Building structure and other factors lead to the performance deterioration of global postioning system (GPS) positioning systems indoors. An adaptive model for Bluetooth-based indoor positioning is proposed in this paper, targeting at the complex indoor environment, to improve the performance of Bluetooth-oriented indoor positioning systems. More accurate Received Signal Strength Indicator (RSSI) calibration which is optimized via Gaussian filtering, together with the environment-dependent attenuation coefficient optimization, results in a more precise hybrid model in the complicated indoor environment. Experiment results show that the difference between the estimated results and the measured samples is less than 0.25[Formula: see text]m as the target node and reference node is less than 1.5[Formula: see text]m far from each other. As the distance increases to more than 1.5[Formula: see text]m, the relative difference between the estimated values and the measured ones decreases to 7.8% at most, satisfying the requirements for indoor positioning applications.

GPS has a sharp performance decline in terms of accuracy indoors due to the complex building structure. A combined algorithm, targeting at received signal strength indication (RSSI) calibration optimisation, depending on deep neural network training via input vector Γ and the target output vector Ψ, termed reference signal optimisation algorithm (RSOA) is proposed to improve the positioning accuracy in the indoor Bluetooth positioning networks. Experimental results show that the relative error of the proposed RSOA between the estimated results and the measured ones can reach as low as 0.2%, and the absolute errors can be reduced to 0.13 m at most within 10 m.

China accounts for more than 22% of the total energy consumption worldwide. Building energy consumption, among which consumption in public buildings was about 40% took the second place. With the problems of high energy waste, error rate and complexity of the control systems available, an indoor intelligent lighting system based on occupants’ location is proposed in this paper to improve the energy efficiency of the current lighting systems indoors. The transmission model of electromagnetic wave in free space is optimized in both aspects of reference signal strength and attenuation coefficient radiation in complex environment dynamically based on which occupants’ positions are obtained. The smart lighting system will turn on or off corresponding lights adaptively to provide a more energy efficient platform. Experimental results show that the proposed system is able to improve the energy efficiency of indoor lighting by at least 15%, with a lower error rate below 2% compared with the existing lighting systems based on voice control.

Surveys reveal that modern people spend more than 90% of their life in an indoor environment. Therefore, the environments indoors have great influence on both the efficiency and body health on the occupants. Nevertheless, the impact of indoor environment on occupants can't be measurement directly nowadays. An indoor comfort evaluation method is proposed in this paper based on the fuzzy AHP. Metrics such as thermal comfort and lighting comfort are considered. Our method is able to derive numeric values for indoor environment so that the occupants are capable of understanding the comfort index for their surrounding environment. Experiment results show that the system can effectively detect the parameters of the indoor environment thermal comfort, and make a quantitative display of the comfort via the fuzzy AHP algorithm. The system has some practicality in the construction of intelligent, air conditioning and other fields.