利用本地蜂巢图像与视频文件的数据处理与优化实现鲁棒传感器网络设计
Enabling robust sensor network design with data processing and optimization making use of local beehive image and video files
AI总结：
针对鲁棒传感器网络中蜂巢图像与视频大数据的高效管理问题，提出结合边缘计算、新型压缩算法和数据聚合技术的整体优化方案，以提升传输与存储效率并支持资源受限环境下的可持续物联网应用。
摘要：
鉴于鲁棒传感器网络的动态特性及其对数据驱动方法日益增长的依赖，迫切需要创造性的方法来提高资源效率。面临的一个关键挑战是高效管理从传感器网络收集的大型数据文件，例如最优的蜂巢图像和视频数据文件。我们提出一种革命性范式，利用前沿的边缘计算技术来优化数据传输和存储，以应对这一问题。我们的方法包括图像和视频的数据压缩，并结合针对数值数据的数据聚合技术。具体而言，我们提出一种新颖的压缩算法，在数据压缩比和吞吐量方面均优于传统的Bzip2。我们还额外设计了一种数据聚合算法，该算法通过减少处理单个数据包开销的时间，从而减少网络流量，表现非常出色。我们方法的一个关键方面是能够在资源受限的环境中运行，例如本地蜂巢农场应用中通常所处的环境，我们从中获取了各种数据集。为实现这一目标，我们仔细探索了吞吐量、延迟容忍度、压缩率和数据重传等关键参数。这确保我们的方法能够满足鲁棒网络管理的独特需求，同时最大限度地减少对资源的影响。总体而言，我们的研究提出并主要聚焦于一种整体解决方案，用于优化鲁棒传感器网络中的数据传输和处理，特别是针对本地蜂巢图像和视频数据文件。我们的方法有潜力显著提高鲁棒传感器网络管理的效率和有效性，从而支持各种物联网应用中的可持续实践，例如蜂巢数据管理。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
