Abstract—Coverage analysis in WSN being a fundamental quality of service parameter needs utmost and precise assessment. Node sensing capability is a function of sensor characteristics and environmental factors which needs to be judiciously factored in sensing model used for coverage estimation. The probabilistic sensing models reported earlier consider only a certain subset of characteristics resulting in an incomplete sensing model. This paper employs a newly formulated and proposed model, Composite Probabilistic Sensing Model (CPSM) that cumulates all possible effects. Typically, nodes are prone to failures during deployment phase itself due to manufacturing defect, severe environmental condition or physical damage. Such node failures should also be addressed for coverage analysis. In addition, during network operation, harsh weather conditions and overloading may lead to early death of certain nodes making the network dysfunctional when 1-coverage is opted. To offer better network reliability, and to cater to increased sensitivity of critical applications, network designers opt for k-coverage. Hence, this paper focuses on investigation of estimation of node density for varied coverage requirements. It also includes a parametric study of effect and dependency of various factors on the required node density. This results in a comprehensive study that aid in WSN design/ analysis/ implementation.

Index Terms—Wireless sensor network, random deployment, probabilistic sensing model, k-coverage, node density estimation.

Cite: Nitika Rai and Rohin Daruwala, "A Comprehensive Approach for Implementation of Randomly Deployed Wireless Sensor Networks,” vol. 14, no. 10, pp. 915-925, 2019. Doi: 10.12720/jcm.14.10.915-925.