Hindawi Publishing Corporation
International Journal of Distributed Sensor Networks
Volume 2013, Article ID 389874, 7 pages
http://dx.doi.org/10.1155/2013/389874
Research Article
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Correspondence should be addressed to Haigang Gong; hggong@uestc.edu.cn
Received 8 November 2012; Accepted 11 December 2012
Academic Editor: Ming Liu
Copyright © 2013 H. Gong and X. Wang. is is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
Data delivery in mobile social network is a challenging task due to the nodal mobility and intermittent connectivity. It is natural to
utilize the inherent social properties to assist in making forwarding decisions. However, existing routing schemes seldom consider
sel�shness of nodes and assume that nodes are willing to forward messages for others. In the real world, most people are sel�sh
and nodes attached to people behave sel�shly too. Based on the regularity of human behavior, we propose a hot-area-based sel�sh
routing protocol (HASR) tailored for mobile social networks in this paper. Data transmission is based on the active degree of the
node, which is calculated by the weight of hot areas that nodes will visit, when there are no sel�sh nodes. When nodes behave
sel�shly, routing decision is made by the contribution index that indicates the contributions to data transmissions of the network
made by nodes. Simulation results show that HASR performs better when nodes behave sel�shly.
e portal devices such as smart phone, laptop, and tablet
computer have been very popular in the world with the
rapid development of the technologies of wireless communi-
cation and integrated circuit. ese devices with the wireless
capabilities such as Bluetooth, WiFi, or 3G are oen carried
by people and cooperate with each other to form an ad
hoc network for exchanging and sharing their data. Social
behavior analysis has been introduced to resolve the routing
issues when nodes are attached to the human and could
achieve better performance by using social relationship or
human behavior in real life environment. Hui et al. [1] named
the network Pocket Switch Networks (PSNs), a type of Delay
Tolerant Networks (DTNs) [2]. Because there have been some
inherent social features in the network, the network is also
called Mobile Social Networks (MSNs).
Since mobile social networks have the great potentials
of collaborative data exchanging, opportunistic routing for
MSNs has attracted a great interest. Unfortunately, it is hard
to �nd an end-to-end path between the source node and the
destination node in the networks and the network is usually
intermittently connected due to the nodal mobility and the
spare distribution of nodes, which pose great challenges for
data delivering in mobile social networks. Different from
traditional delay tolerant networks, nodes in mobile social
networks are oen controlled by people so that nodes have
some social features due to the social relationships or social
ties among people. It is natural to utilize the inherent social
properties to assist in making forwarding decisions.
Some institutes try to �nd social properties of the mobile
socialnetworksintherealworldbasedonthedataset
collected from the portable devices attached to human, for
example, Reality Mining [3], Topology Discovery [4], and
Haggle [5]. And there have been lots of routing strategies
for mobile social networks, such as LABEL [6], BUBBLE
[7], SimBet [8], Peoplerank [9], and PLBR [10], which
all employ the social network properties to help message
forwarding. However, in the previous routing techniques,
there is a common assumption that all nodes in the network
areunsel�shandcoordinated.atistosay,eachnodeis
willing to receive and relay the messages sent by other nodes.
Intherealworld,mostpeoplearesel�shandtherewould
be some sel�sh nodes dominated by human. For example,
the resources (energy, buffer, bandwidth, etc.) of nodes are
usually limited and nodes try to preserve their own resources