Context-aware network selection in heterogeneous wireless networks

Monteiro, Alex; Souto, Eduardo; Pazzi, Richard W.; Nogueira, Michele

doi:10.1016/j.comcom.2018.11.006

Cited by 20 publications

(6 citation statements)

References 11 publications

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“…Since it does not exploit contextual information, such as movement velocity and application requirements for enhancing HM, SSF can lead to unnecessary and frequent handovers, decreasing throughput, increasing packet loss and even causing network service disruption. In [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ] handover decisions are made by using multicriteria and techniques such as Fuzzy Logic, Network Management Policies or Multiple Attribute Decision Making (MADM) Algorithms. Nonetheless, these approaches neglect an information model that disregards criteria from one or more information domains, such as network characteristics and status, application requirements, end-user profile, end-user device features, or handover history, which are relevant for advancing HM.…”

Section: Related Workmentioning

confidence: 99%

“…The Context layer includes contextual data about the user, network, device, application, and handover. Contextual data are essential for carrying out HM in environments with multiple wireless networks [ 20 ]. As in [ 21 , 33 ], this layer is divided into static and dynamic sublayers.…”

Section: Sim-knowmentioning

confidence: 99%

“…These handover issues can decrease throughput, increase packet loss and even cause network service disruption [ 10 , 11 , 12 ]. The multicriteria-based approaches in [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ] use RSSI and context information as criteria for ranking the available networks; the top-ranked network is selected by the end-user device for performing the connection process. These approaches disregard one or more relevant criteria, such as wireless network characteristics (e.g., coverage area), user device features (e.g., battery consumption), application requirements (e.g., real-time response) or user peculiarities (e.g., mobility pattern), leading to the failure of handovers and wrong network selection, negatively impacting the network’s performance [ 12 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

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A Semantic and Knowledge-Based Approach for Handover Management

Vivas

Rendón

Nieves

2021

Sensors

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Handover Management (HM) is pivotal for providing service continuity, enormous reliability and extreme-low latency, and meeting sky-high data rates, in wireless communications. Current HM approaches based on a single criterion may lead to unnecessary and frequent handovers due to a partial network view that is constrained to information about link quality. In turn, HM approaches based on multicriteria may present a failure of handovers and wrong network selection, decreasing the throughput and increasing the packet loss in the network. This paper proposes SIM-Know, an approach for improving HM. SIM-Know improves HM by including a Semantic Information Model (SIM) that enables context-aware and multicriteria handover decisions. SIM-Know also introduces a SIM-based distributed Knowledge Base Profile (KBP) that provides local and global intelligence to make contextual and proactive handover decisions. We evaluated SIM-Know in an emulated wireless network. When the end-user device moves at low and moderate speeds, the results show that our approach outperforms the Signal Strong First (SSF, single criterion approach) and behaves similarly to the Analytic Hierarchy Process combined with the Technique for Order Preferences by Similarity to the Ideal Solution (AHP-TOPSIS, multicriteria approach) regarding the number of handovers and the number of throughput drops. SSF outperforms SIM-Know and AHP-TOPSIS regarding the handover latency metric because SSF runs a straightforward process for making handover decisions. At high speeds, SIM-Know outperforms SSF and AHP-TOPSIS regarding the number of handovers and the number of throughput drops and, further, improves the throughput, delay, jitter, and packet loss in the network. Considering the obtained results, we conclude that SIM-Know is a practical and attractive solution for cognitive HM.

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Section: Related Workmentioning

confidence: 99%

Section: Sim-knowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Semantic and Knowledge-Based Approach for Handover Management

Vivas

Rendón

Nieves

2021

Sensors

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“…The use of different interfaces or communication protocols in the same network (heterogeneous networks) may be an interesting solution to maintain reliable communication links in networks composed of mobile nodes, given the vast number of successful cases in other mobile networks. Examples are the mobile ad hoc networks (MANET) and the vehicular ad hoc networks (VANET) [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Communication Interface Manager for Improving Performance of Heterogeneous UAV Networks

Ribeiro

Müller

Becker

2021

Sensors

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Exchanging messages with stable connections in missions composed of multiple unmanned aerial vehicles (UAV) remains a challenge. The variations in UAV distances from each other, considering their individual trajectories, and the medium dynamic factors are important points to be addressed.In this context, to increase the stability of UAV-to-UAV (U2U) communication with link quality, this paper presents an interface manager (IM) that is capable of improving communication in multi-UAV networks.Given a predefined set of available individual wireless interfaces, the proposed IM dynamically defines the best interface for sending messages based on on-flight conditions sensed and calculated dynamically from the wireless medium. Different simulation scenarios are generated using a complex and realistic experimental setup composed of traditional simulators such as NS-3, Gazebo, and GzUAV. IEEE 802.11n 2.4 GHz and 802.11p 5 GHz interfaces are used for the IM selection. The IM performance is evaluated in terms of metrics from the medium-access-control (MAC) and physical layers, which aim to improve and maintain the connectivity between the UAVs during the mission, and from the application layer, which targets the reliability in the delivery of messages. The obtained results show that compared with the cases where a single interface is used, the proposed IM is able to increase the network throughput and presents the best proportion of transmitted and received packets, reception power (−60 dBm to −75 dBm), and loss (−80 dB to −85 dB), resulting in a more efficient and stable network connections.

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“…For the access selection issue in a HWN, although many scholars have proposed solutions, most existing access selection algorithms are designed to select a network with the best comprehensive performance for users, ignoring the user's service characteristics and preferences [ 27 , 28 ]. Therefore, how to design a user-oriented intelligent access selection algorithm from the user's perspective and according to the user's service characteristics and preferences becomes the motivation of this paper.…”

Section: Introductionmentioning

confidence: 99%

A User-Oriented Intelligent Access Selection Algorithm in Heterogeneous Wireless Networks

Liang

Guo

Fang

2020

Computational Intelligence and Neuroscience

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A heterogeneous wireless network (HWN) contains many kinds of wireless networks with overlapping areas of signal coverage. One of the research topics on HWNs is how to make users choose the most suitable network. This paper designs a user-oriented intelligent access selection algorithm in HWNs with five modules (input, user preference calculation, candidate network score calculation, output, and learning). Essentially, the input module uses a utility function to calculate the utility value of the judgment parameter; the user preference calculation module calculates the weight of the judgment parameter using the fuzzy analysis hierarchy process (FAHP) approach; the candidate network score calculation module calculates the network score through a fuzzy neural network; the output module calculates the error between the actual output value and the expected output value; and the learning module corrects the parameter of the membership function in the fuzzy neural network structure according to the error. Simulation results show that the algorithm proposed in this paper can enable users to select the most suitable network according to service characteristics and can enable users to obtain higher gains.

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Context-aware network selection in heterogeneous wireless networks

Cited by 20 publications

References 11 publications

A Semantic and Knowledge-Based Approach for Handover Management

A Semantic and Knowledge-Based Approach for Handover Management

Communication Interface Manager for Improving Performance of Heterogeneous UAV Networks

A User-Oriented Intelligent Access Selection Algorithm in Heterogeneous Wireless Networks

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