Scalable Database Solutions for Managing Massive IoT Data Volumes
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The rapid proliferation of Internet of Things (IoT) devices has led to an unprecedented explosion in data generation, characterized by high velocity, volume, and variety. Traditional relational database management systems struggle to handle the con tinuous, time-stamped data streams produced by modern IoT networks, particularly in industrial and smart city applications. This chapter explores scalable database solutions specifically designed for managing massive IoT data volumes, with a primary focus on Time-Series Databases (TSDBs) and NoSQL architectures. We present a comprehensive analysis of horizontal scaling strategies, including data sharding, time-partitioning, and distributed replication mechanisms that ensure high availability and fault tolerance. Fur thermore, we propose a multi-tier database architecture optimized for edge-to-cloud IoT deployments. Through extensive simulation using industry-standard benchmark datasets, we evaluate the performance of leading database solutions (TDengine, InfluxDB, and TimescaleDB) across critical metrics such as ingestion throughput, query latency, storage efficiency, and resource utilization. The results demonstrate that purpose-built time-series databases with advanced compression and continuous aggregation capabilities significantly outperform generalized solutions, providing a robust foundation for scalable and secure IoT data management.
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