National Telecommunication Institute (NTI), 5 Mahmoud El Miligui Street, 6th District-Nasr City, Cairo 11768, Egypt.
Faculty of Engineering, Minia University, Minia 61519, Egypt.
Sensors (Basel). 2022 Jul 7;22(14):5106. doi: 10.3390/s22145106.
The Internet of Things (IoT) era is mainly dependent on the word "Smart", such as smart cities, smart homes, and smart cars. This aspect can be achieved through the merging of machine learning algorithms with IoT computing models. By adding the Artificial Intelligence (AI) algorithms to IoT, the result is the Cognitive IoT (CIoT). In the automotive industry, many researchers worked on self-diagnosis systems using deep learning, but most of them performed this process on the cloud due to the hardware limitations of the end-devices, and the devices obtain the decision via the cloud servers. Others worked with simple traditional algorithms of machine learning to solve these limitations of the processing capabilities of the end-devices. In this paper, a self-diagnosis smart device is introduced with fast responses and little overhead using the Multi-Layer Perceptron Neural Network (MLP-NN) as a deep learning technique. The MLP-NN learning stage is performed using a Tensorflow framework to generate an MLP model's parameters. Then, the MLP-NN model is implemented using these model's parameters on a low cost end-device such as ARM Cortex-M Series architecture. After implementing the MLP-NN model, the IoT implementation is built to publish the decision results. With the proposed implemented method for the smart device, the output decision based on sensors values can be taken by the IoT node itself without returning to the cloud. For comparison, another solution is proposed for the cloud-based architecture, where the MLP-NN model is implemented on Cloud. The results clarify a successful implemented MLP-NN model for little capabilities end-devices, where the smart device solution has a lower traffic and latency than the cloud-based solution.
物联网 (IoT) 时代主要依赖于“智能”这个词，例如智慧城市、智能家居和智能汽车。这方面可以通过将机器学习算法与物联网计算模型融合来实现。通过将人工智能 (AI) 算法添加到物联网中，得到的是认知物联网 (CIoT)。在汽车行业，许多研究人员使用深度学习研究自我诊断系统，但由于终端设备的硬件限制，大多数研究人员都在云端执行此过程，而设备通过云服务器获取决策。还有一些人使用简单的传统机器学习算法来解决这些终端设备处理能力的限制。在本文中，引入了一种具有快速响应和低开销的自我诊断智能设备，该设备使用多层感知机神经网络 (MLP-NN) 作为深度学习技术。使用 Tensorflow 框架执行 MLP-NN 的学习阶段，以生成 MLP 模型的参数。然后，在低成本的终端设备（如 ARM Cortex-M 系列架构）上使用这些模型的参数来实现 MLP-NN 模型。在实现 MLP-NN 模型后，构建物联网实现来发布决策结果。使用智能设备的提出的实现方法，基于传感器值的输出决策可以由物联网节点本身做出，而无需返回云端。作为对比，还提出了一种基于云的架构的解决方案，其中在云端实现 MLP-NN 模型。结果表明，对于能力较低的终端设备，成功实现了 MLP-NN 模型，智能设备解决方案的流量和延迟都低于基于云的解决方案。
