Addressing Rogue Nodes and Trust Management: Leveraging Deep Learning-Enhanced Hybrid Trust to Optimize Wireless Sensor Networks Management

Comprising a multiplicity of AdHoc sensors working in concert to monitor a range of environmental and physical factors for the targeted area, wireless sensor networks (WSNs). These sensors are used to provide continuous environmental status like temperature, pressure, and humidity by forwarding vital data to the internet through a base station. Aiming to greatly increase the security and performance of WSNs, this study presents a new framework that is a combination of the Deep Learning-Enhanced Hybrid Trust (DLEHT) model and the Machine Learning-Enhanced Fuzzy-Based Routing Protocol (ML-EFBRP). In this research, enhanced packet delivery, packet drop reduction, and the rogue nodes addressed in WSN from source to sink using a probabilistic approach, which depends on the experience of data with the integration of a sum-rule weight mechanism in HMM (Hidden Markov Model). Integration methodology played a major role in deep learning to observe the normal and abnormal node behavior with historic data. It enhanced the throughput and lowered latency with successful detection and addressing of rogue nodes by the integrated strategy. The proposed work, reflects an improvement in performance, both in terms of throughput and latency. The delay hyperparameters are observed, which vary from 7.48 to 26.22 ms with an average of 15.855 ms. And the packet is controlled and decreased by 7%, showcasing more improvement compared to existing work. Simulation results show considerable improvements in network accuracy, reliability, energy efficiency, and resistance during node failures and security concerns for network correctness. These findings show the combination of DLEHT and ML-EFBRP models provides stronger monitoring systems, hence enhancing operational efficiency in settings with limited resources.