Event Detection in Clustered Wireless Sensor Networks Using Dynamic Cell Structures Neural Networks

Localization of intrusions at country borders is a very important task for surveillance applications. This task can be approached within the framework of Wireless Sensor Networks (WSN). In this context, we investigate the localization of an intruder in a clustered WSN using Dynamic Cell Structures Neural Networks (DCSNN). Sensors are randomly deployed according to a Poisson Point Process and the Region of Interest (ROI) is considered as one, four, and nine clusters respectively in the simulations. We start by presenting a novel scenario of the communication model, where we add a new condition to any Cluster Head (CH) having its sensor nodes detecting, justified the true hypothesis test. Thus, only one CH is elected by the Fusion Center (FC) to run the DCSNN algorithm for each intrusion. This can help us to avoid the false alarm due to the additive noise sensing to the power of the signal. On other hand, we prove our choice of DCSNN through a comparative study between it and the MLPNN. The results collected by the simulations about localization and taking into account the variation of some important parameters like Probability of false alarm (Pfa), the deployment density (lambda), and the transmission power of sensors (P-0), show that more the region is divided into clusters, the smaller the localization mean error becomes in a short time.