An Efficient Hierarchical Mobile IPv6 Group-Based BU Scheme for Mobile Nodes in IoT Network

Ahmed, Aftab; Jabbar, Sohail; Iqbal, M.Munwar; Erbad, Aiman; Song, Houbing

doi:10.1109/jiot.2022.3231989

Cited by 10 publications

(1 citation statement)

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“…Several obstacles must be overcome to disclose higher-quality films to moving vehicles, including the network's dynamic architecture and high mobility. The Doppler Effect, attenuation, packet loss, quickly changing network topology, and many other issues can occur in VANETs [3]. Additionally, the daily operation of the video streaming process requires enormous resources.…”

Section: Introductionmentioning

confidence: 99%

Deep Learnıng Based Enhanced Secure Emergency Vıdeo Streamıng Approach by Leveraging Blockchain Technology for Vehicular AdHoc 5G Networks

Awais,

Ali,

saeed

et al. 2024

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%