Mobility Support for Vehicular Cloud Radio-Access-Networks with Edge Computing

Kim, Yonggang; An, Namwon; Park, Jaehyoung; Lim, Hyuk

doi:10.1109/cloudnet.2018.8549365

Cited by 8 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kim et al [75] proposed a technique to offer the consistent sub-RRH and sub-MEC server flexibility help of communications and routing services with centralised control [76] through a vehicular Cloud Radio Access Network (vCRAN) with Remote Radio Heads (RRHs) and Mobile Edge Computing (MEC). Chen et al proposed a method which provides complete information flexibility, control and an immediate and secure interaction with its client focussed on a data-centred and diverse network transfer.…”

Section: Mobility Management In MCC and Wsnsmentioning

confidence: 99%

See 1 more Smart Citation

Mobile Cloud Computing and Wireless Sensor Networks: A review, integration architecture, and future directions

et al. 2021

View full text Add to dashboard Cite

In today's fast advancing world, sensors are used in various applications to provide complete data about different objects present around us. The sensor data when integrated with Mobile Cloud Computing (MCC) is used for further computation to provide useful results advanced warning, forecasting, planning, and plethora of other applications in this uncertain world. Few works have been proposed where sensor nodes are integrated with Wireless Sensor Networks (WSNs) which give a new direction in modern research for developing new technologies which help the users for fast access of sensory data over mobile devices. Herein, a systematic literature review of both MCC and WSNs are conducted and subsequently assimilated them via an architecture which is efficient and achievable within short time-span for a variety of applications. The MCC applications are reviewed from two main perspectives that are energy and information management. The communication aspect in MCC and WSNs has been discussed and the need for integration between the two has been justified in this work. The work done on mobility management in MCC and WSNs is reviewed. Also, the challenges in MCC-WSNs integration along with the comprehensive analysis and findings of the review are identified. Finally, the conclusion and specific future direction of research in this area are provided. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Mobility Management In MCC and Wsnsmentioning

confidence: 99%

“…Kim et al. [75] proposed a technique to offer the consistent sub‐RRH and sub‐MEC server flexibility help of communications and routing services with centralised control [76] through a vehicular Cloud Radio Access Network (vCRAN) with Remote Radio Heads (RRHs) and Mobile Edge Computing (MEC). Chen et al.…”

Section: Overview Of MCC and Wsnsmentioning

confidence: 99%

Mobile Cloud Computing and Wireless Sensor Networks: A review, integration architecture, and future directions

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Vehicles can be regarded as mobile devices to collect and process environmental data and support various information services. The development of Internet of Vehicles (IoV) and wireless technology provides a way to improve the driving experience [1]. Due to high latency and network energy consumption [2], traditional mobile cloud computing is not suitable for latency-sensitive applications [3,4], which affects the experience of vehicle users.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning Empowered Edge Collaborative Caching Scheme for Internet of Vehicles

Liu¹,

Xu²,

Yang³

et al. 2022

Computer Systems Science and Engineering

View full text Add to dashboard Cite

With the development of internet of vehicles, the traditional centralized content caching mode transmits content through the core network, which causes a large delay and cannot meet the demands for delay-sensitive services. To solve these problems, on basis of vehicle caching network, we propose an edge collaborative caching scheme. Road side unit (RSU) and mobile edge computing (MEC) are used to collect vehicle information, predict and cache popular content, thereby provide low-latency content delivery services. However, the storage capacity of a single RSU severely limits the edge caching performance and cannot handle intensive content requests at the same time. Through content sharing, collaborative caching can relieve the storage burden on caching servers. Therefore, we integrate RSU and collaborative caching to build a MEC-assisted vehicle edge collaborative caching (MVECC) scheme, so as to realize the collaborative caching among cloud, edge and vehicle. MVECC uses deep reinforcement learning to predict what needs to be cached on RSU, which enables RSUs to cache more popular content. In addition, MVECC also introduces a mobility-aware caching replacement scheme at the edge network to reduce redundant cache and improving cache efficiency, which allows RSU to dynamically replace the cached content in response to the mobility of vehicles. The simulation results show that the proposed MVECC scheme can improve cache performance in terms of energy cost and content hit rate.

show abstract

A Computing Offloading Resource Allocation Scheme Using Deep Reinforcement Learning in Mobile Edge Computing Systems

Li¹

2021

J Grid Computing

View full text Add to dashboard Cite

Mobility Support for Vehicular Cloud Radio-Access-Networks with Edge Computing

Cited by 8 publications

References 7 publications

Mobile Cloud Computing and Wireless Sensor Networks: A review, integration architecture, and future directions

Mobile Cloud Computing and Wireless Sensor Networks: A review, integration architecture, and future directions

Deep Reinforcement Learning Empowered Edge Collaborative Caching Scheme for Internet of Vehicles

A Computing Offloading Resource Allocation Scheme Using Deep Reinforcement Learning in Mobile Edge Computing Systems

Contact Info

Product

Resources

About