DIAL: A Distributed Adaptive-Learning Routing Method in VDTNs

Abstract

In this paper, we focus on designing an efficient routing method in Vehicle Delay Tolerant Networks (VDTNs) without centralized infrastructures. Current routing methods either have limited routing efficiency or need the help of centralized infrastructures to improve the routing performance, which is deviated from the goal of building VDTNs without centralized infrastructures. In this paper, we propose a DIstributed Adaptive-Learning routing method for VDTNs, namely DIAL, by taking advantage of the human beings' communication feature, that is, most interactions are generated by pairs of people who interacted often previously. DIAL consists of two components: the information fusion based routing method and the adaptive-learning framework. The information fusion based routing method enables DIAL to improve the routing performance by sharing and fusing multiple kinds of routing information without centralized infrastructures. Furthermore, based on the information shared by information fusion based routing method, the adaptive-learning framework enables DIAL to design personalized routing strategies for different vehicle pairs without centralized infrastructures. Therefore, DIAL can not only share and fuse multiple kinds of routing information of each vehicle without centralized infrastructures, but also design a personalized routing strategy for each vehicle pair. Extensive trace-driven simulation demonstrates that DIAL has better routing success rate and shorter average delay in comparison with state-of-the-art routing methods, which need the help of centralized infrastructures in VDTNs.