Indoor Positioning and Indoor Navigation (IPIN) research still faces important challenges regarding transparency, reproducibility, and long-term reusability of scientific results. Although Open Science practices are gradually gaining momentum in the field, the sharing of Open Research Data (ORD) and Open Code remains relatively uncommon, limiting the reproducibility and comparability of published works.
The ROAD-TRIPIN project aims to strengthen the integration of Open Research Data and Open Code into Indoor Positioning research through a community-driven and institutional approach. Building upon previous efforts and inspired by similar initiatives in other research communities, such as the AGILE conference series, the project develops guidelines, supporting documentation, and practical tools to facilitate the adoption of reproducible and transparent research practices in the field, in a formal context.
A major objective of ROAD-TRIPIN is to support the integration of the provision of Open Research Data and Open Code into the review process of the IPIN conference, nurturing cultural change and stimulating the broader adoption of reproducible research practices within the community. In parallel, the project develops tools to improve the discoverability and usability of existing Open Research Data resources for Indoor Positioning research.
ROAD-TRIPIN follows a strong team-science and community-engagement approach, involving collaboration with domain experts, workshops, dissemination activities, and iterative feedback from the research community to ensure broad adoption and long-term impact.
The ROAD-TRIPIN project is funded by the Open Science II programme of swissuniversities. Moreover, HES-SO has also supported the project, guaranteeing the needed matching funds.
Results at a glance
Within ROAD-TRIPIN, the project activities managed to introduce a new component in the evaluation process of the IPIN conference 2026, namely the Reproducibility Review process, an optional post-acceptance process that offers authors recognition for how their work facilitates reproducibility, while also providing constructive feedback to further strengthen this effort.
The review process validates which aspects of the Reproducibility Guidelines (developed within ROAD-TRIPIN) are addressed by the submitted work, encouraging authors to provide Open Research Data, Open Code, and supporting material accompanying their publications.
The Principal Investigator of ROAD-TRIPIN, Grigorios Anagnostopoulos, serves as Reproducibility Chair ... Read more
The Dream FAIRer project addresses the growing need for reproducible, reusable, and FAIR-compliant research software in localization systems research. Building upon previous work on the ProxyFAUG data augmentation method for fingerprint-based localization, the project focuses on improving the alignment of research software and associated datasets with the FAIR and FAIR4RS principles. The project introduces improvements in reproducibility, interoperability, and long-term usability through explicit environment specification, structured versioning, temporal dataset splitting strategies aligned with community practices, clearer documentation of input and execution workflows, and repository consolidation. By providing openly accessible and reproducible research artifacts, Dream FAIRer contributes to more transparent and evidence-based evaluation practices in localization research while supporting broader adoption of Open Research Data and FAIR research software principles.
The Dream FAIRer project was funded by the HES-SO Open Research Data programme dedicated to software and source code (2025 call).
Results at a glance
Within Dream FAIRer, the project activities resulted in the FAIRification and consolidation of the ProxyFAUG research artifacts into a fully documented and reproducible open repository. The project introduced a reproducible Conda environment specification, clarified dependency and version management, documented the expected input structure and workflow of the ProxyFAUG method, and implemented a temporal train/validation/test data splitting strategy aligned with domain-relevant evaluation conventions. The project also provides a reproducible open implementation of a rule-based fingerprint augmentation baseline for localization systems research, enabling transparent and evidence-based comparison of future methods under FAIR-compliant conditions. The resulting repository improves interoperability, reproducibility, and long-term reusability of the research outputs while contributing to broader Open Research Data and FAIR4RS adoption in the Indoor Positioning research community.
Read more
The HYPER-AI project aims to revolutionize the field of distributed computing by addressing the complexities of integrating Internet of Things (IoT), Edge, and Cloud computing domains. It focuses on the creation of smart virtual computing nodes, optimizing data-processing applications across a distributed network to enhance performance and efficiency. HYPER-AI leverages autonomous, self-organizing computing swarms, enabling dynamic connectivity and cooperation among diverse computing resources. This approach facilitates the seamless integration of resources across all levels of the computing continuum, from edge devices to cloud infrastructure, through innovative semantic representation and orchestration techniques.
The project envisions a new era of computing ecosystems that are adaptable, efficient, and capable of supporting highly demanding applications in various domains such as healthcare and energy, thus contributing significantly to the advancement of digital technologies and sustainable development goals.
The DMML group from HES-SO participates in the project, to provide Reinforcement learning expertise. More particularly, our group will focus on Hierarchical Multi-Agent Deep Reinforcement Learning for Data Management.
Read more
CoORDinance - Shaping A Code of Conduct Concerning the ORD of the Indoor Positioning Research Field
The CoORDinance project aims to develop a comprehensive Code of Conduct for Open Research Data (ORD) in the Indoor Positioning (IP) field, addressing the current lack of standardized ORD practices and its fragmented ORD landscape. CoORDinance builds on the findings of the previous CoORDinates project, which performed a systematic study of the ORD landscape of the field, identified significant challenges of the IP community in terms of its ORD practices, and elaborated on lessons learned, on shortcomings, and identified best practices that emerged through its systematic study. CoORDinance aims to capitalize on these solid foundations to propose a clear Code of Conduct, in line with the Swiss National ORD Strategy and the FAIR principles. This initiative will involve collaborating with leading experts to ensure the guidelines are practical, comprehensive, and widely accepted. By promoting principles of Open Science, the project seeks to promote rigorous, transparent and reproducible (RTR) research practices in IP research, ultimately fostering a culture of collaboration and rigor, assisting the advancement of the field.
CoORDinance will integrate the lessons learned from CoORDinates project, to develop a concise and unambiguous Code of Conduct that will facilitate the motivated researcher of the field to engage with ORD, responsibly and adequately. CoORDinance aims to focus deeper on ethical considerations and scientific best practices, ensuring that the guidelines not only cover practical and technical aspects but also emphasize the ethical responsibilities of data providers and users. To achieve this, we will conduct a thorough review of existing codes of conduct and guidelines of other fields to inform our approach, ensuring that the Code of Conduct we develop is grounded in proven best practices.
The project is funded by the Swiss Academies of Arts and Sciences.
Read more
CoORDinates – Towards a Consensus-based ORD Standard for the Indoor Positioning Community
The community of indoor positioning research has identified the need for a paradigm shift towards more reproducible and open research. Over the last years, a slowly increasing tendency of openly sharing data and code has been observed in field, facilitating the establishment of baselines and benchmarks, assisting the reproducibility, verifiability, and comparability of scientific results. Nevertheless, accompanying research results with ORD still constitutes the exception rather than the rule in publications of the field. Moreover, since there exist no relevant standards or suggested guidelines, important aspects regarding the data format, the metadata and the overall documentation are typically decided individually by the authors. The data that might accompany scientific publications of the field usually do not undergo peer review as the published manuscripts does, and therefore the extent to which those data are well documented and reusable in practice relies on the rigour and the motivation of the authors.
In this project, we aim at exploring the existing landscape of ORD within the Indoor Positioning community, specifying guidelines leveraging experiences of existing ORD practices of other fields, moving towards a community consensus on the ORD practices that the Indoor Positioning community can adopt. To achieve these goals, we will engage active researchers and other stakeholders with track record on ORD sharing, planning research synergies which will culminate in a public event of this community. The outcome of the project will be a clear step towards a consensus-based ORD standard for the Indoor Positioning community.
The CoORDinates project has been funded by the Swiss Open Research Data Grants (CHORD) of SwissUniversities, under the Track A: Explore Projects funding tool. Moreover, HES-SO has also supported the project, guaranteeing the needed matching funds.
Results at a glance:
In the work "Evaluating Open Science Practices in Indoor Positioning and Indoor Navigation Research", we performed a comprehensive review of Open Science practices followed in recent publications in the field, analyzing all reference papers from the 2022 and 2023 ... Read more