Modeling Real-Life Urban Sensor Networks Based on Open Data
Epidemics and pandemics dramatically affect mobility trends around the world, which we have witnessed recently and expect more of in the future. A global energy crisis is looming ahead on the horizon and will redefine the transportation and energy usage patterns, in particular in large cities and me...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/23/9264 |
| _version_ | 1797462150997868544 |
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| author | Bartosz Musznicki Maciej Piechowiak Piotr Zwierzykowski |
| author_facet | Bartosz Musznicki Maciej Piechowiak Piotr Zwierzykowski |
| author_sort | Bartosz Musznicki |
| collection | DOAJ |
| description | Epidemics and pandemics dramatically affect mobility trends around the world, which we have witnessed recently and expect more of in the future. A global energy crisis is looming ahead on the horizon and will redefine the transportation and energy usage patterns, in particular in large cities and metropolitan areas. As the trend continues to expand, the need to efficiently monitor and manage smart city infrastructure, public transportation, service vehicles, and commercial fleets has become of higher importance. This, in turn, requires new methods for dissemination, collection, and processing of data from massive number of already deployed sensing devices. In order to transmit these data efficiently, it is necessary to optimize the connection structure in wireless networks. Emerging open access to real data from different types of networked and sensing devices should be leveraged. It enables construction of models based on frequently updated real data rather than synthetic models or test environments. Hence, the main objective of this article is to introduce the concept of network modeling based on publicly available geographic location data of heterogeneous nodes and to promote the use of real-life diverse open data sources as the basis of novel research related to urban sensor networks. The feasibility of designed modeling architecture is discussed and proved with numerous examples of modeled spatial and spatiotemporal graphs, which are essential in opportunistic routing-related studies using the methods which rely on graph theory. This approach has not been considered before in similar studies and in the literature. |
| first_indexed | 2024-03-09T17:32:25Z |
| format | Article |
| id | doaj.art-eebf2d9192884b8e83e3075e9a62d005 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T17:32:25Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-eebf2d9192884b8e83e3075e9a62d0052023-11-24T12:11:16ZengMDPI AGSensors1424-82202022-11-012223926410.3390/s22239264Modeling Real-Life Urban Sensor Networks Based on Open DataBartosz Musznicki0Maciej Piechowiak1Piotr Zwierzykowski2Faculty of Computing and Telecommunications, Poznań University of Technology, 60-965 Poznań, PolandInstitute of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, PolandFaculty of Computing and Telecommunications, Poznań University of Technology, 60-965 Poznań, PolandEpidemics and pandemics dramatically affect mobility trends around the world, which we have witnessed recently and expect more of in the future. A global energy crisis is looming ahead on the horizon and will redefine the transportation and energy usage patterns, in particular in large cities and metropolitan areas. As the trend continues to expand, the need to efficiently monitor and manage smart city infrastructure, public transportation, service vehicles, and commercial fleets has become of higher importance. This, in turn, requires new methods for dissemination, collection, and processing of data from massive number of already deployed sensing devices. In order to transmit these data efficiently, it is necessary to optimize the connection structure in wireless networks. Emerging open access to real data from different types of networked and sensing devices should be leveraged. It enables construction of models based on frequently updated real data rather than synthetic models or test environments. Hence, the main objective of this article is to introduce the concept of network modeling based on publicly available geographic location data of heterogeneous nodes and to promote the use of real-life diverse open data sources as the basis of novel research related to urban sensor networks. The feasibility of designed modeling architecture is discussed and proved with numerous examples of modeled spatial and spatiotemporal graphs, which are essential in opportunistic routing-related studies using the methods which rely on graph theory. This approach has not been considered before in similar studies and in the literature.https://www.mdpi.com/1424-8220/22/23/9264urban sensor networksopen dataopportunistic routinggraph modeling |
| spellingShingle | Bartosz Musznicki Maciej Piechowiak Piotr Zwierzykowski Modeling Real-Life Urban Sensor Networks Based on Open Data Sensors urban sensor networks open data opportunistic routing graph modeling |
| title | Modeling Real-Life Urban Sensor Networks Based on Open Data |
| title_full | Modeling Real-Life Urban Sensor Networks Based on Open Data |
| title_fullStr | Modeling Real-Life Urban Sensor Networks Based on Open Data |
| title_full_unstemmed | Modeling Real-Life Urban Sensor Networks Based on Open Data |
| title_short | Modeling Real-Life Urban Sensor Networks Based on Open Data |
| title_sort | modeling real life urban sensor networks based on open data |
| topic | urban sensor networks open data opportunistic routing graph modeling |
| url | https://www.mdpi.com/1424-8220/22/23/9264 |
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