Poster: bacteria inspired mitigation of selfish users in ad-hoc social networks

Abstract

In data management protocols for Ad-hoc Social Networks (ASNETs), involvement of selfish users can pose a serious threat to network performance and fairness. Therefore, it is essential to detect and mitigate their effects on other well behaving users. We contribute to this line of research by combining the benefits of users' social behavior (social tie) with a biologically inspired approach in ASNETs. We designed a bio-inspired scheme (BoDMaS) to detect and mitigate selfish users in replication operations. Its goals include providing greater accessibility and effective detection of selfish users. The proposed scheme not only guarantees accessibility and effective detection rate, but also ensures the reliability of replica allocation operations.