Hadoop Based Architecture for a Smart, Secure and Efficient Insurance Solution Using IoT

Abstract

The “Internet of Things” (IoT) is among the most highly subsidized and promising topics in both academia and industry these days. Contemporary developments in digital technology have raised the interest of many researchers towards implementation in this area. The influence of IoT within the insurance field is vital. This chapter asserts an innovative concept of IoT pooled with an insurance application, which is beneficial for insurance companies to monitor and analyze the health of their clients continuously. Numerous insurance companies are clustered together to provide a standardized health status monitoring of clients. Since there is a large amount data generated by the system, we adopt Hadoop in the background to map the data effectively and to reduce it into a simpler format. We assimilate Sqoop tool to enable data transfer between Hadoop and RDBMS, in consort with Apache Hive for providing a database query interface to the Hadoop. By consuming the output from Hadoop MapReduce, a non-probabilistic binary linear classifier predicts the policyholder’s chances of developing some health problems. Ultimately, the resultant outcomes are presented on the user’s smartphones. The Apache Ranger framework interweaved with the Hadoop ecosystem aims to ensure data confidentiality. The endowments are granted to the policy holders based on the health report generated by our system. To evaluate the efficiency of the system, experiments are conducted using various policyholder’s health datasets and from the results, it is observed that SVM predicts sepsis with an accuracy of approximately 86%. While testing with the medical dataset, SVM proved to be more accurate than the C4.5 algorithm.