Abstract
Exploiting social information to improve routing performance is an increasing trend in Opportunistic Mobile Social Networks (OMSNs). Selecting the next message’s relay node based on the user’s social behavior is a critical factor in attaining a high delivery rate. So, to ascertain the most efficient selection of the next relay, the correlation between daily social activities and the social characteristics in the user profiles can be exploited. In this paper, we consider the impact of the social characteristics on mobile user activities during certain periods of the day and then rank these characteristics based on their relative importance in order to be included in the routing protocol. These processes consolidate the proposed Ranked Social-based Routing (R-SOR) protocol to provide an effective way for data dissemination in OMSN. We use the real data set INFOCOM06 to evaluate the proposed protocol. The experimental results show that the proposed protocol has higher routing efficiency than flooding-based protocols such as Epsoc and Epidemic, prediction-based protocols such as PRoPHET, and social-based protocols such as MSM and Bubble Rap.
Highlights
INTRODUCTIONSmart mobile devices are present in all areas of human activity. They transform the ways of sharing data into a new paradigm of Mobile Social Networks (MSNs) [1,2,3,4]
Nowadays, smart mobile devices are present in all areas of human activity
Our contribution is focused on designing a ranked social-based routing protocol which is referred to as Ranked Social-based Routing (R-SOR) protocol that seeks to improve the performance of Opportunistic Mobile Social Networks (OMSNs) by considering the regularity of users’ social behavior and by exploiting the relative impact of the social features during each day period
Summary
Smart mobile devices are present in all areas of human activity. They transform the ways of sharing data into a new paradigm of Mobile Social Networks (MSNs) [1,2,3,4]. The main objective of this work is to design an efficient forwarding scheme in order to achieve high delivery ratio and to decrease the network overhead This is carried out by involving the ranking of social features with regard to a specific day slicing mechanism in the routing process. Our contribution is focused on designing a ranked social-based routing protocol which is referred to as R-SOR protocol that seeks to improve the performance of OMSNs by considering the regularity of users’ social behavior and by exploiting the relative impact of the social features during each day period. We developed the proposed R-SOR algorithm by selecting the best relay node having the highest rank of social feature similar to that of the destination in the current day period. The result of the simulation experiments exhibits the efficiency of the R-SOR protocol, in comparison to 5 benchmark routing protocols, namely flooding-based protocols such as Epsoc and Epidemic, prediction-based protocols such as PRoPHET, and social-based protocols such as MSM and Bubble Rap
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