AQuaMoHo: Localized Low-Cost Outdoor Air Quality Sensing over a Thermo-Hygrometer
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929686760652800 |
|---|---|
| author | Pramanik, Prithviraj Karmakar, Prasenjit Sharma, Praveen Kumar Chatterjee, Soumyajit Roy, Abhijit Mandal, Santanu Nandi, Subrata Chakraborty, Sandip Saha, Mousumi Saha, Sujoy |
| author_facet | Pramanik, Prithviraj Karmakar, Prasenjit Sharma, Praveen Kumar Chatterjee, Soumyajit Roy, Abhijit Mandal, Santanu Nandi, Subrata Chakraborty, Sandip Saha, Mousumi Saha, Sujoy |
| contents | Efficient air quality sensing serves as one of the essential services provided in any recent smart city. Mostly facilitated by sparsely deployed Air Quality Monitoring Stations (AQMSs) that are difficult to install and maintain, the overall spatial variation heavily impacts air quality monitoring for locations far enough from these pre-deployed public infrastructures. To mitigate this, we in this paper propose a framework named AQuaMoHo that can annotate data obtained from a low-cost thermo-hygrometer (as the sole physical sensing device) with the AQI labels, with the help of additional publicly crawled Spatio-temporal information of that locality. At its core, AQuaMoHo exploits the temporal patterns from a set of readily available spatial features using an LSTM-based model and further enhances the overall quality of the annotation using temporal attention. From a thorough study of two different cities, we observe that AQuaMoHo can significantly help annotate the air quality data on a personal scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_11484 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | AQuaMoHo: Localized Low-Cost Outdoor Air Quality Sensing over a Thermo-Hygrometer Pramanik, Prithviraj Karmakar, Prasenjit Sharma, Praveen Kumar Chatterjee, Soumyajit Roy, Abhijit Mandal, Santanu Nandi, Subrata Chakraborty, Sandip Saha, Mousumi Saha, Sujoy Computers and Society Human-Computer Interaction Machine Learning Efficient air quality sensing serves as one of the essential services provided in any recent smart city. Mostly facilitated by sparsely deployed Air Quality Monitoring Stations (AQMSs) that are difficult to install and maintain, the overall spatial variation heavily impacts air quality monitoring for locations far enough from these pre-deployed public infrastructures. To mitigate this, we in this paper propose a framework named AQuaMoHo that can annotate data obtained from a low-cost thermo-hygrometer (as the sole physical sensing device) with the AQI labels, with the help of additional publicly crawled Spatio-temporal information of that locality. At its core, AQuaMoHo exploits the temporal patterns from a set of readily available spatial features using an LSTM-based model and further enhances the overall quality of the annotation using temporal attention. From a thorough study of two different cities, we observe that AQuaMoHo can significantly help annotate the air quality data on a personal scale. |
| title | AQuaMoHo: Localized Low-Cost Outdoor Air Quality Sensing over a Thermo-Hygrometer |
| topic | Computers and Society Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2204.11484 |