Location Prediction Weighted Clustering Algorithm Based on WNNM in Ad Hoc Network

DOI

10.2991/iccasm.2012.3How to use a DOI?

Keywords

Ad hoc network, Weighted clustering algorithm (WCA), Location prediction, Wavelet neural network model (WNNM)

Abstract

Introducing a wavelet neural network model (WNNM) at a route maintenance stage to predict the position of nodes in ad hoc networks, a new weighted clustering algorithm (WCA) is presented. Using the nodes’ position information from GPS, this algorithm predicts the position of nodes at next time by WNNM, then calculates the time the neighbor nodes spend moving out of the coverage of cluster heads by the predicted values and takes it as a measurement of aggregate holding time. If the cluster structure tends to be unstable, a pre-repair mechanism will function before the link fails, thus avoiding frequent break of links and improving the network performance. Simulation results show that compared to the Lowest-ID WCA and Location-based WCA, the algorithm proposed can increase the packet delivery rate by 9% and 6%, respectively, and decrease the numbers of link break by about 70% and 50%, respectively.

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yi Sha
AU  - Li Huang
AU  - Jiafu Chu
AU  - Lili Zhang
PY  - 2012/08
DA  - 2012/08
TI  - Location Prediction Weighted Clustering Algorithm Based on WNNM in Ad Hoc Network
BT  - Proceedings of the 2012 International Conference on Computer Application and System Modeling (ICCASM 2012)
PB  - Atlantis Press
SP  - 8
EP  - 11
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasm.2012.3
DO  - 10.2991/iccasm.2012.3
ID  - Sha2012/08
ER  -