Tunable Multilevel Probabilistic Sensing Model based Intrusion Detection in Gaussian Distributed WSNs

Intrusion detection is a process of sensing the advent of any unautorized user. Especially, this mechanism is very much needed in the areas which are belligerent, inhospitable or unfavorable. The quality of detection significantly depends on the sensing model and spatial distribution of the wireless nodes. In general, the classical Boolean model is used for sensing. However, in this sensing model, beyond a specific sensing limit the object detectability abruptly becomes zero. This limitation can be overcome by using the Tunable Multilevel Probabilistic Sensing Model (TMSM) whose range of sensing is greater than that of boolean sensing model. Further, when the nodes are distributed spatially in a Gaussian way, the presence of intruder is faithfully detected only at the proximity of the dense node distribution. In addition, by forming a k-Gaussian distributed network, the proportion of dense node region in the field of interest increases. Consequently, the chances of detecting the intruder should also increase. To this end, we have incorporated the TMSM in k-Gaussian distributed network. The results show a consistent behavior of intruder detectability using TMSM.