Improving Quality of Experience in multimedia Internet of Things leveraging machine learning on big data

2018 ◽  
Vol 86 ◽  
pp. 1413-1423 ◽  
Author(s):  
Xiaohong Huang ◽  
Kun Xie ◽  
Supeng Leng ◽  
Tingting Yuan ◽  
Maode Ma
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Quality Of Experience ◽  
Multimedia Internet Of Things

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 A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 318
Author(s):  
Merima Kulin ◽  
Tarik Kazaz ◽  
Eli De Poorter ◽  
Ingrid Moerman
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Performance Improvement ◽  
Quality Of Experience ◽  
Data Driven ◽  
Protocol Stack ◽  
Comprehensive Survey ◽  
Network Prediction ◽  
Network Quality

This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

Introduction to Smart City and Agricultural Revolution

2019 ◽  
pp. 135-176
Author(s):  
Rajesh Angadi
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Smart City ◽  
Agricultural Development ◽  
Agricultural Revolution ◽  
Information And Communication ◽  
Development Goals ◽  
Smart Agriculture ◽  
National Food Security

In this chapter, a discussion is presented about what Big Data and Internet of Things (IoT) really is and what intricacies are used while building big data and internet of things. Further Big Data and Internet of Things have been used for building an application used for Smart City & Agriculture. A smart city is an urban development vision to integrate multiple information and communication technology (ICT) solutions. Smart city's goal is to improve quality of life with technology to improve the efficiency of services and meet residents' needs. Smart agriculture approach is to develop, transform and reorient agricultural development under new realities of climate change. It increases productivity enhances resilience (adaptation), reduces mitigation with achievement of national food security and development goals. This chapter includes detailed discussion on Smart City and Smart Agriculture along with planning, designing as well as various approaches used to build and implement them effectively.

Sign in / Sign up

Export Citation Format

Share Document