Abstract
Today, wearable health products play a crucial role in most locations, such as constant wellness monitoring of people, street traffic management, weather forecasting, along with smart house. These sensor devices constantly generate massive amounts of data and are kept in cloud computing. This particular chapter proposes Internet of Things design to store and system scalable sensor information for healthcare apps. Proposed architecture comprises 2 primary architecture, specifically, MetaFog-Redirection and Choosing and Grouping architecture. Though cloud computing offers scalable data storage, effective computing platforms must process it. There's a requirement for scalable algorithms to process the big sensor information and recognize the helpful patterns. To conquer this problem, this particular chapter proposes a scalable MapReduce based logistic regression to process such massive quantities of sensor information. Apache Mahout includes scalable logistic regression to system BDA in a distributed way. This particular chapter uses Apache Mahout with Hadoop Distributed File System to process the sensor information produced by the wearable health units.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
