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DC-Gossip: An Enhanced Broadcast Protocol in Hyperledger Fabric Based on Density Clustering Read chapter Read first chapter

2022 | OriginalPaper | Chapter

BP-CODS: Blind-Spot-Prediction-Assisted Multi-Vehicle Collaborative Data Scheduling

Authors : Tailai Li, Chaokun Zhang, Xiaobo Zhou

Published in: Wireless Algorithms, Systems, and Applications

Publisher: Springer Nature Switzerland

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Abstract

The most important thing for Connected and Automated Vehicles (CAVs) is to ensure driving safety and prevent the loss of life and property due to danger. The existence of vehicle blind spots can lead to incomplete or ineffective access to information, which will bring risks. At the same time, the transmission of a large amount of duplicate data will lead to information redundancy and bandwidth waste. In this paper, we design BP-CODS, which uses blind-spot prediction assistance to schedule image data between vehicles with the support of the Edge Server. We model the data scheduling transmission as two processes of uploading and downloading, form the set coverage problem, and propose a heuristic algorithm to solve it. We conduct extensive simulation experiments in CARLA to verify the effectiveness of BP-CODS in reducing a large number of redundant data.

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previous chapter A Scalable Blockchain-Based Trust Management Strategy for Vehicular Networks
next chapter Performance Analysis of Partition-Based Caching in Vehicular Networks
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Metadata
Title
BP-CODS: Blind-Spot-Prediction-Assisted Multi-Vehicle Collaborative Data Scheduling
Authors
Tailai Li
Chaokun Zhang
Xiaobo Zhou
Copyright Year
2022
Book
Wireless Algorithms, Systems, and Applications

Print ISBN: 978-3-031-19210-4

Electronic ISBN: 978-3-031-19211-1

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-031-19211-1_25