Optimized Parallel Computing Framework for Real-Time IoT Data Processing

Abstract

Advent of Internet of Things (IoT) sensor networks of scale, the amount and speed of data produced has become a major problem in real-time data processing and analytics. Conventional computing models cannot provide real-time insights because are constrained by scalability, resource management, and computational capabilities. The paper discusses how parallel computing frameworks, including Apache Spark, Apache Flink, and CUDA, can be used to improve the efficiency of processing and analyzing massive sensor data in real time. These models allow distributed data processing across multiple nodes, significantly reducing latency and enhancing throughput. The load balancing, fault tolerance, and data partitioning problems are some of the challenges that proposed approach will address using these frameworks to ensure significant performance gains in large-scale IoT settings. Data processing is found to be significantly faster, and the analytics latency is reduced, which is shown by experimental results, thereby showing the potential of parallel computing in real-time IoT analytics. The contributions made in this work are the design and implementation of an optimized framework of IoT sensor networks and the performance evaluation of a comprehensive framework across different real-world conditions.