Mobility-aware personalized service recommendation in mobile edge computing

Abstract With the proliferation of smartphones and an increasing number of services provisioned by clouds, mobile edge computing (MEC) is emerging as a complementary technology of cloud computing. It could provide cloud resources and services by local mobile edge servers, which are normally nearby u...

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Autores principales:	Hongxia Zhang, Yanhui Dong, Yongjin Yang
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Mobile edge computing Mobility Edge service Service recommendation Quality of service(QoS) Telecommunication TK5101-6720 Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/287aa930303c409eab0c7105506cc70d
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spelling	oai:doaj.org-article:287aa930303c409eab0c7105506cc70d2021-12-05T12:06:32ZMobility-aware personalized service recommendation in mobile edge computing10.1186/s13638-021-02068-11687-1499https://doaj.org/article/287aa930303c409eab0c7105506cc70d2021-12-01T00:00:00Zhttps://doi.org/10.1186/s13638-021-02068-1https://doaj.org/toc/1687-1499Abstract With the proliferation of smartphones and an increasing number of services provisioned by clouds, mobile edge computing (MEC) is emerging as a complementary technology of cloud computing. It could provide cloud resources and services by local mobile edge servers, which are normally nearby users. However, a significant challenge is aroused in MEC because of the mobility of users. User trajectory prediction technologies could be used to cope with this issue, which has already played important roles in service recommendation systems with MEC. Unfortunately, little attention and work have been given in service recommendation systems considering users mobility. Thus, in this paper, we propose a mobility-aware personalized service recommendation (MPSR) approach based on user trajectory and quality of service (QoS) predictions. In the proposed method, users trajectory is firstly discovered by a hybrid long-short memory network. Then, given users trajectories, service QoS is predicted, considering the similarity of different users and different edge servers. Finally, services are recommended by a center trajectory strategy through MPSR. Experimental results on a real dataset show that our proposed approach can outperform the traditional recommendation approaches in terms of accuracy in mobile edge computing.Hongxia ZhangYanhui DongYongjin YangSpringerOpenarticleMobile edge computingMobilityEdge serviceService recommendationQuality of service(QoS)TelecommunicationTK5101-6720ElectronicsTK7800-8360ENEURASIP Journal on Wireless Communications and Networking, Vol 2021, Iss 1, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mobile edge computing Mobility Edge service Service recommendation Quality of service(QoS) Telecommunication TK5101-6720 Electronics TK7800-8360
spellingShingle	Mobile edge computing Mobility Edge service Service recommendation Quality of service(QoS) Telecommunication TK5101-6720 Electronics TK7800-8360 Hongxia Zhang Yanhui Dong Yongjin Yang Mobility-aware personalized service recommendation in mobile edge computing
description	Abstract With the proliferation of smartphones and an increasing number of services provisioned by clouds, mobile edge computing (MEC) is emerging as a complementary technology of cloud computing. It could provide cloud resources and services by local mobile edge servers, which are normally nearby users. However, a significant challenge is aroused in MEC because of the mobility of users. User trajectory prediction technologies could be used to cope with this issue, which has already played important roles in service recommendation systems with MEC. Unfortunately, little attention and work have been given in service recommendation systems considering users mobility. Thus, in this paper, we propose a mobility-aware personalized service recommendation (MPSR) approach based on user trajectory and quality of service (QoS) predictions. In the proposed method, users trajectory is firstly discovered by a hybrid long-short memory network. Then, given users trajectories, service QoS is predicted, considering the similarity of different users and different edge servers. Finally, services are recommended by a center trajectory strategy through MPSR. Experimental results on a real dataset show that our proposed approach can outperform the traditional recommendation approaches in terms of accuracy in mobile edge computing.
format	article
author	Hongxia Zhang Yanhui Dong Yongjin Yang
author_facet	Hongxia Zhang Yanhui Dong Yongjin Yang
author_sort	Hongxia Zhang
title	Mobility-aware personalized service recommendation in mobile edge computing
title_short	Mobility-aware personalized service recommendation in mobile edge computing
title_full	Mobility-aware personalized service recommendation in mobile edge computing
title_fullStr	Mobility-aware personalized service recommendation in mobile edge computing
title_full_unstemmed	Mobility-aware personalized service recommendation in mobile edge computing
title_sort	mobility-aware personalized service recommendation in mobile edge computing
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/287aa930303c409eab0c7105506cc70d
work_keys_str_mv	AT hongxiazhang mobilityawarepersonalizedservicerecommendationinmobileedgecomputing AT yanhuidong mobilityawarepersonalizedservicerecommendationinmobileedgecomputing AT yongjinyang mobilityawarepersonalizedservicerecommendationinmobileedgecomputing
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Mobility-aware personalized service recommendation in mobile edge computing

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