Relative Mobility Prediction Based <i>k</i>-Hop Clustering Algorithm in AdHoc Networks

Luoming MENG; Yanfu JIANG; Yanjun LIU; Han SU; Siya XU; Feng QI

doi:10.11999/JEIT180192

Volume 40 Issue 12

Nov. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(12): 2954-2961

Luoming MENG, Yanfu JIANG, Yanjun LIU, Han SU, Siya XU, Feng QI. Relative Mobility Prediction Based k-Hop Clustering Algorithm in AdHoc Networks[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2954-2961. doi: 10.11999/JEIT180192

Citation:

Luoming MENG, Yanfu JIANG, Yanjun LIU, Han SU, Siya XU, Feng QI. Relative Mobility Prediction Based k-Hop Clustering Algorithm in AdHoc Networks[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2954-2961. doi: 10.11999/JEIT180192

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Relative Mobility Prediction Based k-Hop Clustering Algorithm in AdHoc Networks

doi: 10.11999/JEIT180192

1.
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
2.
State Grid Hebei Electric Power Company, Shijiazhuang 050021, China

Funds: The National Natural Science Foundation of China (61702048)

Received Date: 2018-02-11
Rev Recd Date: 2018-08-13

Available Online: 2018-08-21

Publish Date: 2018-12-01

Abstract

Abstract

To solve the problem of network structure change and route failure caused by random movement of network nodes, a relative mobility prediction based k-hop clustering algorithm is proposed, the movement of nodes are analyzed and predicted, the cluster structure is adjusted adaptively, the stability of cluster structure is improved. First, the Doppler shift is used to calculate the relative moving speed and obtain the link expiration time between nodes. Then, during the cluster formation stage, the MAX-MIN heuristic algorithm is used to select the cluster head according to the average link expiration time of the node. Furthermore, during the cluster maintenance stage, a network adaptive adjustment method is proposed based on node motion. On the one hand, the node information transmission cycle is adjusted to balance the data overhead and accuracy; On the other hand, the cluster structure is adjusted by predicting the link disconnection to reduce link reconstruction time and improve the quality of network operation. Simulation results show that the proposed algorithm can effectively prolong the duration of cluster head and improve the stability of cluster structure in dynamic environment.
- AdHoc network,
- Clustering,
- Mobility prediction,
- Doppler shift,
- MAX-MIN algorithm

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References

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