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.

中文翻译：

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通过 ANN 和 KNN 优化 IoT 设备和数据可用性

人们进行了大量研究，重点关注瞬时故障率和温度的快速预测，以提高物联网设备和数据的可用性。温度在设备可用性中起着至关重要的作用，因为它显着影响设备性能和使用寿命。它是预测设备故障的重要指标，并通过有效的温度管理提高可用性和效率。在所提出的物联网设备和数据可用性优化方案中，利用人工神经网络（ANN）算法和K最近邻（KNN）算法来驱动神经网络。选择可用性优化的初步算法，目标分为数据优化和设备优化两部分。为每个部分构建合适的模型，并采用KNN驱动的神经网络算法来求解所提出的优化模型。验证结果清楚地证明了所提出方案的有效性。与基准方法、可用性前向容错方法和启发式优化算法相比，最高温度成功降低至2.0750℃。此外，物联网设备的平均可用性显着提高，与上述方法相比分别提高了27.03%、15.76%和10.85%。三种算法的瞬时失败率分别为100%、87.89%和84.4%。事实证明，该优化算法在消除故障信号和优化限时满意度预测方面非常有效。此外，它在决策过程中表现出战略远见。