There is an immediate need for creative ways to improve resource ef iciency given the dynamic nature of robust sensor networks and their increasing reliance on data-driven approaches.One key challenge faced is ef iciently managing large data files collected from sensor networks for example optimal beehive image and video data files. We of er a revolutionary paradigm that uses cutting-edge edge computing techniques to optimize data transmission and storage in order to meet this problem. Our approach encompasses data compression for images and videos, coupled with a data aggregation technique for numerical data. Specifically, we propose a novel compression algorithm that performs better than the traditional Bzip2, in terms of data compression ratio and throughput. We also designed as an addition a data aggregation algorithm that basically performs very well by reducing on the time to process the overhead of individual data packets there by reducing on the network traf ic. A key aspect of our approach is its ability to operate in resource-constrained environments, such as that typically found in a local beehive farm application from where we obtained various datasets. To achieve this, we carefully explore key parameters such as throughput, delay tolerance, compression rate, and data retransmission. This ensures that our approach can meet the unique requirements of robust network management while minimizing the impact on resources. Overall, our study presents and majorly focuses on a holistic solution for optimizing data transmission and processing across robust sensor networks for specifically local beehive image and video data files. Our approach has the potential to significantly improve the ef iciency and ef ectiveness of robust sensor network management, thereby supporting sustainable practices in various IoT applications such as in Bee Hive Data Management.

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