Projects Current Projects

Please, see the list of LCCN offered projects:

Project Title:

MANET Traffic Performance Prediction

Supervisors:
Itzik Ashkenazi,
Description:
Mobile Ad-hoc NETworks (MANET) is a communication platform for wireless first response units that creates a temporary network without any help of any centralized support. MANET is characterized by its rapidly changing connectivity and bandwidth over the communication links. Mobile Ad Hoc Network is a collection of wireless hosts that creates a temporary network without any help of any centralized support. At the same time, the application runs on the units often requires strict availability of end to end bandwidth and delay. It is essential to be build an optimization tool that will be able to predict the traffic bandwidth or the delay performance once the network topology changes or a new application starts running. Developing such tool requires network modeling. Nowadays, network models are either based on packet-level simulators or analytical models (e.g., queuing theory). Packet–level simulators are very costly computationally, while the analytical models are fast but not accurate. Hence, Machine Learning (ML) arises as a promising solution to build accurate network models able to operate in real time and to predict the resulting network performance according to the target policy, i.e maximum bandwidth or minimum end-to-end delay. Recently, Graph Neural Networks (GNN) have shown a strong potential to be integrated into commercial products for network control and management. Early works using GNN have demonstrated capability to learn from different network characteristics that are fundamentally represented as graphs, such as the topology, the routing configuration, or the traffic that flows along a series of nodes in the network. In contrast to previous ML-based solutions, GNN enables to produce accurate predictions even in networks unseen during the training phase.
Picture of MANET Traffic Performance Prediction Project
 
Project Title:

MPEG-DASH Proxy Live Streaming from Multiple Servers in Unstable Environment

Supervisors:
Aviel Glam (Rafael), Itzik Ashkenazi,
Description:
MPEG-DASH (Moving Picture Experts Group - Dynamic Adaptive Streaming over HTTP) is a vendor independent, international standard ratified in 2012. One of the main benefits of MPEG-DASH is reduction of startup delays and buffering/stalls during the video and continued adaptation to the bandwidth situation of the client. Today, MPEG-DASH is gaining more and more deployments, accelerated by services such as Netflix or Google, which recently switched to this new standard. With these two major sources of internet traffic, 50% of total internet traffic is already MPEG-DASH. The basic idea of MPEG-DASH is as follows: chop the media file into different bitrates or spatial resolutions encoded segments. The segments are provided on a Web server and can be downloaded through HTTP standard compliant GET requests where the HTTP Server serves different qualities, chopped into segments of equal length. Since the client knows its capabilities, received throughput and the context of the user best - the adaptation to the best bitrate or resolution is done on the client side for each segment. In certain cases, there is a need for multiple clients to receive the same video stream. Since the client’s bandwidth and connection quality can vary, the challenge is to stream to each client the best possible quality, using MPEG-DASH Proxy, while maintaining live streaming. A greater challenge is to support in addition cases where there are multiple servers and the proxy-servers connection is unstable.
Picture of MPEG-DASH Proxy Live Streaming from Multiple Servers in Unstable Environment Project
 
Project Title:

MPEG-DASH Live Streaming in Unstable Environment

Supervisors:
Aviel Glam (Rafael), Itzik Ashkenazi,
Description:
MPEG-DASH (Moving Picture Experts Group - Dynamic Adaptive Streaming over HTTP) is a vendor independent, international standard ratified in 2012. One of the main benefits of MPEG-DASH is reduction of startup delays and buffering/stalls during the video and continued adaptation to the bandwidth situation of the client. Today, MPEG-DASH is gaining more and more deployments, accelerated by services such as Netflix or Google, which recently switched to this new standard. With these two major sources of internet traffic, 50% of total internet traffic is already MPEG-DASH. The basic idea of MPEG-DASH is as follows: chop the media file into different bitrates or spatial resolutions encoded segments. The segments are provided on a Web server and can be downloaded through HTTP standard compliant GET requests where the HTTP Server serves different qualities, chopped into segments of equal length. Since the client knows its capabilities, received throughput and the context of the user best - the adaptation to the best bitrate or resolution is done on the client side for each segment. In previous semester, we managed to achieve MPEG-DASH live streaming (sub 2 second delay) in an un-stable environment by improving the client’s rate adaptation algorithm. The traffic instability was simulated by Netem tool. In this project we will use MiniNet-WiFi that will emulate WiFi mobile clients and will research the MPEG-DASH client rate adaption in various mobility models
Picture of MPEG-DASH Live Streaming in Unstable Environment Project
 
Project Title:

Smart Mobile LoRaWAN Gateway

Supervisors:
Aviel Glam (Rafael), Itzik Ashkenazi,
Description:
Low-power WAN (LPWAN) is a wireless wide area network specification that interconnects low-bandwidth, battery-powered sensors with low bit rates over long ranges. To meet the challenges of long range, low power consumption and secure data transmission, the sensors are based on LoRa Technology and on LoRaWAN media access control (MAC) layer protocol that manages communication between LPWAN sensors and the Gateway. Not in all circumstances its possible for an end node sensor to communicate with the outside world. This requires to use mobile gateway utilized on drone. The drone on its flight path can reach the remote location where the sensor device is running and collect its data. The challenge in this solution is to establish a communication link with every sensor node, by being at the correct location at the right sensor duty cycle time.
Picture of Smart Mobile LoRaWAN Gateway Project
 
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