Open AccessNode density optimisation using composite probabilistic sensing model in wireless sensor networks
Author(s) -
Rai Nitika,
Daruwala Rohin
Publication year - 2019
Publication title -
iet wireless sensor systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.433
H-Index - 27
eISSN - 2043-6394
pISSN - 2043-6386
DOI - 10.1049/iet-wss.2018.5048
Subject(s) - wireless sensor network , computer science , probabilistic logic , parametric statistics , node (physics) , cover (algebra) , wireless network , quality of service , data mining , wireless , computer network , mathematics , engineering , artificial intelligence , statistics , telecommunications , mechanical engineering , structural engineering
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.