Adaptive Decision of Wireless Access Network for Higher User Satisfaction

Kim, Hyun-Soon; Bae, Mungyu; Lee, Woonghee; Kim, Hwangnam

doi:10.1155/2018/3427238

Cited by 6 publications

(2 citation statements)

References 25 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the view of the service providers, the network is actually divided into three parts: wireless access network, mobile core network, and application network. Among them, the wireless access network is composed of base stations, which are responsible for the access of mobile terminals [10][11][12]. e mobile core network is composed of a bunch of high-performance routers and servers, which are responsible for connecting the wireless base station to the external network [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

On Improving the Robustness of MEC with Big Data Analysis for Mobile Video Communication

Zhao

Zeng

Liu

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

Mobile video communication and Internet of Things are playing a more and more important role in our daily life. Mobile Edge Computing (MEC), as the essential network architecture for the Internet, can significantly improve the quality of video streaming applications. The mobile devices transferring video flow are often exposed to hostile environment, where they would be damaged by different attackers. Accordingly, Mobile Edge Computing Network is often vulnerable under disruptions, against either natural disasters or human intentional attacks. Therefore, research on secure hub location in MEC, which could obviously enhance the robustness of the network, is highly invaluable. At present, most of the attacks encountered by edge nodes in MEC in the IoT are random attacks or random failures. According to network science, scale-free networks are more robust than the other types of network under the random failures. In this paper, an optimization algorithm is proposed to reorganize the structure of the network according to the amount of information transmitted between edge nodes. BA networks are more robust under random attacks, while WS networks behave better under human intentional attacks. Therefore, we change the structure of the network accordingly, when the attack type is different. Besides, in the MEC networks for mobile video communication, the capacity of each device and the size of the video data influence the structure significantly. The algorithm sufficiently takes the capability of edge nodes and the amount of the information between them into consideration. In robustness test, we set the number of network nodes to be 200 and 500 and increase the attack scale from 0% to 100% to observe the behaviours of the size of the giant component and the robustness calculated for each attack method. Evaluation results show that the proposed algorithm can significantly improve the robustness of the MEC networks and has good potential to be applied in real-world MEC systems.

show abstract

Section: Introductionmentioning

confidence: 99%

On Improving the Robustness of MEC with Big Data Analysis for Mobile Video Communication

Zhao

Zeng

Liu

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Wi-5 SDN framework selects the RAT according to the QoS requirement of downlink flows determined by the fittingness factor. Authors in [41], presented a device centric framework called SIMLESS that ensures low-cost seamless connectivity by considering the WiFi preferences and history collected through the mobility trace module based on machine learning models. A network selection framework named TYDER presented in [42] monitors the network QoS level over time and considers user feedback to ensure stable connectivity and QoS.…”

Section: Introductionmentioning

confidence: 99%

QAAs: QoS provisioned artificial intelligence framework for AP selection in next-generation wireless networks

Priya¹,

Malhotra²

2020

Telecommun Syst

View full text Add to dashboard Cite

Emerging trend of ubiquitous data access is driving the demand for effective wireless communication connectivity. In essence to this, wireless local area network (WLAN) technology seems to be a reliable and cost effective access for the next-generation wireless ecosystem. But the pivotal challenge for WLAN in the next generation wireless networks is to cater the legions of heterogeneous services with characteristic sets of quality of service requirements. However, the strategies present in the existing literature are not accoutered for the application-agnostic association and are incompetent in handling the enormous WLAN state space. Realising the pitfalls of the existing strategies, a novel software-defined networking enabled artificial intelligence framework has been proposed. The proposed framework implements a novel invalid action reduction scheme and double deep reinforcement learning to guarantee the flow based association in a multi-service WLAN environment. Moreover, it allows the multi-parametric optimisation of the association decision and faster convergence to the stable solution. The analytical results validated through the extensive simulations revealed that the proposed scheme achieves high performance gain in terms of convergence, stability and network utility as compared to the other solutions in the literature.

show abstract

Coexistence Standards in IEEE 802

2021

Wireless Coexistence

View full text Add to dashboard Cite

Adaptive Decision of Wireless Access Network for Higher User Satisfaction

Cited by 6 publications

References 25 publications

On Improving the Robustness of MEC with Big Data Analysis for Mobile Video Communication

On Improving the Robustness of MEC with Big Data Analysis for Mobile Video Communication

QAAs: QoS provisioned artificial intelligence framework for AP selection in next-generation wireless networks

Coexistence Standards in IEEE 802

Contact Info

Product

Resources

About