A comprehensive survey on Machine Learning techniques in opportunistic networks: Advances, challenges and future directions

Abstract

Machine Learning (ML) is growing in popularity and is applied in numerous fields to solve complex problems. Opportunistic Networks are a type of Ad-hoc Network where a contemporaneous path does not always exist. So, forwarding methods that assume the availability of contemporaneous paths does not work. ML techniques are applied to resolve the fundamental problems in Opportunistic Networks, including contact probability, link prediction, making a forwarding decision, friendship strength, and dynamic topology. The paper summarises different ML techniques applied in Opportunistic Networks with their benefits, research challenges, and future opportunities. The study provides insight into the Opportunistic Networks with ML and motivates the researcher to apply ML techniques to overcome various challenges in Opportunistic Networks.