TY - GEN
T1 - Poster Abstract
T2 - 2020 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020
AU - Rashid, Md Tahmid
AU - Zhang, Daniel Yue
AU - Wang, Dong
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - While computational model-based wildfire prediction provides reasonable accuracy in predicting wildfire behaviour, they are often limited due to lack of constant availability of real-time data. By contrast, social sensing is an emerging sensing paradigm able to obtain early signs of forest fires from online social media users (e.g. smoke in nearby cities), but suffers from inconsistent reliability due to unreliable social signals. Meanwhile, UAV-based physical sensing utilizes onboard physical sensors to perform reliable wildfire sensing, but requires manual efforts to be narrowed down to fire infested regions. In this poster, we present CompDrone, a novel computational model-driven social media and drone-based wildfire monitoring framework that exploits the collective strengths of computational modeling, social sensing, and drone-based physical sensing for reliable wildfire monitoring. In particular, the CompDrone framework leverages techniques from cellular automata, constrained optimization, and bottom-up game theory to solve a few technical challenges involved in monitoring wildfires. The evaluation results using a real-world forest fire monitoring application show that CompDrone outperforms the state-of-the-art monitoring schemes.
AB - While computational model-based wildfire prediction provides reasonable accuracy in predicting wildfire behaviour, they are often limited due to lack of constant availability of real-time data. By contrast, social sensing is an emerging sensing paradigm able to obtain early signs of forest fires from online social media users (e.g. smoke in nearby cities), but suffers from inconsistent reliability due to unreliable social signals. Meanwhile, UAV-based physical sensing utilizes onboard physical sensors to perform reliable wildfire sensing, but requires manual efforts to be narrowed down to fire infested regions. In this poster, we present CompDrone, a novel computational model-driven social media and drone-based wildfire monitoring framework that exploits the collective strengths of computational modeling, social sensing, and drone-based physical sensing for reliable wildfire monitoring. In particular, the CompDrone framework leverages techniques from cellular automata, constrained optimization, and bottom-up game theory to solve a few technical challenges involved in monitoring wildfires. The evaluation results using a real-world forest fire monitoring application show that CompDrone outperforms the state-of-the-art monitoring schemes.
UR - http://www.scopus.com/inward/record.url?scp=85091496391&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091496391&partnerID=8YFLogxK
U2 - 10.1109/INFOCOMWKSHPS50562.2020.9162586
DO - 10.1109/INFOCOMWKSHPS50562.2020.9162586
M3 - Conference contribution
AN - SCOPUS:85091496391
T3 - IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020
SP - 1362
EP - 1363
BT - IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 July 2020 through 9 July 2020
ER -