Gaussian Distributive Clustering Based Multi-Objective Truncated Grasshopper Optimal Path Selection for Energy Efficient Routing in WSN

WSN is network that contain numerous SN deployed within a specific region for sensing and collecting data as well as transmitting to the BS. WSNs consistently face challenges in delivering the information to the base station with minimal delay, energy utilization, and packet loss. Energy efficiency is also considered one of the major issues in the WSNs through the routing process. An efficient routing protocol is required to mention these constraints as well as enhance effectiveness of WSNs. Motivated by these challenges, a Gaussian Distributive Clustering-based Multi-Objective Truncated Grasshopper Optimization (GDC-MTGO) method is developed for effective data packet routing in WSN through high packet delivery ratio as well as minimal delay. GDC-MTGO method includes sensor node clustering, optimal route path identification, route maintenance. At first, number of SN is taken as input. Afterward, all SN in WSN are clustered depend on their energy level using the energy-aware Gaussian distributive Jenks Natural Break Node clustering technique. For every cluster, SN through superior residual energy considered as CH. Secondly, optimal route paths between cluster heads are identified with multi-objective truncated grasshopper optimization for broadcasting data packets to BS for further processing. In optimization process, the population of available route paths between source and sink node are initialized. Fitness of every route path is calculated depend on multi-objective functions. Truncated selection process is used to choose global best optimal path for resource-aware data broadcast. Finally, route maintenance is carried out by identifying substitute optimal route path when link malfunction happens. Experimental evaluation is carried out with different performance metrics with number of SN and number of data packets. Quantitatively analyzed outcomes denote GDC-MTGO method achieving higher data delivery, throughput and minimal energy consumption, delay, loss rate compared to existing methods.