplications, less has been said about the business side, and specifically how business modelsfor IIoT are changing currently. This paper seeks to explore how industrial IoT business modelsare changing the key drivers in the now. Some of the key finding include the fact that thischange is expressed most within the value proposition, collaborations and partnerships, newskill sets, internal departmental convergences etc. The key drivers are mainly bothtechnologically and market driven with mostly reasons of IIoT adoption being cost cutting andefficiency in operations. It is also found out that standardization and regulations also play akey role but only to arbitrate (issue like privacy security, ownership interoperability etc.) whathas or is already been put to service, in most of the cases e.g., The General Data ProtectionRegulation (GDPR).

The Internet of Things (IoT) provide the possibility to build dynamic industrial systems and applications to improve the quality of production in industrial areas. There are many Industrial IoT Gateways (IoTGWs) available on the market, all of which have different functionalities and properties. Here, the different properties of the IIoTGWs were explored, with the researcher’s as well as vendor’s perspective in mind. The most important properties were identified using research papers and technical data sheets, based on this, a model was created. The model showed the best two gateways available in the database. The results show that methods used can be applied in future research.

<p dir="ltr">We are in the midst of the fourth industrial revolution, and manufacturers are looking<br>to digitally transform their processes in order to leverage new technologies such as adap-<br>tive automation, virtual reality and digital twin driven simulation. A key aspect of this<br>revolution compared to previous is the increased availability of data and accessibility of<br>machines throughout the production process enabled by cyber-physical systems (CPS) and<br>IoT. However, the integration of many devices is challenging, requiring significant capital<br>and expertise. This can limit smaller players from benefiting from technological gains as<br>well as stymie research, particularly advanced human-computer-interaction (HCI) investiga-<br>tions which are becoming increasingly relevant.<br>Thus in this thesis we develop a framework for CPS creation and communication that<br>is amenable to the needs of HCI and convergence research. We develop several middleware<br>components to bridge the communication gap of many common fabrication machines and<br>other devices. The middleware translates device specific protocols into a shared language to<br>alleviate the user interface (UI) programs of this responsibility and promote reuse. Addi-<br>tionally, we develop an extension to the glTF model format to leverage this shared protocol<br>to enable the UI to load and interact with an arbitrary number of devices in an intuitive<br>manner at runtime. Finally, we discuss several applications to demonstrate the system’s<br>utility for research.</p>

The Internet of Things (IoT) has the vision to interconnect everything of the physical world and the virtual world. Advanced automated and adaptive connectivity of objects, systems, and services is expected to be achieved under the IoT context, especially in the industrial environment. Industry 4.0 with the goal of intelligent and self-adaptable manufacturing is driven by the IoT. The Object Layer, where real-time and reliable information acquisition from the physical objects carried out, is the basic enabler in the 3-layer industrial IoT system. Such acquisition system features deterministic access, reliable communication with failure resistance mechanism, latency-aware real-time response, deployable structure/protocol, and adaptive performance on various QoS demands. This thesis proposes a reliable RFID communication system for acquisition in the industrial environment. A discrete gateway structure and a contention-free communication protocol are designed to fulfill the unique system requirements. Such gateway structure offers a flexible configuration of readers and RF technologies. It enables a full duplex communication between the objects and the gateway. The designed MF-TDMA protocol can enhance the failure resistance and emergency report mechanism thanks to the separation of control link and data link in the gateway. Specifically, an optional ARQ mechanism, an independent/uniform synchronization and control method, and a slot allocation optimization algorithm are designed besides time-division and frequency-division multiplexing. Protocol implementations for different industrial situations illustrate the system ability for supporting the demands of various QoS. Finally, a 2.4-GHz/UWB hybrid positioning platform is explored based on the introduced RFID system. Taking advantage of the UWB technology, the positioning platform can achieve positioning accuracy from meter level to centimeter level. Hybrid tag prototype and specific communication process based on the MF-TDMA protocol are designed. An SDR UWB reader network, capable of evaluating multiple algorithms, is built to realize accurate positioning with an improved algorithm proposed. / <p>QC 20161109</p>

In the last few years, the popularity of Industrial IoT has grown a lot, and it is expected to have an impact of over 14 trillion USD on the global economy by 2030. One application of Industrial IoT is using data pipelining tools to move raw data from industry machines to data storage, where the data can be processed by analytical instruments to help optimize the industrial operations. This thesis analyzes and evaluates a data pipeline setup for Industrial IoT built with the tool Apache NiFi. A data flow setup was designed in NiFi, which connected an SQL database, a file system, and a Kafka topic to a distributed file system. To evaluate the NiFi data pipeline setup, some tests were conducted to see how the system performed under different workloads. The first test consisted of determining which size to merge a FlowFile into to get the lowest latency, the second test if data from the different data sources should be kept separate or be merged together. The third test was to compare the NiFi setup with an alternative setup, which had a Kafka topic as an intermediary between NiFi and the endpoint. The first test showed that the lowest latency was achieved when merging FlowFiles together into 10 kB files. In the second test, merging together FlowFiles from all three sources gave a lower latency than keeping them separate for larger merging sizes. Finally, it was shown that there was no significant difference between the two test setups.

Internet of Things (IoT) is a revolutionary paradigm approaching both industries and consumers everyday life. It refers to a network of addressable physical objects that contain embedded sensing, communication and actuating technologies, to sense and interact with the environment where being deployed. It can be considered as a modern expression of Mark Weiser's vision of ubiquitous computing where tiny networked computers become part of everyday objects, fusing together the virtual world and the physical word. Recent advances in hardware solutions have led to the emergence of powerful wireless IoT systems that are entirely energy-autonomous. These systems extract energy from their environment and operate intermittently, only as power is available. Battery-less sensors present an opportunity for the pervasive wide-spread of remote sensor deployments that require little maintenance and have low cost. As the number of IoT endpoint grows -- industry forecast trillions of connected smart devices in the next few years -- new challenges to program, manage and maintain such a huge number of connected devices are emerging. Web technologies can significantly ease this process by providing well-known patterns and tools - like cloud computing - for developers and users. However, the existing solutions are often too heavyweight or unfeasible for highly resource-constrained IoT devices. This dissertation presents a comprehensive analysis of two of the biggest problems that the IoT is currently facing: R1) How are we going to provide connectivity to all these devices? R2) How can we improve the quality of service provided by these tiny autonomous motes that rely only on limited energy scavenged from the environment? The first contribution is the study and deployment of a Low-Power Wide-Area-Network as a feasible solution to provide connectivity to all the expected IoT devices to be deployed in the following years. The proposed technology offers a novel communication paradigm to address discrete IoT applications, like long-range (i.e., kilometers) at low-power (i.e., tens of mW). Moreover, results highlight the effectiveness of the technology also in the industrial environment thanks to the high immunity to external noises. In the second contribution, we focus on smart metering presenting the design of three smart energy meters targeted to different scenarios. The first design presents an innovative, cost-effective smart meter with embedded non-intrusive load monitoring capabilities intended for the domestic sector. This system shows an innovative approach to provide useful feedback to reduce and optimize household energy consumption. We then present a battery-free non-intrusive power meter targeted for low-cost energy monitoring applications that lower both installation cost due to the non-intrusive approach and maintenance costs associated to battery replacement. Finally, we present an energy autonomous smart sensor with load recognition capability that dynamically adapts and reconfigures its processing pipeline to the sensed energy consumption. This enables the sensor to be energy neutral, while still providing power consumption information every 5 minutes. In the third contribution, we focus on the study of low-power visual edge processing and edge machine learning for the IoT. Two different implementations are presented. The first one discusses an energy-neutral IoT device for precision agriculture, while the second one presents a battery-less long-range visual IoT system, both leveraging on deep learning algorithms to avoid unnecessary wireless data communication. We show that there is a clear benefit from implementing a first layer of data processing directly in-situ where the data is acquired, providing a higher quality of service to the implemented application.

The global value creation networks have experienced increased volatility and dynamic behavior in recent years, resulting in an acceleration of a trend already evident in the shortening of product and technology cycles. In addition, the manufacturing industry is demonstrating a trend of allowing customers to make specific adjustments to their products at the time of ordering. Not only do these changes require a high level of flexibility and adaptability from the cyber-physical systems, but also from the employees and the supervisory production planning. As a result, the development of control and monitoring mechanisms becomes more complex. It is also necessary to adjust the production process dynamically if there are unforeseen events (disrupted supply chains, machine breakdowns, or absences of staff) in order to make the most effective and efficient use of the available production resources. In recent years, reinforcement learning (RL) research has gained increasing popularity in strategic planning as a result of its ability to handle uncertainty in dynamic environments in real time. RL has been extended to include multiple agents cooperating on complex tasks as a solution to complex problems. Despite its potential, the real-world application of multi-agent reinforcement learning (MARL) to manufacturing problems, such as flexible job-shop scheduling, has been less frequently approached. The main reason for this is most of the applications in this field are frequently subject to specific requirements as well as confidentiality obligations. Due to this, it is difficult for the research community to obtain access to them, which presents substantial challenges for the implementation of these tools. ...

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 structures. It also expands the literature on IT/OT convergence by taking a broader ecosystem perspective than only IT and OT companies. Our framework provides practical contributions for managers by identifying key challenges and activities and how these relate to each other. Limitations and Future Research – Our study is limited to a single case study on wind power generation in northern Europe. Therefore, future studies are recommended to investigate if our findings apply to other companies, industry sectors, and geographical areas.

As society becomes more digitalized, cyberattacks are increasingly common and severe. Security in the Internet of Things (IoT) is essential, and IoT devices must be updated to patch vulnerabilities. The thesis aims to investigate the question "How can the Software Updates for Internet of Things (SUIT) specification be applied to develop a technology-agnostic and interoperable update architecture for heterogeneous networks of Internet of Things devices?" The thesis project studied the SUIT specifications to gain an understanding of what such an architecture must provide. Five high-level domains were identified and further discussed:1) roles of devices, servers, and operators, 2) key management, 3) device profiles, 4) authorization, and 5) update handling. The architecture was shown to fulfill the requirements SUIT imposes on the architecture and information model, while being flexible and extensible. A prototype was developed in the Contiki-NG operating system to evaluate the feasibility of the architecture. The thesis found that applying the proposed architecture to constrained systems is feasible and would enable updates in heterogeneous IoT networks. / I takt med att samhället blir digitaliserat blir digitala attacker vanligare och får ökade konsekvenser. Säkerhet inom Internet of Things (IoT) är kritiskt och IoT-enheter måste kunna uppdateras för att laga sårbarheter. Denna uppsats ämnar att undersöka frågan "Hur kan Software Updates for Internet of Things (SUIT)-specifikationen appliceras för att utveckla en teknologiskt agnostisk och kompatibel uppdateringsarkitektur för heterogena nätverk av Internet of Things-enheter?"Uppsatsen studerade SUIT-specifikationen för att förstå vad en sådan arkitektur måste erbjuda. Fem abstrakta domänområden identifierades och diskuterades: 1) roller för enheter, uppdateringsservrar, och operatörer, 2) nyckelhantering, 3) enhetsprofiler, 4) auktorisering, och 5) lokal uppdateringshantering. Arkitekturen visades uppfylla de krav SUIT ställer på en arkitektur och informationsmodell samt var flexibel och kunde utökas. En prototyp utvecklades i Contiki-NG operativsystemet för att utvärdera genomförbarheten hos arkitekturen. Uppsatsen fann att det är rimligt att applicera den föreslagna arkitekturen på resursbegränsade enheter, vilket skulle möjliggöra uppdateringar för heterogena IoT-nätverk.