The messages routing in a DTN network is a complicated challenge, due on the one hand of intermittent connection between the nodes, the lack of the end-to-end path between source / destination and on the other hand, the constraints related to the capacity of the buffer and the battery. To ensure messages delivery in such an environment, the proposed routing protocols use multiple copies of each message in order to increase the delivery ratio. Most of these routing protocols do not take into account the remaining energy of nodes and the history on the relays that have already received a copy of the message in order to select the nodes that will participate in the message routing. This paper proposes a new approach named EERPFAnt inspired by the ant colony intelligence and improved by the fuzzy logic technique to select the best relay by combining the energy level of the nodes, as well as the information on the relay that have already received a copy of the message to estimate intelligently, the energy level of the nodes at the time of encounter with the desired destination. Simulation results will show that the proposed approach performances are better than those of Epidemic routing protocols, Spray and Wait and ProPHET.

E-reputation management has become an important challenge for firms that try to improve their notoriety across the web and more specifically in social media. Indeed, the power of online communities to impact a brand’s image is undeniable and companies need a powerful system to measure their reputation as perceived by connected society. Moreover, they need to follow its variation and forecast its evolution to anticipate any impacting change. For this purpose we have implemented an Intelligent Reputation Measuring System (IRMS) that assesses reputation in online social networks on the basis of members’ activity and popularity. In this paper, we add a predictive module to IRMS that forecasts the evolution of reputation score using influence propagation algorithms.