Trustworthiness-Based WSN Routing Analysis and Design of a Deep Learning-Based Hybrid Methodology

Wireless Sensor Networks (WSNs) are gaining popularity in industries such as environmental monitoring, process control, and healthcare. WSNs, on the other hand, have constraints such as restricted energy sources and communication capacity, which result in decreased network performance and security concerns. This study gives a survey of the literature on WSN routing, with a focus on trustworthiness and security challenges. An Enhanced Hybrid Trustworthy Routing Algorithm is proposed, which employs deep learning (LSTM networks) to accurately forecast mobile sink sites. The study is divided into two parts: improving the routing algorithm and developing a reliable model. The method collects data, trains LSTM models, and simulates WSNs. Trust values are calculated depending on node behaviour, allowing for dynamic security modifications. The suggested method should increase accuracy, security, and dependability. Extensive simulations will demonstrate its efficacy, assisting in the development of trust-based routing and secure WSN infrastructures.