Deep learning based enhanced secure emergency video streaming approach by leveraging blockchain technology for Vehicular AdHoc 5G Networks

Awais, Muhammad, Saeed, Yousaf, Ali, Abid, Jabbar, Sohail, Ahmad, Awais, Alkhrijah, Yazeed, Raza, Umar ORCID: https://orcid.org/0000-0002-9810-1285 and Saleem, Yasir (2024) Deep learning based enhanced secure emergency video streaming approach by leveraging blockchain technology for Vehicular AdHoc 5G Networks. Journal of Cloud Computing: Advances, Systems and Applications, 13 (1). 130. ISSN 2192-113X

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Abstract

VANET is a category of MANET that aims to provide wireless communication. It increases the safety of roads and passengers. Millions of people lose their precious lives in accidents yearly, millions are injured, and others incur disability daily. Emergency vehicles need clear roads to reach their destination faster to save lives. Video streaming can be more effective as compared to textual messages and warnings. To address this issue, we proposed a methodology to use visual sensors, cameras, and OBU to record emergency videos. Initially, the frames are detected. After re-recording, the frames detection algorithm detects the specific event from the video frames. Blockchain encrypts an emergency or specific event using hashing algorithms in the second layer of our proposed framework. In the third layer of the proposed methodology, encrypted video is broadcast with the help of 5G wireless technology to the connected nodes in the VANET. The dataset used in this research comprises up to 72 video sequences averaging about 120 seconds per video. All videos have different traffic conditions and vehicles. The ResNet-50 model is used for the feature extraction process of extracted frames. The model is trained using Tensorflow and Keras deep learning models. The Elbow method finds the optimal K number for the K Means model. This data is split into training and testing. 70% is reserved for training the support vector machine (SVM) model and test datasets, while 30%. 98% accuracy is achieved with 98% precision and 99% recall as results for the proposed methodology.