Optimization and Implementation of IoT Embedded Software Based on Big Data

With the rapid growth of Internet of Things (IoT) devices, the explosive increase in data volume poses significant challenges for traditional embedded systems in handling massive data and managing multiple transactions. To address this issue, this project proposes an IoT embedded software optimization and implementation method based on big data technology. The research integrates big data into the IoT architecture to achieve efficient data processing and management. Relevant file systems and drivers were ported into the embedded system to ensure the stability of big data processing and the real-time nature of data handling. Additionally, the study designs a multi-transaction priority management mechanism and a load balancing strategy, effectively enhancing the performance of IoT systems in high-concurrency scenarios. Experimental results show that the optimized embedded system maintains stable data transmission when faced with an increasing number of transactions, demonstrating high stability. The downtime recovery time is reduced to within 0.5 seconds, with no deadlock occurrences. This research provides technical support for the further development of IoT systems.