TY - GEN
T1 - Carefull Tread
T2 - 8th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2023
AU - Khan, Junaid Ahmed
AU - Wang, Weiyi
AU - Ozbay, Kaan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Mobility data generated from Connected and Autonomous Vehicles (CAVs) and micro-mobility devices (e-scooters, e-bikes and smartphones) is critical to understand different safety and future Intelligent Transportation System (ITS) needs. Blockchain is used to endorse such mobility data in a secure manner, however, trusting large amount of data from massive number of devices is challenging as results in scalability issues along incurring longer delays. To cater this, this paper employs reinforcement learning, specifically, Q-learning towards a lightweight mobility data validation at scale. We derive the optimal policy for a node to endorse mobility data generated by nearby nodes in a blockchain network. We present a novel consensus protocol for the node to learn the block sampling rates and time delay thresholds based on the neighborhood topology and network connectivity. We simulate the proposed consensus protocol in NS3, and it has shown to achieve lower delays, overhead and high throughput for up to 50 nodes simultaneously endorsing each others mobility data in New York city.
AB - Mobility data generated from Connected and Autonomous Vehicles (CAVs) and micro-mobility devices (e-scooters, e-bikes and smartphones) is critical to understand different safety and future Intelligent Transportation System (ITS) needs. Blockchain is used to endorse such mobility data in a secure manner, however, trusting large amount of data from massive number of devices is challenging as results in scalability issues along incurring longer delays. To cater this, this paper employs reinforcement learning, specifically, Q-learning towards a lightweight mobility data validation at scale. We derive the optimal policy for a node to endorse mobility data generated by nearby nodes in a blockchain network. We present a novel consensus protocol for the node to learn the block sampling rates and time delay thresholds based on the neighborhood topology and network connectivity. We simulate the proposed consensus protocol in NS3, and it has shown to achieve lower delays, overhead and high throughput for up to 50 nodes simultaneously endorsing each others mobility data in New York city.
KW - Blockchain
KW - Connected and Automated Vehicles
KW - Micromobility
KW - Reinforcement Learning
KW - Trajectory data
UR - http://www.scopus.com/inward/record.url?scp=85166253805&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85166253805&partnerID=8YFLogxK
U2 - 10.1109/MT-ITS56129.2023.10241675
DO - 10.1109/MT-ITS56129.2023.10241675
M3 - Conference contribution
AN - SCOPUS:85166253805
T3 - 2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2023
BT - 2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 June 2023 through 16 June 2023
ER -