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An environmental thermal comfort model has previously been quantified based on the predicted mean vote (PMV) and the physical sensors parameters, such as temperature, relative humidity, and air speed in the indoor environment. However, first, the relationship between environmental factors and physiology parameters of the model is not well investigated in the smart home domain. Second, the model that is not mainly for an individual human model leads to the failure of the thermal comfort system to fulfill the human’s comfort preference. In this paper, a cyber–physical human centric system (CPHCS) framework is proposed to take advantage of individual human thermal comfort to improve the human’s thermal comfort level while optimizing the energy consumption at the same time. Besides that, the physiology parameter from the heart rate is well-studied, and its correlation with the environmental factors, i.e., PMV, air speed, temperature, and relative humidity are deeply investigated to reveal the human thermal comfort level of the existing energy efficient thermal comfort control (EETCC) system in the smart home environment. Experimental results reveal that there is a tight correlation between the environmental factors and the physiology parameter (i.e., heart rate) in the aspect of system operational and human perception. Furthermore, this paper also concludes that the current EETCC system is unable to provide the precise need for thermal comfort to the human’s preference. [ABSTRACT FROM AUTHOR]

This paper proposes a new joint received signal strength (RSS)-based Voronoi and factor graph (RVFG) wireless geolocation technique. The RSS-based Voronoi (RV) technique is used to provide the initial position for the RSS-based factor graph (RFG) technique. The initial point obtained by the RV is used to select the appropriate four monitoring spots which are covering the target for the RFG. We also modify the RV so that it can be performed in the fusion center. The proposed technique is tested in outdoor environment where only path-loss is taken into account. The performance of the proposed technique in term of the root mean square (RMSE) is compared to that of the conventional RV only technique. The results show that the accuracy of the proposed technique outperforms the conventional RV technique. The material in this paper was presented in part at Asia Modelling Symposium, Kuala Lumpur, September 2015 [1]. [ABSTRACT FROM AUTHOR]

This paper proposes a new joint received signal strength (RSS)-based Voronoi and factor graph (RVFG) wireless geolocation technique. The RSS-based Voronoi (RV) technique is used to provide the initial position for the RSS-based factor graph (RFG) technique. The initial point obtained by the RV is used to select the appropriate four monitoring spots which are covering the target for the RFG. We also modify the RV so that it can be performed in the fusion center. The proposed technique is tested in outdoor environment where only path-loss is taken into account. The performance of the proposed technique in term of the root mean square (RMSE) is compared to that of the conventional RV only technique. The results show that the accuracy of the proposed technique outperforms the conventional RV technique. The material in this paper was presented in part at Asia Modelling Symposium, Kuala Lumpur, September 2015 [1]. [ABSTRACT FROM AUTHOR]