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Item type:Publication, Access status: without files , A systematic survey of Internet of Things frameworks for smart city applicationsRecent developments in smart sensing technologies have fostered the wide-spread utilization of smart city applications, which rely on Internet of Things (IoT) frameworks to work efficiently. The terms “smart city” and “IoT framework”, however, have been given several definitions, without consensus. Consequently, definitions of the terms “smart city” and “IoT framework” need to be condensed, consolidating concepts and guidelines of smart cities and IoT frameworks, as will be shown in this study. In addition, a systematic survey of IoT frameworks for smart city applications is presented, summarizing and comparing the technologies and architectures of IoT frameworks for smart city applications. As a result of this study, trends in IoT frameworks for smart city applications and a definition of the term “smart city” are provided. Materializing the findings achieved in this study, an abstract IoT framework concept for smart city applications is proposed. It is expected that the definition of the term “smart city” may be used as a basis for a generally accepted formal definition and that the proposed IoT framework concept may provide a strong foundation for successful IoT framework implementations in the context of smart city applications.Publicationtype: Journal Article98 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Explainable Artificial Intelligence to Advance Structural Health MonitoringIn recent years, structural health monitoring (SHM) applications have significantly been enhanced, driven by advancements in artificial intelligence (AI) and machine learning (ML), a subcategory of AI. Although ML algorithms allow detecting patterns and features in sensor data that would otherwise remain undetected, the generally opaque inner processes and black-box character of ML algorithms are limiting the application of ML to SHM. Incomprehensible decision-making processes often result in doubts and mistrust in ML algorithms, expressed by engineers and stakeholders. In an attempt to increase trust in ML algorithms, explainable artificial intelligence (XAI) aims to provide explanations of decisions made by black-box ML algorithms. However, there is a lack of XAI approaches that meet all requirements of SHM applications. This chapter provides a review of ML and XAI approaches relevant to SHM and proposes a conceptual XAI framework pertinent to SHM applications. First, ML algorithms relevant to SHM are categorized. Next, XAI approaches, such as transparent models and model-specific explanations, are presented and categorized to identify XAI approaches appropriate for being implemented in SHM applications. Finally, based on the categorization of ML algorithms and the presentation of XAI approaches, the conceptual XAI framework is introduced. It is expected that the proposed conceptual XAI framework will provide a basis for improving ML acceptance and transparency and therefore increase trust in ML algorithms implemented in SHM applications.Publicationtype: Journal Article151