In modern Industry, I4.0, artificial intelligence technology like Machine Learning (ML) and Deep Learning (DL) are increasingly used to fully realize the digital transformation. And is no news that Sustainability and Sustainable Digitalization are key. To this end, automatic anomaly detection is a concrete area for improvement in production lines, focusing on processes. In this paper, we investigate how to build an optimal Intelligent Defect Detection (IDD) model for furniture manufacturing, by taking the case of kitchen cabinets. We study (ML) Support Vector Machine, K-Neighbour Network, and (DL) YOLO models on different datasets and by analyzing training time, accuracy, precision, recall, F1-score, and robustness to lighting conditions. We contribute with an optimal IDD and a critical discussion. Our conclusions are based on the experiments conducted on the real world industrial manufacturing of kitchen cabinets.

A number of challenging aspects have to be considered, when the Internet of Things (IoT) and Artificial Intelligence (AI) are combined into intelligent IoT systems. A key aspect that demands high attention is trustworthiness. As part of the investigations we conduct in this area in collaboration with partner companies, the need of a holistic view for trustworthiness in Intelligent IoT systems has emerged. To address such need, and to identify suitable support for it, we analyzed existing ISO standards and literature and we found out that they lack a holistic view for trustworthiness in intelligent IoT systems.To bridge this gap, we propose a conceptual model for trustworthiness in intelligent IoT systems that includes stakeholders, systems, and primary concerns, and is built upon existing standards and literature. Our model can support the design, development, operations, evolution of and communication about intelligent IoT systems. We received positive confirmation of the validity of the conceptual model from industrial practitioners working in four companies in the intelligent IoT systems area. Together with our partner companies, we plan to develop and operate approaches leveraging the conceptual model as next step.

The integration of Artificial Intelligence (AI) into Internet of Things (IoT) systems has garneredconsiderable attention for its ability to enhance efficiency, functionality, and decision making.To drive further research and practical applications, it is essential to gain a deeper understandingof the different roles of AI in IoT systems. In this systematic literature review, we analyze103 articles describing Artificial Intelligence of Things (AIoT) systems found in three databases,i.e. Scopus, IEEE Xplore, and Web of Science. For each article, we examined the tasks for whichAI was used, the input and output data, the application domain, the maturity level of the system,the AI methods used, and where the AI components were deployed. As a result, we identified sixgeneral tasks of AI in IoT systems, and thirteen subtasks, the most frequent being prediction,object and event recognition, and operational decision-making. Moreover, we conclude thatmost AI components in IoT systems process numeric data as input and that healthcare isthe most common application domain followed by farming and transportation. Our analysisfurther revealed that most AIoT systems are in early development stages not validated in realenvironments. We also identified that Convolutional Neural Networks is the most frequentlyemployed AI method, with supervised learning being the dominant approach. Additionally, wefound that both AI deployment, either in the cloud or at the edge, are frequent, but that hybriddeployment is not that common. Finally, we identified key gaps in current AIoT research andbased on this, we suggest directions for future research.

IoT systems face significant challenges adapting to user needs, often under-specified and evolving with changing environmental contexts. To address these complexities, users should be able to explore possibilities. At the same time, IoT systems must learn and support users in providing proper services, e.g., to serve novel experiences. The IoT-Together paradigm aims to meet this demand through the Mixed-Initiative Interaction (MII) paradigm that facilitates a collaborative synergy between users and IoT systems, enabling the co-creation of intelligent and adaptive solutions that are precisely aligned with user-defined goals. This work is a realization of IoT-Together by integrating Large Language Models (LLMs) into its architecture. The presented work enables intelligent goal interpretation through a three-pass dialogue framework and dynamic IoT systems generation according to user needs. To demonstrate the efficacy of our methodology, we design and implement the system in the context of a smart city tourism case study. We evaluated the system performance using agent-based simulation and user studies. Results indicate efficient and accurate service identification and high adaptation quality. The empirical evidence indicates that integrating Large Language Models (LLMs) into IoT architectures can significantly enhance the architectural adaptability of the system while ensuring real-world usability.

Given the environmental impact and cost-efficiency challenges of the conventional central District Heating (DH) systems, there is a shift towards hybrid solutions. The demand for small-scale Heat Pumps (HPs), integral components of these systems, has surged due to their electrically driven, cost-effective operation, and potential to meet environmental goals. This paper conducts a systematic literature review by investigating and highlighting hybrid heating solutions and their role in decarbonizing the built environment. It compares and discusses the potential benefits and challenges of various hybrid HP-DH systems against conventional DH-only heating approaches. The study evaluates these systems based on economic, environmental, and energy efficiency aspects, and it explores the use of intelligent and AI-based algorithms. The results indicate that, from an economic perspective, the hybrid approach can potentially offer cost savings over the long term, considering factors such as initial investment and operating expenses. The findings of the reviewed works suggest that in a DH-HP configuration, an operational cost saving between 5% and 27%, and a CO2 reduction of up to 32.3% can be achieved without additional resources. Additionally, the environmental impact analysis indicates a significant decrease in greenhouse gas emissions, aligning with global efforts to mitigate global warming.

With more hybrid heating systems available, there is a need to optimize energy use intelligently from the end-consumer perspective. This paper focuses on a multi-criteria heating system optimization to optimize cost, carbon emission, and comfort level of building occupants. A discrete Multi-Objective Model Predictive Controller (MO-MPC) algorithm is proposed to optimally utilize two heating sources connected to a building, namely district heating (DH) and a building-integrated electrical heat pump (HP). The model is tested on a real-world building case simulated with a gray box building model. The results are compared to a conventional PID controller as well as the MPC scheme, each with a single heating input, and eight different cases are constructed to make this comparison more visible. The results indicate that, using MO-MPC, a cost saving of up to 10% and emission saving of up to 13% can be reached without additional thermal discomfort, while the potential savings on cost and emission with the hybrid system can be up to 25% and 77%, respectively. Further, a sensitivity analysis on price and emission parameters is conducted to investigate the changes in the provided solution.

The Internet of Things (IoT) has transformed our daily life by enabling devices and objects to collect data, communicate, and collaborate to provision novel types of services. Engineering IoT systems is a complex process that should consider a number of quality characteristics to meet the systems’ goals. Towards identifying the key quality characteristics of IoT systems, in this study, we conduct semi-structured interviews with seven companies developing IoT solutions within smart energy, smart healthcare, smart surveillance, and smart buildings application areas. The study used the ISO/IEC 25010 model as a reference and a qualitative research approach, i.e., we conducted semi-structured interviews with ten experts and performed content analysis on the data collected from the interviews. The study findings reveal that the ISO/IEC 25010 model does not include the following key quality characteristics that practitioners consider when engineering IoT systems: trust, privacy, and energy consumption. Additionally, we report about trade-offs between quality characteristics, architectural constraints, and challenges related to the achievement of the identified quality characteristics when engineering IoT systems in practice.

The use of Artificial Intelligence (AI) in Internet of Things (IoT) systems has gained significant attention due to its potential to improve efficiency, functionality and decision-making. To further advance research and practical implementation, it is crucial to better understand the specific roles of AI in IoT systems and identify the key application domains. In this article we aim to identify the different roles of AI in IoT systems and the application domains where AI is used most significantly. We have conducted a systematic mapping study using multiple databases, i.e., Scopus, ACM Digital Library, IEEE Xplore and Wiley Online. Eighty-one relevant survey articles were selected after applying the selection criteria and then analyzed to extract the key information. As a result, six general tasks of AI in IoT systems were identified: pattern recognition, decision support, decision-making and acting, prediction, data management and human interaction. Moreover, 15 subtasks were identified, as well as 13 application domains, where healthcare was the most frequent. We conclude that there are several important tasks that AI can perform in IoT systems, improving efficiency, security and functionality across many important application domains.

The IEEE International Conference on Software Architecture (ICSA) is the premier gathering of practitioners and researchers interested in software architecture, component-based software engineering, and quality aspects of complex software systems. The 21st IEEE International Conference on Software Architecture (ICSA 2024) continued the tradition of a working conference, where attendees met and where software architects were able to explain the challenges they face and try to influence the future of the field. Interactive working sessions were the place where researchers met practitioners to identify opportunities to shape the future of our field.