WSN and RFID Integration to Support Intelligent Monitoring in Smart Buildings Using Hybrid Intelligent Decision Support Systems
F. Alshahranya, M. Abbod a and I. Moualek b
aCollege I of Engineering Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, United Kingdom
bHybrid Intelligent Decision Systems, Jerjer House, Nottingham, United Kingdom
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The real time monitoring of environment context aware activities is becoming a standard in the service delivery in a wide range of domains (child and elderly care and supervision, logistics, circulation, and other). The safety of people, goods and premises depends on the prompt reaction to potential hazards identified at an early stage to engage appropriate control actions. This requires capturing real time data to process locally at the device level or communicate to backend systems for real time decision making. This research examines the wireless sensor network and radio frequency identification technology integration in smart homes to support advanced safety systems deployed upstream to safety and emergency response. These systems are based on the use of hybrid intelligent decision support systems configured in a multi-distributed architecture enabled by the wireless communication of detection and tracking data to support intelligent real-time monitoring in smart buildings. This paper introduces first the concept of wireless sensor network and radio frequency identification technology integration showing the various options for the task distribution between radio frequency identification and hybrid intelligent decision support systems. This integration is then illustrated in a multi-distributed system architecture to identify motion and control access in a smart building using a room capacity model for occupancy and evacuation, access rights and a navigation map automatically generated by the system. The solution shown in the case study is based on a virtual layout of the smart building which is implemented using the capabilities of the building information model and hybrid intelligent decision support system.

DOI: 10.12693/APhysPolA.128.B-152
PACS numbers: 07.05.Tp