Academic Work
Here is a list of my academic publications. You can also check out my Google Scholar profile for a Google indexed list of my publications.
HM-Req: A Framework for Embedding Values within CPS Human Monitoring Requirements
Monitoring humans, for example, their movement or location, is essential for safe and efficient human-machine collaboration in Cyber-Physical Systems (CPS). This information allows CPS to ensure safety properties, adapt their behaviour dynamically, and coordinate with humans. To ensure that the design of a CPS respects ethical principles and the privacy of its stakeholders, system requirements, particularly those related to human monitoring, must reflect the human values of all involved stakeholders. However, human values are often underrepresented in Software Engineering -- particularly during requirements elicitation and system design, crucial phases when introducing ethically critical functionality. Stakeholder values are often implicit and conflicting, yet rarely systematically captured. Furthermore, unstructured natural language requirements introduce ambiguity and vagueness, complicating conflict resolution. To address these problems, we propose HM-Req, a novel requirements elicitation framework including a Controlled Natural Language (CNL) for defining human monitoring requirements. These requirements are then augmented with human values from relevant stakeholders and integrated into a Value Dashboard to detect potential conflicts that require further discussion and resolution. Validation results, applying the CNL to different datasets and conducting a survey and expert interview, confirm the CNL's ability to capture diverse human monitoring requirements and demonstrate HM-Req's usefulness for requirements elicitation activities.
Human-Machine Collaboration systems work because they monitor humans. Think of a heavy-tooling industrial follow robot that needs to track a worker's position to avoid collisions. In our paper, we argue that while these monitoring functions are needed for the systems' functionality, we must not forget to think about what this means regarding privacy and ethics of involved stakeholders. Such issues must be considered during the design of systems, including Requirements Engineering. HM-Req is a framework including a Controlled Natural Language (similar to a DSL) for defining "monitoring requirements". In addition, we built a proof-of-concept dashboard that allows relevant stakeholders to assign their respective human values to requirements and detect potential conflicts that require further discussion and resolution.
Transforming Privacy Artifacts into Accessible Reports for Non-Technical Stakeholders
The transition toward Industry 5.0 is reshaping industrial work environments with an emphasis on human-centricity, enabling close collaboration between humans and machines to enhance productivity and flexibility. However, such systems typically require monitoring of human workers and operators, often involving sensitive data, raising significant privacy concerns. As a result, affected workers and unions frequently reject human-machine collaboration features due to a lack of transparency regarding privacy threats and implemented mitigation strategies. To enable early stakeholder involvement, establish trust, and support informed decision-making, privacy implications must be communicated in a way understandable to non-technical stakeholders. Yet, current Requirements Engineering (RE) practices provide limited methodological support for making privacy threats and mitigations accessible to non-technical stakeholders (e.g., individual workers or their representative unions). In this RE@Next paper, we propose a conceptual framework that guides software design from human monitoring-related use cases and requirements to informed decision-making guidance focusing on non-technical stakeholders. Building on principles such as Privacy by Design, the framework leverages Large Language Models (LLMs) to transform technical artifacts into accessible privacy reports. We share initial insights from two industry use cases, evaluate the quality of the generated reports, and outline future research directions toward integrating privacy transparency into RE processes for human-centric industrial systems.
Human-Machine Collaboration and Ethical Considerations in Adaptive Cyber-Physical Systems
Adaptive Cyber-Physical Systems (CPS) are systems that integrate both physical and computational capabilities, which can adjust in response to changing parameters. Furthermore, they increasingly incorporate human-machine collaboration, allowing them to benefit from the individual strengths of humans and machines. Human-Machine Teaming (HMT) represents the most advanced paradigm of human-machine collaboration, envisioning seamless teamwork between humans and machines. However, achieving effective and seamless HMT in adaptive CPS is challenging. While adaptive CPS already benefit from feedback loops such as MAPE-K, there is still a gap in integrating humans into these feedback loops due to different operational cadences of humans and machines. Further, HMT requires constant monitoring of human operators, collecting potentially sensitive information about their actions and behaviour. Respecting the privacy and human values of the actors of the CPS is crucial for the success of human-machine teams. This research addresses these challenges by: (1) exploring and developing methods and processes for integrating HMT into adaptive CPS, focusing on human-machine interaction principles and their incorporation into adaptive feedback loops found in CPS, and (2) creating frameworks for integrating, verifying, and validating ethics and human values throughout the system lifecycle, starting from requirements engineering.
Towards a Value-Complemented Framework for Enabling Human Monitoring in Cyber-Physical Systems
[Context and Motivation]: Cyber-Physical Systems (CPS) have become relevant in a wide variety of different domains, integrating hardware and software, often operating in an emerging and uncertain environment where human actors actively or passively engage with the CPS. To ensure correct and safe operation, and self-adaptation, monitors are used for collecting and analyzing diverse runtime information. [Problem]: However, monitoring humans at runtime, collecting potentially sensitive information about their actions and behavior, comes with significant ramifications that can severely hamper the successful integration of human-machine collaboration. Requirements engineering (RE) activities must integrate diverse human values, including Privacy, Security, and Self-Direction during system design, to avoid involuntary data sharing or misuse. [Principal Ideas]: In this research preview, we focus on the importance of incorporating these aspects in the RE lifecycle of eliciting and creating runtime monitors. [Contribution]: We derived an initial conceptual framework, building on the value taxonomy introduced by Schwartz and human value integrated software engineering by Whittle, further leveraging the concept of value tactics. The goal is to tie functional and non-functional monitoring requirements to human values and establish traceability between values, requirements, and actors. Based on this, we lay out a research roadmap guiding our ongoing work.
FORTE: An Open-Source System for Cost-Effective and Scalable Environmental Monitoring
Forests are an essential part of our biosphere, regulating climate, acting as a sink for greenhouse gases, and providing numerous other ecosystem services. However, they are negatively impacted by climatic stressors such as drought or heat waves. In this paper, we introduce FORTE, an open-source system for environmental monitoring with the aim of understanding how forests react to such stressors. It consists of two key components: (1) a wireless sensor network (WSN) deployed in the forest for data collection, and (2) a Data Infrastructure for data processing, storage, and visualization. The WSN contains a Central Unit capable of transmitting data to the Data Infrastructure via LTE-M and several spatially independent Satellites that collect data over large areas and transmit them wirelessly to the Central Unit. Our prototype deployments show that our solution is cost-effective compared to commercial solutions, energy-efficient with sensor nodes lasting for several months on a single charge, and reliable in terms of data quality. FORTE's flexible architecture makes it suitable for a wide range of environmental monitoring applications beyond forest monitoring. The contributions of this paper are three-fold. First, we describe the high-level requirements necessary for developing an environmental monitoring system. Second, we present an architecture and prototype implementation of the requirements by introducing our FORTE platform and demonstrating its effectiveness through multiple field tests. Lastly, we provide source code, documentation, and hardware design artifacts as part of our open-source repository.
Concepts and Implementation of a Wireless Sensor Network for Forest Environment Monitoring
Forests are a crucial part of the environment, serving as bio-diverse ecosystems that regulate climate, water supply, and provide resources such as timber. However, various stressors like climate change, air pollution, and pests negatively impact forests. To understand how forests react to these stressors, we require long-term, multi-location data on aspects like humidity, temperature, soil water content, and tree circumference increment. In this thesis, we develop an open-source, low-power Wireless Sensor Network (WSN) named FORTE-WSN that can be deployed in the forest to autonomously collect and transmit such data to a centralized server. With FORTE-WSN, we enable researchers and forest managers to better understand the forest ecosystem and make informed decisions in research or forestry based on the collected data. This thesis defines the necessary requirements for developing a forest environment monitoring solution based on a literature review of existing WSNs in forest environment monitoring. Further, we provide a detailed description of our architecture and implementation of our prototype WSN subsystem, which was deployed over a span of three months in Neustift in Tirol. Our prototype deployment showed promising results, with less than 5% packet loss between September 3rd and October 3rd, 2023 while being highly power efficient. Finally, we evaluate our WSN prototype based on our defined requirements.