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@article{167327,
        author = {M S Lakshmi Devi},
        title = {SCALABLE IOT DATA MANAGEMENT IN CLOUD ENVIRONMENTS: TECHNIQUES AND CHALLENGES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {5},
        number = {3},
        pages = {374-382},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167327},
        abstract = {This paper explores various techniques designed to address these challenges, focusing on edge computing, fog computing, data partitioning, sharding, stream processing, and data compression. Edge computing and fog computing are examined for their roles in reducing latency and bandwidth consumption by decentralizing data processing. Data partitioning and sharding are analyzed for their contributions to scalability and fault tolerance in distributed storage systems. Stream processing is evaluated for its ability to handle real-time data analysis, while data compression techniques are assessed for their impact on bandwidth and storage efficiency. Each technique's strengths and limitations are discussed, highlighting their implications for scalability, latency, security, data management, and energy efficiency. The paper concludes that a hybrid approach integrating these techniques is essential for effective IoT data management, and future research should focus on optimizing these methods to meet the demands of complex and data-intensive applications.},
        keywords = {Edge Computing, Latency, Bandwidth, Stream processing, Scalability},
        month = {August},
        }