Multi-Level Trust Based Talented Clustering with Compressive Sensing for Wireless Sensor Networks

Wireless Sensor Network (WSN) comprises a tiny sensor that comprises large number of sensors. WSN is subjected to a distinct number of small sensors with reduced deployed costs. WSN comprises a vast range of applications such as target tracking, pollution monitoring, detection of the enemy, medical care, and so on. Generally, sensor nodes are constrained to limited battery power with a limited energy supply. The WSN is subjected to the detection of the malicious node those need to be addressed to improve the energy consumption and delivery of data. WSN are constrained to energy limitation of the packet loss with presence of malicious node those need to be addressed to increase the performance of network with reduced consumption of energy and delay. In order to provide security to the Talented Clustering with Compressive Sensing (TCCS) network we used multi-level trust module in the TCCS network which is MT-TCCS network. This research work considers the cluster head (CH)selection, Compressive Sensing (CS) theory, Connection based Decentralized Clustering (CDC), Relay node selection, Multi-Level (ML) Trust management and Multi Objective Genetic Algorithm (MOGA). The initial stage provided a theoretical revision to the concepts of network construction, compressive sensing, which impacted the improvement of network lifetime. In the second stage developed Multi-Level (ML) Trust management for calculating the trust worthiness of each node in the network. In final stage multi objective genetic algorithm is used for optimal path selection in the network. The proposed method provides the contemplates the improvement of lifetime, security and also improves the overall quality of service (QOs) of the network. The proposed method with generates less delay and less packet loss when compared to the existing technique.