An Improved Stochastic Fractal Search Based on Entropy Multitrust Fusion Model for IoT-Enabled WSNs Clustering

In mission-critical Internet of Things (IoT)-enabled wireless sensor networks (WSNs), the demand for reliable sensing data is significantly increasing. Meanwhile, the major challenge of maximizing network lifetime is energy dissipation optimization, whereas the existing major clustering protocols are proposed to reduce energy consumption without considering the malicious nodes in the network. This article proposes an improved stochastic fractal search (SFS) based on the multitrust fusion model for energy-efficient and secure clustering in IoT-enabled WSNs. The multitrust fusion model based on the entropy weight method (EWM) is established to calculate the integrated trust of sensors. After trust evaluation, an improved SFS using differential mutation and adaptive mutation factor is introduced to clustering. During clustering, the selection strategy of cluster heads adopts a new fitness function, which considers five parameters: integrated trust, residual energy, spatial density, the distance to the base station, and the dissipated energy for the next round. Experimental results reveal that the proposed protocol outperforms other existing protocols in all scenarios.