Exploring the role of edge computing on the legal effect of secure collaborative download protocol Online publication date: Tue, 26-Jul-2022
by Peifang Zhong
International Journal of Grid and Utility Computing (IJGUC), Vol. 13, No. 2/3, 2022
Abstract: To make people's car driving experience safer and more comfortable, the architecture of intrusion recognition model based on the K-nearest neighbour classification Deep Neural Networks (K-DNN) is proposed to classify and identify various network intrusion factors, thereby strengthening the security level of Internet of Vehicles (IoV) and alleviating the hardware resource scarcity for IoV. Then, a secure collaborative download system based on edge computing is proposed, which can accurately and timely collect the information of roads, vehicles and nearby infrastructure in the driving process and facilitate people to safely and quickly download the required content. In the proposed system, the vehicle encrypts the content download request and sends it to the Roadside Unit (RSU) and the content server, respectively. The content server sends the content corresponding to the download request to the vehicle through the RSU.
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