## 4 Content Analysis and Data Mining
### Group 3: Energy Management and Artificial Intelligence
#### 4.3.3 Care Systems
A local interface for the user is presented using two LCD displays. Raspberry pi, as a home gateway, links all of the sensors and gathers their data from Arduinos through a USB port and also executes the communication with the energy gateway. Data visualization is provided by a web page showing graphs, which represent the sensor's data. Energy and health data are visualized separately on two different tabs on the web page. Furthermore, a tab for teleconsultation is also included for remote consultation with a doctor. Tele-consultation is provided through a WebRTC multimedia session with the doctor and wearable medical sensors for sending health data simultaneously. With the home energy management system (HEMS) implementation, two tasks are carried out in this system--firstly, the gateway gathers data in the MongoDB database, and essential interfaces are provided subsequently, and next, the encrypted communication is established by a data concentrator with the head-end metering system, which provides a short-term and long-term memory storage of energy information. Following this, the on-demand calculations are also stored by another MongoDB database for up to three days. A database of Microsoft SQL records the measurement history to be accessed by an API presented by the manufacturer. The LabVIEW-based application disputes on-demand measurements from the advanced metering infrastructure (AMI) network and logging alerts, events, or errors. The control unit application sends signals and set points to actuators and device controllers. Three most important information conduits are connected to the HEMS from forecasting (weather and load forecasting) and electricity price services. Then, optimum scheduling is provided and transferred to the gateway and is implemented in every device. The designed architecture facilitates the implementation of two services (healthcare and energy management) at the same time. Its validity is tested in a lab-scale environment using real systems and appliances. Linking the smart home with third parties and developing the idea of the smart city is provided in this paper. Therefore, tracking the users' motion profile and comparing different levels of physical activities with healthcare providers' recommendations are achieved simultaneously. The main challenges faced by this study are (1) the interchanging process of different data formats (health and energy) and concentrating them and (2) applying deeper analysis for security and privacy services. In future works, a significant amount of data in case of having extra services, such as management or entertainment, can be calculated by using big data and fog computing. [PERSON] et al. [84] proposed a solution to distinguish the IoT devices' performance and events in a smart home scenario and to characterize the behavior of IoT devices representing in smart homes, which involves resources with the low computational process and conserve consumers' privacy. Five algorithms for traffic classification are used at the evaluation level, which are k-nearest neighbors (k-NN), decision tree, random forest, support vector machine (SVM), and majority voting. Besides, python scripts with the use of the panda library are utilized in this paper. A prototype is implemented for IoT devices identification by using a three-stage process, as follows: (1) IoT/non-IoT device identification; (2) Specific IoT device identification; and (3) IoT device events identification. A binary classifier and a multi-class classifier are used for device type identification and type of device determination, respectively (a type of device is a motion sensor, camera, etc.). At the final step, a multi-class classifier detects the event, which generates the traffic.