| Abstract: |
The integration of Artificial Intelligence (AI) with Internet of Things (IoT) systems has emerged as a transformative technological paradigm, creating intelligent ecosystems capable of autonomous decision-making and predictive analytics. This research investigates the convergence of AI and IoT technologies, examining their synergistic applications across various domains. A quantitative research methodology was employed, analyzing data from 250 IoT deployments across Indian smart cities, healthcare facilities, and industrial sectors between 2023 and 2025.
The primary objective was to evaluate performance metrics, energy efficiency, and decision-making accuracy in AI-integrated IoT systems compared with traditional IoT frameworks. The hypothesis posited that AI-enhanced IoT systems demonstrate superior operational efficiency, reduced latency, and improved predictive capabilities.
Results revealed that AI-IoT integration led to a 78.4% improvement in prediction accuracy, a 62.3% reduction in energy consumption, and an 84.7% enhancement in real-time decision-making compared to conventional IoT systems. Statistical analysis showed significant correlations between the deployment of machine learning algorithms and system performance optimization. The findings further indicate that deep learning-based edge computing architectures provide optimal results for smart city applications, whereas lightweight AI models are most effective for resource-constrained IoT devices.
Overall, the research offers valuable insights for stakeholders seeking to implement intelligent IoT infrastructures in developing economies. |
