Localization in complex indoor environments with RF signal is a challenging task. Via this technique, the signal is easily affected by obstacles and environmental noise due to the broadcast nature of RF signal transmission. In this paper, we observe that seismic signal is more oriented than the RF signal, and thus is more suitable for localization in an complex indoor environment with obstacles. Motivated by this observation, we propose a device-free passive indoor localization system, namely Floc, that can fight against environmental impact and achieve high localization accuracy. FLoc is composed of three modules, which are sensing module that collects seismic signal from footsteps, footstep detection module that remove the environmental impact and recover the clean footsteps, and localization module that leverages seismic signal from footsteps for positioning. We implement FLoc on credit-card sized single-board computer Raspberry Pi equipped with geophones. To verify the effectiveness of our system, we conduct extensive experiments for different scenario in a complex indoor environment with the area of 6 × 8 square meter. The experimental results demonstrates that Floc can achieve up to 7cm localiztion accuracy on average, and can outperform the existing acoustic signal-based localization techniques.

Assessing the performance of algorithms in solving building energy optimization (BEO) problems with different properties is essential for selecting appropriate algorithms to achieve the best design solution. This study begins with a classification of the properties of BEO problems from three perspectives, namely, design variables, objective functions, and constraints. An analytical approach and a numerical approach are proposed to determine the properties of BEO problems. Six BEO test problems with different properties, namely, continuous vs. discrete, convex vs. non-convex, linear vs. non-linear, uni-modal vs. multimodal, and single-dimensional vs. multi-dimensional, are composed to evaluate the performance of algorithms. The selected optimization algorithms for performance assessment include the discrete Armijo gradient, Particle Swarm Optimization (PSO), Hooke-Jeeves, and hybrid PSO and Hooke-Jeeves. The assessment results indicate that multimodality can cause Hooke-Jeeves and discrete Armijo gradient algorithms to fall into local optima traps. The convex, non-convex, linear and non-linear properties of uni-modal BEO problems have little impact on the performance behavior of the algorithms. The discrete Armijo gradient and Hooke-Jeeves are not recommended for solving discrete and multi-dimensional BEO problems.

Determination of interior daylight illuminance is the key step in daylighting schemes. Recently, climate-based daylight metrics (CBDMs) which considers the real climatic data for the location has been adopted to evaluate the dynamic daylight performance. However, the usual method to calculate the CBDMs is full scale computer simulations which are quite time demanding and designated skills are required. Architects and building practitioners prefer simple methods to assess daylight performance particularly during initial design process when various building schemes and concepts are being appraised. Daylight factor (DF) is the traditional daylight metric and it has a strong relationship with room parameters which can be simply modified to fit the design criteria. This paper puts forward a series of simple mathematical expressions to correlate the CBDMs with DF metrics (DFMs). The vertical outdoor illuminance at the window center point and the 49 interior points were simulated via the RADIANCE software. The results showed that there are strong correlations between these daylight metrics. The proposed approach would be useful to building professionals conducted in visual comfort, fenestration and daylighting design and evaluation in the preliminary design phase.