Energy Optimization through Dual step based probable Cluster Head selection in WSN Environment

In a nearby future, applications of WSN are expected to bring an evolution in data collection, data processing and others. However, Energy consumption is one of the crucial issues when it comes to designing the Network. Apart from the energy consumption other issues like network lifetime, network failure also influences the design of the network. Several methods have been proposed for improving the network lifetime however these methods fail in addressing issues such as complexity, redundancy. Hence in this research work we have proposed a methodology called DSPCH (Dual step based probable cluster head)-selection to enhance network lifetime, achieve efficient transmission and network development. The main aim of DSPCH is to optimize the energy for efficient transmission and hence to improve the network life time. DSPCH follows a two-step mechanism to select cluster head. First step is through designed probability mechanism and second step is based on the node lifetime considering optimization in both the steps. Further, DSPCH model is evaluated considering the different parameters like energy consumption, network lifetime in terms of number of rounds and number of dead nodes. Comparative analysis is carried out with the existing model in terms of the same parameters to evaluate and prove the model efficiency.