Optimizing nodes proportion for intrusion detection in uniform and Gaussian distributed heterogeneous WSN

Abstract

In wireless sensor networks (WSN), intrusion detection applications have gained significant importance because of diverse implementations including tracking malicious intruder in the battlefield. Network parameters such as allowable distance, sensing range, transmission range, and node density plays important role in designing a model according to specific applications. Numerous models have been proposed to efficiently deploy WSNs for these applications. However, deviated requirements of different applications make it difficult to develop a generic model. Another important factor with significant contribution towards the performance of a WSN is the strategy adopted for distribution of the sensor nodes in the area of interest. The most common method is to deploy the sensors is either through uniform or gaussian distribution. Several performance comparisons have been reported to evaluate the detection probability and analyze its dependency on various network parameters. Another aspect fundamental to the performance of a sensor network is heterogeneity. Practically, for economic or logistic reasons, it may not be possible to ensure availability of nodes with identical features e.g. sensing range, transmission/ detection capability etc. It is, therefore, important to assess the detection performance of the network when the nodes do not possess same sensing range. In this paper we analyze the impact of various node densities in calculating detection probability in a Uniform and Gaussian distributed heterogeneous network under K-sensing model. Experimental results provide optimal values of node densities for efficient deployment in heterogeneous WSN environment.