ABSTRACT
This chapter enables the targeted troubleshooting and remediation of IoT-based technology with AI. Reliability modeling is another key AI application in IoT. By analyzing historical data, AI algorithms can create probabilistic models that assess the reliability of IoT systems. These models estimate failure rates, identify failure modes, and predict system behavior under different conditions. This helps in understanding the reliability characteristics of IoT deployments and optimizing system designs. The paper also highlights the role of AI in the adaptive control and optimization of IoT systems. AI algorithms continuously analyze sensor data to adapt system parameters and configurations in real-time. This adaptive control ensures that the system operates within safe, reliable limits, minimizing the risk of failures and performance degradation. Furthermore, AI contributes to the cyber-security of IoT networks. Machine learning algorithms detect and mitigate cyber security threats by analyzing network traffic, identifying suspicious patterns, and predicting potential attacks. AI-powered intrusion detection systems help maintain the integrity and availability of IoT systems. Lastly, the paper discusses how AI can be utilized as a decision support system in IoT environments.
