Resource Allocation in Vehicular Cloud Computing Systems with Heterogeneous Vehicles and Roadside Units

Chun-Cheng Lin, Der Jiunn Deng*, Chia Chi Yao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

112 Scopus citations


Vehicular cloud computing (VCC) system coordinates the vehicular cloud (consisting of vehicles' computing resources) and the remote cloud properly to provide in-Time services to users. Although pervious works had established the models for resource allocation in the VCC system based on semi-Markov decision processes (SMDPs), few of them discussed heterogeneity of vehicles and influences of roadside units (RSUs). Heterogeneous vehicles made by different manufacturers may be equipped with different amount of computing resources; and furthermore, RSU can enhance the computing capability of VCC. Therefore, this paper creates an SMDP model for VCC resource allocation that additionally considers heterogeneous vehicles and RSUs, and proposes an approach for finding the optimal strategy of VCC resource allocation. The two additional features significantly elaborate the SMDP model, and demonstrate different results from the original model. Simulation shows that the resource allocation in the VCC system can be captured by the proposed model, which performs well in terms of long-Term expected values (consisting of consumption costs of power and time), under various parameter settings.

Original languageEnglish
Article number7891877
Pages (from-to)3692-3700
Number of pages9
JournalIEEE Internet of Things Journal
Issue number5
StatePublished - Oct 2018


  • Intelligent Transportation System (ITS)
  • semi-Markov decision processes (SMDPs)
  • vehicular ad hoc network (VANET)
  • vehicular cloud computing (VCC)


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