Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 453564, 15 pages
Research Article

Location Prediction-Based Data Dissemination Using Swarm Intelligence in Opportunistic Cognitive Networks

1Computing Center, Northeastern University, Shenyang 110819, China
2Key Laboratory of Networked Control System, The Chinese Academy of Sciences, Shenyang 110016, China
3College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
4Software College, Northeastern University, Shenyang 110819, China
5Information and Technology Center of China Mobile Group Liaoning Co., Ltd., Liaoning 110179, China

Received 8 June 2014; Revised 8 August 2014; Accepted 11 August 2014; Published 25 September 2014

Academic Editor: Baozhen Yao

Copyright © 2014 Jie Li 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.


Swarm intelligence is widely used in the application of communication networks. In this paper we adopt a biologically inspired strategy to investigate the data dissemination problem in the opportunistic cognitive networks (OCNs). We model the system as a centralized and distributed hybrid system including a location prediction server and a pervasive environment deploying the large-scale human-centric devices. To exploit such environment, data gathering and dissemination are fundamentally based on the contact opportunities. To tackle the lack of contemporaneous end-to-end connectivity in opportunistic networks, we apply ant colony optimization as a cognitive heuristic technology to formulate a self-adaptive dissemination-based routing scheme in opportunistic cognitive networks. This routing strategy has attempted to find the most appropriate nodes conveying messages to the destination node based on the location prediction information and intimacy between nodes, which uses the online unsupervised learning on geographical locations and the biologically inspired algorithm on the relationship of nodes to estimate the delivery probability. Extensive simulation is carried out on the real-world traces to evaluate the accuracy of the location prediction and the proposed scheme in terms of transmission cost, delivery ratio, average hops, and delivery latency, which achieves better routing performances compared to the typical routing schemes in OCNs.