Abstract
The combination of edge computing and deep learning helps make intelligent edge devices that can make several conditional decisions using comparatively secured and fast machine learning algorithms. An automated car that acts as the data-source node of an intelligent Internet of vehicles or IoV system is one of these examples. Our motivation is to obtain more accurate and rapid object detection using the intelligent cameras of a smart car. The competent supervision camera of the smart automobile model utilizes multimedia data for real-time automation in real-time threat detection. The corresponding comprehensive network combines cooperative multimedia data processing, Internet of Things (IoT) fact handling, validation, computation, precise detection, and decision making. These actions confront real-time delays during data offloading to the cloud and synchronizing with the other nodes. The proposed model follows a cooperative machine learning technique, distributes the computational load by slicing real-time object data among analogous intelligent Internet of Things nodes, and parallel vision processing between connective edge clusters. As a result, the system increases the computational rate and improves accuracy through responsible resource utilization and active–passive learning. We achieved low latency and higher accuracy for object identification through real-time multimedia data objectification.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Cited by
3 articles.
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1. Privacy Protection Technology for Internet of Vehicles;2023 IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud)/2023 IEEE 9th International Conference on Edge Computing and Scalable Cloud (EdgeCom);2023-07
2. Stochastic modeling and performance analysis in balancing load and traffic for vehicular ad hoc networks: A review;International Journal of Network Management;2023-03-17
3. Real-Time Target Detection System for Intelligent Vehicles Based on Multi-Source Data Fusion;Sensors;2023-02-06