A New Trust-Based Optimal Stochastic Data Scheduling for Wireless Sensor Networks

Heterogeneous Wireless sensor networks (HWSNs) have several uses currently across too many different fields. Measurement of climate-related events and multi-hop transmission of sensing data to the sink are the major goals of HWSNs. The two major issues facing devices are increasing network longevity and reducing energy usage in mobile sensors. Compressive sensing (CS), as scholars have recently considered, represents one of the most successful methods for lowering energy consumption in HWSNs. Furthermore, providing network security also needs equal concentration; hence the network consists of several threads and malfunctions. For this research, Multilevel Trust Based Optimal Stochastic Data Scheduling Model (MTODS) is proposed. This approach is mainly classified into the multilevel trust model and optimal stochastic data scheduling. An effective clustering and beta distribution are performed through a multilevel trust model, which greatly helps increase energy efficiency. On the other hand, optimal stochastic data scheduling is performed using hybrid particle swarm optimization (hybrid-PSO), which helps to reduce the delay occurrence. The simulation is performed in NS2, and the results are analysis concerned with the number of nodes and the varying speed of the network. The results of the comparative analysis are compared with the earlier approaches, such as CDAS-WSN, EEPC-WSN and TCCS-WSN. The outcome proves that the proposed MTODS-HWSN outperforms in energy efficiency and delivery ratio when compared with the baseline methods.