TY - GEN
T1 - Demo
T2 - 42nd IEEE International Conference on Distributed Computing Systems, ICDCS 2022
AU - Trihinas, Demetris
AU - Agathocleous, Michalis
AU - Avogian, Karlen
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - As drone technology penetrates even more application domains, Machine Learning (ML) is becoming a key driver enabling intelligence in the sky. However, ML Practitioners and Drone Application Operators are faced with several challenges when wanting to test ML-driven drone applications early in the design phase. These include the development and configuration of experiment use-cases over a robotics simulator along with the collection and assessment of desired KPIs which can range from ML algorithm accuracy to drone resource utilization and the impact of "intelligence"to the drone's energy footprint. This demonstration showcases FlockAI, an open and modular by design framework supporting users with the rapid deployment and repeatable testing during the design phase of ML-driven drone applications over the Webots robotics simulator. Through realistic use-cases, the demonstration will show how FlockAI can be used to design drone testbeds with "ready-to-go"drone templates, deploy ML models, configure on-board/remote inference, monitor and export drone resource utilization, network overhead and energy consumption to pinpoint performance inefficiencies and understand if various trade-offs can be exploited.
AB - As drone technology penetrates even more application domains, Machine Learning (ML) is becoming a key driver enabling intelligence in the sky. However, ML Practitioners and Drone Application Operators are faced with several challenges when wanting to test ML-driven drone applications early in the design phase. These include the development and configuration of experiment use-cases over a robotics simulator along with the collection and assessment of desired KPIs which can range from ML algorithm accuracy to drone resource utilization and the impact of "intelligence"to the drone's energy footprint. This demonstration showcases FlockAI, an open and modular by design framework supporting users with the rapid deployment and repeatable testing during the design phase of ML-driven drone applications over the Webots robotics simulator. Through realistic use-cases, the demonstration will show how FlockAI can be used to design drone testbeds with "ready-to-go"drone templates, deploy ML models, configure on-board/remote inference, monitor and export drone resource utilization, network overhead and energy consumption to pinpoint performance inefficiencies and understand if various trade-offs can be exploited.
KW - Drones
KW - Edge Computing
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85140916465&partnerID=8YFLogxK
U2 - 10.1109/ICDCS54860.2022.00147
DO - 10.1109/ICDCS54860.2022.00147
M3 - Conference contribution
AN - SCOPUS:85140916465
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 1318
EP - 1321
BT - Proceedings - 2022 IEEE 42nd International Conference on Distributed Computing Systems, ICDCS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 July 2022 through 13 July 2022
ER -