Context aware qo e modelling, measurement, and prediction in mobile computing systems +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

1. Context-Aware QoE Modelling, Measurement and Prediction in Mobile
Computing Systems
Abstract:
Quality of Experience (QoE) as an aggregate of Quality of Service (QoS)
and human user-related metrics will be the key success factor for current
and future mobile computing systems. QoE measurement and prediction
are complex tasks as they may involve a large parameter space such as
location, delay, jitter, packet loss and user satisfaction just to name a few.
These tasks necessitate the development of practical context-aware QoE
models that efficiently determine relationships between user context and
QoE parameters. In this paper, we propose, develop and validate a novel
decision-theoretic approach called CaQoEM for QoE modelling,
measurement and prediction. We address the challenge of QoE
measurement and prediction where each QoE parameter can be measured
on a different scale and may involve different units of measurement.
CaQoEM is context-aware and uses Bayesian networks and utility theory to
measure and predict users’ QoE under uncertainty. We validate CaQoEM
using extensive experimentation, user studies and simulations. The results
soundly demonstrate that CaQoEM correctly measures range-defined QoE
using a bipolar scale. For QoE prediction, an overall accuracy of 98.93%
was achieved using 10-fold cross validation in multiple diverse network

2. conditions such as vertical handoffs, wireless signal fading and wireless
network congestion.
Existing System:
We argue that QoE is a broader construct which encompasses QoS
parameters along with user related factors such as their behavioural,
cognitive and psychological states along with the context in which these
products and services are provided to them.
These factors determine their overall QoE related to a particular application
or service. In mobile computing environments, users’ behaviour is dynamic
as they use applications or services in different scenarios. Thus, for QoE
measurement and prediction, it is important to consider parameters related
to users, their device(s) and environment along with network QoS.
Proposed System:
We propose, develop and validate a context-aware method for QoE
modeling based on context spaces model (CSM) and Bayesian networks.
Our method considers several context attributes and QoE parameters. It
provides a simple and efficient way to determine relationships between
these attributes as well as parameters to measure and predict users’ QoE.
We propose, develop and validate a context-aware, decision-theoretic
method for QoE measurement and prediction under uncertainty on a

3. single scale. We validate CaQoEM using a number of case studies. We
developed a prototype and performed a number of subjective tests.
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• RAM : 256 Mb.
Software Requirements:
• Operating system : - Windows XP.
• Front End : - JSP
• Back End : - SQL Server
Software Requirements:
• Operating system : - Windows XP.
• Front End : - .Net
• Back End : - SQL Server