Research Article
A Self-adaptive Feedback Handoff Algorithm Based Decision Tree for Internet of Vehicles
@INPROCEEDINGS{10.1007/978-3-030-05888-3_17, author={Wenqing Cui and Weiwei Xia and Zhuorui Lan and Chao Qian and Feng Yan and Lianfeng Shen}, title={A Self-adaptive Feedback Handoff Algorithm Based Decision Tree for Internet of Vehicles}, proceedings={Ad Hoc Networks. 10th EAI International Conference, ADHOCNETS 2018, Cairns, Australia, September 20-23, 2018, Proceedings}, proceedings_a={ADHOCNETS}, year={2018}, month={12}, keywords={Internet of Vehicles Decision tree Handoff Feedback decision Mobile Edge Computing}, doi={10.1007/978-3-030-05888-3_17} }- Wenqing Cui
Weiwei Xia
Zhuorui Lan
Chao Qian
Feng Yan
Lianfeng Shen
Year: 2018
A Self-adaptive Feedback Handoff Algorithm Based Decision Tree for Internet of Vehicles
ADHOCNETS
Springer
DOI: 10.1007/978-3-030-05888-3_17
Abstract
In this paper, a self-adaptive feedback handoff (SAFH) algorithm is proposed to address the problem about dynamic handoffs for the Internet of Vehicles (IoVs), aiming at minimizing handoff delay and reducing the ping-pong effect. We first analyze the main attributes and terminal movement trend, and give the respective handoff probability distribution. Based on handoff probability distributions, the structure of multi-attribute decision tree is determined. To update the terminal state, the incremental learning method by feedback mechanism is implemented by adding decision table information at the nodes of the decision tree so as to dynamically catch the splitting attributes of the decision tree. Simulation results show that the proposed SAFH algorithm’s time cost is lower than some existing algorithms. Besides, SAFH algorithm also reduces the ping-pong effect and increases the effectiveness of network connections.