The Dynamics and Implications of the Internet of Things on Data Mining

Abstract

The research explores and understands the thematic dynamics of the Internet of Things (IoT) and its complementary and cross-cutting data mining (DM) platform. As part of the process, secondary data is utilized based on user-app searches generated by Google Scholar. A database is compiled, analyzed, and presented. This study also discusses the classification of data mining methods and the key data mining techniques for IoT applications. The research findings indicate that IoT continues to evolve with significant degrees of proliferation. Complementary and trailblazing data mining (DM) with more access to cloud computing platforms has accelerated the achievement of planned technological innovations. The outcome has been myriads of apps currently used in different thematic landscapes. Based on available data on user app searches, between 2016 and 2019, themes like sports, supply chain, and agriculture maintained positive trends over the four years. Moreover, the emerging Internet of Nano-Things was beneficial in many sectors. Wireless Sensor Networks (WSNs) were also emerging with more accurate and effective results in gathering information and processing data and communication technologies. However, data mining in IoT applications faces significant security, complexity, and privacy challenges. In summary, available data indicate that IoT is happening and has a significant implication for data mining. All indications suggest that it will continue to grow and increasingly affect how the world interacts with "things." A backdrop of concerns exists, from developing standard protocols to protecting individual privacy. This study recommends various potential solutions; however further studies are required to determine the practicality of the suggested solutions.