Identifying core IoT technologies using ARM and FCM: A comprehensive data-driven method

Abstract

The internet of things (IoT) technology has garnered significant attention in recent years due to its wide-ranging applications. IoT, with its high connectivity capabilities, integrates various industrial, domestic, and agricultural devices into a smart and remotely controllable software and hardware platform. The field of IoT technology is expansive and encompasses a multitude of sub-technologies. Identifying core technologies in this domain is crucial for guiding research and development efforts by companies. Given the interrelation of these core technologies and their combination with recent decision-making approaches, network-based strategies have recently received special attention. The developed methods are based on static conditions and the assumption of stability, while in emerging technologies like IoT, the pace of changes over time is high. This leads to changes in the importance of technologies under various scenarios.

In this study, in order to analyze the extracted patent data, association rule mining (ARM) algorithms were applied to identify the relationships between technologies and social network analysis was used to analyze the relationships between technologies and estimate their initial weights. Finally, fuzzy cognitive map (FCM) were used to estimate the final weights of technologies and rank them. The fcm approach allows for simultaneous modeling of both static and dynamic states of the system and, on the other hand, by calculating under various scenarios, suggests a core technology that is sustainable.

The research results show that digital information transmission technologies, digital or electrical data processing, and wireless communication networks are the most important sub-technologies of Internet of things.