Wireless technology and relative powerful mobile devices enable advanced applications, such as video streaming to be set up in areas without infrastructure. These complex networks are called Mobile Ad-Hoc Networks (MANETs). Improving the stability and performance of video streaming over MANETs is an active area of research.The motivation behind this thesis is to visualize network simulations for such complex networks. With the effect that the visualization tool may help research and development off protocols to solve the many network related challenges. The existing visualization tool Bienvisto was developed to help in this research as part of the DT- Stream project. Bienvisto visualized nodes present in the network simulation, the routes and the network transmission between these nodes. Bienvisto lacks some features to help users to better understand such complex network. These are:
1. To better visualize routes between nodes, both from a global and a local perspective. 2. An overview of node properties and metrics, including their identifier to be able to distinguish between the nodes in the network. 3. Provide statistics and metrics measured from the video stream transmitted over the network.
During early testing of our rough models of the features we wanted to support, we were faced with some challenges. We found several issues that forced us to re-evaluate the underlying architecture of Bienvisto. Thus, we developed the new visualization tool Broad. It is based on Bienvisto, and most of the visual aspects such as how nodes, routes and network transmissions between these nodes are visualized, only the chart presentation was kept unchanged. The required features have been addressed and implemented, which represent the main contributions of this master thesis:
1. Visualization of routes from a global perspective, and the ability to inspect routes from a local perspective. Finally we support analyzing paths between nodes. 2. Ability to distinguish between the different nodes in the network, inspect their properties and related metrics. 3. Statistics and metrics related to the video stream transmitted over the network. 4. Topology of the network as adjacency matrices both from a global and a localperspective.