Organizer:
Anna Forster, University of Lugano, anna.egorova.foerster@lu.unisi.ch
Frederick Ducatelle, IDSIA-SUPSI, frederick@idsia.ch
Gianni Di Caro, IDSIA-SUPSI, gianni@idsia.ch
Ganesh Venayagamoorthy, Missouri University of Science and Technology, ganeshv@mst.edu

Wireless ad hoc networks are communication networks that consist entirely of wireless nodes, placed together in an ad hoc manner, i.e. with minimal planning. Nodes can enter or leave the network at any time, and may be mobile, so that the network topology continuously experiences alterations during deployment. Examples include mobile ad hoc networks and sensor networks. Wireless ad hoc networks pose substantially different challenges compared to more traditional communication networks: they are highly dynamic, have limited resources (in terms of bandwidth, computation power, battery, etc.), have a highly decentralized organization, rely on often unreliable wireless communication channels, etc. As a consequence, new algorithms and protocols are needed for all aspects of the organization of these networks. Swarm Intelligence methods seem particularly interesting in this context. This is because their ability to solve difficult tasks in a distributed and robust way using locally interacting simple agents fits closely to the typical environment created by an ad hoc network.

In this special session, we solicit novel and interesting contributions in this area. Topics of interest include but are not limited to:

* Distributed medium access control using Swarm Intelligence
* Swarm Intelligence routing protocols in ad hoc networks
* Swarm Intelligence transport layer protocols in ad hoc networks
* Swarm Intelligence for QoS provisioning
* Self-configuration, self-optimization and self-adaptation through swarm intelligence
* Off-line optimization for ad hoc networks through swarm intelligence
* Comparisons between Swarm Intelligence and other algorithms for ad hoc networks
* Implementation of Swarm Intelligence approaches in testbeds
* Evaluation of Swarm Intelligence approaches in real-world settings