Sensors, Vol. 19, Pages 2374: FSF: Applying Machine Learning Techniques to Data Forwarding in Socially Selfish Opportunistic Networks

Sensors, Vol. 19, Pages 2374: FSF: Applying Machine Learning Techniques to Data Forwarding in Socially Selfish Opportunistic Networks Sensors doi: 10.3390/s19102374 Authors: Camilo Souza Edjair Mota Diogo Soares Pietro Manzoni Juan-Carlos Cano Carlos T. Calafate Enrique Hernández-Orallo Opportunistic networks are becoming a solution to provide communication support in areas with overloaded cellular networks, and in scenarios where a fixed infrastructure is not available, as in remote and developing regions. A critical issue, which still requires a satisfactory solution, is the design of an efficient data delivery solution trading off delivery efficiency, delay, and cost. To tackle this problem, most researchers have used either the network state or node mobility as a forwarding criterion. Solutions based on social behaviour have recently been considered as a promising alternative. Following the philosophy from this new category of protocols, in this work, we present our “FriendShip and Acquaintanceship Forwarding” (FSF) protocol, a routing protocol that makes its routing decisions considering the social ties between the nodes and both the selfishness and the device resources levels of the candidate node for message relaying. When a contact opportunity arises, FSF first classifies the social ties between the message destination and the candidate to relay. Then, by using logistic functions, FSF assesses the relay node selfishness...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research