Contact predication based large-scale mobile delay tolerant network cluster-based routing method and system thereof

Abstract

The invention relates to a contact predication based large-scale mobile delay tolerant network cluster-based routing method and a system thereof. The method includes: analyzing a truthful data set of a large-scale urban taxi network to disclose the intrinsic contact and clustering characteristics; designing a contact model among nodes and determining a probability distribution expressing form of contact time interval among the nodes; designing and implementing a contact predication based distributed cluster-based routing algorithm, and dividing nodes with high mutual contact probability into a same cluster under the large-scale dynamic environment; and designing and implementing a direct delivery based in-cluster routing algorithm and a flooding based inter-cluster routing algorithm to guarantee effectiveness and expandability in routing transmission on the basis of clustering. The contact predication based large-scale mobile delay tolerant network cluster-based routing method and the system thereof can be widely applied to message communication of large-scale urban transportation networks such as dynamic navigation systems, automatic vehicle driving systems and vehicular entertainment systems.

Description

technical field [0001] The invention relates to the fields of computer network communication, delay-tolerant network and vehicle network, in particular to a large-scale mobile delay-tolerant network cluster routing method and system based on contact prediction. Background technique [0002] The research on clustering routing algorithms in traditional networks is relatively mature, but these routing algorithms are mostly used in networks with less dynamic network topology, so it is difficult to directly apply them to mobile delay-tolerant networks. Because of the particularity of the mobile delay-tolerant network environment, the research on cluster routing in this network is relatively scarce. [0003] Both the LIC algorithm and the Max-Min D-Clustering algorithm belong to the identifier-based clustering algorithm. This type of algorithm assigns a unique ID number to all nodes in the network, and elects the node with the smallest ID number in the area to be the cluster head...

AI Technical Summary

Problems solved by technology

However, in the environment of dynamic topological changes, it is difficult to accurately describe the contact probability between nodes

[0007] 2) Due to the strong randomness of topology changes, the estimation of metric values ​​is prone to bias, resulting in many small-scale clustering fragments in the network

[0008] 3) Due to the movement of nodes, cluster member nodes and gateway nodes are prone to change. When a node enters or exits, it is difficult to maintain the consistency of regional topology information

[0009] 4) In the actual mobile delay-tolerant network, nodes have certain mobility patterns. When their mobility patterns change due to certain factors (such as time factors), the connectivity between nodes and other nodes in the same cluster may be reduced.

However, these algorithms only use historical or current data to estimate the connectivity performance, and lack the prediction of the future state of the network, which leads to low stability of clustering and requires frequent cluster update process

[0013] Furthermore, these algorithms have too strong scene dependence, such as bus network, satellite network, etc., and the assumed network scale is limited, which makes the clustering algorithm not universal, and it is difficult to apply to the real taxi network. in the scene

[0014] Finally, like most clustering algorithms of MANET networks, the above algorithm assumes that the network is in a static state during the initialization phase, and the cluster head node performs centralized cluster management. Unrealistic in Latency Tolerant Web Applications

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