## 4 Discussion  
This research paper implements a method for an IoT-based framework to collect environmental indoor room parameter-based multivariate time-series data to be uploaded on the central gateway database using MQTT protocol as shown in Figure 3. The collected data was analyzed by deep learning algorithms for the presence of human beings to be rescued from the building. This is to inform the fire brigade of the location of the presence of human beings in a building. Comparative analysis of deep learning algorithms was also conducted with error measurement techniques. The reason for choosing MQTT over HTTP communication protocol is to allow real-time data ingestion into the database as HTTP protocol considered as client/server presents a communication overhead. The system was configured in the publish-subscribe paradigm in such a way that sent parameters are considered as topics and the continuous sent data are considered as messages, in another word it is topic/message policy as shown in Figure 4. The configuration of the mist node is an MQTT-client publisher to the MQTT-broker with the role of publishing data to the broker. The database is configured as an MQTT-client subscriber to be precise. The messages acquired from the mist node are collected to the gateway server database as shown in Figure 4. In this research a no-relational database management system MongoDB is used, which allows scalability, and replication. The API (web services) was developed to read data from the database to display them to the user interface as shown in Figure 9b. The experimental setup is tested for 15 days and the data are saved within a 1-min time interval to a time series data set as shown in Figure 9a. The time-series data collected is exported in CSV format for further processing in the next phase as shown in Figure 10. The research paper developed a method to predict room occupancy using deep learning-based methodology as a regression problem. All parameters are analyzed by considering the correlation between each variable to explore  
Figure 14: Prediction results: Comparative analysis of LSTM with Naive Bayes Classifier, Support Vector Machine, and Multilayer Perceptron Feed Forward Network. the possibility of inferring the knowledge as shown in Figure 11. The modeled data were considered as multivariate multi-step time series data. The background of machine learning techniques used in this research paper explored the techniques that model the real-time collected data efficiently with LSTM as shown in Figure 6 predicting the best accuracy amongst all chosen as shown in Table 5.  
In particular, LSTM shows to over-perform the widely used machine learning algorithms such as Naive Bayes and Support Vector Machines model. The implemented LSTM model is compared with another deep learning algorithm such as Multilayer Perceptron Feed Forward Network that confirms a reasonable result compared to the other machine learning techniques as shown in Figure 14.  
In conclusion, the proposed LSTM model using captured real-time collected data is compared for performance metrics with other algorithms as shown in Figure 14. The LSTM model's accuracy is 96% on the tested multivariate captured data that is nearly 16% better than the best of the others. The LSTM model does not over-fit from the used data and minimizes the loss that can infer their prediction. The various limitations in this research use different parameters such as temperature, humidity, light, humidity ratio, and occupancy as data to predict the presence of an occupant within the room. A lot of observations had been explored as limitations of variables or biases to affect the prediction results. CO\({}_{2}\), pressure, and humidity parameters were reported to be noisy when the experimental setup is not conducted in a controlled environment hence, it brought unconvincing prediction results. Research also shows that the light parameter can be noisy too due to daylight differentiation.