The internet of things (IoT) is prominently used in the present world. Although it has vast potential in several applications, it has several challenges in the real-world. One of the most important challenges is conservation of battery life in devices used throughout IoT networks. Since many IoT devices are not rechargeable, several steps to conserve the battery life of an IoT network can be taken using the early prediction of battery life. In this study, a machine learning based model implementing a random forest regression algorithm is used to predict the battery life of IoT devices. The proposed model is experimented on ‘Beach Water Quality – Automated Sensors’ data set generated from sensors in an IoT network from the city of Chicago, USA. Several pre-processing techniques like normalisation, transformation and dimensionality reduction are used in this model. The proposed model achieved a 97% predictive accuracy. The results obtained proved that the proposed model performs better than other state-of-art regression algorithms in preserving the battery life of IoT devices.

中文翻译：

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物联网网络中电池寿命的预测模型

物联网（IoT）在当今世界中得到了广泛使用。尽管它在多种应用中具有巨大的潜力，但在现实世界中却面临着一些挑战。最重要的挑战之一是保存整个IoT网络中使用的设备的电池寿命。由于许多物联网设备都是不可充电的，因此可以使用电池寿命的早期预测来采取一些步骤来保存物联网网络的电池寿命。在这项研究中，基于机器学习的模型实现了随机森林回归算法，用于预测IoT设备的电池寿命。在从美国芝加哥市物联网网络中的传感器生成的“海滩水质-自动化传感器”数据集上对提出的模型进行了实验。几种预处理技术，例如归一化，此模型中使用了变换和降维。提出的模型实现了97％的预测准确性。获得的结果证明，该模型在保留物联网设备的电池寿命方面比其他最新的回归算法表现更好。