TY - GEN
T1 - InsideOut
T2 - 17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2020
AU - Devarakonda, Srinivas
AU - Chittaranjan, Senthil
AU - Kwak, Daehan
AU - Nath, Badri
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/12/7
Y1 - 2020/12/7
N2 - The current pollution measurement methodology is coarse-grained where the pollution measurements are spatiotemporally few and far in-between. Our vision is to provide broadly accessible, fine-grained pollution information to a variety of end-users, and in turn, allow them to make better informed decisions using a new, more accurate information stream. To this end, this study proposes a new neural network model to estimate Carbon Monoxide (CO) concentrations outside vehicle from crowd-sourced CO measurements inside vehicles measured using mobile devices (dosimeters). End-users can benefit from the fine-grained pollution information generated by this prediction model along with data from direct measurements. A neural network is used to model the dynamic relationship between the CO measurements inside and outside a moving vehicle. The resulting neural network model is then used to predict outside CO concentrations from CO measurements inside vehicles. Mobile CO dosimeters were used inside and outside vehicles to collect measurements used in training a neural network based regression model. For this regression task, a new neural network architecture was designed using Convolutional layers and Gated Recurrent Unit (GRU) layers. The results show that outside CO concentrations can be estimated from inside vehicle CO measurements with high accuracy. The proposed neural network model provides a promising new and novel source of fine-grained pollution information along with direct measurement streams.
AB - The current pollution measurement methodology is coarse-grained where the pollution measurements are spatiotemporally few and far in-between. Our vision is to provide broadly accessible, fine-grained pollution information to a variety of end-users, and in turn, allow them to make better informed decisions using a new, more accurate information stream. To this end, this study proposes a new neural network model to estimate Carbon Monoxide (CO) concentrations outside vehicle from crowd-sourced CO measurements inside vehicles measured using mobile devices (dosimeters). End-users can benefit from the fine-grained pollution information generated by this prediction model along with data from direct measurements. A neural network is used to model the dynamic relationship between the CO measurements inside and outside a moving vehicle. The resulting neural network model is then used to predict outside CO concentrations from CO measurements inside vehicles. Mobile CO dosimeters were used inside and outside vehicles to collect measurements used in training a neural network based regression model. For this regression task, a new neural network architecture was designed using Convolutional layers and Gated Recurrent Unit (GRU) layers. The results show that outside CO concentrations can be estimated from inside vehicle CO measurements with high accuracy. The proposed neural network model provides a promising new and novel source of fine-grained pollution information along with direct measurement streams.
KW - Crowd-sourced Data Collection
KW - Internet of Things
KW - Mobile Pollution Sensing
KW - Neural Networks
UR - http://www.scopus.com/inward/record.url?scp=85112723063&partnerID=8YFLogxK
U2 - 10.1145/3448891.3448942
DO - 10.1145/3448891.3448942
M3 - Conference contribution
AN - SCOPUS:85112723063
T3 - ACM International Conference Proceeding Series
SP - 206
EP - 214
BT - Proceedings of the 17th EAI International Conference on Mobile and Ubiquitous Systems
PB - Association for Computing Machinery
Y2 - 7 December 2020 through 9 December 2020
ER -