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Wireless Communications and Mobile Computing
Volume 2018, Article ID 3806907, 19 pages
Research Article

Learning Automata Based Caching for Efficient Data Access in Delay Tolerant Networks

College of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Correspondence should be addressed to Ke Shi; nc.ude.tsuh.liam@ihsek

Received 26 September 2017; Revised 11 January 2018; Accepted 29 January 2018; Published 5 March 2018

Academic Editor: Daniele Pinchera

Copyright © 2018 Zhenjie Ma 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.


Effective data access is one of the major challenges in Delay Tolerant Networks (DTNs) that are characterized by intermittent network connectivity and unpredictable node mobility. Currently, different data caching schemes have been proposed to improve the performance of data access in DTNs. However, most existing data caching schemes perform poorly due to the lack of global network state information and the changing network topology in DTNs. In this paper, we propose a novel data caching scheme based on cooperative caching in DTNs, aiming at improving the successful rate of data access and reducing the data access delay. In the proposed scheme, learning automata are utilized to select a set of caching nodes as Caching Node Set (CNS) in DTNs. Unlike the existing caching schemes failing to address the challenging characteristics of DTNs, our scheme is designed to automatically self-adjust to the changing network topology through the well-designed voting and updating processes. The proposed scheme improves the overall performance of data access in DTNs compared with the former caching schemes. The simulations verify the feasibility of our scheme and the improvements in performance.