Socio-Environmental Sensor Networks for Community Sensing
We are living in a time of extraordinary urban changes. Research has shown that cities can bring economic wealth and improved quality of life by fostering diverse economies, dense knowledge exchanges, and efficient district performance. However, it is also true that scientists have associated cities...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/145088 |
| _version_ | 1826188763787689984 |
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| author | Rico Medina, Andrés |
| author2 | Larson, Kent |
| author_facet | Larson, Kent Rico Medina, Andrés |
| author_sort | Rico Medina, Andrés |
| collection | MIT |
| description | We are living in a time of extraordinary urban changes. Research has shown that cities can bring economic wealth and improved quality of life by fostering diverse economies, dense knowledge exchanges, and efficient district performance. However, it is also true that scientists have associated cities with crowding, segregation, environmental degradation, and other significant challenges. Sensors, Data, and Artificial Intelligence can lead to a better understanding of urban settings and their challenges by providing opportunities for insight into their social and environmental performance. Many of these sensing initiatives are carried out in a top-down fashion. Top-down sensing generates datasets that capture large-scale patterns across populations. This data could be complemented by bottom-up community-based approaches that capture more granular information emerging from the specific needs of individuals. Through a series of case studies, this thesis illustrates how to use a variety of community-scale sensor and machine intelligence implementations to measure aspects of socio-environmental cycles that emerge in different urban and environmental contexts. These studies explore possibilities for providing communities with access to localized information about socio-environmental systems that, if fully deployed, could enable bottom-up transformation of collective behavior, policies, and infrastructure to address the great challenges that future cities will face. |
| first_indexed | 2024-09-23T08:04:40Z |
| format | Thesis |
| id | mit-1721.1/145088 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T08:04:40Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1450882022-08-30T04:07:57Z Socio-Environmental Sensor Networks for Community Sensing Rico Medina, Andrés Larson, Kent Program in Media Arts and Sciences (Massachusetts Institute of Technology) We are living in a time of extraordinary urban changes. Research has shown that cities can bring economic wealth and improved quality of life by fostering diverse economies, dense knowledge exchanges, and efficient district performance. However, it is also true that scientists have associated cities with crowding, segregation, environmental degradation, and other significant challenges. Sensors, Data, and Artificial Intelligence can lead to a better understanding of urban settings and their challenges by providing opportunities for insight into their social and environmental performance. Many of these sensing initiatives are carried out in a top-down fashion. Top-down sensing generates datasets that capture large-scale patterns across populations. This data could be complemented by bottom-up community-based approaches that capture more granular information emerging from the specific needs of individuals. Through a series of case studies, this thesis illustrates how to use a variety of community-scale sensor and machine intelligence implementations to measure aspects of socio-environmental cycles that emerge in different urban and environmental contexts. These studies explore possibilities for providing communities with access to localized information about socio-environmental systems that, if fully deployed, could enable bottom-up transformation of collective behavior, policies, and infrastructure to address the great challenges that future cities will face. S.M. 2022-08-29T16:32:00Z 2022-08-29T16:32:00Z 2022-05 2022-06-07T17:53:58.484Z Thesis https://hdl.handle.net/1721.1/145088 0000-0002-8085-9354 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Rico Medina, Andrés Socio-Environmental Sensor Networks for Community Sensing |
| title | Socio-Environmental Sensor Networks for Community Sensing |
| title_full | Socio-Environmental Sensor Networks for Community Sensing |
| title_fullStr | Socio-Environmental Sensor Networks for Community Sensing |
| title_full_unstemmed | Socio-Environmental Sensor Networks for Community Sensing |
| title_short | Socio-Environmental Sensor Networks for Community Sensing |
| title_sort | socio environmental sensor networks for community sensing |
| url | https://hdl.handle.net/1721.1/145088 |
| work_keys_str_mv | AT ricomedinaandres socioenvironmentalsensornetworksforcommunitysensing |