Showing posts with label wireless networks. Show all posts
Showing posts with label wireless networks. Show all posts

Monday, May 11, 2020

Remember the Conferences?

After a couple of weeks in self-isolation due to the global epidemic of the COVID-19 virus, we are getting used to having conferences entirely online. To cheer you up we are posting some impressions from one of our last conference visits that actually took place physically.
Casa Convalescència

We had been at WiMob 2019, the 15th International Conference on Wireless and Mobile Computing, Networking and Communications. The event brought together top researchers and practitioners and created a forum for the exchange of experience and knowledge among researchers and developers concerned with wireless and mobile technology.
In addition to presenting our paper at a top conference, we also enjoyed the nice conference place - the event took place in Barcelona and was organized at the venerable Casa Convalescència. The building is one of the great works of Catalan Modernism, and was declared Historical Artistic Monument in 1978 and World Cultural Heritage Site by UNESCO in 1997. The building is part of the historic site of the Hospital de la Santa Creu i Sant Pau.
Plenary at WiMob'19

Being in such an inspiring environment, the conference went great. Martina Umlauft presented our paper "Topology Characterization for Position-based Wireless Network Topology Generators" in front of an interesting crowd with great success. In the paper, we discuss methods to characterize network topology based solely on the spatial positions of the nodes on the terrain are necessary. Topologies are usually characterized in terms of their network graph; usually by investigating their degree frequency, rank/degree, or hop/count distributions. Wireless network simulation, on the other hand, typically does not use network graphs. Instead, in most wireless simulations, nodes are first positioned on the terrain based on some positioning algorithm and then a radio propagation model is used to determine connectivity dynamically at simulation run-time. We propose several metrics and show how they can be used to evaluate position-based topologies: the nearest neighbor distance distribution, a threshold, and a probabilistic node degree measure, and the application of an inhomogeneity measure for spatial distributions.

Please find the presented paper here:

Martina Umlauft and Wilfried Elmenreich. Topology Characterization for Position-based Wireless Network Topology Generators. In Anna Maria Vegni, editor, 15th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2019), Piscataway (NJ), Oktober 2019. IEEE.

We hope this little report helped to remember the better times. See you hopefully soon at the next real conference ;-)


Friday, March 11, 2011

Self-organized positioning of mobile relays

Quadcopter from Microdrones
The Fifth International Workshop on Self-Organizing Systems (IWSOS 2011) in Karlsruhe was a great success. Helmut Lindner won the best student poster award with his work on self-organizing mobile drones.
In catastrophic scenarios, wireless communication is an important means for coordinating rescue and saving operations. However, in such situations, the standard communication infrastructure is often not available. One possibility to solve this problem would be the usage of helicopter drones as flying relay stations. For the positioning of the drones, we would have to cope with disturbances of wireless media (interference from other nets, signal fading, etc.), an unknown landscape, as well as the need to add or remove relay nodes as they need to recharge.
Simulation of movement patterns for four drones
At IWSOS 2011, Helmut Lindner presented a evolution-based algorithm for a self-organizing positioning of the drones. The ground stations are connected by multi-hop communication over drone relays. For each possible route a “flow” value Phi is calculated, which serves as a local fitness function for each drone. The drones are moving around in order to increase their Phi value. A movement that worsened the Phi value is reversed, while for an improvement, the current direction is kept. This way, the drones execute a distributed (1+1) evolution strategy.