In this review paper we have discussed the existing state-of-the-art practices of improved intelligent features, controlling parameters and Internet of things (IoT) infrastructure required for smart building. The main focus is on sensing, controlling the IoT infrastructure which enables the cloud clients to use a virtual sensing infrastructure using communication protocols. The following are some of the intelligent features that usually make building smart such as privacy and security, network architecture, health services, sensors for sensing, safety and overall management in smart buildings. As we know, the internet of things (IoT) describes the ability to connect and control the appliances through the network in smart buildings. The development of sensing technology, control techniques and IoT infrastructure give rise to a smart building more efficient. Therefore, the new and problematic innovation of smart buildings in the context of IoT is to a great extent and scattered. The conducted review organised in a scientific manner for future research direction which presents the existing challenges, and drawbacks.

Most of the electrical energy consumption occurs in residential buildings in maintaining the desired comfort level for occupants. Since comfort level and energy consumption are conflicting in nature, there is a need for energy-efficient building management and information system. In this article, Artificial Intelligence (AI) based building management and information system with multi-agent topology for the energy-efficient building is proposed. The multi-agent topology building management and information system are based on minimizing the energy consumption and maximizing the level of comfort by reducing the error between the actual parameters of the environment and the desired environmental parameters. Firstly, the constrained nonlinear optimization algorithm is applied in the first optimization, and secondly the optimization using artificial intelligence incorporating deep learning concept training and validation to obtain a set of optimized solutions. These solutions comprise values of temperature, illumination level, and concentration of CO₂ for maximum comfort level in terms of thermal, visual and air quality and minimum energy consumption at the same time. The developed system is energy efficient and maintains a high comfort level for occupants.

Energy-efficient and sustainable buildings have become a specialized research theme for energy planning and the green building industry. Rapidly increased energy consumption in the residential area has attracted researchers' attention to minimize energy consumption without affecting occupant comfort. Accordingly, an effective energy management system is required for energy-efficient buildings to utilize energy resources efficiently and maintain desired comfort. In this paper, data-driven based optimization models are compared to maximize the comfort index and minimize energy consumption simultaneously. Heating-cooling systems are used to maintain indoor thermal comfort, which consumes energy according to the environmental temperature and indoor temperature difference. Therefore, the environmental temperature parameter is optimized using optimization techniques Genetic Algorithm (GA), Bat, Neural Network Algorithm (NNA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). The main objective of the optimization is to reduce the gap/error between the temperature specified by the user and the environmental temperature. Afterward, the discrepancy between optimized temperature and actual temperature is fed to the designed ML (Machine Learning) based controller. The controller's output is further provided to coordinator agents, which give the power to the actuators accordingly. The linearity of the proposed model improves the performance of the ML algorithm. The obtained dataset from fuzzy temperature controller has been used to design an ML controller. The experimental results show that the designed system significantly improves energy efficiency and occupant comfort for energy-efficient buildings. The BAT model has shown effectiveness in achieving a high comfort index with minimum power consumption compared to other considered models.

The present work focuses on the development of a fire prevention system for buildings. The first subsystem identifies electrical fire causes. The system integrates nonvisual and visual fire detection techniques. The nonvisual approach is based on a fuzzy logic model that fuses electric wire current and temperature information. The visual technique is based on an artificial intelligence approach for fire detection. A deep learning model is deployed on an embedded platform, the Raspberry Pi 3B+. The transfer learning approach applies to a MobileNet deep learning model. The second subsystem identifies the general causes of fire and the necessary actions to be taken to prevent it. The developed fire detection system aims at the early detection of fire with fewer false positives.