Enhanced handover mechanism using mobility prediction in wireless networks


Autoři: Khong-Lim Yap aff001;  Yung-Wey Chong aff001;  Weixia Liu aff002
Působiště autorů: National Advanced IPv6 Centre, Universiti Sains Malaysia, USM, Penang, Malaysia aff001;  Internet Innovation Research Center, Minjiang University, Fuzhou, China aff002
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0227982

Souhrn

The rapid increase in the usage of the mobile internet has led to a great expansion of cellular data networks in order to provide better quality of service. However, the cost to expand the cellular network is high. One of the solutions to provide affordable wireless connectivity is the deployment of a WiFi access point to offload users’ data usage. Nevertheless, the frequent and inefficient handover process between the WiFi AP and cellular network, especially when the mobile device is on the go, may degrade the network performance. Mobile devices do not have the intelligence to select the optimal network to enhance the quality of service (QoS). This paper presents an enhanced handover mechanism using mobility prediction (eHMP) to assist mobile devices in the handover process so that users can experience seamless connectivity. eHMP is tested in two wireless architectures, homogeneous and heterogeneous networks. The network performance significantly improved when eHMP is used in a homogeneous network, where the network throughput increases by 106% and the rate of retransmission decreases by 85%. When eHMP is used in a heterogeneous network, the network throughput increases by 55% and the retransmission rate decreases by 75%. The findings presented in this paper reveal that mobility prediction coupled with the multipath protocol can improve the QoS for mobile devices. These results will contribute to a better understanding of how the network service provider can offload traffic to the WiFi network without experiencing performance degradation.

Klíčová slova:

Algorithms – Communication equipment – Computer networks – Forecasting – Hidden Markov models – Human mobility – Intelligence – Internet


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Článek vyšel v časopise

PLOS One


2020 Číslo 1