An intelligent handoff optimization algorithm for network selection in heterogeneous networks

Dhand, Pooja; Mittal, Sumit; Sharma, Geeta

doi:10.1007/s41870-021-00710-1

Cited by 10 publications

(5 citation statements)

References 35 publications

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“…Dhand et al [24] developed a network selection model consisting of fuzzy AHP and ELECTRE, where fuzzy AHP is utilized to obtain the weight of each criterion, and ELECTRE is utilized to rate networks. Results showed that the model could effectively select the optimal network, but it has not been validated against the RRP.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced MCDM Model for Cloud Service Provider Selection

Abdelaziz¹,

Harb²,

Zaghloul³

et al. 2023

IJACSA

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Multi-Criteria Decision-Making (MCDM) techniques are often used to aid decision-makers in selecting the best alternative among several options. However, these systems have issues, including the Rank Reversal Problem (RRP) and decision-making ambiguity. This study aimed to propose a selection model for a Cloud Service Provider (CSP) that addresses these issues. This research used the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank the alternatives. The entropy technique is utilized to determine the weight of the criteria, and Single Valued Neutrosophic (SVN) is employed to address uncertainty. To select the best cloud provider based on Quality of Service (QoS) criteria, we used a dataset from Cloud Harmony for this study. The results indicated that the suggested model could effectively resolve the RRP under conditions of uncertainty. This research is novel and is the first to address both the problem of uncertainty in decision-making and RRP in MCDM.

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Section: Related Workmentioning

confidence: 99%

An Enhanced MCDM Model for Cloud Service Provider Selection

Abdelaziz¹,

Harb²,

Zaghloul³

et al. 2023

IJACSA

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“…To resolve this issue, there is a requirement for an instrument to foresee the traffic observing around the reach and afterward anticipating it through the available data sets. [18] Secondly, it is great challenge to define an edge of a cell when there is a collision two or more different networks. This zone should be treated as a genuine dynamic region for which different arrangement of measurements can be considered and applied for.…”

Section: Challenges Of Proposed Algorithmmentioning

confidence: 99%

Mobility Management Framework in Edge Computing using Multi Criteria Decision Making

Chopra¹,

Sharma²,

Dhand³

2023

Emerging Trends in Engineering and Management

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The urge for seamless and ubiquitous connectivity for heterogeneous devices and networks to deliver desired results has gathered a lot of attention of researchers, academicians and industry experts. To a large extent, this issue has been resolved by cloud computing through task offloading either partially or fully but sending so much data on cloud create overheads. So, a particular task has to be uploaded or not and handoff to be initiated or not is a major decision. Through this paper, an algorithm to take the decision for handoff and switching to the most lucrative network has been proposed. The scheme aims to preserve and provide the users with Quality of Service and Quality of Experience while keeping in mind the criteria of offloading traffic to another cell or network based on the number of mobile users, type of application being used on the mobile, available bandwidth, and network load. The proposed mobile edge architecture may improve the timely execution of the handover process, greatly reduces the ping-pong rate and terminal overhead.

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“…In order to differentiate from previous studies on heterogeneous network selection [30], the network density of the studied network selection problem is highlighted. The system model in this paper not only increases the type and number of networks, but also adds the number of networks.…”

Section: System Modelmentioning

confidence: 99%

“…According to feedback from users, eMMB services have greater demands on network bandwidth and delay, while URLLC services are more sensitive to network delay and jitter and mMTC services have higher requirements for network packet loss rate and price. Therefore, to effectively distinguish the different needs of different services for the network, it is necessary to assign different weights of network attribute parameters to different services when constructing the reward function [32], [33]. In this paper, the AHP is used to assign subjective weights to different network attributes.…”

Section: Determination Of Network Parameter Attribute Weightsmentioning

confidence: 99%

Research on 5G Heterogeneous Ultra Dense network selection algorithm based on Dueling- DDQN

Xie

Zhu

2023

Preprint

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In the 5G Heterogeneous Ultra Dense Network environment, due to the dense deployment of the network and the diversity of user service types, a more flexible network selection algorithm is required to reduce the network blocking rate and improve the user’s quality of service (QoS). Considering the QoS requirements and preferences of the users, a network selection algorithm based on Dueling-DDQN is proposed by using deep reinforcement learning, which defines the state, action space and reward function of the different services to maximize the cumulative reward value of the network selection algorithm. The simulation results show that compared with other algorithms, the proposed algorithm can effectively reduce the network blocking rate while reducing the switching times.

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An intelligent handoff optimization algorithm for network selection in heterogeneous networks

Cited by 10 publications

References 35 publications

An Enhanced MCDM Model for Cloud Service Provider Selection

An Enhanced MCDM Model for Cloud Service Provider Selection

Mobility Management Framework in Edge Computing using Multi Criteria Decision Making

Research on 5G Heterogeneous Ultra Dense network selection algorithm based on Dueling- DDQN

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