Keywords
Mobile edge computing
Deep reinforcement learning
Submitted : 2025-04-29
Accepted : 2025-05-14
Published : 2025-05-29
Abstract
With the rapid development of intelligent transportation systems, vehicular networks (VANETs) have become an essential part of the intelligent transportation infrastructure. Due to the high dynamics of efficient vehicular network nodes and the low latency requirement of data interaction, the traditional cloud computing model makes it difficult to meet the real-time and performance criteria, and the storage optimization strategy based on edge computing can effectively improve the data access efficiency and system response. This paper explores optimizing the data caching mechanism of intelligent telematics using edge computing to reduce network latency, improve data availability, and enhance overall system performance.
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