\input zb-basic
\input zb-ioport
\iteman{io-port 06090728}
\itemau{Sha, Yi; Huang, Ye; Huang, Li; Zhang, Lili}
\itemti{The WNNP-based clustering algorithm for ad hoc networks.}
\itemso{J. Northeast. Univ., Nat. Sci. 32, No. 9, 1233-1236 (2011).}
\itemab
Summary: According to the dynamic characteristics of ad hoc network topology, a wavelet neural network prediction (WNNP) model is used to predict the geometrical location of the nodes. Comparing the predicted total holding time with the threshold, the stabilization of a cluster in next time can be measured. If the cluster tends to be unstable in next time, a routing pre-repair mechanism can be initiated before the link failure to avoid frequent breaks of links. Thus the network performance is significantly improved. Simulation results show that compared with the lowest-identifier (lowest ID) algorithm and location-based WCA (LWCA) which has no prediction model, the WNNP-LWCA can improve by 7\% and 5\% of the packet delivery rate, reduce by 63\% and 50\% of the broken routing number, and maintain the stabilization of the cluster.
\itemrv{~}
\itemcc{}
\itemut{ad hoc network; weighted clustering algorithm; location prediction; wavelet neural network prediction}
\itemli{}
\end