Edge Intelligence (EI) is an emerging computing and communication paradigm that enables Artificial Intelligence (AI) functionality at the network edge. In this article, we highlight EI as an emerging and important field of research, discuss the state of research, analyze research gaps and highlight important research challenges with the objective of serving as a catalyst for research and innovation in this emerging area. We take a multidisciplinary view to reflect on the current research in AI, edge computing, and communication technologies, and we analyze how EI reflects on existing research in these fields. We also introduce representative examples of application areas that benefit from, or even demand the use of EI.

Globally, manufacturing ecosystems are facing the challenge of twin transition, i.e., how to utilize digitalization for improving or transforming the sustainability of manufacturing operations. Here, operations refer widely to the upstream of manufacturing, while the entire product lifecycle also covers the downstream and end-of-life operations. Here, sustainability is understood to consider the impact of the product lifecycle at environmental, social, and governance (ESG) levels. In this article, we explore this progress through the digitalization concept of business-to-business data sharing, and through one example of a manufacturing ecosystem in Finland. We discuss the federated data space concept and the international data spaces (IDS) architecture as technological building blocks of twin transition, and report the first results from an industry−research shared-risk project. Semi-structured interviews and a diary-style reporting from an industry−research IDS proof-of-concept (PoC) experiment are presented and analyzed within a design science research method framework. The findings give the first indications that while data sharing is seen as important and increasing in relevance in industry, it is currently challenging for companies to see how an open standard architecture creates value beyond a single limited ecosystem view. We also highlight possible avenues for further research.

We introduce evaluation probes for conducting emic, experiential evaluation of urban technologies "in the wild" without direct researcher presence. We commence with a thorough discussion and analysis of the original cultural probes, used by Gaver, Dunne and Pacenti to gain design inspiration, and their subsequent variations. We develop the concept of evaluation probes through careful re-conceptualization and application of the cultural probes in three successive studies conducted in the wild. We recount and reflect on our use of evaluation probes and discuss their merits and limitations in experiential emic evaluation.

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.

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