A SCALABLE AI AND CLOUD-BASED FRAMEWORK FOR SMART AUTOMATION IN IOT NETWORKS

Abstract

Background

The rapid evolution of the Internet of Things (IoT), as well as the Artificial Intelligence (AI) and cloud computing have transformed the modern digital ecosystems, making them intelligent, connected, and automated. However, the classical IoT systems lack scalability, real-time processing, and efficient data management, which necessitates more advanced solutions.

Objective

This paper seeks to discuss and analyze a scalable AI and cloud-based framework of smart automation in IoT networks, its effectiveness, scalability, challenges associated with its implementation, and it’s potential to be adopted in the future.

Methodology

A quantitative research methodology was employed based on a structured questionnaire that was sent to 300 professionals, including IoT engineers, data scientists, cloud experts, IT managers, and researchers. The analysis of data was performed with the help of descriptive statistics, including mean, standard deviation, frequency, and percentage, and the reliability was evaluated with the help of Cronbach’s Alpha to ensure the internal consistency.

Results

The research proves that there is a high level of support to the use of AI and cloud computing in IoT-based automation systems. The data is very reliable (Cronbachs alpha = 0.931) and respondents are very aware and adopters of these technologies. AI-based automation is more efficient and reliable in decision-making, as well as in data management and scalability, cloud computing is more efficient and reliable. Nevertheless, there are still challenges like privacy of data, security threats, and cost of implementation. In general, the prospects are the most optimistic, and the integration of AI and IoT is likely to become the key to significant technological improvements.

Conclusion

The paper concludes that AI and cloud-based IoT solutions can be of much advantage in smart automation and improving the performance of systems. However, to be sustainable, security, cost and scalability issues must be considered. The findings have significant implications to intelligent and scalable design of IoT solutions to researchers, practitioners, and policymakers