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Optimal User Association Strategy for Large-Scale IoT Sensor Networks with Mobility on Cloud RANs.
Kim, Taewoon; Chun, Chanjun; Choi, Wooyeol.
  • Kim T; School of Software, Hallym University, Chuncheon 24252, Korea. taewoon@hallym.ac.kr.
  • Chun C; Future Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, Korea. chanjunchun@kict.re.kr.
  • Choi W; Department of Computer Engineering, Chosun University, Gwangju 61452, Korea. wyc@chosun.ac.kr.
Sensors (Basel) ; 19(20)2019 Oct 12.
Article en En | MEDLINE | ID: mdl-31614801
ABSTRACT
In networking systems such as cloud radio access networks (C-RAN) where users receive the connection and data service from short-range, light-weight base stations (BSs), users' mobility has a significant impact on their association with BSs. Although communicating with the closest BS may yield the most desirable channel conditions, such strategy can lead to certain BSs being over-populated while leaving remaining BSs under-utilized. In addition, mobile users may encounter frequent handovers, which imposes a non-negligible burden on BSs and users. To reduce the handover overhead while balancing the traffic loads between BSs, we propose an optimal user association strategy for a large-scale mobile Internet of Things (IoT) network operating on C-RAN. We begin with formulating an optimal user association scheme focusing only on the task of load balancing. Thereafter, we revise the formulation such that the number of handovers is minimized while keeping BSs well-balanced in terms of the traffic load. To evaluate the performance of the proposed scheme, we implement a discrete-time network simulator. The evaluation results show that the proposed optimal user association strategy can significantly reduce the number of handovers, while outperforming conventional association schemes in terms of load balancing.
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Risk_factors_studies Idioma: En Año: 2019 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Risk_factors_studies Idioma: En Año: 2019 Tipo del documento: Article