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http://dx.doi.org/10.7840/kics.2017.42.5.1009

Personalized Service Recommendation for Mobile Edge Computing Environment  

Yim, Jong-choul (Electronics and Telecommunications Research Institute)
Kim, Sang-ha (Chungnam National Univ. Department of Computer Science & Engineering)
Keum, Chang-sup (Electronics and Telecommunications Research Institute)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.42, no.5, 2017 , pp. 1009-1019 More about this Journal
Abstract
Mobile Edge Computing(MEC) is a emerging technology to cope with mobile traffic explosion and to provide a variety of services having specific requirements by means of running some functions at mobile edge nodes directly. For instance, caching function can be executed in order to offload mobile traffics, and safety services using real time video analytics can be delivered to users. So far, a myriad of methods and architectures for personalized service recommendation have been proposed, but there is no study on the subject which takes unique characteristics of mobile edge computing into account. To provide personalized services, acquiring users' context is of great significance. If the conventional personalized service model, which is server-side oriented, is applied to the mobile edge computing scheme, it may cause context isolation and privacy issues more severely. There are some advantages at mobile edge node with respect to context acquisition. Another notable characteristic at MEC scheme is that interaction between users and applications is very dynamic due to temporal relation. This paper proposes the local service recommendation platform architecture which encompasses these characteristics, and also discusses the personalized service recommendation mechanism to be able to mitigate context isolation problem and privacy issues.
Keywords
MEC; Edge; Context; Personalized; Recommendation; Mobile;
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