Early Detection System for Gas Leakage and Fire in Smart Home Using Machine Learning
Early Detection System for Gas Leakage and Fire in Smart Home Using Machine Learning
复制标题
DOI：
10.1109/icce.2019.8661990
复制
发表时间：
2019-01
期刊：
影响因子：
--
通讯作者：
L. Salhi;T. Silverston;Taku Yamazaki;T. Miyoshi
中科院分区：
文献类型：
--
作者：
L. Salhi;T. Silverston;Taku Yamazaki;T. Miyoshi
使住房更具包容性、更安全、更有弹性和可持续性是每个社会都必须实现的重要要求。智能住宅中的燃气泄漏和火灾是造成人员死亡和财产损失的严重问题。目前，预防和警报系统已广泛使用。然而，它们通常是具有基本功能的单个单元，没有足够的多传感能力和与现有机器对机器（M2M）家庭网络以及Internet等外部网络的交互能力。事实上，在不久的将来，这种通信模式显然将是M2M家庭网络中最主要的。在本文中，我们提出了一种高效的系统模型，使用低成本的设备将气体泄漏和火灾检测系统集成到集中式M2M家庭网络中。然后，通过机器学习的方法，我们将数据挖掘方法与感知到的信息结合起来，以隐藏的模式检测异常的空气状态变化，从而对风险事件进行早期预测。这项工作将有助于提高智能房屋的安全性和保护财产。
Making houses more inclusive, safer, resilient and sustainable is an important requirement that must be achieved in every society. Gas leakage and fires in smart houses are serious issues that are causing people’s death and properties losses. Currently, preventing and alerting systems are widely available. However, they are generally individual units having elementary functions without adequate capabilities of multi-sensing and interaction with the existing Machine-to-Machine (M2M) home network along with the outside networks such as Internet. Indeed, this communication paradigm will be clearly the most dominant in the near future for M2M home networks. In this paper, we are proposing an efficient system model to integrate the gas leakage and fire detection system into a centralized M2M home network using low cost devices. Then, through machine learning approach, we are involving a data mining method with the sensed information and detect the abnormal air state changes in hidden patterns for early prediction of the risk incidences. This work will help to enhance safety and protect property in smart houses.