Node density optimisation using composite probabilistic sensing model in wireless sensor networks

Network coverage is a measure of efficiency that signifies the extent to which the deployed nodes collectively cover the network area. It is a fundamental and critical quality of service (QoS) parameter for designing wireless sensor networks (WSNs). Various sensing models are reported which can be used to predict the coverage fraction for a given number of nodes in a predetermined network area. However, each of these reported models consider a subset of parameters. In this study, a novel formulation and hence a new model, composite probabilistic sensing model (CPSM) is proposed which combines the cumulative effects of all the possible factors, thus resulting in a realistic study. Further, the model is revisited to estimate the optimal density of randomly deployed nodes required to attain the desired network area coverage. An exhaustive parametric study is carried out and the results obtained are used to empirically derive a formula based on regression analysis using least square polynomial curve fitting technique. The formulation can be readily and accurately used to design any practical WSN system.