University Institute of Control Systems and Industrial Computing (ai2), Universitat Politècnica de València (UPV) Camino de Vera, s/n. 46022 Valencia, Spain.
Sensors (Basel). 2019 Dec 23;20(1):112. doi: 10.3390/s20010112.
Object recognition, which can be used in processes such as reconstruction of the environment map or the intelligent navigation of vehicles, is a necessary task in smart city environments. In this paper, we propose an architecture that integrates heterogeneously distributed information to recognize objects in intelligent environments. The architecture is based on the IoT/Industry 4.0 model to interconnect the devices, which are called smart resources. smart resources can process local sensor data and offer information to other devices as a service. These other devices can be located in the same operating range (the edge), in the same intranet (the fog), or on the Internet (the cloud). smart resources must have an intelligent layer in order to be able to process the information. A system with two smart resources equipped with different image sensors is implemented to validate the architecture. Our experiments show that the integration of information increases the certainty in the recognition of objects by 2-4%. Consequently, in intelligent environments, it seems appropriate to provide the devices with not only intelligence, but also capabilities to collaborate closely with other devices.
目标识别可用于环境地图重建或车辆智能导航等过程，是智慧城市环境中的一项必要任务。在本文中，我们提出了一种架构，该架构集成了异构分布式信息，以识别智能环境中的对象。该架构基于物联网/工业 4.0 模型来互联设备，这些设备被称为智能资源。智能资源可以处理本地传感器数据，并将信息作为服务提供给其他设备。这些其他设备可以位于相同的操作范围（边缘）、相同的内部网（雾）或互联网（云）中。智能资源必须具有智能层才能处理信息。实现了一个配备有不同图像传感器的两个智能资源的系统，以验证该架构。我们的实验表明，信息的集成将对象识别的确定性提高了 2-4%。因此，在智能环境中，为设备提供不仅智能，而且还提供与其他设备密切协作的能力似乎是合适的。
