Cloud Computing Framework for Vehicle Multimedia with Dynamic Priority-based Efficient Resource Allocation

International Journal of Electrical and Electronics Engineering
© 2024 by SSRG - IJEEE Journal
Volume 11 Issue 6
Year of Publication : 2024
Authors : Mohammad Khaja Nizamuddin, Zunaira Begum, C. Atheeq, Ruhiat Sultana, Ahsan Saud Qadri
pdf
How to Cite?

Mohammad Khaja Nizamuddin, Zunaira Begum, C. Atheeq, Ruhiat Sultana, Ahsan Saud Qadri, "Cloud Computing Framework for Vehicle Multimedia with Dynamic Priority-based Efficient Resource Allocation," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 6, pp. 22-30, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I6P103

Abstract:

 Intelligent transportation systems rely on smart vehicles equipped with a diverse array of sensory devices to deliver a spectrum of multimedia applications, including driving assistance, traffic status updates, weather forecasts, safety alerts, and entertainment features. However, the substantial volume of multimedia data generated by these vehicles overwhelms the processing due to their restricted processing speed and storage capacity, standalone onboard computer systems. Consequently, a shift in networking and computational paradigms is imperative to accommodate these multimedia services effectively. Cloud computing emerges as a viable solution for seamlessly integrating vehicles into the cloud infrastructure. Nevertheless, challenges related to multimedia content processing, encompassing resource costs, swift service response times, and optimized user experiences, can significantly influence vehicular communication performance. We provide effective resource allocation and computation architecture designed specifically for vehicle multimedia cloud computing to overcome these issues. Through the Cloudsim simulator, this framework’s performance is thoroughly assessed with an emphasis on user experience, service response times, and resource costs.

Keywords:

Intelligent transportation system, Smart vehicles, Efficient resource allocation, Cloud computing, QoS.

References:

[1] Tesnim Mekki et al., “Vehicular Cloud Networks: Challenges, Architectures, and Future Directions,” Vehicular Communications, vol. 9, pp. 268-280, 2017.
[Cross Ref] [Google Scholar] [Publisher Link]
[2] Sadip Midya et al., “Multi-Objective Optimization Technique for Resource Allocation and Task Scheduling in Vehicular Cloud Architecture: A Hybrid Adaptive Nature-Inspired Approach,” Journal of Network and Computer Applications, vol. 103, pp. 58–84, 2018.
[Cross Ref] [Google Scholar] [Publisher Link]
[3] Mario Gerla et al., “Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds,” Proceedings of IEEE World Forum on Internet of Things (WF-IoT), Seoul, South Korea, pp. 241-246, 2014.
[Cross Ref] [Google Scholar] [Publisher Link]
[4] Jiann-Liang Chen et al., “IMS Cloud Computing Architecture for High-Quality Multimedia Applications,” Proceedings of 7th IEEE International Conference on Wireless Communications and Mobile Computing (IWCMC), Istanbul, Turkey, pp. 1463-1468. 2011.
[Cross Ref] [Google Scholar] [Publisher Link]
[5] Yu Wu et al., “Cloud Media: When Cloud on Demand Meets Video on Demand,” Proceedings of IEEE 31st International Conference on Distributed Computing Systems (ICDCS), Minneapolis, MN, USA, pp. 268-277, 2011.
[Cross Ref] [Google Scholar] [Publisher Link]
[6] Mohammed Abdul Lateef et al., “Data Aegis Using Chebyshev Chaotic Map-Based Key Authentication Protocol.” Intelligent Manufacturing and Energy Sustainability: Proceedings of ICIMES 2022, Singapore: Springer Nature, Singapore, pp. 187-195, 2023.
[Cross Ref] [Google Scholar] [Publisher Link]
[7] Wenwu Zhu et al., “Multimedia Cloud Computing,” IEEE Signal Processing Magazine, vol. 28, no. 3, pp. 59-69, 2011.
[Cross Ref] [Google Scholar] [Publisher Link]
[8] Ming-Kai Jiau et al., “Multimedia Services in Cloud-Based Vehicular Networks,” IEEE Intelligent Transportation Systems Magazine, vol. 7, no. 3, pp. 62-79, 2015.
[Cross Ref] [Google Scholar] [Publisher Link]
[9] Amjad Ali et al., “Priority-Based Cloud Computing Architecture for Multimedia Enabled Heterogeneous Vehicular Users,” Journal of Advanced Transportation, pp. 1-12, 2018.
[Cross Ref] [Google Scholar] [Publisher Link]
[10] G. Zhang et al., “Dynamic Resource Allocation for Vehicular Cloud Computing Based on Vehicular Data,” IEEE Transactions on Vehicular Technology, vol. 66, no. 10, pp. 8775-8788, 2017.
[11] Q. Zhang et al., “Dynamic Resource Allocation for Multimedia Cloud Computing in Vehicular Networks,” IEEE Transactions on Multimedia, vol. 20, no. 6, pp. 1577-1588, 2018.
[12] J. Li et al., “A Priority-based Resource Allocation Scheme for Vehicular Cloud Computing,” IEEE Access, vol. 6, pp. 30348-30355, 2018.
[13] H. He et al., “An Efficient Resource Allocation Scheme for Vehicular Cloud Computing,” Proceedings of the IEEE International Conference on Cloud Computing, San Francisco, CA, USA, pp. 536-543, 2017.
[14] W. Chen et al., “Efficient Resource Allocation for Vehicular Multimedia Cloud Computing,” Proceedings of the IEEE Global Communications Conference, Singapore, pp. 1-6, 2017. [15] L. Zhang et al., “A Dynamic Resource Allocation Scheme Based on QoS Requirements for Vehicular Cloud Computing,” Proceedings of the IEEE International Conference on Communications, Paris, France, pp. 1-6, 2017.
[16] X. Zhang, X. Qiao, and H. Li, “Dynamic Resource Allocation for Vehicular Cloud Computing Using Fuzzy Logic and Genetic Algorithm,”  Proceedings of the IEEE International Conference on Fuzzy Systems, Naples, Italy, pp. 1-6, 2017.
[17] S. Wang et al., “A Dynamic Resource Allocation Scheme Based on QoS and Energy Efficiency for Vehicular Cloud Computing,”  Proceedings of the IEEE International Conference on Green Computing and Communications, Liverpool, UK, pp. 233-240, 2018.
 [18] M. Ma et al., “Efficient Resource Allocation Scheme for Multimedia Services in Vehicular Cloud Computing,” Journal of Network and Computer Applications, vol. 119, pp. 55-64, 2018.