A Distributed Incremental Processing Framework for Large-Scale IoT Data Streams

With the widespread deployment of the Internet of Things (IoT), high-frequency data streams generated by large-scale sensor networks pose significant challenges to traditional stream processing systems, particularly in terms of limited real-time performance, inefficient handling of redundant data, and unbalanced system load. To address these issues, this paper presents an optimized algorithmic framework that integrates distributed scheduling with incremental computation, aiming to improve processing efficiency and system scalability in IoT environments. The proposed system incorporates core modules including data preprocessing, dynamic task scheduling, and state-aware incremental computing. It effectively reduces redundant computation and dynamically balances workload across processing nodes while ensuring low-latency performance. A simulated urban environmental monitoring platform was constructed to validate the method. Experimental results demonstrate that the proposed approach outperforms mainstream solutions in key metrics such as latency, throughput, and resource utilization—achieving more than 45% reduction in average processing delay and significantly improving load balancing. This work not only enhances the performance of IoT data stream processing systems but also provides a technical reference for deploying efficient real-time computing frameworks in applications such as smart cities and industrial monitoring.