We propose a novel localization method using angle-of-arrival (AoA) measurements with two-step error variance-weighted least squares (TELS). The first step is to estimate the terminal location provisionally using least squares. The second step is to estimate the terminal location using weighted least squares, with the weights for each anchor and each evaluation-function term, calculated from the error variance based on the first step. The proposed method does not require previous information on the environment while achieving high performance. The simulation results indicate that a root mean square error (RMSE) of the proposed method is superior to that of the existing hybrid received signal strength (RSS)/AoA localization methods. When 11 anchors are deployed inside a cube with edge length 15 m, and the standard deviations of measurements are small, the RMSE of the proposed method reaches about 0.34 m. It is nearly equal to that of Cramer-Rao lower bound (CRLB) on AoA. INDEX TERMS Angle-of-arrival (AoA), received signal strength (RSS), localization, error variance, least squares (LS), wireless sensor networks (WSN) I. INTRODUCTION

Personalized Environmental Control Systems (PECS) allow control over indoor environmental quality (IEQ) parameters of the microclimate around individual occupants. The present study reports on the results of human subject experiments evaluating a prototype of PECS. The tested prototype had heating, cooling, and ventilation (air circulation through a filter and an ultraviolet germicidal irradiation component) functions. The objective of the present experiment was to obtain the occupants' subjective responses and physiological parameters such as skin temperature, with and without the use of PECS. The occupants' interaction with the PECS prototype was also observed. Experiments were conducted with 24 university students (12 male and 12 female subjects) over a 5-week period between February and March 2022. Different ambient temperature settings between 18 and 28 °C were tested each week. In each week, subjects participated in two 3-hour sessions, once with PECS and once without it. Subjects with PECS were able to adjust the PECS functions freely throughout the measurements, and the changes they made were recorded in an internal log of the PECS. Subjective responses such as thermal sensation and acceptability were compared with their PECS operation to evaluate the effect of PECS, together with each occupant’s interaction with PECS.

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Personalised Environmental Control Systems (PECS) are devices that cater to the individual needs by providing micro-climate heating, cooling, and ventilation. However, to ensure comfort, energy savings, and productivity, a comfort model based automatic control is required. For its development, thermal preference, physiological information, and data on the surrounding indoor climate were gathered from 24 subjects when using a newly developed PECS with heating, cooling, and ventilation functions. Since PECS should ensure a high level of comfort while providing energy savings through background temperature relaxation, multiple steady-state ambient temperature settings ranging from 18 to 28 °C were tested. The data were clustered according to the subject’s self-assessed general thermal preference, namely neutral, warmer, and colder. Machine learning was used to generate a cluster-based personalised comfort model using environmental, physiological, and behavioural indicators. The prediction performance of the models was 11 to 18 percent points higher than that of current group comfort models, predicted mean vote (PMV), which is independent of occupant similarities. The advantage of the personalised approach was the increased performance of the thermal comfort prediction at no expense of occupant sensitive information. Although reliant on estimates of physiological indicators, the models’ performance may be increased using real-time data acquisition.