Cross-layer Design and Mechanisms
The LDST exhibits research in the area of cross layer design that utilize information available of different layers in the protocol stack in wireless network in order to improve end-to-end Quality of Service (QoS). More specifically the LDST uses cross layer techniques to address the problems of power management, and content, video and in general multimedia data transmission over mobile ad-hoc networks.
Power Management in Wireless Networks
Efficient power management for content transmission over 802.11x wireless networks is important for both energy conservation and Quality of Experience (QoE) for multimedia applications.
LDST has been working in this area using both simulation software (ns-2) and actual implementations on Linux-based operating systems. ns-2 simulations have evaluated proposed algorithms that utilize TFRC feedback reports in order to adjust the transmission power level.
The algorithms focus on quick convergence to near-optimal values, and have been further extended to interact with the application layer, in case of video transmission, so that more important frames are prioritized in terms of available transmission power utilization. The extensions include interaction with H.264 SVC standard information.
Linux-based implementations that were developed by LDST focus on adjusting the wireless card power level using the RSSI (Received Signal Strength Indication) measurements, and can be extended with cross-layer optimizations.
Related Information
Efficient power management for content transmission over 802.11x wireless networks is important for both energy conservation and Quality of Experience (QoE) for multimedia applications.
LDST has been working in this area using both simulation software (ns-2) and actual implementations on Linux-based operating systems. ns-2 simulations have evaluated proposed algorithms that utilize TFRC feedback reports in order to adjust the transmission power level.
The algorithms focus on quick convergence to near-optimal values, and have been further extended to interact with the application layer, in case of video transmission, so that more important frames are prioritized in terms of available transmission power utilization. The extensions include interaction with H.264 SVC standard information.
Linux-based implementations that were developed by LDST focus on adjusting the wireless card power level using the RSSI (Received Signal Strength Indication) measurements, and can be extended with cross-layer optimizations.
LDST has been working in this area using both simulation software (ns-2) and actual implementations on Linux-based operating systems. ns-2 simulations have evaluated proposed algorithms that utilize TFRC feedback reports in order to adjust the transmission power level.
The algorithms focus on quick convergence to near-optimal values, and have been further extended to interact with the application layer, in case of video transmission, so that more important frames are prioritized in terms of available transmission power utilization. The extensions include interaction with H.264 SVC standard information.
Linux-based implementations that were developed by LDST focus on adjusting the wireless card power level using the RSSI (Received Signal Strength Indication) measurements, and can be extended with cross-layer optimizations.
Related Information
Video/Multimedia Transmission in MANETs/Ad-hoc Networks
Mobile Ad-hoc networks (MANETs) and ad-hoc networks in general, are becoming more essential to wireless communications and more relevant, due to the growing popularity of mobile devices, and the need for ubiquitous access.
However, multimedia data transmission and especially video streaming are facing a number of technical challenges due the mobility factor and the continuous changing topology.
The LDST work in this area focuses on cross layer design in order to improve video streaming over these networks (allowing for more concurrent video streams to be transmitted and played without interruption or severe quality degradation).
In particular the LDST work exploits information available only on the physical layer of wireless networks (such as the SNR) to affect the way the network layer (and especially routing) operates (in order to avoid connection outages due to link degradation or disconnection), as well as to affect the way video is encoded at the application layer (in order to adjust the video quality to the transmission capacity of the underlying network).
LDST also exploits information available on the application layer (such as the possible priority of some data) to affect how the network and the physical layer handle the respective data.
Finally, LDST researches the application of these mechanisms in the use of ad-hoc networks in crisis management situations (e.g., first responders, the fire brigade, and other related agencies in disaster areas).
However, multimedia data transmission and especially video streaming are facing a number of technical challenges due the mobility factor and the continuous changing topology.
The LDST work in this area focuses on cross layer design in order to improve video streaming over these networks (allowing for more concurrent video streams to be transmitted and played without interruption or severe quality degradation).
In particular the LDST work exploits information available only on the physical layer of wireless networks (such as the SNR) to affect the way the network layer (and especially routing) operates (in order to avoid connection outages due to link degradation or disconnection), as well as to affect the way video is encoded at the application layer (in order to adjust the video quality to the transmission capacity of the underlying network).
LDST also exploits information available on the application layer (such as the possible priority of some data) to affect how the network and the physical layer handle the respective data.
Finally, LDST researches the application of these mechanisms in the use of ad-hoc networks in crisis management situations (e.g., first responders, the fire brigade, and other related agencies in disaster areas).
Related Information
Feedback-based Adaptation for Improved Power Consumption
As networks and connected devices become more mobile and thus energy constrained, and as the requirements for lower energy consumption become more demanding, the issue of consumed power by wireless network cards is becoming an intensively researched topic.
The LDST works in this area on simulations and on field tests. This is an implementation with the purpose to minimize the power consumption on wireless networks by reducing the Strength of the signal if possible. The algorithms is based on feedback reports of the received signal strength indicator.
The LDST works in this area on simulations and on field tests. This is an implementation with the purpose to minimize the power consumption on wireless networks by reducing the Strength of the signal if possible. The algorithms is based on feedback reports of the received signal strength indicator.
The above work depends on a specific adapter and adjusts the power according the received signal strength indicator (RSSI).
TheLDST has also implemented the Signal Adaptation Mechanism (SAM). SAM is driver independent and optimizes the power depending on the quality of the connection instead of RSSI. The quality of the connection is measured through the Signal to Noise Ratio (SNR) and the mechanism guarantees a fair SNR value (if feasible) and better quality of service.
TheLDST has also implemented the Signal Adaptation Mechanism (SAM). SAM is driver independent and optimizes the power depending on the quality of the connection instead of RSSI. The quality of the connection is measured through the Signal to Noise Ratio (SNR) and the mechanism guarantees a fair SNR value (if feasible) and better quality of service.