Not all research leads to fruitful results; trying new ways or methods may surpass state of the art, but sometimes the hypothesis is not proven, the improvement is insignificant, or the system fails because of a design error done years ago in previous works. In a systems discipline like pervasive computing, there are many sources of errors, from hardware issues over communication channels to heterogeneous software environments. However, failure to succeed is not a failure to progress. It is essential to create platforms for sharing insights, experiences, and lessons learned when conducting research in pervasive computing so that the same mistakes are not repeated. And sometimes, a problem is a symptom of discovering new research challenges. Based on the collective input of the First International Workshop on Negative Results in Pervasive Computing (PerFail 2022), co-located with the 20th International Conference on Pervasive Computing and Communications (PerCom 2022), this article presents a comprehensive discussion on perspectives on publishing negative results, useful failures, and lessons learned in pervasive computing.@en

As a fast evolving domain that merges edge computing, data analytics and AI/ML, commonly referred as Edge AI, the community of Edge AI is establishing and gradually finds its way to connect with mainstream research communities of distributed systems, IoT, and embedded machine learning. Meanwhile, despite of its well-claimed potential to transform cloud and IoT industry, Edge AI is still a complex subject that faces critical challenges from the trustworthy and sustainable concerns. To shed light on these pressing matters, this paper aims to develop a research agenda for trustworthy and sustainable Edge AI. We clarify the concepts, define the proper scoping and propose a research agenda for Edge AI to be trustworthy and sustainable. To illustrate the research agenda in practice, we highlight two active RD projects: the SPATIAL project on trustworthy Edge AI and the APROPOS project on sustainable computing. The projects serve as concrete use cases to explore the agenda development. Our goal is to equip researchers, engineers, service providers, government and public sectors with a better understanding of the underlying concepts and to raise awareness of emerging directions in trustworthy and sustainable Edge AI. @en