In the era of Internet of things (IoT), network Connection of an enormous number of agriculture machines and service centers is an expectation. However, it will be with a generation of massive volume of data, thus overwhelming the network traffic and storage system especially when manufacturers give maintenance service typically by various data analytic applications on the cloud. The situation is more complex in the context of low latency applications such as health monitoring of agriculture machines, although require emergency responses. Performing the computational intelligence on edge devices is one of the best approaches in developing green communications and managing the blast of network traffic. Due to the increasing usage of smartphone applications, the edge computation on the smartphone can highly assist the network traffic management. In connection with the mentioned point, in the context of exploiting the limited computation power of smartphones, the design of an AI-based data analytic technique is a challenging task. On the other hand, the users’ need for economic technology makes it not to be easily pierced. This research work aims both targets by presenting a bi-level genetic algorithm approach of an optimized data analytic AI technique for monitoring the health of the agriculture vehicles which can be economically utilized on smartphone end-devices using the built-in microphones instead of expensive IoT sensors.

在物联网（IoT）时代，人们期望建立大量农业机械和服务中心的网络连接。但是，它将产生大量的数据，从而使网络流量和存储系统不堪重负，尤其是当制造商通常通过云上的各种数据分析应用程序提供维护服务时。尽管需要紧急响应，但在低延迟应用程序（如农业机械的健康监控）中，情况更为复杂。在边缘设备上执行计算智能是开发绿色通信和管理网络流量爆炸的最佳方法之一。由于智能手机应用程序的使用不断增加，智能手机上的边缘计算可以极大地帮助网络流量管理。结合上述观点，在利用智能手机有限的计算能力的背景下，基于AI的数据分析技术的设计是一项艰巨的任务。另一方面，用户对经济技术的需求使其不容易被刺穿。这项研究工作针对这两个目标，通过提出一种优化遗传数据分析AI技术的双层遗传算法方法来监控农用车辆的健康状况，可以使用内置麦克风而不是昂贵的物联网将其经济地用于智能手机终端设备传感器。用户对经济技术的需求使其不容易被刺穿。这项研究工作针对这两个目标，提出了一种优化遗传数据分析AI技术的双层遗传算法方法，用于监控农业车辆的健康状况，可以使用内置麦克风而不是昂贵的IoT在智能手机终端设备上经济地利用农业车辆传感器。用户对经济技术的需求使其不容易被刺穿。这项研究工作针对这两个目标，提出了一种优化遗传数据分析AI技术的双层遗传算法方法，用于监控农业车辆的健康状况，可以使用内置麦克风而不是昂贵的IoT在智能手机终端设备上经济地利用农业车辆传感器。