TY - GEN
T1 - FLOATING
T2 - 5th IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2023
AU - Khan, Junaid Ahmed
AU - Wang, Weiyi
AU - Ozbay, Kaan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Trajectory data from mobile, micro-mobility devices (e-scooter, e-bikes, etc) and vehicles need validation regarding its trustworthiness for utility in different applications. Sharing of false trajectories from compromised devices can lead to potentially fatal consequences for safety-related applications. There is no scalable method to assess the truthfulness of trajectory data in real-time, therefore, this paper proposes FLOATING, leveraging federated reinforcement learning to automate trajectory validation on a private-by-design blockchain. FLOATING employs a three-tier consensus process for nodes in each others vicinity to endorse trajectories in real-time. We evaluate FLOATING using NS-3 and it shows to achieve lower delays and network overhead for a network size of up to 50 nodes participating in the consensus, while reducing network resource utilization by 10 times.
AB - Trajectory data from mobile, micro-mobility devices (e-scooter, e-bikes, etc) and vehicles need validation regarding its trustworthiness for utility in different applications. Sharing of false trajectories from compromised devices can lead to potentially fatal consequences for safety-related applications. There is no scalable method to assess the truthfulness of trajectory data in real-time, therefore, this paper proposes FLOATING, leveraging federated reinforcement learning to automate trajectory validation on a private-by-design blockchain. FLOATING employs a three-tier consensus process for nodes in each others vicinity to endorse trajectories in real-time. We evaluate FLOATING using NS-3 and it shows to achieve lower delays and network overhead for a network size of up to 50 nodes participating in the consensus, while reducing network resource utilization by 10 times.
KW - Blockchain
KW - Connected and Automated Vehicles
KW - Federated Reinforcement Learning
KW - Micro-mobility
KW - Trajectory data
UR - http://www.scopus.com/inward/record.url?scp=85166173076&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85166173076&partnerID=8YFLogxK
U2 - 10.1109/ICBC56567.2023.10174956
DO - 10.1109/ICBC56567.2023.10174956
M3 - Conference contribution
AN - SCOPUS:85166173076
T3 - 2023 IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2023
BT - 2023 IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 May 2023 through 5 May 2023
ER -