TMSM-based optimal clustering in a Gaussian distributed wireless sensor network

Deployment of wireless sensor nodes can follow a Gaussian or a uniform distribution. In a given sensing field, sensor nodes can often be grouped into non overlapping subsets called clusters. Gaussian distribution is preferably used for realistic applications and can provide a reduction in energy hole problem and enhance intrusion detection capability. In this research paper, we proposed the cluster size optimization for the Gaussian distributed sensor nodes, while, Base Station (BS) follows a Tunable Multilevel Sensing Model (TMSM). Using this model, we obtained an expression to find optimal clusters. Herein, the communication between Cluster Heads (CH) and Base Station (BS) is considered to be of single-hop mode. Subsequently, it has illustrated the effectiveness of proposed model in comparison with two contemporary models (a) Gaussian distribution of sensor nodes, where a BS follows a simple (classical) sensing model (GSS) (b) Uniform distribution of sensor nodes, where a BS follows the simple (classical) sensing model (USS). Using the proposed method, it is noted that a significant reduction of total energy consumption is achieved using optimal clustering.