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The rapid evolution of the Internet of Things (IoT) ecosystem has underscored the critical role of parallel and distributed computing in handling the vast data streams generated by IoT devices. Challenges such as processing bottlenecks from large data volumes and device heterogeneity pose significant implementation hurdles. To mitigate these issues, advanced technologies like MapReduce, parallel databases, and platforms such as Apache Hadoop and Spark are employed. These methodologies distribute computational tasks across multiple nodes, enhancing data processing and analysis speeds. By leveraging parallel and distributed computing, organizations can efficiently process IoT data, derive real-time insights, and improve service delivery through optimized analysis.

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Internet of Thing Based Healthcare Monitoring System

  • 会议论文

  • 发表日期：

    2025年

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  • Fajer Danish,
  • Nader Alkhater,
  • Layla Alhalwachi,
  • Muaeyd Aldhamin,
  • Amina Alobaid

摘要

The rapid evolution of the Internet of Things (IoT) ecosystem has underscored the critical role of parallel and distributed computing in handling the vast data streams generated by IoT devices. Challenges such as processing bottlenecks from large data volumes and device heterogeneity pose significant implementation hurdles. To mitigate these issues, advanced technologies like MapReduce, parallel databases, and platforms such as Apache Hadoop and Spark are employed. These methodologies distribute computational tasks across multiple nodes, enhancing data processing and analysis speeds. By leveraging parallel and distributed computing, organizations can efficiently process IoT data, derive real-time insights, and improve service delivery through optimized analysis.