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Mobile Information Systems
Volume 2016 (2016), Article ID 7908328, 11 pages
http://dx.doi.org/10.1155/2016/7908328
Research Article

ODMBP: Behavior Forwarding for Multiple Property Destinations in Mobile Social Networks

1School of Computer Science & Technology, Nanjing University of Posts and Telecommunications, Jiangsu, Nanjing 210003, China
2Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Jiangsu, Nanjing 210003, China
3Institute of Computer Technology, Nanjing University of Posts and Telecommunications, Jiangsu, Nanjing 210003, China
4Department of Information Technology, Nanjing General Hospital of Nanjing Military Command, Jiangsu, Nanjing 210002, China

Received 2 November 2015; Accepted 3 January 2016

Academic Editor: Tingting Chen

Copyright © 2016 Jia Xu et al. This 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.

Abstract

The smartphones are widely available in recent years. Wireless networks and personalized mobile devices are deeply integrated and embedded in our lives. The behavior based forwarding has become a new transmission paradigm for supporting many novel applications. However, the commodities, services, and individuals usually have multiple properties of their interests and behaviors. In this paper, we profile these multiple properties and propose an Opportunistic Dissemination Protocol based on Multiple Behavior Profile, ODMBP, in mobile social networks. We first map the interest space to the behavior space and extract the multiple behavior profiles from the behavior space. Then, we propose the correlation computing model based on the principle of BM25 to calculate the correlation metric of multiple behavior profiles. The correlation metric is used to forward the message to the users who are more similar to the target in our protocol. ODMBP consists of three stages: user initialization, gradient ascent, and group spread. Through extensive simulations, we demonstrate that the proposed multiple behavior profile and correlation computing model are correct and efficient. Compared to other classical routing protocols, ODMBP can significantly improve the performance in the aspect of delivery ratio, delay, and overhead ratio.