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Skip Navigation LinksEffat University > Effat University > Research > Research Centers > Smart Building Research Center
Smart Buildings (SB) are energy efficient buildings that adapt to users and the environment for providing comfort, safety and security. Smart buildings will play a vital role in transforming our future environment into a socially and physically sustainable one. Bennett et al. (1987, in Atkin, 1988), highlight the three attributes of smart buildings:
  • ​Buildings should ‘know’ what is happening inside and react immediately to what is outside.
  • Buildings should ‘decide’ the most efficient way of providing a convenient, comfortable and productive environment for its’ occupants
  • Buildings should ‘respond quickly to occupants’ requests.  
Smart Building Effat Research Center.pngThe main aim of the smart building research center is investigate use of innovative and smart platform technologies integrated witha plethora of intelligent building components. There is a focus on re al-time optimization of the energy consumption in buildings, taking into account users’ requirements, needs and expectations as well as the characteristics of the building design itself. The center’s research and development looks at six drivers of change for smart buildings:
  1. Smart building materials
  2. Flexible building components
  3. Multi-functional bio-reactive facades
  4. Integrated renewable energies & sustainable design
  5. Building Information Modeling -based data visualization interface
  6. Smart systems for services, performance measurement and monitoring 

Integrated Renewable Energies & Sustainable Design:

The Water and Energy Research Group​

The water and energy group work with faculty and students on research applying systems analyses techniques on sustainable infrastructure systems. The application of engineering systems in the areas of water and energy allows designing, analyzing and developing infrastructure systems which with well-integrated socioeconomic, technical, environmental, and political sustainability aspects, and are aligned with national and regional strategies. The interdisciplinary nature of the area dictates the investigation and the analysis of engineering and information systems that interface between the technology and the society. The group focuses on the technologies which enhance clean water and clean energy, production and management, and policy development. Some examples of the research undertaken by the water and energy group includes:​

  • Water desalination and wastewater treatment systems analysis and development
  • Water resource management, policies, and economics
  • Energy portfolio analysis and optimization
  • Environmental issues related to water and energy supply and growth
  • Modeling and visualization of integrated water and energy systems

Smart Systems for Services, Performance Measurement and Monitoring:

The Biometrics Lab​

The field of biometrics examines the unique physical or behavioral traits that can be used to determine a person’s identity. Biometric recognition is the automatic recognition of a person based on for example, fingerprint, face, iris or veins, voice, handwriting or typing rhythm. The study of biometric systems deals with aspects of image processing, security information retrieval, mathematical modeling, human physiology and cognitive science – making research in this field a truly multidisciplinary endeavor. Researchers will find opportunities for interdisciplinary research across campus of Effat University, including a new state-of-the-art biometrics laboratory. Some examples of the research projects that can be undertaken within the umbrella of biometric security systems may be:

  •  Seamless integration of systems into building design
  • Context dependent security systems
  • User training and education for security systems
  • Reducing error rate of biometric systems to prevent user deterrence
  • Gait pattern identification
  • Behavioral analysis and cognitive modeling for more complex systems
  • Combining several characteristics to improve biometric accuracy