Interoperability in Heterogeneous Wireless Networks Using FIS-ENN Vertical Handover Model

Rajinikanth, E.; Jayashri, S.

doi:10.1007/s11277-019-06406-x

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…The methods, such as ABC algorithm (Murad et al , 2017), fast game-based handoff (Tseng et al , 2017), VHO in wireless 4 G network (Buragohain, 2017), FIS-ENN (Rajinikanth and Jayashri, 2019) and ES-DQN (Jiani et al , 2020) are used for the comparison with the proposed Fractional Dolphin-based Handoff. The comparative analysis of the developed method is analyzed based on delay, energy consumption, throughput and average stay time is depicted in Figure 9.…”

Section: Simulation and Resultsmentioning

confidence: 99%

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A network controlled vertical handoff mechanism for heterogeneous wireless network using optimized support vector neural network

Patil¹,

Patil²

2021

IJPCC

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Purpose Vertical handoff mechanism (VHO) becomes very popular because of the improvements in the mobility models. These developments are less to certain circumstances and thus do not provide support in generic mobility, but the vertical handover management providing in the heterogeneous wireless networks (HWNs) is crucial and challenging. Hence, this paper introduces the vertical handoff management approach based on an effective network selection scheme. Design/methodology/approach This paper aims to improve the working principle of previous methods and make VHO more efficient and reliable for the HWN.Initially, the handover triggering techniques is modelled for identifying an appropriate place to initiate handover based on the computed coverage area of cellular base station or wireless local area network (WLAN) access point. Then, inappropriate networks are eliminated for determining the better network to perform handover. Accordingly, a network selection approach is introduced on the basis ofthe Fractional-dolphin echolocation-based support vector neural network (Fractional-DE-based SVNN). The Fractional-DE is designed by integrating Fractional calculus (FC) in Dolphin echolocation (DE), and thereby, modifying the update rule of the DE algorithm based on the location of the solutions in past iterations. The proposed Fractional-DE algorithm is used to train Support vector neural network (SVNN) for selecting the best weights. Several parameters, like Bit error rate (BER), End to end delay (EED), jitter, packet loss, and energy consumption are considered for choosing the best network. Findings The performance of the proposed VHO mechanism based on Fractional-DE is evaluated based on delay, energy consumption, staytime, and throughput. The proposed Fractional-DE method achieves the minimal delay of 0.0100 sec, the minimal energy consumption of 0.348, maximal staytime of 4.373 sec, and the maximal throughput of 109.20 kbps. Originality/value In this paper, a network selection approach is introduced on the basis of the Fractional-Dolphin Echolocation-based Support vector neural network (Fractional-DE-based SVNN). The Fractional-DE is designed by integrating Fractional calculus (FC) in Dolphin echolocation (DE), and thereby, modifying the update rule of the DE algorithm based on the location of the solutions in past iterations. The proposed Fractional-DE algorithm is used to train SVNN for selecting the best weights. Several parameters, like Bit error rate (BER), End to end delay (EED), jitter, packet loss, and energy consumption are considered for choosing the best network.The performance of the proposed VHO mechanism based on Fractional-DE is evaluated based on delay, energy consumption, staytime, and throughput, in which the proposed method offers the best performance.

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Section: Simulation and Resultsmentioning

confidence: 99%

“…Identifying the criteria that can influence VHO is a demanding task. Rajinikanth and Jayashri (2019) developed a decision method, named FIS-ENN (Fuzzy interference system Elman Neural Network) for HWNs. This method achieved maximum throughput without any interference and loss.…”

Section: Related Workmentioning

confidence: 99%