Ella Peltonen

Heidi Hietala, Nirnaya Tripathi, Prabhash Rathnayake et al.

The automotive industry is shifting from hardware-centric development toward software-defined vehicles (SDVs), where software drives functionality, value creation, and competitive differentiation. Growing software complexity renders firm-centric and proprietary software development models insufficient, prompting a shift toward ecosystem collaboration among OEMs, suppliers, and software firms. Yet, how these SDV ecosystems emerge and operate in response to software-driven development remains insufficiently understood. This study enhances our understanding of SDV ecosystems, outlines their collaborative structures, identifies stakeholders, their roles and authority, and highlights associated challenges and opportunities. This study identifies six levels of collaboration involving twelve stakeholder groups shaping SDV ecosystem transformation. These collaborations are influenced by five dimensions of authority. SDV ecosystems face six core software development challenges alongside six organisational, six industry and market, and four regulatory, legal, and ethical challenges. The literature also highlights five key software development opportunities complemented by six organisational, four industry and market, and two public value and ethical opportunities. SDV ecosystem research is primarily technical, concentrating on architectures and standardisation, while lacking studies on governance and collaborative software business models that reflect regional characteristics and power dynamics. We reposition SDVs as multi-level socio-technical ecosystems where software functions as the core structuring principle but does not alone determine ecosystem success. We develop a multi-level SDV ecosystem model, integrating stakeholders, collaborative structures, and governance across ecosystem levels, and outline directions for future research and practice.

Aaron Yi Ding, Ella Peltonen, Tobias Meuser et al.

Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation, optimization, and deployment of distributed AI/ML pipelines with given quality of experience, trust, security and privacy targets. The Edge AI community investigates novel ML methods for the edge computing environment, spanning multiple sub-fields of computer science, engineering and ICT. The goal is to share an envisioned roadmap that can bring together key actors and enablers to further advance the domain of Edge AI.

Wiebke Toussaint, Alejandra Gomez Ortega, Jered Vroon et al.

Mobile applications and online service providers track our virtual and physical behaviour more actively and with a broader scope than ever before. This has given rise to growing concerns about ethical personal data management. Even though regulation and awareness around data ethics are increasing, end-users are seldom engaged when defining and designing what a future with ethical personal data management should look like. We explore a participatory process that uses design futures, the Future workshop method and design fictions to envision ethical personal data management with end-users and designers. To engage participants effectively, we needed to bridge their differential expertise and make the abstract concepts of data and ethics tangible. By concretely presenting personal data management and control as fictitious entities called Data Daemons, we created a shared understanding of these abstract concepts, and empowered non-expert end-users and designers to become actively engaged in the design process.

Ella Peltonen, Mehdi Bennis, Michele Capobianco et al.

In this white paper we provide a vision for 6G Edge Intelligence. Moving towards 5G and beyond the future 6G networks, intelligent solutions utilizing data-driven machine learning and artificial intelligence become crucial for several real-world applications including but not limited to, more efficient manufacturing, novel personal smart device environments and experiences, urban computing and autonomous traffic settings. We present edge computing along with other 6G enablers as a key component to establish the future 2030 intelligent Internet technologies as shown in this series of 6G White Papers. In this white paper, we focus in the domains of edge computing infrastructure and platforms, data and edge network management, software development for edge, and real-time and distributed training of ML/AI algorithms, along with security, privacy, pricing, and end-user aspects. We discuss the key enablers and challenges and identify the key research questions for the development of the Intelligent Edge services. As a main outcome of this white paper, we envision a transition from Internet of Things to Intelligent Internet of Intelligent Things and provide a roadmap for development of 6G Intelligent Edge.