Network Selection in Wireless Heterogeneous Networks: a Survey

Bendaoud, Fayssal; Abdennebi, Marwen; Didi, Fedoua

doi:10.26636/jtit.2018.126218

Cited by 13 publications

(8 citation statements)

References 35 publications

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“…In the article [ 33 ], a hybrid decision-making algorithm is implemented by merging the Fuzzy Analytic Hierarchy Process (FAHP) and Dynamic Analytic Hierarchy Process (DAHP) algorithms to be applied to Intelligent Transport Systems. Finally, we mention the article [ 34 ], which uses optimization methods for network selection based on various criteria covering quality of service, mobility, cost, energy, battery life, etc.…”

Section: Related Workmentioning

confidence: 99%

Case Studies with the Contiki-NG Simulator to Design Strategies for Sensors’ Communication Optimization in an IoT-Fog Ecosystem

Ferreira

Azevedo

Estrella

et al. 2023

Sensors

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With the development of mobile communications and the Internet of Things (IoT), IoT devices have increased, allowing their application in numerous areas of Industry 4.0. Applications on IoT devices are time sensitive and require a low response time, making reducing latency in IoT networks an essential task. However, it needs to be emphasized that data production and consumption are interdependent, so when designing the implementation of a fog network, it is crucial to consider criteria other than latency. Defining the strategy to deploy these nodes based on different criteria and sub-criteria is a challenging optimization problem, as the amount of possibilities is immense. This work aims to simulate a hybrid network of sensors related to public transport in the city of São Carlos - SP using Contiki-NG to select the most suitable place to deploy an IoT sensor network. Performance tests were carried out on five analyzed scenarios, and we collected the transmitted data based on criteria corresponding to devices, applications, and network communication on which we applied Multiple Attribute Decision Making (MADM) algorithms to generate a multicriteria decision ranking. The results show that based on the TOPSIS and VIKOR decision-making algorithms, scenario four is the most viable among those analyzed. This approach makes it feasible to optimally select the best option among different possibilities.

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Section: Related Workmentioning

confidence: 99%

Case Studies with the Contiki-NG Simulator to Design Strategies for Sensors’ Communication Optimization in an IoT-Fog Ecosystem

Ferreira

Azevedo

Estrella

et al. 2023

Sensors

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“…16 F I G U R E 2 Network selection process. 5 The RAN selection operation is initiated by a mobile device when it needs to establish a connection to a wireless network, such as when a user wants to make a call, send a message, or access the internet. The RAN selection procedure is a mechanism that allows a mobile device to choose the suitable available wireless RAT based on multiple elements like signal strength, network availability, and the user's subscription profile.…”

Section: Network Selection Problemmentioning

confidence: 99%

“…The RAN selection is a crucial task in HWNs that integrate different RATs to provide high‐quality connectivity and data throughput. In HWNs, it is fundamental to optimise network performance and capacity and one of the main issues is selecting the most appropriate RAT when a service is requested, Figure 1, To address this issue, researchers have proposed adaptive and efficient algorithms that consider the user's quality of service (QoS) requirements and network performance 1–3 . These algorithms use different techniques such as game theory, 4 joint resource management and others to facilitate the seamless communications and the adaptive QoS.…”

Section: Introductionmentioning

confidence: 99%

A machine learning access network selection in a heterogeneous wireless environment

Bendaoud,

Abdennebi

2023

Concurrency and Computation

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In the context of Heterogeneous Wireless Networks (HWN), achieving the best connectivity is crucial for users. With the availability of multiple Radio Access Technologies (RATs), users are constantly looking to be “Always Best Connected” (ABC) by connecting to the best possible RAT. This challenge is also important in Next‐Generation Networks (NGN), where researchers are proposing models and algorithms to make the ABC paradigm a reality. One such challenge is the network selection problem, which involves the automatic and transparent selection of the best RAT at a given time. Many papers in this field have used Multi‐Attribute Decision‐Making (MADM) methods, which have been implemented with various objectives such as increasing quality of service (QoS), reducing energy consumption and costs etc. This paper proposes a modified machine learning algorithm, specifically an adapted K‐means algorithm, to address this challenge. The results show that this proposal outperforms legacy MADM methods in terms of latency, jitter, packet loss, and data rate. So, the paper presents a new approach to network selection in HWN and NGN, and the results suggest that it could be a promising solution to this important challenge.

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“…The next generation of wireless network is characterized by the coexistence of various wireless access networks such as, the basic WLAN IEEE802.11 family, UMTS, HSPA and the LTE The heterogeneous environment resides in the different radio access links. This heterogeneous system (Bendaoud et al, 2018) has different characteristics such as, network availability, coverage and Quality of Service (QoS). The main difference between the different RATs is related to the speed of movement of the mobile node.…”

Section: Introductionmentioning

confidence: 99%

Mobility Aware Network Selection in a Heterogeneous Wireless Environment

Bendaoud

Abdennebi

Didi

2020

Transport and Telecommunication Journal

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Different Radio Access Technologies (RATs) coexist in the same area has encouraged the researchers to get profit from the available networks by selecting of the best RAT at each moment of the call session to satisfy the user requirements. In this paper, we address a real-world problem which is the frequent mobility of the users in heterogeneous networks. We present in this paper a framework that allows users to select the best networks for the whole call session especially form a mobility perspective. The framework consists of several steps, starting by the path prediction which is performed using a Markov model order 2. The second step is to make the network selection on the zones of each predicted path, while in the third step; we get the best RAT’s configuration for each predicted path. Finally, we use another function to select one of the best configurations to be used for all the possible used paths. The results show that our proposal performs very well by eliminating the unnecessary vertical handover while maintaining a good Quality of Service (QoS).

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Network Selection in Wireless Heterogeneous Networks: a Survey

Cited by 13 publications

References 35 publications

Case Studies with the Contiki-NG Simulator to Design Strategies for Sensors’ Communication Optimization in an IoT-Fog Ecosystem

Case Studies with the Contiki-NG Simulator to Design Strategies for Sensors’ Communication Optimization in an IoT-Fog Ecosystem

A machine learning access network selection in a heterogeneous wireless environment

Mobility Aware Network Selection in a Heterogeneous Wireless Environment

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