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.

翻译：随着鲁棒传感器网络的动态特性及其对数据驱动方法依赖性的日益增强，迫切需要创新方法以提高资源利用效率。关键挑战之一在于高效管理传感器网络收集的大型数据文件，例如最优蜂箱图像与视频数据文件。为应对该问题，我们提出一种利用前沿边缘计算技术优化数据传输与存储的革命性范式。该方法涵盖图像与视频数据压缩技术，并结合数值型数据聚合技术。具体而言，我们提出一种新型压缩算法，在数据压缩比与吞吐量方面均优于传统Bzip2算法。此外，我们设计了数据聚合算法，该算法通过减少单个数据包处理开销来降低网络流量，从而表现优异。该方法的核心优势在于能够在资源受限环境中运行，例如本地蜂箱养殖应用场景（本文数据集源于此）。为实现这一目标，我们深入探索了吞吐量、延迟容忍度、压缩率及数据重传等关键参数，确保该方法在最小化资源影响的同时满足鲁棒网络管理的独特需求。总体而言，本研究提出并重点聚焦于针对本地蜂箱图像与视频数据文件，在鲁棒传感器网络中优化数据传输与处理的整体解决方案。该方法有望显著提升鲁棒传感器网络管理的效率与效果，从而支持蜜蜂数据管理等各类物联网应用中的可持续实践。