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 proposed model categorizes the behavior of devices as positive, neutral, or negative, facilitating real-time monitoring 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.