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- Delsing, Jerker, 1957-
(författare)
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Smart City Solution Engineering
- 2021
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Ingår i: SMART CITIES. - : MDPI. - 2624-6511. ; 4:2, s. 643-661
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Tidskriftsartikel (refereegranskat)abstract
- Many smart city applications have been proposed and demonstrated over the years; however, moving to large-scale deployment is still scarce. A contributing factor to this scarcity is the lack of well-established engineering methodologies for large-scale smart city applications. This paper addresses engineering methodologies and tools for large-scale smart city application engineering, implementation, deployment, and evolution. A model-based engineering approach based on IoT, SOA, and SysML is proposed and applied to a smart streetlight application. Engineering considerations for streetlight area enlargement and updated technology generations with additional capabilities are discussed. The proposed model-based engineering approach provides considerable scaling simplifications and opportunities for considerable savings on engineering costs. The model-based engineering approach also provides good documentation that enables technology evolution specifications that support both maintenance and emerging behaviours.
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| 2. |
- Englund, Cristofer, 1977-, et al.
(författare)
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AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control
- 2021
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Ingår i: Smart Cities. - Basel : MDPI. - 2624-6511. ; 4:2, s. 783-802
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Tidskriftsartikel (refereegranskat)abstract
- Smart Cities and Communities (SCC) constitute a new paradigm in urban development. SCC ideates on a data-centered society aiming at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with internet of things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, Smart Traffic Control and Driver Modelling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, the availability of data from different stakeholders is need. Further, though AI technologies provide accurate predictions and classifications there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability, while the models have difficulties explaining how they come to a certain conclusions it is difficult for humans to trust it. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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| 3. |
- Osman, Ahmed M. Shahat, et al.
(författare)
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Smart Cities and Big Data Analytics: A Data-Driven Decision-Making Use Case
- 2021
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Ingår i: Smart Cities. - Basel, Switzerland : MDPI. - 2624-6511 .- 2624-6511. ; 4:1, s. 286-313
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Tidskriftsartikel (refereegranskat)abstract
- Interest in smart cities (SCs) and big data analytics (BDA) has increased in recent years, revealing the bond between the two fields. An SC is characterized as a complex system of systems involving various stakeholders, from planners to citizens. Within the context of SCs, BDA offers potential as a data-driven decision-making enabler. Although there are abundant articles in the literature addressing BDA as a decision-making enabler in SCs, mainstream research addressing BDA and SCs focuses on either the technical aspects or smartening specific SC domains. A small fraction of these articles addresses the proposition of developing domain-independent BDA frameworks. This paper aims to answer the following research question: how can BDA be used as a data-driven decision-making enabler in SCs? Answering this requires us to also address the traits of domain-independent BDA frameworks in the SC context and the practical considerations in implementing a BDA framework for SCs' decision-making. This paper's main contribution is providing influential design considerations for BDA frameworks based on empirical foundations. These foundations are concluded through a use case of applying a BDA framework in an SC's healthcare setting. The results reveal the ability of the BDA framework to support data-driven decision making in an SC.
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