A ROBUST NEUTROSOPHIC CLUSTERING BASED RAVEN ROOSTING OPTIMIZATION TO ALLEVIATE VOID HOLE AND ENERGY DEPLETION IN IOT ENABLED WIRELESS SENSOR NETWORK

Abstract

In the modern internet era, the usage of Internet of Things need is grown rapidly with the assistance of Wireless Sensor Networks (WSN). This necessitates the importance of quality assured network coverage with high speed of data transmission is highly demanded. The random positioning of the sensor nodes in WSN and improper distribution of the load are the primary factors which consequence to void hole and energy depletion in the network. Though, there are many routing policies are in existence, the presence of noisy and imprecise positioning of the senor nodes in WSN is the major challenge which affects the performance of the routing strategies. Thus, this paper focuses on handling two important factors void hole and energy depletion by developing a two-stage optimization routing policy. First, neutrosophic clustering is applied to represent each sensor node in terms of membership degree of truthfulness, falseness and indeterminacy. The cluster head is selected using the Neutrosophic C Means clustering and the sensor nodes are clustered depending on their nearest cluster head. This strategy overcomes the problem of void hole by handling the noisy and boundary nodes by position management using neutrosophic clustering. The second stage, performs forwarding management by deploying Raven Roosting Optimization selection which selects the robust forwarding nodes based on their fittest value with its foraging behavior of optimized best route. Thus, the energy depletion is reduced by choosing the optimized forwarding nodes with high residual energy, minimum hop count and distance are considered as best. The simulation results proved that the robustly constructed Neutrosophic Clustering based Raven Roosting Optimizationto alleviate void hole and energy depletion reduction (NRR-AVE)) achieves best performance compared to other existing routing strategies