Abstract
Delay Tolerant Networks (DTNs) are novel wireless mobile networks, which suffer from frequent disruption, high latency, and the lack of a complete path from source to destination. Vehicular Delay Tolerant Network (VDTN) is a special type of DTNs with vehicles as nodes. In VDTN, most nodes have specific movement patterns, however, traditional routing algorithms in DTNs do not take this characteristic into considerations very well. In this paper, a new routing algorithm based on Bayesian Network (BN) is proposed to construct the prediction model, which intends to predict the movement patterns of nodes in the real VDTN scenarios. Firstly, a comprehensive BN model is established, where more attributes of nodes are selected to improve the accuracy of the model prediction. Then, considering the complexity of the structure learning problem of BN, a novel structure learning algorithm, K2 algorithm based on Genetic Algorithm (K2-GA), is proposed to search the optimal BN structure efficiently. At last, Junction Tree Algorithm (JTA) is adopted in the inference of BN, which can accelerate the inference process through variable elimination and calculation sharing for large scale BN. The simulation results show that the proposed VDTN routing algorithm based on the BN model can improve the delivery ratio with a minor forwarding overhead.
Highlights
Traditional networks rely on end-to-end paths from source to destination and special network protocols to transmit data in minimal delay and high reliability
According to our previous work [19], a routing algorithm based on Bayesian Network (BN) was proposed for Vehicular Delay Tolerant Network (VDTN), which utilizes the BN model to preserve the dependency of the attributes of node and obtain the movement patterns
Since the relay nodes belonging to the shortest path often have great ability to transmission messages, we propose a classifier with BN model to evaluate the ability of node for transmitting messages to destination
Summary
Traditional networks rely on end-to-end paths from source to destination and special network protocols (such as TCP/IP or UDP) to transmit data in minimal delay and high reliability. According to our previous work [19], a routing algorithm based on BN was proposed for VDTN, which utilizes the BN model to preserve the dependency of the attributes of node (e.g., location, time, contact, etc.) and obtain the movement patterns. The main contributions of this paper are as follows: 1) A new BN model is proposed, where more attributes of nodes are introduced to predict the delivery ability of nodes accurately. These attributes are supposed to be able to reflect the movement patterns, which will reduce the number of copies of messages in VDTN.
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