Autor: |
Ayaz, Muhammad, Iqbal, Jawaid, Adnan, Muhammad, Hussain, Saddam S., Alabrah, Amerah, Amin, Noor Ul, Al-Hadhrami, Suheer, Mizanur Rahman, Sk. Md. |
Předmět: |
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Zdroj: |
Journal of Advanced Transportation; 4/12/2022, p1-16, 16p |
Abstrakt: |
Due to the ability of Vehicular Ad Hoc Networks (VANETs) to improve mobility and create innovative services, they have received increasing attention from both industry and academia fields. Named Data Networking (NDN) is a network service that has been developed for Internet's host-based packet delivery model. In VANETs, NDN is used as an emerging architecture for improving the quality of service (QoS) of the networks connected cloud and mobile Internet of Things (IoT). We can achieve this improvement using an efficient content dissemination and forwarding technique that depends on name-based routing, in-network content caching, and interest-based content retrieval. The large amount of data processed by several IoT sensors or nodes of VANETs makes content prioritizing and forwarding mechanism is crucial, especially when many end-users need common content simultaneously. Therefore, in this paper, we propose a content dissemination technique based on priority for boosting the service requirements of NDN-based cloud and mobile IoT nodes in the VANETs. The approach prioritizes the data flow of traffic into four classes: urgent, emergency, least, and average using a novel constrained location and deadline method, called New method employing Deadline Distance and Size of Data (NDDS). The highest priority is assigned to the data traffic with the emergency class, then urgent, average, and least class. To evaluate this proposed technique, a numerical simulation experiment is conducted by using the CloudSim toolkit. The simulation results demonstrate that the emergency data flow has minimum processing time, compared to the average, urgent, and least data flow, which can significantly enhance the QoS of NDN protocol-based Ad Hoc networks. [ABSTRACT FROM AUTHOR] |
Databáze: |
Complementary Index |
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