  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.
