A Cloud- based Data Processing Platform for the Internet of Th ings: Architecture, Implementation, and Performance Evaluation
Xia Peng
International Journal of Web Applications, Vol. 17, No. 4, 0000.
DOI: https://doi.org/10.6025/ijwa/2025/17/4/145-152
Link: https://www.dline.info/ijwa/fulltext/v17n4/ijwav17n4_1.pdf
Abstract:
The paper presents a cloud-based data processing platform for the Internet of Things (IoT), designed to handle the collection, storage, processing, and visualization of sensor data. It leverages cloud computing to address the challenges posed by the massive volumes of data generated by IoT devices, utilizing scalable technologies like Hadoop and Hive for distributed storage and Map Reduce based analytics. The system architecture includes a TCP server module built with PHP and the Swoole extension to manage real time sensor data ingestion, and a middleware layer based on Redis to decouple components and enable efficient inter module communication. The platform offers two primary external interfaces: one for task execution (via Hive) and another for data uploading to HDFS. Performance tests on the TCP server show high data processing efficiency at low concurrency, with performance degrading beyond 500 concurrent connections still sufficient for most real world IoT scenarios where data acquisition rates are lower. The Hadoop cluster demonstrates robust functionality, including dynamic node addition/removal without service interruption. While domestic IoT analytics platforms in China lag behind global counterparts, this work contributes a customizable, open framework that supports secondary development and real time monitoring. The authors conclude that the platform meets typical production needs and suggest future improvements in API design and developer usability to lower the barrier for customization and deployment.