## 2 Review Method
### Step 3: Bibliography and Content Analysis
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**Topic and Objective** & **Technology and Feature** & **Outcome and Future Suggestion** \\ \hline PLUG-N-HARVEST architecture is used for controlling near-zero building envelope (ADBE) with a safe connection between cloud systems and building infrastructure [41]. & The architecture part is based on the “energy consumption” and ‘comfort conditions’ are upgraded by the use of cloud intelligent management system (EMS) used, including the intelligent management and, this result is based on the prototype implemented in Spain. Have suggested evaluating the whole performance of EMS with other infrastructures, such as ADBE, renewable energy sources, and energy storage. \\ \hline A network of wireless sensors, electrical appliances, a cloud-based data collector, AI, and a programming algorithm that is dynamic is the component of this system. The wireless sensors use message queue telemetry transport (MQTT), which is a lightweight messaging protocol. & System evaluation: a test bench, including four smart homes, which take advantage of the proposed EMS system. The test results showed that the uncertainty of the power exchanged between the grid and the user falls to 2.88\% and also showed a fall of 3.23\% in the electricity  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Topic and Objective** & **Technology and Feature** & **Outcome and Future Suggestion** \\ \hline A wearable health monitoring system to decrease the nightmares of those who suffer from post-traumatic stress disorder (PTSD) by monitoring sleep conditions [51]. & A wearable device for monitoring; a hub controller called INSTCON; different types of countermeasures suppressing; wearable devices for vibration alert and lighting smart outlets; machine learning tools for the optimization of countermeasure selection. & The system does not interfere in the normal life of the user. It increases the quality of sleep and life of patients with PTSD. The system can be integrated into patients’ personal IoT. Future suggestions: Bluetooth can make insecurity in the system and should be considered in future studies. The spread of private user data is an issue that should be addressed in future studies. \\ \hline An intelligent hybrid remote patient-monitoring model with a cloud-based framework for knowledge discovery using hybrid architecture in case of internet disconnections [52]. & A model for patients under supervision at home (IIHCAM-PUSH) contains ambient, cloud-based framework for knowledge cloud (RMC), local database (LDB), medical service provider cloud (MSPC). & The system relies on MapReduce programming and data mining assignments, such as association rules. The results showed minimal fault in real-time dig data acquisition and analysis, patient monitoring, and emergency cases (blood pressure) detection. The operation of the system for other diseases is suggested to be examined further. \\ \hline \hline \multirow{2}{*}{Develop a laboratory voice user interface (VUI) to integrate laboratory devices into an IoT environment in order to control them and read out their specific data [53].} & The VUI is a platform to control lab devices and read out specific device data; photometer; VA-enabled device; cloud services (voice model and server, skill host, device shadow); local services (server); natural language processing (NLP) algorithms. & Analysis of the speech examples showed that the standard setting of this system proves a high mean accuracy (95\(\%\pm 3.62\)) of voice identification. This solution makes a hands-free device control and will be an assistant to physically impaired or low vision users. Future suggestions: Security solutions that reflect common privacy rules are suggested to be worked on more. \\ \hline \hline \end{tabular}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} Bridging e-Health and the IoT; sensor platform for healthcare in a residential environment (SPHERE) to identify different health conditions [54]. & Environmental sensors, a Zigbee or 6 LoWPAN-based wireless sensor networks, video, and Bluetooth sensors connected to a cellular network, home gateway, data hub. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of selected papers focusing on the care system theme.  
\begin{table}
**Topic and Objective** & **Technology and Feature** & **Outcome and Future Suggestion** \\ \hline A deep-learning for monitoring nutrition called smart log in the IoT [48] & An optical character recognition (OCR) for obtaining nutrition data; Belief network (BN) or Bayesian for food classification is used. & The accuracy of the smart-log is 98.6\%. Have suggested tracking user activities for precise automated diet prediction, integrating smart-log with a physiological observing mechanism. & The accuracy of the smart-log is 98.6\%. Have suggested tracking user activities for precise automated diet prediction, integrating smart-log with a physiological observing mechanism. \\ \hline A home-based human-robot interaction (HRI) system object-tracking mechanism for non-GPS environments using an improved deep neural network (HOT) installed camera. Amazon web services. Alexa custom skill software. Deep convolutional neural networks (CNN). & A resulting plot of a chair detected, while the camera frame moves closer to the chair, shows that raw confidence level of a special object that is tracked by the multi-object detector is noisy; while the filtered data shows a general boosting trend of confidence, providing a true positive detection of the object. Suggested integrating all the decoupled systems to make a comprehensive robotic assistant. \\ \hline \hline \multirow{4}{*}{A multi-agent system for gamification between householders and also between families managing smart homes [50].} & Includes environmental sensors, such as motion sensors and gas sensors; backpropagation for numerical values, or RMSProp for graphical data; graphics processor and numerical value processor to make the analysis process faster. & The system is evaluated in two groups of experiments, including three families. The gamification results showed that the participants had lots of fun, but in some cases, its effect of enthusiasm for getting more score had some negative results. For example, the agents turned the lights on during the sleeping time at night. \\ \hline Two-tier anomaly detection based on traffic profiling of the home automation system [42]: To find anomalies by using the network operator in the security of a home area network & A two-tier intrusion detection system, which takes advantage of a machine learning method to look for strange anomalies at the data center. The main algorithm used is a data classification algorithm, namely, a naive Bayesian classifier (NBC). & The system does not interfere in the normal life of the user. It increases the quality of sleep and life of patients with PTSD. The system can be integrated into patients’ personal IoT. Future suggestions: Bluetooth can make insecurity in the system and should be considered in future studies. The spread of private user data is an issue that should be addressed in future studies. \\ \hline \hline \end{tabular}