Abstract:
The term delay-tolerant network (DTN) is coined to describe and include all types of long-delay,
disconnected, disrupted or intermittently-connected networks. So, in such network,
communication is established with the help of encounter opportunities between nodes and those
nodes follow store- carry and forward approach to achieve data delivery. In this approach, each
node stores the messages in their buffers until they encounter another node and also to achieve a
high delivery ratio most of DTN routing transmits multiple copies of messages. But the
combination of long term storage and multiple copies of message transmission in this process
impose greater transmission overhead and buffer occupancy. Most of the previous efforts on
improving the performance of routing do not consider the issue of resource limitations like buffer
and bandwidth. However, hand held devices which are having limited storage and bandwidth are
used in many of DTN applications. Therefore, buffer management policies have a direct impact
on routing performance of DTNs routing. Basing on this point, in this paper a Social aware wait
based buffer management policy has been proposed in the situations where there is short contact
duration, limited bandwidth and buffer.
Very little attention has been directed towards applying the social characteristics of the users to
mitigate the congestion in DTN. This paper presents a buffer management strategy for DTNs that
takes into account a value associated with the social relationship strength among the users. In
addition to the social relationship between nodes we also used a weight criterion to schedule
messages to be drooped and/or transmitted based on message properties. We evaluated this
policy with Epidemic routing algorithms. By means of a thoroughly planned set of steady-state
simulation experiments with two real world traces, we found that the proposed scheme has
outperformed the DCB, HBD & MOFO policies in terms of delivery rate & delivery delay.
When compared to DCB in terms of delivery rate, in different buffer size (20M, 40M, 60M, and
80M) SAWBD has a delivery rate of (55%, 63%, 77%, and 84%) whereas DCB has (50%, 59%,
64%, and 78%) and when compared to HBD, HBD has (42%, 45%, 57% and 60%) delivery rate.
This shows that the proposed policy has better performance than those policies used in our
simulation test.
