## 1 Introduction  
The Internet of Things (IoT) paradigm will be the next wave in the era of computing [1]. IoT is the basic concept of basically enabling a global connectivity of any devices with an on and off switch to the Internet between the real world and a virtual world. Considering the increasingly popular smart devices that intuitively adjust the temperature, humidity, and light based on residents' preferences and climatic conditions, IoT smart space applications [2, 3] aim to enable features such as dramatically more efficient building operations, automation and environment control. Smart buildings is the highest user of IoT in 2017 according to Gartner's recent smart city forecasts report [4]. The majority of Heating, Ventilation and Air Conditioning (HVAC) systems [5] for thermal comfort are either based on a single temperature control loop or, in some cases, on a multivariable temperature and relative humidity control loop. Nonetheless, when considering the optimization of human thermal comfort, other parameters should also be considered in order to meet the comfort requirements of residents in the building [6]. The thermal comfort model is designed to take into account all these considerations. Predicted Mean Vote (PMV) is among the most widely adopted comfort models for evaluating global thermal comfort in buildings with mechanical cooling. Thermal comfort is roughly half from air convection and half from the mean radiant temperature [7]. For a certain temperature, the PMV index can vary as air temperature and mean radiant temperature change, with a consequent shift of the environmental category. Reading both air temperature and the mean radiant temperature gives the more accurate operative temperature, which keeps people more comfortable and uses less energy at the same time. In general, control and optimization methodologies of the thermal comfort can be generalized into two approaches that are real-time based control of the building [8; 9] or offline-based strategic choices for structural changes [10; 11]. Moreover, in order to reach an optimal comfort situation and reduce the energy consumption in the meantime, the trade-off between users' comfort and energy efficiency should be taken into consideration. As a result, Model-based Predictive Control (MPC) systems are universally used to design and build control systems with the purpose of minimizing the optimal cost function to reach the best trade-off point between users' comfort and energy consumption without affecting users' welfare. MPC is intended to forecast the future evolvement of the control system by using the model of the plant and buildings [12; 13]. It has successfully been accepted as the occupation standard in the process industry for complex constrained multi-variable control problems [14] due to its unique capacity to effectively deal with hard constraints on control and states. In the past few years, many researchers have made efforts in dealing with MPC for building energy control and management. Among them, the work presented in [PERSON] et al. [15] is possible to bring attention to, where a linear time-dependent based MPC model is presented. The benefits that an MPC can provide towards especially old buildings designed with poor construction materials and systems are shown and commented in [PERSON] et al. [16]. RC models are utilized to model the residence and experimental results indicate that the proposed system results in lower energy usage between 10% and 15% compared to traditionally used simple ON-OFF controllers. Generally, these systems are only designed with constant parameters schemes and they don't consider direct knowledge of the control system. Most of the control strategies only have a concern with energy consumption savings disregarding the human comfort factor [17], which bring about many limitations for Indoor Environmental Quality (IEQ) control.