AI-Driven Sentiment-Enhanced Secure IoT Communication Model Using Resilience Behavior Analysis

Wireless technologies and the Internet of Things (IoT) are being extensively utilized for advanced
development in traditional communication systems. This evolution lowers the cost of the extensive use of sensors,
changing the way devices interact and communicate in dynamic and uncertain situations. Such a constantly evolving
environment presents enormous challenges to preserving a secure and lightweight IoT system. Therefore, it leads to
the design of effective and trusted routing to support sustainable smart cities. This research study proposed a Genetic
Algorithm sentiment-enhanced secured optimization model, which combines big data analytics and analysis rules to
evaluate user feedback. The sentiment analysis is utilized to assess the perception of network performance, allowing
the classification of device behavior as positive, neutral, or negative. By integrating sentiment-driven insights, the IoT
network adjusts the system configurations to enhance the performance using network behaviour in terms of latency,
reliability, fault tolerance, and sentiment score.Accordingly to the analysis, the proposedmodel categorizes the behavior
of devices as positive, neutral, or negative, facilitating real-timemonitoring for crucial applications. Experimental results
revealed a significant improvement in the proposed model for threat prevention and network efficiency, demonstrating
its resilience for real-time IoT applications.