Novel IoT market solutions and research promise IoT modules that do not require  programming or electrical setup, yet shop floor personnel need to face problem solving  activities to create technical solutions. This paper introduces the Karakuri card deck and  presents a case study composed of four workshop sessions in four manufacturing settings,  where shop floor personnel tested the cards as a means of ideating and presenting  conceptual IoT solutions in the form of diagrams. The results indicate the validity of the  proposed conceptual solutions and suggest prototyping as a next step.

The increasing functionality and complexity of automotive applications requires not only the use of more powerful hardware, e.g., multi-core processors, but also efficient methods and tools to support design decisions. Component-based software engineering proved to be a promising solution for managing software complexity and allowing for reuse. However, there are several challenges inherent in the intersection of resource efficiency and predictability of multi-core processors when it comes to running component-based embedded software. In this paper, we present a software design framework addressing these challenges. The framework includes both mapping of software components onto executable tasks, and the partitioning of the generated task set onto the cores of a multi-core processor. This paper aims at enhancing resource efficiency by optimizing the software design with respect to: 1) the inter-software-components communication cost, 2) the cost of synchronization among dependent transactions of software components, and 3) the interaction of software components with the basic software services. An engine management system, one of the most complex automotive sub-systems, is considered as a use case, and the experimental results show a reduction of up to 11.2% total CPU usage on a quad-core processor, in comparison with the common framework in the literature.

Embedded systems are today often self-sufficient with limited and predefined communication. However, this traditional view of embedded systems is changing through advancements in technologies such as, communication, cloud technologies, and advanced analytics including machine learning. These advancements have increased the benefits of building Systems of Systems (SoS) that can provide a functionality with unique capabilities that none of the included subsystems can accomplish separately. By this gain of functionality the embedded system is evolving towards a “dual use” purpose¹¹In this paper we define dual usage as a control system having two purposes. In other contexts such as politics, diplomacy and export control, the term “dual-use” refers to technology that can be used for both peaceful and military aims, e.g., nuclear power technology., The use is dual in the sense that the system still needs to handle its original task, e.g., control and protect of an asset, and it must provide information for creating the SoS. Larger installations, e.g., industry plants, power systems and generation, have in most cases a long expected life-cycle, some up to 30–40 years without significant updates, compared to analytical functions that evolve and change much faster, i.e., requiring new types of data sets from the subsystems, not know at its first deployment. This difference in development cycles calls for new solutions supporting updates related to new requirements inherent in analytical functions. In this paper, within the context of “dual usage” of systems and subsystems, we analyze the impact on an embedded system, new or legacy, when it is required to provide analytic data with high quality. We compare a reference system, implementing all functions in one CPU core, to three other alternative solutions: a) a multi-core system where we are using a separate core for analytics, b) using a separate analytics CPU and c) analytics functionality located in a separate subsystem. Our conclusion is that the choice of analytics information collection method should to be based on intended usage, along with resulting complexity and cost of updates compared to hardware cost.

Embedded systems are today often self-sufficient systems with limited communication. However, this traditional view of an embedded system is changing rapidly. Embedded systems are nowadays evolving, e.g., an evolution pushed by the increased functional gain introduced with the concept of System of Systems (SoS) that is connecting multiple subsystems to achieve a combined functionality and/or information of a higher value. In such a SoS the subsystems will have to serve a dual purpose in a) the initial purpose that the subsystem was originally designed and deployed for, e.g., control and protection of the physical assets of a critical infrastructure system that could be up and running for 30-40 years, and b) at the same time provide information to a higher-level system for a potential future increase of system functionality as technology matures and/or new opportunities are provided by, e.g., greater analytics capabilities. In this paper, within the context of a “dual purpose use” of a) and b), we bring up three central challenges related to i) information gathering, ii) life-cycle management, and iii) data governance, and we propose directions for solutions to these challenges that need to be evaluated already at design time.

This paper investigates the interplay of cloud computing, fog computing, and Internet of Things (IoT) in control applications targeting the automation industry. In this context, a prototype is developed to explore the use of IoT devices that communicate with a cloud-based controller, i.e., the controller is offloaded to cloud or fog. Several experiments are performed to investigate the consequences of having a cloud server between the end device and the controller. The experiments are performed while considering arbitrary jitter and delays, i.e., they can be smaller than, equal to, or greater than the sampling period. This paper also applies mitigation mechanisms to deal with the delays and jitter that are caused by the networks when the controller is offloaded to the fog or cloud.

Internet of Things and cloud services are one of main enablers in fourth industrial revolution. Real-time industrial systems have high availability requirements of 99.9% to 99.999% whereas architectures built on regional cloud services and IoT do not provide similar guarantees or Service Level Agreement. These differences of QoS and SLA availability between Operational Technology and Information Technology has become a main challenge in adoption of Industrial Internet of Things (IIoT) for real-time applications.This work presents an approach to find end-to-end QoS and availability for an IIoT architecture. Device-to-cloud, cloud-to-cloud and inside-cloud experiments have been performed over eight weeks where each experiment have more then four million QoS measurements. Our availability analysis shows that a remote IoT connected to a less busy cloud region gives higher availability as compared to an IoT device inside a busy cloud region. IIoT and regional cloud services provide good QoS with 99% to 99.9% availability for 1sec soft real-time requirements. In 100ms applications, more efforts are required to achieve higher then 95% availability and design industrial SLA. IIoT applications with 10sec latency like machine learning models can get 99.9% availability with cloud. Availability loss due to communication is almost 1% for 100ms applications. These results also provide requirements and future work of industrial edge computing for IIoT on real-time cloud.

Although scholars and practitioners are actively discussing the potential benefits of introducing Internet of Thing (IoT) in production, IoT is still as an expensive solution in terms of investment and high technological threshold. Manufacturing companies seek a simpler and lower-cost approach to adopting IoT technologies in production, allowing companies to take advantage of the knowledge and innovation capabilities of people close to shop floor operations. This paper introduces the concept of “Karakuri IoT” – simple and low-cost IoT-aided improvements driven by the people close to shop floor operations. A pre-study is conducted to examine the feasibility of the concept. This paper presents the results of the pre-study.

Smart Flow aimed at applying AI within process industry through using machine learning, simulations, industrial IoT and cloud technology, specifically in the case of district heating and the district heating network (DH-Net). The goal was to make a concept evaluation of AI for decision support in a core industrial process. The project has through a successful concept evaluation shown the technological possibilities and the economic potential within the energy utility application. Furthermore, the project has identified many challenges related to industrial use of AI.

Some of the highlights from the project include: • Successful development of AI algorithms that make detailed predictions of consumer and network behaviour. • An operational IIoT and Cloud pilot in the cloud that manages 15 000 meters daily and more than 3000 meters every 15 minutes. • A physical simulation model showing a potential energy saving in a single DH area of up to 4.2 MW. • Several articles in sector specific media, e.g., Fjärrvärmetidningen and Nordiska Projekt, sharing the ideas of the project. • Many event presentations of project results and experiences, including at Internetdagarna 2017, Microsoft and Dagens Industri AI event 2018, and Science and Innovation day 2019. • A film made by Microsoft that shows the concepts of Smart Flows and its application to utilities.

Mälarenergi has identified that many savings can be possible by optimizing the DH-net, and by getting a more detailed knowledge of how the net behaves one can make large improvements and developments. The keywords for Mälarenergi to continue to be successful is to focus on” the right amount of energy at the right time and place with the right quality”. By creating smartness, and tools for a better understanding, Mälarenergi will reach a higher level of decisions making and support for both the operators and data-analysis group. Instead of working with the production of heat such as the total amount produced from the Combined Heat and Power- Plant (CHP-plant), which is the traditional way to make forecast and heat demand, Smart Flow has focused on working with the consumers data. By trying to sum up the consumption from all the customers one can find another approach to know what to produce from the plant. Smart Flow demonstrated that using machine learning one can estimate how much energy is needed from the customers point of view. Moreover, the results show that it is possible to estimate the heat demand from different zones in the DH-net, using the same type of techniques. It was also found out that the time delays for different areas in Västerås are an important key for a better understanding of how the DH-net behaves. Indeed, knowing the time delays in advance, the operators will know when and how to react, to optimize the delivery of heat. A data-based methodology was developed, which can be used to estimate the time delays for different zones in the DH-net