Abstract
Background/Objectives: The information of IoT devices processed in a cloud environment has a costly problem in optimizing information of IoT devices due to different types of information generated by IoT devices and large-scale information processing. In the cloud environment, reducing costs and increasing transmission capacity remain the biggest issues to efficiently manage information generated by heterogeneous IoT devices. Methods/Statistical analysis: The various parameters used for simulation in the proposed technique performance evaluation based on the mean of Monte Carlo simulations. Among the performance evaluation results of the proposed techniques derived in this environment, IoT processing time averaged 11.85% improvement since IoT processing time is classified into multi-subset sized network regions and then generated cumulative use of transactions through similarity between IoT information. Findings: In this paper, we propose an optimization management technique for heterogeneous IoT information using hierarchical distributed polynomials to minimize the cost of processing information generated by IoT devices. The proposed technique not only enables batch distribution processing of IoT information by deep learning IoT information, but also performs multi-dimensional distribution processing of IoT information hierarchically in the process of sending and receiving IoT information. Improvements/Applications: The proposed technique can synchronize the frequency of use according to the number of IoT information by applying the n-order distribution of IoT information to manage IoT information as efficiently as possible. The proposed technique improves bandwidth and processing time over existing techniques in the process of sending and receiving large amounts of IoT information in a short time when distributing heterogeneous IoT information during the IoT information linkage process.
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