An Enhance the Performance of Mining Vehicular and Machinery Security Systems Using Artificial Intelligence in VANET Cloud Computing

Abstract

Over the recent decades, incorporating Vehicular Ad-hoc Network (VANET) into Cloud computing plays a vital role, since it provides a reliable safety journey to vehicular drivers, passengers, etc. However, attaining security and emergency message dissemination is still major bottleneck in VANET combined Cloud, due to the dynamic nature of vehicles and wireless communication. Our major intention is to provide high level security in VANET-Cloud environment. In addition to it, we also reduce delay in emergency dissemination. Our proposed Delay aware Emergency Message Dissemination and Data Retrieval in secure (DEMD22RS) VANET-Cloud is composed of four sequential processes: Authentication, Clustering, Data Retrieval and Data dissemination. In regard to maintaining security for both Road Side Unit (RSU) and Vehicles, we propose Hash based Credential Authentication Scheme (HCAS) that affords authentication using Secure Hash Algorithm-3 (SHA-3) and Elliptic Curve Points (ECP). To sustain a stable cluster, Firm Aware Clustering Scheme (FACS) is pursued where Stud Krill Herd (SKH) algorithm is exploited. In the data retrieval process, cloud provides requested information to the RSU in encrypted form using the Twofish algorithm. RSU discover the path to deliver received data through executing Artificial Neural Network (ANN) algorithm. In order to diminish delay in emergency message dissemination, best disseminator is selected by cluster head using Fuzzy-Topsis (FT) algorithm. Our DEMD22RS VANET-Cloud network is implemented in Network Simulator 3 tool. Finally, the evaluation of DEMD22RS work performance is achieved by computing consequent metrics that are Throughput, Packet Delivery Ratio, Transmission delay, Average delay, Key generation time, Encryption time and Decryption time.