Autonomic Information Diffusion in Intermittently Connected Networks

In this work, we introduce a framework for designing autonomic information diffusion mechanisms in intermittently connected wireless networks. Our approach is based on the use of techniques and tools drawn from evolutionary computing research, which enable to embed evolutionary features in epidemic-style forwarding mechanisms. In this way, it is possible to build a system in which information dissemination strategies change at run-time to adapt to the current network conditions in a distributed autonomic fashion. A case study is then introduced, for which design and implementation choices are presented and discussed. Simulation results are reported to validate the ability of the proposed protocol to converge to the optimal operating point (or close to it) in unknown and changing environments.