Flow/Interface Association for multi-connectivity in heterogeneous wireless networks: e-Health case

Senouci, Mohamed Abdelkrim; Senouci, Hadj; Senouci, Mustapha Réda; Ferdosian, Nasim; Mellouk, Abdelhamid

doi:10.1016/j.adhoc.2019.101942

Cited by 5 publications

(1 citation statement)

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“…An energy consumption as a criterion in the Flow/Interface Association (FIA) was considered [12]. A novel method called Smart Tabu Search (STS) was proposed that took into account network conditions, the monetary cost of the network, QoS requirements of the applications, user preferences, and energy consumption of the mobile device to select the optimal FIA that achieved the best trade-off among all the considered criteria.…”

Section: Background and Related Workmentioning

confidence: 99%

Switching of Interfaces and Self-optimization of weights using Backpropagation ANN in WHN Enviornment

Rani

2023

Preprint

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In today’s scenario, mobile communication is facing a healthy competition due to different networks, interfaces, channels, and many more available in wireless heterogeneous environment. The problem arises when customers/users get the availability of many interfaces at the same time. At that time users need an intelligent or smart mechanism to connect them to the best services according to their requirements/preferences. Interface management manages available interfaces and connects the user with the best. In this paper, Interface management with Artificial Neural Network (ANN) allows the smart use of different radio accesses/interfaces. The selection is made with different parameters of different networks. This paper proposed a backpropagation neural network that is used for the switching in between different networks-3G, WLAN, 4G and 5G. The different parameters of a network are used as the selection parameters with assigning proper weights. Weights are initialized with fuzzy AHP and optimized with Back Propagation Neural Network (BPNN). The target value and the actual value is compared and their difference used as the adjusting value for the weights to get the optimum value. The backpropagation is used to train the network. The comparison among the projected algorithm and the existing algorithm shows the valuablity of the new method and the best connectivity of the network.

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Section: Background and Related Workmentioning

confidence: 99%