Challenges in Implementing Machine Learning-Driven IoT Solutions in Semiconductor Design and Wireless Communication System

Abstract

he integration of machine learning and Internet of Things has great potential to revolutionize several industries including semiconductor design and wireless communication techniques as it can. However, the application of ML in the IoT poses great problems such as compatibility with existing systems, real-time decision making, scalability and cost. This research seeks to investigate these challenges by identifying major technical and operational impediments that hinder IoT based ML application in these industries.

The current research adopted the quantitative research approach and the data was collected through an online questionnaire completed by the professionals in the semiconductor and wireless communication industries. Questionnaire was distributed and filled by 300 participants, to evaluate their experience about ML-IoT integration. The survey encompasses a number of aspects such as technical issues, practical problems, the questions of expansion of the usage and questions of data protection. Quantitative data were analyzed descriptively and inferentially employing Chi-square test, ANOVA, multiple regression analysis, correlation analysis. These techniques enabled the assessment of the issues and interactions between the factors of industry focus, company size and ML-IoT implementation.

The article concluded that the main challenges still persists and they are chiefly evidenced in the issues of legacy system integration whereby semiconductor design still finds itself in the lager of hauling old architectures that cannot support computation of today’s convolutional ML algorithms. The identified key issue in wireless communication environment was real-time decision-making because it could not afford a time delay in processing of large amount of data when required. The issue of scalability was identified to be widely affecting both industries as they attempt to handle the increasing amount of IoT data in efficient and performant manners. Further, the data privacy and security were reported to be slightly higher in the wireless communication system and the participants called for enhanced legal safeguards and privacy-preserving methodologies in the ML domain.

The regression analysis revealed that the difficulty level of the ML-IoT challenges was dependent on the size of the organization and the number of years spent on such projects: While larger organizations had more potential to come to terms with the issues of scalability of the projects, they required in order to advance, they still struggled with the costs involved in the projects and the matter of data privacy. The article suggests that the use of techniques like edge computing and federated learning can eliminate the challenges posed by real-time processing while cloud environments can also be the possibility of cost-saving. More emphasis must be placed on security solutions that facilitate data privacy such as privacy-preserving technologies and commitment to more technological advances for creating more scalable ML models to be used in IoT systems.