Joint Task Partitioning and Resource Allocation in RAV-Enabled Vehicular Edge Computing Based on Deep Reinforcement Learning

Vehicle edge computing (VEC) leverages compact cloud computing at the mobile network edge to meet the processing and latency needs of vehicles. By bringing computation closer to the vehicles, VEC reduces data transmission, minimizes latency, and boosts performance for compute-intensive applications....

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Vydáno v:	IEEE internet of things journal Ročník 12; číslo 11; s. 15453 - 15466
Hlavní autoři:	Liang, Hongbin, Zhang, Han, Ale, Laha, Hong, Xintao, Wang, Lei, Jia, Qiong, Zhao, Dongmei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.06.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Automation Autonomous aerial vehicles Cloud computing Computation offloading Computational modeling Data transmission Deep learning Deep reinforcement learning deep reinforcement learning (DRL) Design optimization Edge computing Energy sources Heuristic algorithms Internet of Things Markov processes Mobile computing Multi-access edge computing Multiagent systems Optimization Partitioning Partitioning algorithms Peak hour traffic remote aerial vehicles (RAVs) Resource allocation Resource management Servers task partitioning vehicle edge computing (VEC)
ISSN:	2327-4662, 2327-4662
On-line přístup:	Získat plný text
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