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Light Weight CNN based Robust Image Watermarking Scheme for Security
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AUTOMATION USING IOT IN GREENHOUSE ENVIRONMENT
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Principle of 6G Wireless Networks: Vision, Challenges and Applications
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Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach
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Light Weight CNN based Robust Image Watermarking Scheme for Security
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VIRTUAL REALITY GAMING TECHNOLOGY FOR MENTAL STIMULATION AND THERAPY
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Design of Digital Image Watermarking Technique with Two Stage Vector Extraction in Transform Domain
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PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
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Image Augmentation based on GAN deep learning approach with Textual Content Descriptors
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Comparative Analysis for Personality Prediction by Digital Footprints in Social Media
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Volume - 2 | Issue - 3 | september 2020
Published
17 September, 2020
Intelligent transport system is one of thriving research domain and in particular multimedia vehicular networks gains more attention. The deployment of multimedia services in vehicle ad-hoc networks (VANETs) provides real time support to users and provides better traffic management with high safety measures. Most of the vehicles has multimedia applications such as weather sensors, real time map applications etc., and it requires adequate resources to process. Resource allocation to a static network is simple and various routing models are evolved. In case of dynamic network like VANETs, routing is still in progress in order to obtain a better model. Routing directly related to quality of services of network and user, it is essential to develop a better dynamic routing strategy for multimedia wireless networks. The proposed work aims to provide an optimized dynamic routing strategy for multimedia networks. For efficient routing, k-means clustering is used to organize the clusters and exchanges the routing information and inverted ant colony optimization is used to obtain the optimal path for multimedia access. Proposed model is experimentally verified and compared to conventional ant colony optimization and grey wolf optimization in terms of parameters such as end to end delay, throughput, target used, average computation time and efficiency.
KeywordsMultimedia Vehicular Networks (MVANets) K-means clustering Inverted Ant Colony optimization (IACO)
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