Software engineering model based smart indoor localization system using deep-learning

Abstract

During the last few years, the allocation of objects or persons inside a specific building is highly required. It is well known that the global positioning system (GPS) cannot be adopted in indoor environment due to the lack of signals. Therefore, it is important to discover a new way that works inside. The proposed system uses the deep learning techniques to classify places based on capturing images. The proposed system contains two parts: software part and hardware part. The software part is built based on software engineering model to increase the reliability, flexibility, and scalability. In addition, this part, the dataset is collected using the Raspberry Pi III camera as training and validating data set. This dataset is used as an input to the proposed deep learning model. In the hardware part, Raspberry Pi III is used for loading the proposed model and producing prediction results and a camera that is used to collect the images dataset. Two wheels' car is adopted as an object for introducing indoor localization project. The obtained training accuracy is 99.6% for training dataset and 100% for validating dataset.

Smarter dam based on cyber-physical system utilizing Raspberry Pi4 and NodeMCU ESP8266

“…To monitor and control the system, the server operator creates a graphical user interface (GUI) using Node-RED [24]. The dashboard, shown in Figure 4, is the system's ultimate user interface and is a web-based GUI [25]. It has controls that open the dam's gates at the proper angle and historical charts that display pertinent data, including water level, rainfall, and evaporation.…”