Journal
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
Volume 6, Issue 4, Pages 1155-1165Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCCN.2020.3002253
Keywords
Vehicular networks; routing protocol; collaborative learning; multi-access vehicular environment; fuzzy logic; reinforcement learning
Categories
Funding
- JSPS KAKENHI [18KK0279, 19H04092, 20H04174, 19H04093]
- Telecommunications Advanced Foundation
- ROIS NII Open Collaborative Research [2020-20FA02]
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Some Internet-of-Things (IoT) applications have a strict requirement on the end-to-end delay where edge computing can be used to provide a short delay for end-users by conducing efficient caching and computing at the edge nodes. However, a fast and efficient communication route creation in multi-access vehicular environment is an underexplored research problem. In this paper, we propose a collaborative learning-based routing scheme for multi-access vehicular edge computing environment. The proposed scheme employs a reinforcement learning algorithm based on end-edge-cloud collaboration to find routes in a proactive manner with a low communication overhead. The routes are also preemptively changed based on the learned information. By integrating the proactive and preemptive approach, the proposed scheme can achieve a better forwarding of packets as compared with existing alternatives. We conduct extensive and realistic computer simulations to show the performance advantage of the proposed scheme over existing baselines.
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