Mobility Prediction for Multimedia Services

2011 ◽  
pp. 491-506 ◽  
Author(s):  
Damien Charlet ◽  
Frédéric Lassabe ◽  
Philippe Canalda ◽  
Pascal Chatonnay ◽  
François Spies
Keyword(s):  
Mobility Prediction ◽  
Mobile Multimedia ◽  
Positioning System ◽  
Multimedia Services ◽  
Mobile Access ◽  
Rich Media ◽  
New Challenges ◽  
New Applications ◽  
Indoor Positioning System ◽  
Network Scalability

Advances in technology have enabled a proliferation of mobile devices and a broad spectrum of novel and out breaking solutions for new applications and services. In the present, more and more people and companies are demanding mobile access to multimedia services such as real-time rich media. Today, it is necessary to be able to predict adaptation behaviour which concerns and addresses not only the mobile usage or the infrastructure availability, but the service quality especially the continuity of service. Our chapter provides insight to new challenges of mobile multimedia services and applications: Wifi indoor positioning system adapted to heterogeneous building, static and learning mobility prediction, predictive handover policy for multimedia cache management, mobile multimedia guide (such as museum), and network scalability.

Mobility Prediction for Multimedia Services

2008 ◽  
pp. 1766-1780
Author(s):  
Damien Charlet ◽  
Frédéric Lassabe ◽  
Philippe Canalda ◽  
Pascal Chatonnay ◽  
François Spies
Keyword(s):  
Mobility Prediction ◽  
Mobile Multimedia ◽  
Positioning System ◽  
Multimedia Services ◽  
Mobile Access ◽  
Rich Media ◽  
New Challenges ◽  
New Applications ◽  
Indoor Positioning System ◽  
Network Scalability

Advances in technology have enabled a proliferation of mobile devices and a broad spectrum of novel and out breaking solutions for new applications and services. In the present, more and more people and companies are demanding mobile access to multimedia services such as real-time rich media. Today, it is necessary to be able to predict adaptation behaviour which concerns and addresses not only the mobile usage or the infrastructure availability, but the service quality especially the continuity of service. Our chapter provides insight to new challenges of mobile multimedia services and applications: Wifi indoor positioning system adapted to heterogeneous building, static and learning mobility prediction, predictive handover policy for multimedia cache management, mobile multimedia guide (such as museum), and network scalability.

Mobility Prediction for Multimedia Services

2010 ◽  
pp. 961-974
Author(s):  
Damien Charlet ◽  
Frédéric Lassabe ◽  
Philippe Canalda ◽  
Pascal Chatonnay ◽  
François Spies
Keyword(s):  
Mobility Prediction ◽  
Mobile Multimedia ◽  
Positioning System ◽  
Multimedia Services ◽  
Mobile Access ◽  
Rich Media ◽  
New Challenges ◽  
New Applications ◽  
Indoor Positioning System ◽  
Network Scalability

Advances in technology have enabled a proliferation of mobile devices and a broad spectrum of novel and out breaking solutions for new applications and services. In the present, more and more people and companies are demanding mobile access to multimedia services such as real-time rich media. Today, it is necessary to be able to predict adaptation behaviour which concerns and addresses not only the mobile usage or the infrastructure availability, but the service quality especially the continuity of service. Our chapter provides insight to new challenges of mobile multimedia services and applications: Wifi indoor positioning system adapted to heterogeneous building, static and learning mobility prediction, predictive handover policy for multimedia cache management, mobile multimedia guide (such as museum), and network scalability.

Mobility within Rich Multimedia Services

2010 ◽  
pp. 804-819
Author(s):  
Frédéric Lassabe ◽  
Philippe Canalda ◽  
Damien Charlet ◽  
Pascal Chatonnay ◽  
François Spies
Keyword(s):  
Broad Spectrum ◽  
Mobile Multimedia ◽  
Positioning System ◽  
Multimedia Services ◽  
Mobile Access ◽  
Rich Media ◽  
New Challenges ◽  
New Applications ◽  
Indoor Positioning System ◽  
Network Scalability

Advances in technology have enabled a proliferation of mobile devices and a broad spectrum of novel and outbreaking solutions for new applications and services. Presently more and more people and companies are demanding mobile access to multimedia services such as real-time rich media. Today, it is necessary to be able to predict adaptation behavior that concerns and addresses not only the mobile usage or the infrastructure availability, but also the service quality, especially the continuity of service. Our chapter provides insight to new challenges of mobile multimedia services and applications: wifi indoor positioning system adapted to heterogeneous building, static and learning mobility prediction, predictive handover policy for multimedia cache management, mobile multimedia guide (e.g., museums), and network scalability.

scholarly journals High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3701
Author(s):  
Ju-Hyeon Seong ◽  
Soo-Hwan Lee ◽  
Won-Yeol Kim ◽  
Dong-Hoan Seo
Keyword(s):  
High Precision ◽  
Distance Estimation ◽  
Sampling Period ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Location Determination ◽  
Indoor Positioning System

Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

Sign in / Sign up

Export Citation Format

Share Document