With the rise of both embedded technologies and the Internet of Things (IoT) integration, it is increasingly possible to realize complex systems in which, for example, vehicles are connected to the IoT domain capable of interacting directly with applications on mobile or embedded devices. In this work, a Machine to Machine (M2M) based platform able to recognize the driving style will be presented. An embedded device (On-Board Diagnostic (OBD)) has been considered to implement the Internet of Vehicles (IoV) paradigm exporting recognized data to other systems to promote an eco-friendly driving style by suggesting corrective actions to drivers. The architecture consists of the following elements: an on board device able to obtain information regarding speed, acceleration, jerk and fuel consumption, a cloud able to store all collected data, an M2M protocol such as Message Queuing Telemetry Transport (MQTT) to provide scalability to the architecture, and a Fuzzy Inference System to classify user behavior. All these elements are harmonized to achieve the common target of informing users about an Aggressive or Very-Aggressive driving style with the aim of reducing both speed and acceleration. The smart alerting system will promote a more eco-friendly driving style, contributing a reduction in $C{O}_2$ emissions and fuel consumption. Performance evaluation confirms the efficiency of the proposed classifier and the overall benefits for drivers in fuel consumption reduction.

随着嵌入式技术和物联网 (IoT) 集成的兴起，实现复杂系统的可能性越来越大，例如，车辆连接到物联网域，能够直接与移动或嵌入式设备上的应用程序交互。在这项工作中，将展示一个能够识别驾驶风格的基于机器对机器 (M2M) 的平台。嵌入式设备（车载诊断（OBD））已被考虑实施车联网（IoV）范式，将识别数据导出到其他系统，以促进环保驾驶风格通过向司机建议纠正措施。该架构由以下元素组成：能够获取有关速度、加速度、加加速度和燃料消耗的信息的车载设备，能够存储所有收集到的数据的云，M2M 协议（如消息队列遥测传输 (MQTT)）以提供可扩展性到体系结构，以及用于对用户行为进行分类的模糊推理系统。所有这些元素都协调一致，以实现告知用户激进或非常激进的驾驶风格的共同目标，目的是降低速度和加速度。智能警报系统将促进更环保的驾驶方式，有助于减少$C{哦}_2$排放和燃料消耗。性能评估证实了所提出的分类器的效率以及驾驶员在降低燃料消耗方面的整体利益。