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SDN-assisted efficient LTE-WiFi aggregation in next generation IoT networks

Anbalagan, Sudha and Kumar, Dhananjay and J, Mercy Faustina and Raja, Gunasekaran and Ejaz, Waleed and Bashir, Ali Kashif (2017) SDN-assisted efficient LTE-WiFi aggregation in next generation IoT networks. Future Generation Computer Systems. pp. 1-11. ISSN 0167-739X

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Restricted to Repository staff only until 1 December 2019.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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Abstract

Currently, the increasing demands of user terminals has surged drastically and pulling up the global data traffic along. According to 3GPP, offloading is one of the most beneficial and advantageous options to handle this critical traffic bottleneck, however, both Long Term Evolution (LTE) and Wireless Local Area Network (WLAN) are loosely coupled. To mitigate the User Equipment (UE) from latency issues during offloading and for tighter integration of LTE and WLAN radio networks, LTE-WLAN Aggregation (LWA) was introduced by 3GPP which is apparently suitable for Internet of Things (IoT) devices. However, LWA is not suitable for high mobility scenarios as UEs’ information need to be updated for every new environment because of the frequent aggregation triggers which are mostly non-optimal and demands for a high-level controller. To resolve the disadvantage of non-optimal aggregation triggers, in this paper, we proposed Software Defined Networking (SDN) based approach for LWA, named as LWA under SDN Assistance (LWA-SA). In this approach, SDN initiates aggregation appropriately between LTE and an optimal WLAN Access Point (AP) which avoids frequent reconnections and deprived services. As multiple parameters are required for selection of an optimal WLAN AP, so we use Genetic Algorithm (GA) that considers each parameter as fitness value for the selection of optimal WLAN AP. This maximizes the throughput of UE and reduces the traffic pressure over licensed spectrum. Further, mathematical model is formulated that uses Karush-Kuhn-Tucker (KKT) to find the maximum attainable throughput of a UE. Using NS-3, we compared our approach with offloading scenarios and LWA. The simulation results clearly depict that LWA-SA outperforms existing schemes and achieves higher throughput.

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