Fuzzy Based Mobility Management in 4G Wireless Networks

Selvi, M. Phemina; Sendhilnathan, S.

doi:10.1590/1678-4324-2016161047

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…The fuzzy logic system has been a great deal of attention during last two decades and has achieved great success in many different real-world application like ATM networks, wireless mobile network, wireless sensor networks, and traffic congestion control. The literature survey has shown that previously the researcher has faced so many difficulties to handle the uncertainties because of a hard decision boundary, but with the advancement of FL, logic systems are characterized by membership functions with soft decision boundaries [21][22][23][24].…”

Section: Previous Workmentioning

confidence: 99%

Neuro-Fuzzy based Call Admission Control for next Generation Mobile Multimedia Networks

2019

ijeat

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As the demand of the mobile users are increasing day by day, wireless/mobile multimedia networks still need advancement in terms of, reliable traffic performance, link availability, efficient bandwidth utilization, and user mobility, that can attain extremely consistent wireless communication and data transmission over the networks. Due to the emerging demand of multimedia services a high-speed network and call admission control (CAC) scheme is required, which not only guarantees the quality of services (QoS) for new and handoff calls but also results in optimum resource utilization in bursty traffic network environments. The main objective of this integrated neural fuzzy based CAC scheme is to improve QoS with decent resource allocation, such that it minimizes the probability of call dropping and call blocking in mobile multimedia networks. The proposed neural fuzzy CAC scheme is a hybrid approach that integrates the semantic rule ability of fuzzy logic (FL) controller and self-training capability of a neural network (NN) which is further enhanced to construct an efficient computational model for traffic control and fair radio resources allocation for new calls and handoff calls. The simulation results conclude that a neural fuzzy based CAC can achieve minimal call dropping probabilities and maximum resource utilization in high-speed networks as compared to fuzzy logic based CAC and conventional CAC or existing CAC schemes

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Section: Previous Workmentioning

confidence: 99%

Neuro-Fuzzy based Call Admission Control for next Generation Mobile Multimedia Networks

2019

ijeat

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“…It is noteworthy that the algorithm focuses on QoS, but there is also need to reduce the number of handover (network latency) and which type of parameters should considered for proper vertical handover decision. In [25], Phemina and Sendhilnathan, proposed a Mobility management in 4G wireless network using FL (fuzzy logic). The Algorithm used five input parameters which are "Received Signal Strength (RSS), BW, Monetary Cost, User Preference (UP) and UE Velocity (V)" while the output is the HO decision.…”

Section: International Journal Of Advanced Engineering Management Anmentioning

confidence: 99%

Handover in Mobile Wireless Communication Network - A Review

Abolade¹,

Fakolujo²,

Orimogunje³

2017

IJAEMS

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Abstract-Handover is the process of achieving Continuous service as the user moves in-between cells. Handover is needed during cell-crossing or/and signal quality degradation in the current channel. During handover, there is switching among networks and thereby transfer users to another network or BS [45]. The fundamental of 5G is heterogeneous networks and in such networks, seamless handover in non-negotiable [46]. Phases of handover are: discovery, decision and execution. Network discovery finds appropriate network that satisfies user desired QoS. Decision phase is when the handover should take place (also known as a handover initiation phase). These phases determine seamlessness of the handover. Wrong time of initiation leads to unnecessary handover or call drop rate is increased and thereby result to poor QoS. Therefore, handover should take place at the right time by trigger handover decision considering all the parameters properly to ensure QoS is not affected and unnecessary handover is avoided.

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“…They have been applied to solving many different real-world problems such as classification, regression, control, decision making, prediction, etc. [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Flower Pollination Algorithm in Optimization of Interval Type-2 Fuzzy for Telemedical Problem

Umoh

Abayomi

Udoh

et al. 2021

Advances in Intelligent Systems and Computing

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Determining the best parameters of the membership functions (MFs) in developing interval type-2 fuzzy logic controller (IT2FLC) is always difficult and complex because of the presence of high level of uncertainty and imprecision. Flower Pollination Algorithm (FPA) is a nature-inspired global optimization technique that imitates the pollination behavior of flowering plants, which avoids trapping in local optimum solution. This paper describes the optimization of IT2FLC using FPA for telemedical problem and applies to predict shock level in cardiac patients. The optimized IT2FLC is used to handle the uncertainties inherent in the membership functions (MFs) of the type-2 using cardiac patients' data for remote vital signs monitoring. The parameters of the MFs of the IT2FLC had not been previously optimized as this study considers applying Flower Pollination Algorithm (FPA) to optimize interval Footprint of Uncertainty (FOU) MFs of IT2FLS. We use the FPA to find the best MFs parameters of the IT2FL and the choice of FPA is because of the linear nature of the algorithm which gives attractive performances when compared to state-of-the-art algorithms. The empirical comparison is made on the designed system using cardiac patients' health datasets obtained both from real-time and clinical scenarios. Simulation results indicate that FPAbased interval type-2 fuzzy algorithm outperforms those results of a single IT2FL algorithm.

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Fuzzy Based Mobility Management in 4G Wireless Networks

Abstract: Handover is the key procedure in wireless networks to provide required qual ity of service to the users during mobility Handover.

Cited by 8 publications

References 10 publications

Neuro-Fuzzy based Call Admission Control for next Generation Mobile Multimedia Networks

Neuro-Fuzzy based Call Admission Control for next Generation Mobile Multimedia Networks

Handover in Mobile Wireless Communication Network - A Review

Flower Pollination Algorithm in Optimization of Interval Type-2 Fuzzy for Telemedical Problem

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