A QoS/Mobility-Aware Model for Mobile Internet

Lopes, Nuno Vasco; Nicolau, Maria João; Santos, Alexandre

doi:10.24138/jcomss.v7i2.177

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…These tables show the envisioned traffic load, for the traffic generated in the simulations, on pAR and nAR, after applying the admission control to new flows and after MNs have completed the handover. The proposed model is able to work rightly under any traffic load condition, however, the model can be more efficient if the △min i parameter is tuned to the traffic dynamics [22].…”

Section: Model Performanceandresultsmentioning

confidence: 99%

Evaluation of a Mobility/QoS-aware Architecture for Mobile Internet

Lopes¹,

Nicolau

Santos³

2012

JCOMSS

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This paper addresses the issue of mobility and QoS management principles as well as the mobility and QoSmanagement integration in the sense of build a QoS-awarearchitecture for mobile Internet. After covering the mobilityand QoS management principles and integration, this paper also proposes a new QoS-aware architecture for mobile Internet. This new architecture takes into account the specific characteristics of mobile networks in order to design an integrated Mobility/QoS-aware management architecture suitable for realtime applications requirements.The solution avoids network congestion as well as the starvation of less priority DiffServ classes while increasing resource utilization for priority classes while maintaining the QoS offer to MN’s applications by making each DiffServ class unchangeable with MNs mobility. The proposed mobility management model is simple, easy to implement, and takes into consideration mobile internet requirements. It proved to be capable of providing Internet applications running in mobile devices with acceptable levels of quality of service. The simulation results indicate that the suggested architecture is able to provide acceptable QoS levels to real-time applications that are running in mobiles devices.

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Section: Model Performanceandresultsmentioning

confidence: 99%

Evaluation of a Mobility/QoS-aware Architecture for Mobile Internet

Lopes¹,

Nicolau

Santos³

2012

JCOMSS

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“…In a service available for the network not only the Quality of Service is important [5], [6] but also the user Quality of Experience (QoE) [7] is an important factor to be considered. The users QoE is a measure used to evaluate the degree of pleasure, or discomfort, when trying out a product or service.…”

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confidence: 99%

A Recommendation System for Shared-Use Mobility Service through Data Extracted from Online Social Networks

Rosa

Lasmar

Rodríguez

2018

JCOMSS

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In recent years, the shared mobility service has increased in many countries across the world because its low cost and several shared-use mobility applications on mobile devices [1]. Commonly, if a ride is shared between people with similar preferences, users likely feel both more comfortable and safe. In this context, the main goal of this article is to classify users with similar preferences, in automatic manner, to improve user's quality of experience in ridesharing service. To obtain initial data, subjective tests are carried out using questionnaires and their results are used to determine ridesharing profiles. Then, some basic user profile information is extracted from Online Social Networks (OSN) to determine an user profile based on preferences in ridesharing service. The user profile classification is performed through different machine learning algorithms, which use as input the data extracted from OSN. Two case studies of shared-mobility are treated, (i) sharing a ride with a passenger with a similar hobby [2], and (ii) sharing a ride with people that support an opposite football teams. The novelty of this work consists in the application of the Hybrid Discriminative Restricted Boltzmann Machines (HDRBM) algorithm for classification whose performance overperforms widely used algorithms such as Random Forest, SVM and DRBM. The experimental results presented a correctly classified instance of 96.9% and 97.3% for the cases of sharing a ride with people with similar hobby and support different football team, respectively. Finally, a Recommendation System (RS) is proposed, which efficiency is compared with a basic RS, obtaining a Pearson correlation coefficient of 0.97 and 0.71, respectively. Index Terms-Recommendation system; ridesharing; mobile applications, online social networks, machine learning, HDRBM, social web analysis tool. I. INTRODUCTION T HE mobile devices and applications have facilitated the manner of people request services. The urban mobility is a kind of service that became very popular, in it is common that people share the rides among others. The advantages of this service are related to benefits for urban traffic, such as reduction of environment pollution, and low cost.

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A QoS/Mobility-Aware Model for Mobile Internet

Cited by 2 publications

References 21 publications

Evaluation of a Mobility/QoS-aware Architecture for Mobile Internet

Evaluation of a Mobility/QoS-aware Architecture for Mobile Internet

A Recommendation System for Shared-Use Mobility Service through Data Extracted from Online Social Networks

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