Integrating Artificial Intelligence (AI) with the Industrial Internet of Things (IIoT) has transformed industrial processes, enhancing productivity, quality control, and operational efficiency. However, ensuring the precision and reliability of sensor-generated data remains a critical challenge due to the evolving nature of industrial processes and the limitations of conventional validation methods. Traditional rule-based and supervised learning approaches struggle to adapt to process shifts, drifts, and novel anomalies, making sensor data validation an ongoing issue. This article introduces UDAVA (Unsupervised Learning Approach using Process Mining for Sensor Data Validation in IIoT), a novel AI-driven pipeline designed to automate the identification of reference patterns in sensor data and validate subsequent production cycles by recognizing deviations from expected behaviors. UDAVA employs a multi-stage process that includes preprocessing sensor data, clustering recurring patterns, and assessing deviations. It supports semi-supervised learning by integrating manual labels where available, improving interpretability and accuracy. One of UDAVA’s key strengths lies in its ability to extract features from sensor data rather than relying on raw time series similarity, making it robust against noise and diverse process variations. Additionally, UDAVA integrates process mining techniques—process discovery and conformance checking—to enhance its ability to detect even subtle anomalies and deviations in industrial workflows. We conduct a comprehensive evaluation of UDAVA using three industrial datasets, demonstrating its effectiveness in identifying high-level process behaviors, detecting process shifts and drifts, and ensuring data validation across multiple production cycles. The results highlight UDAVA ’s adaptability across different industrial processes, making it a valuable tool for optimizing operations and ensuring sensor data reliability in IIoT environments.

The growing adoption of digital technologies in agriculture has led to a proliferation of heterogeneous data from sources such as drones, robotic platforms, and IoT sensors.However, the lack of interoperability across these data streams poses major challenges for integration into decision support systems.This paper presents an approach to harmonising such data using NGSI-LD and Smart Data Models, developed within the Norwegian research project SMARAGD.We demonstrate how domain-specific semantic models and linked data principles can be applied to standardise and enrich geospatial and temporal metadata across three key agritech domains: aerial imagery, robotic sensing, and environmental monitoring.The resulting information assets are integrated into a shared, FIWAREcompatible data space, enabling cross-platform visualisation, querying, and reuse.This work contributes to the development of an open, standards-based digital infrastructure for interoperable, data-driven agriculture in Norway and beyond.

Technical Debt (TD) is a critical challenge in software development, leading to increased maintenance costs and reduced software quality over time. While considerable research has focused on identifying and managing TD in Java projects, studies on. NET (C#) projects remain limited. Additionally, existing approaches often rely on a single tool for TD detection, overlooking the benefits of combining multiple tools. In this paper, we analyze the effectiveness of Arcan, CodeScene, Designite, and DV8 on four industrial C#. NET 8 software products to address these research gaps. To validate and enrich our findings, we conducted online seminars and interviews with developers, architects, and managers involved in these projects, gathering practitioner insights on TD relevance and tool effectiveness. By leveraging complementary tools and practitioner feedback, we uncover different types of TD, including code-level, design, architectural, and knowledge debt. Our findings highlight each tool's strengths and limitations and demonstrate how integrating their outputs with expert input provides a more comprehensive and actionable TD assessment. Based on these insights, we propose a conceptual model for prioritizing and managing TD, offering guidance for practitioners.

Abstract Workers, especially those involved in jobs requiring extended standing or repetitive movements, often face significant health challenges due to Musculoskeletal Disorders (MSDs). To mitigate MSD risks, enhancing workplace ergonomics is vital, which includes forecasting long-term employee postures, educating workers about related occupational health risks, and offering relevant recommendations. However, research gaps remain, such as the lack of a sustainable AI/ML pipeline that combines sensor-based, uncertainty-aware posture prediction with large language models for natural language communication of occupational health risks and recommendations. We introduce ERG-AI, a machine learning pipeline designed to predict extended worker postures using data from multiple wearable sensors. Alongside providing posture prediction and uncertainty estimates, ERG-AI also provides personalized health risk assessments and recommendations by generating prompts based on its performance and prompting Large Language Model (LLM) APIs, like GPT-4, to obtain user-friendly output. We used the Digital Worker Goldicare dataset to assess ERG-AI, which includes data from 114 home care workers who wore five tri-axial accelerometers in various bodily positions for a cumulative 2913 hours. The evaluation focused on the quality of posture prediction under uncertainty, energy consumption and carbon footprint of ERG-AI and the effectiveness of personalized recommendations rendered in easy-to-understand language.

The Industrial Internet of Things (IIoT) leverages thousands of interconnected sensors and computing devices to monitor and control large and complex industrial processes. Machine learning (ML) applications in IIoT use data acquired from multiple sensors to perform tasks such as predictive maintenance. While remembering useful learning from the past, these applications need to adapt learning for evolving sensor data stemming from changes in industrial processes and environmental conditions. This paper presents a continual learning pipeline to learn from the evolving data while replaying selected parts of the old data. The pipeline is configured to produce ML experiences (e.g., training a baseline neural network model), improve the baseline model with the new data while replaying part of the old data, and infer/predict using a specific model version given a stream of IIoT sensor data. We have evaluated our approach from an AI Engineering perspective using three industrial case studies, i.e., predicting tool wear, remaining useful lifetime, and anomalies from sensor data acquired from CNC machining and broaching operations. Our results show that configuring experiences for replay-driven continual learning allows dynamic maintenance of ML performance on evolving data while minimizing the excessive accumulation of legacy sensor data.

Small and Medium Enterprises (SMEs) are increasingly exposed to cyber risks. Some of the main reasons include budget constraints, the employees’ lack of cybersecurity awareness, cross-sectoral cyber risks, lack of security practices at organizational level, and so on. To equip SMEs with appropriate tools and guidelines that help mitigate their exposure to cyber risk, we must better understand the SMEs’ context and their needs. Thus, the contribution of this paper is a survey based on responses collected from 141 SMEs based in the UK, where the objective is to obtain information to better understand their level of cybersecurity awareness and practices they apply to protect against cyber risks. Our results indicate that although SMEs do apply some basic cybersecurity measures to mitigate cyber risks, there is a general lack of cybersecurity awareness and lack of processes and tools to improve cybersecurity practices. Our findings provide to the cybersecurity community a better understanding of the SME context in terms of cybersecurity awareness and cybersecurity practices, and may be used as a foundation to further develop appropriate tools and processes to strengthen the cybersecurity of SMEs.

Manufacturing has enabled the mechanized mass production of the same (or similar) products by replacing craftsmen with assembly lines of machines. The quality of each product in an assembly line greatly hinges on continual observation and error compensation during machining using sensors that measure quantities such as position and torque of a cutting tool and vibrations due to possible imperfections in the cutting tool and raw material. Patterns observed in sensor data from a (near-)optimal production cycle should ideally recur in subsequent production cycles with minimal deviation. Manually labeling and comparing such patterns is an insurmountable task due to the massive amount of streaming data that can be generated from a production process. We present UDAVA, an unsupervised machine learning pipeline that automatically discovers process behavior patterns in sensor data for a reference production cycle. UDAVA performs clustering of reduced dimensionality summary statistics of raw sensor data to enable high-speed clustering of dense time-series data. It deploys the model as a service to verify batch data from subsequent production cycles to detect recurring behavior patterns and quantify deviation from the reference behavior. We have evaluated UDAVA from an AI Engineering perspective using two industrial case studies.