IoT devices and data availability optimization by ANN and KNN

Abstract

Extensive research has been conducted to enhance the availability of IoT devices and data by focusing on the rapid prediction of instantaneous fault rates and temperatures. Temperature plays a crucial role in device availability as it significantly impacts equipment performance and lifespan. It serves as a vital indicator for predicting equipment failure and enables the improvement of availability and efficiency through effective temperature management. In the proposed optimization scheme for IoT device and data availability, the artificial neural network (ANN) algorithm and the K-Nearest Neighbours (KNN) algorithm are utilized to drive a neural network. The preliminary algorithm for availability optimization is chosen, and the target is divided into two parts: data optimization and equipment optimization. Suitable models are constructed for each part, and the KNN-driven neural network algorithm is employed to solve the proposed optimization model. The effectiveness of the proposed scheme is clearly demonstrated by the verification results. When compared to the benchmark method, the availability forward fault-tolerant method, and the heuristic optimization algorithm, the maximum temperature was successfully reduced to 2.0750 °C. Moreover, significant enhancements in the average availability of IoT devices were achieved, with improvements of 27.03%, 15.76%, and 10.85% respectively compared to the aforementioned methods. The instantaneous failure rates were 100%, 87.89%, and 84.4% respectively for the three algorithms. This optimization algorithm proves highly efficient in eliminating fault signals and optimizing the prediction of time-limited satisfaction. Furthermore, it exhibits strategic foresight in the decision-making process.