###### Abstract
Efficiency ratings of most LEED (Leadership in Energy and Environmental Design) certified buildings require such sensor systems. Combination of the IoT paradigm with the smart objects approaches, the concept of the smart building arose. Smart environments focus on looking at occupancy in the specific indoor area, like tracking, detecting, and identifying occupants as a way to offer quality services, while considering factors [11]. As far as the occupancy monitoring of a building is concerned, it can be categorized into group-based and individual monitoring. The group-based occupancy method refers to the estimation of aggregated space occupancy, while the individual based is to track each specific occupant's location. In the latter case, the system extracts the identity of an occupant to provide tracking information showing the locations of a past time-period [10; 12]. Group-based occupancy monitoring is divided into two categories according to the occupancy granularity. The first is occupancy detection, referring to the detection of presence in a controlled environment. The second is occupancy estimation referring to the inference of the number of occupants. Occupancy estimation refers to the estimation of the exact number of occupants along with an estimation of occupancy density. Occupancy detection is the most feasible task but the estimation of the exact number of occupants in space is the most challenging task. In order to respond to the occupancy detection, intrusive methods were used, like cameras and wearable devices, but these methods suffered from the privacy of occupants, later on, non-intrusive mechanisms were adopted by using sensors to capture the human presence based on different considerable characteristics. It is observed that the deployment of a sensor network in the whole building is cost-effective. This idea led researchers to start focusing on the estimation and prediction methods. At this point, identifying good features considered to increase the accuracy depends on two essential criteria. The first is to select a high-accuracy algorithm. The second is the computation time. In the case of fire rescue, human life and resources need accurate methods to be efficient. In case of emergency especially in densely populated buildings with complex floor plans, it is a challenging task to evacuate people to a safe place. The sensor data must be sent in such a way that it will ease the information complexity. This research is mainly reflecting on the building occupant prediction, as it is tackling the problem of indoor victims trapped when there is a natural disaster (fire or earthquake). The proposed mechanism is to use environmental parameters captured using a designed IoT system. The IoT time series multivariate data captured in a timely manner allowing the LSTM deep learning model to infer the occupant of the specific rooms so that the fire brigade can respond automatically without any delay. The main objective of this research is to design an IoT system to gather time-series-based data of an indoor building. Further is to aggregate those data in the cloud storage by using a Deep learning prediction model with multivariate predictors resulting to draw the inference of building occupancy. The motivation for choosing deep learning is (1) the use of ordered time-series data requiring the previous sequential information to work on the current and next sequences, (2) keeping the memory state of the previous layer, and (3) the expectation of the huge amount of data as they are accumulated in every one minute time interval. Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) model had been used in many domains as a deep learning model to predict the future effects. In air pollution [13] where they developed a prediction model based on the fine Particulate Matter(PM) concentrations demonstrated in 24 stations in Seoul. In environmental disasters, [14] used LSTM neural network model for flood forecasting as they used daily discharge and rainfall data were used for prediction purposes.