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On Social Delay-Tolerant Networking Aggregation, Tie
Detection, and Routing
Abstract
Social-based routing protocols have shown their promising capability to improve the message delivery
efficiency in Delay Tolerant Networks (DTNs). The efficiency greatly relies on the quality of the
aggregated social graph that is determined by the metrics used to measure the strength of social
connections. In this paper, we propose an improved metrics that leads to high-quality social graph by
taking both frequency and duration of contacts into consideration. Furthermore, to improve the
performance of social-based message transmission, we systematically study the community evolution
problem that has been little investigated in the literation. Distributed algorithms based on the obtained
social graph are developed such that the overlapping communities and bridge nodes (i.e., connecting
nodes between communities) can be dynamically detected in an evolutionary social network. Finally, we
take all the results above into our social-based routing design. Extensive trace-driven simulation results
show that our routing algorithm outperforms existing social-based forwarding strategies significantly.
Existing system
Social-based routing protocols have shown their promising capability to improve the message delivery
efficiency in Delay Tolerant Networks (DTNs). The efficiency greatly relies on the quality of the
aggregated social graph that is determined by the metrics used to measure the strength of social
connections.
Proposed system
In this paper, we propose an improved metrics that leads to high-quality social graph by taking both
frequency and duration of contacts into consideration. Furthermore, to improve the performance of

2. social-based message transmission, we systematically study the community evolution problem that has
been little investigated in the literation. Distributed algorithms based on the obtained social graph are
developed such that the overlapping communities and bridge nodes (i.e., connecting nodes between
communities) can be dynamically detected in an evolutionary social network. Finally, we take all the
results above into our social-based routing design. Extensive trace-driven simulation results show that
our routing algorithm outperforms existing social -based forwarding strategies significantly.
SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
 Processor - Pentium –IV
 Speed - 1.1 Ghz
 RAM - 256 MB(min)
 Hard Disk - 20 GB
 Key Board - Standard Windows Keyboard
 Mouse - Two or Three Button Mouse
 Monitor - SVGA
SOFTWARE CONFIGURATION:-
 Operating System : Windows XP
 Programming Language : JAVA
 Java Version : JDK 1.6 & above.