Journal of Theoretical and Applied Information Technology
Future network traffic in WSN can be predicted by time series models. The knowledge of traffic can be used for routing, load balancing and QoS provisioning. S-ARMA model has been proposed to predict the future traffic in WSN. The abnormality in traffic is predicted and it indicates the possibility for Dos attack and it initiates frequency hopping to avoid this. Increase in the frequency hopping time is identified by SARMA model, alerts the network to avoid the anomaly channel. Effectiveness of this model is been proved to be efficient in detecting the anomaly channel from the simulation results since the information about the attackers in the channel can be known using swarm intelligence (ants).