TY - GEN
T1 - Smart Cloud and D2D Communication Driven Trajectories Prediction with Content Caching
AU - Fan, Jiaxin
AU - Wu, Jun
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - As intelligent transportation systems continue to develop, the integration of cloud-assisted device-to-device (D2D) communication and trajectory prediction has become a crucial strategy for enhancing traffic management and safety. However, D2D communication faces challenges such as bandwidth limitations and high latency, which are particularly pronounced in dense urban environments. This paper introduces a novel framework that utilizes self-attention mechanisms and edge content caching to optimize D2D communication and trajectory predictions. By leveraging cloud resources to alleviate computational burdens and employing edge caching to reduce latency and bandwidth consumption, our approach ensures that data transmission between vehicles is both fast and economical. Our innovative method facilitates real-time, accurate trajectory forecasting and efficient data communication among vehicles. We have developed a self-attention model that dynamically prioritizes relevant data points and trajectory information based on historical and contextual traffic data, thereby enabling more precise predictions and a robust communication network. To validate the effectiveness of our proposed framework, we compared its performance with traditional methods. The results demonstrate significant improvements in predictive accuracy and communication efficiency compared to conventional approaches.
AB - As intelligent transportation systems continue to develop, the integration of cloud-assisted device-to-device (D2D) communication and trajectory prediction has become a crucial strategy for enhancing traffic management and safety. However, D2D communication faces challenges such as bandwidth limitations and high latency, which are particularly pronounced in dense urban environments. This paper introduces a novel framework that utilizes self-attention mechanisms and edge content caching to optimize D2D communication and trajectory predictions. By leveraging cloud resources to alleviate computational burdens and employing edge caching to reduce latency and bandwidth consumption, our approach ensures that data transmission between vehicles is both fast and economical. Our innovative method facilitates real-time, accurate trajectory forecasting and efficient data communication among vehicles. We have developed a self-attention model that dynamically prioritizes relevant data points and trajectory information based on historical and contextual traffic data, thereby enabling more precise predictions and a robust communication network. To validate the effectiveness of our proposed framework, we compared its performance with traditional methods. The results demonstrate significant improvements in predictive accuracy and communication efficiency compared to conventional approaches.
KW - D2D communication
KW - edge content caching
KW - self-attention
KW - trajectories prediction
UR - http://www.scopus.com/inward/record.url?scp=85198035738&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85198035738&partnerID=8YFLogxK
U2 - 10.1109/SmartCloud62736.2024.00020
DO - 10.1109/SmartCloud62736.2024.00020
M3 - Conference contribution
AN - SCOPUS:85198035738
T3 - Proceedings - 2024 IEEE 9th International Conference on Smart Cloud, SmartCloud 2024
SP - 72
EP - 76
BT - Proceedings - 2024 IEEE 9th International Conference on Smart Cloud, SmartCloud 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Smart Cloud, SmartCloud 2024
Y2 - 10 May 2024 through 12 May 2024
ER -