Dissertation
| Author | Philipp Kisters |
| Tutor | Winfried Lamersdorf |
| Title | A DISTRIBUTED SERVICE PLATFORM FOR MANAGING STREAMING DATA IN SMART CITIES: A Citizen-Centric Approach to Improve Urban Participation |
| Abgabe am | 30.09.2024 |
| Abstract | Smart Cities aim to enhance urban living by improving efficiency, sustainability, and citizen engagement. However, current Smart City models, primarily driven by technology, have struggled to demonstrate substantial improvements in efficiency and sustainability while facing public skepticism regarding data privacy and surveillance. This thesis proposes a shift from technology-driven towards a citizen-centered approach for Smart Cities, leveraging existing citizen-operated sensors to create a more sustainable and inclusive urban environment. This approach enhances participatory rights and fosters active engagement with urban spaces by minimizing the need for new sensor installations and empowering citizens to manage their data. This dissertation addresses the technical and social challenges associated with implementing such a decentralized data space. Key technical challenges include ensuring interoperability between heterogeneous data sources, discovering relevant sensor data, and assessing data quality in a decentralized system. Social challenges focus on maintaining citizen data sovereignty, building trust through transparency, and ensuring that non-technically trained citizens are included. To address these challenges, this work consists of three key contributions. First, it introduces SkABNet, an attribute-based overlay network enabling efficient semantic search of distributed data streams without a central authority, reducing search overhead by up to 90%. Second, a data sovereignty-respecting framework for distributed preprocessing is developed, allowing citizens to control the processing of their collected data before sharing it with remote services. A user study demonstrates that this approach supports decision-making for non-technical users, helping them understand the usage of their provided data. Finally, a data stream categorization method is proposed, which, on the one hand, enables the identification of shared characteristics from individually placed sensors to help services rate the data quality. On the other hand, microclimate events can be identified to tackle local anomalies that current quality control mechanisms might remove. These contributions collectively advance the vision of a more sustainable, inclusive, and citizen-centered Smart City. vii |
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