A Review: Integration of Computational Material Science with AIoT for Enhanced IoT Applications

Akanksha Verma; Priyanka Mehta; Ganesh Vandile; Deoram Nandanwar; Amar Nandanwar

doi:10.61343/jcm.v3i02.134

Authors

Akanksha Verma S. J. College, Naubasta Road, Kanpur-208021, Uttar Pradesh, India
Priyanka Mehta Dayanand Arya Kanya Mahavidyalaya, Jaripatka, Nagpur-440014, Maharashtra, India
Ganesh Vandile Shri Mathuradas Mohota College of Science, Nagpur-440009, Maharashtra, India
Deoram Nandanwar Shri Mathuradas Mohota College of Science, Nagpur-440009, Maharashtra, India
Amar Nandanwar J. M. Patel Arts, Commerce & Science College, Bhandara-441904, Maharashtra, India

DOI:

https://doi.org/10.61343/jcm.v3i02.134

Keywords:

AI Agents, Edge Computing, Microcontrollers, IoT Applications, Computation material science, Data Processing

Abstract

The rapid growth of the Internet of Things (IoT) has necessitated the development of systems capable of processing data efficiently and in real-time in computation material science. Traditionally, cloud computing has been used to manage and analyze the vast amounts of data generated by IoT devices to store and analyze observed data (as results of the materials) which further use as survey data application. However, cloud-based solutions often face challenges related to latency, bandwidth consumption, periodically survey, comparison with standard data and energy inefficiency, especially in resource-constrained environments. To address these challenges, edge computing has emerged as a promising solution, bringing computation closer to the data source. The integration of Artificial Intelligence (AI) agents in edge computing through microcontrollers can provide enhanced decision-making capabilities in IoT applications, offering both performance and energy efficiency. This review paper explores the advanced implementation of AI agents in edge computing using microcontrollers for IoT applications for material science as computing agent for data storage, comparison, analyzing and many more, with a specific emphasis on material science applications, highlighting the benefits and challenges of deploying lightweight AI models on resource-constrained devices.

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