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Opportunistic networks could become the solution to provide communicationsupport in both cities where the cellular network could be overloaded, and inscenarios where a fixed infrastructure is not available, like in remote anddeveloping regions. A critical issue that still requires a satisfactorysolution is the design of an efficient data delivery solution. Socialcharacteristics are recently being considered as a promising alternative. Mostopportunistic network applications rely on the different mobile devices carriedby users, and whose behavior affects the use of the device itself.  This work presents the "Friendship and Selfishness Forwarding" (FSF)algorithm. FSF analyses two aspects to make message forwarding decisions when acontact opportunity arises: First, it classifies the friendship strength amonga pair of nodes by using a machine learning algorithm to quantify thefriendship strength among pairs of nodes in the network. Next, FSF assesses therelay node selfishness to consider those cases in which, despite a strongfriendship with the destination, the relay node may not accept to receive themessage because it is behaving selfishly, or because its device has resourceconstraints in that moment.  By using trace-driven simulations through the ONE simulator, we show that theFSF algorithm outperforms previously proposed schemes in terms of deliveryrate, average cost, and efficiency.

Friendship and Selfishness Forwarding: applying machine learning techniques to Opportunistic Networks data forwarding