Vehicle as a computational resource: Optimizing quality of experience for connected vehicles in a smart city

2021 ◽  
pp. 100432
Author(s):  
Abdallah H. Salem ◽  
Issam W. Damaj ◽  
Hussein T. Mouftah
Keyword(s):  
Smart City ◽  
Quality Of Experience ◽  
Connected Vehicles ◽  
Computational Resource

ASSESSMENT OF SMART EXPERIENCE OF TOURISTS AND LOCAL CITIZENS IN TOURIST DESTINATION

2020 ◽  
Author(s):  
Genka Rafailova ◽  
◽  
Antonio Hadzhikolev ◽  
Keyword(s):  
Quality Of Life ◽  
Smart City ◽  
Quality Of Experience ◽  
Business Environment ◽  
Competitive Advantages ◽  
Tourist Destination ◽  
The Creation ◽  
Smart Technologies ◽  
Smart Infrastructure

The concept of smart city is used for development of smart destination with purpose of achieving competitive advantages and enhancing the quality of experience for tourists and quality of life for local citizens. There are wide accepted characteristics of smart tourist destination such as built smart infrastructure, smart technologies application as well as criteria for defining smartness of destination as digitalization, accessibility and mobility. But the main focus of development of smart destination is the creation of smart experience and smart business environment. That stresses the importance of assessment of how tourists and local citizens appreciate their experience. The aim of this paper is to present a model for study and assessment of smart experience of tourists and local citizens in tourist destination. The model is applied in tourist destination Varna. Results of conducted research of tourists and local citizens' view and acceptance, based on their experience, are presented.

Quality indicator of the smart city transportation and logistics system

2020 ◽  
Vol 19 (11) ◽  
pp. 2116-2135
Author(s):  
G.V. Savin
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart City ◽  
Urban Areas ◽  
Human Life ◽  
Life Quality ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Logistics System ◽  
Organization And Management

Subject. The article considers functioning and development of process flows of transportation and logistics system of a smart city. Objectives. The study identifies factors and dependencies of the quality of human life on the organization and management of stream processes. Methods. I perform a comparative analysis of previous studies, taking into account the uniquely designed results, and the econometric analysis. Results. The study builds multiple regression models that are associated with stream processes, highlights interdependent indicators of temporary traffic and pollution that affect the indicator of life quality. However, the identified congestion indicator enables to predict the time spent in traffic jams per year for all participants of stream processes. Conclusions. The introduction of modern intelligent transportation systems as a component of the transportation and logistics system of a smart city does not fully solve the problems of congestion in cities at the current rate of urbanization and motorization. A viable solution is to develop cooperative and autonomous intelligent transportation systems based on the logistics approach. This will ensure control over congestion, the reduction of which will contribute to improving the life quality of people in urban areas.

Systematization of the smart city civil engineering infrastructure

2020 ◽  
Vol 18 (4) ◽  
pp. 765-779
Author(s):  
E.V. Popov ◽  
K.A. Semyachkov ◽  
K.V. Zhunusova
Keyword(s):  
Smart City ◽  
Process Management ◽  
Smart Cities ◽  
Digital Technologies ◽  
Urban Infrastructure ◽  
Infrastructure Development ◽  
Economic Innovation ◽  
Civil Engineering Infrastructure ◽  
Functional Transformations

Subject. This article explores the basic elements of the engineering infrastructure of smart cities. Objectives. The article aims to systematize theoretical descriptions of the engineering infrastructure of a smart city. Methods. For the study, we used a logical analysis and systematization. Results. The article highlights the main areas of infrastructure development of smart cities. Conclusions. Improving process management mechanisms, optimizing urban infrastructure, increasing the use of digital technologies, and developing socio-economic innovation improve the quality of the urban environment in a digitalized environment. And improving the efficiency of urban planning and security, studying its properties and characteristics, and forming an effective urban information system lead to its functional transformations.

UBAR

2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  
Keyword(s):  
Resource Management ◽  
Quality Of Experience ◽  
State Of The Art ◽  
State Of Charge ◽  
User Preference ◽  
Management Approach ◽  
High Quality ◽  
Trade Off ◽  
Run Time

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

scholarly journals Quality-of-Experience-Aware Incentive Mechanism for Workers in Mobile Device Cloud

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sajeeb Saha ◽  
Md. Ahsan Habib ◽  
Tamal Adhikary ◽  
Md. Abdur Razzaque ◽  
Md. Mustafizur Rahman ◽  
...  
Keyword(s):  
Mobile Device ◽  
Quality Of Experience ◽  
Incentive Mechanism ◽  
Mobile Device Cloud

What's my App?

2021 ◽  
Vol 48 (4) ◽  
pp. 41-44
Author(s):  
Dena Markudova ◽  
Martino Trevisan ◽  
Paolo Garza ◽  
Michela Meo ◽  
Maurizio M. Munafo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Real Time ◽  
Language Processing ◽  
Traffic Management ◽  
Quality Of Experience ◽  
Broadband Internet ◽  
Management Policies ◽  
Control Traffic

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.

scholarly journals Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

2021 ◽  
Vol 48 (4) ◽  
pp. 37-40
Author(s):  
Nikolas Wehner ◽  
Michael Seufert ◽  
Joshua Schuler ◽  
Sarah Wassermann ◽  
Pedro Casas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Quality Of Experience ◽  
Unique Feature ◽  
Time Series Modeling ◽  
Web Browsing ◽  
Large Dataset ◽  
Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

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