JAVA 2013 IEEE MOBILECOMPUTING PROJECT On exploiting transient social contact patterns for data forwarding in delay tolerant networks
On Exploiting Transient Social Contact Patterns for Data
Forwarding in Delay-Tolerant Networks
We are challenging to achieve effective data forwarding in Delay-Tolerant Networks (DTNs). Most of the
current data forwarding schemes choose the nodes with the best cumulative capability of contacting others as
relays to carry and forward data, but these nodes may not be the best relay choices within a short time period
due to the heterogeneity of transient node contact characteristics.
In this paper, we propose a novel approach to improve the performance of data forwarding with a short time
constraint in DTNs by exploiting the transient social contact patterns. These patterns represent the transient
characteristics of contact distribution, network connectivity and social community structure in DTNs.
We provide analytical formulations on these patterns based on experimental studies of realistic DTN traces. We
then propose appropriate forwarding metrics based on these patterns to improve the effectiveness of data
forwarding strategies, our proposed forwarding metrics achieve much better performance compared to existing
schemes with similar forwarding cost.
Existing Networks using (MANET) is a dynamic wireless network with or without fixed infrastructure. Nodes
may move freely and organize themselves arbitrarily. Sparse Mobile Ad hoc Networks are a class of Ad hoc
networks where node density is low, and contacts between the nodes in the network do not occur very
frequently. MANET network graph is rarely, if ever, connected and message delivery must be delay-tolerant.
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Traditional MANET routing protocols such as AODV, DSR, DSDV and LAR make the assumption that the
network graph is fully connected and fail to route messages if there is not a complete route from source to
destination at the time of sending. For this reason traditional MANET routing protocols cannot be used in sparse
To overcome this issue, node mobility is exploited to physically carry messages between disconnected parts of
the network. These schemes are sometimes referred to as mobility assisted routing that employs the store-carry-
and-forward model. Mobility-assisted routing consists of each node independently making forwarding decisions
that take place when two nodes meet. A message gets forwarded to encountered nodes until it reaches its
We propose a novel approach to improve the performance of data forwarding with a short time constraint in
DTNs by exploiting the transient social contact patterns. These patterns represent the transient characteristics of
contact distribution, network connectivity and social community structure in DTNs, and we provide analytical
formulations on these patterns based on experimental studies of realistic DTN traces.
We propose data forwarding metrics by exploiting the stochastic node contact process based on experimental
and theoretical analysis metrics based on the prediction of node mobility and its probability of contacting the
destination. However, the performance of these schemes is limited due to the randomness of human mobility
and thus the low prediction accuracy.
Data forwarding is divided into two stages:
First, node centrality is evaluated at the global scope which includes all the nodes in the network, to ensure that
data is carried and forwarded by relays with higher capability of contacting other nodes.
Second, when a relay contacts a node within the same community of the destination, data is forwarded to that
community. Afterward, node centrality is evaluated within the local community scope, and data is forwarded
directly to the destination.
We improve the performance of data forwarding by exploiting the transient social contact patterns from the
Transient Contact Distribution
Transient Connectivity (TC)
Transient Community Structure
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In this paper, we propose effective forwarding metrics to improve the performance of data forwarding in DTNs,
by exploiting the transient social contact patterns. We formulate these patterns based on experimental
observations from realistic DTN traces, and exploit these patterns for more accurate prediction on the node
contact capability. Through extensive trace-driven experiments, we show that our approach significantly
improves the data delivery ratio, while keeping similar forwarding cost with existing schemes.
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