KCC 2021
Current Result Document : 18 / 18
ÇѱÛÁ¦¸ñ(Korean Title) |
Collaborative Task Offloading for Cloud Assisted MEC-Enabled Vehicular Networks |
¿µ¹®Á¦¸ñ(English Title) |
Collaborative Task Offloading for Cloud Assisted MEC-Enabled Vehicular Networks |
ÀúÀÚ(Author) |
Md Delowar Hossain
Waqas ur Rahman
Tangina Sultana
Md Alamgir Hossain
Md Imtiaz Hossain
Md Abu Layek
Ga-Won Lee
Eui-Nam Huh
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¿ø¹®¼ö·Ïó(Citation) |
VOL 48 NO. 01 PP. 0153 ~ 0155 (2021. 06) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (English Abstract) |
Vehicular Edge Computing (VEC) is a promising technology to extend the computation and storage capabilities of vehicular networks through task offloading. However, one of the challenging issues in the vehicular networks is task offloading because of the overloaded problem for handling a huge number of vehicle offloading requests and mobility scenarios. Therefore, it degrades the quality of vehicular performance. To handle the above-mentioned problem and compute the latency-intensive vehicular applications in VEC networks, a collaborative task offloading scheme between local RSU computing (LRC) with cloud via cellular network (CN) is proposed in this study. Our proposed scheme cooperatively shares the computation tasks between the MEC server with cloud and surety the low computation latency. Extensive simulation experiments affirm that our proposed scheme fulfills the performance guarantees which can reduce the response time and task failure rate at almost 21.6% and 75.5% respectively, when compared with the LRC scheme. Moreover, the reduced rates are approximately 3.2% and 28.4% respectively, when compared to the LRC with cloud via RSU scheme. |
Å°¿öµå(Keyword) |
Vehicular Networks
Task Offloading
Vehicular Edge Computing
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