Due to the massive deployment of WiFi APs and its accessibility to various positioning elements, WiFi positioning is a key enabler to provide seamless and ubiquitous location services to users. There are various kinds of WiFi positioning technologies, depending on the concerned positioning element. Among them, round-trip time (RTT) measured by a fine-timing measurement protocol has received great attention recently. It provides an acceptable ranging accuracy near the service requirements in favorable environments when a line-of-sight (LOS) path exists. Otherwise, a signal is detoured along with non-LOS paths, making the resultant ranging results different from the groundtruth. The difference between the two is called an RTT bias, which is the main reason for poor positioning performance. To address it, we aim at leveraging the history of user mobility detected by a smartphone's inertial measurement units, called pedestrian dead reckoning (PDR). Specifically, PDR provides the geographic relation among adjacent locations, guiding the resultant positioning estimates' sequence not to deviate from the user trajectory. To this end, we describe their relations as multiple geometric equations, enabling us to render a novel positioning algorithm with acceptable accuracy. The algorithm is designed into two phases. First, an RTT bias of each AP can be compensated by leveraging the geometric relation mentioned above. It provides a user's relative trajectory defined on the local coordinate system of the concerned AP. Second, the user's absolute trajectory can be found by rotating every relative trajectory to be aligned, called trajectory alignment. The proposed algorithm gives a unique position when the number of detected steps and APs is at least 4 and 3 for linear mobility and 5 and 2 for arbitrary mobility. Various field experiments extensively verify the proposed algorithm's effectiveness that the average positioning error is approximately 0.369 (m) and 1.705 (m) in LOS and NLOS environments, respectively.
Photoelectric smoke detectors, which operate by reacting to the scattering of light caused by particles entering the light path, are widely used and extremely sensitive. Owing to higher standards imposed by Underwriters Laboratories, researchers have begun analyzing the properties of smoke particles. In particular, several wavelengths are used to classify particles by their scattering reactivity. The performances of actual smoke detectors are limited by their hardware and price. Therefore, properties that can distinguish particle types in these limited conditions must be determined. In addition, algorithms for extracting valid data intervals from unstable scattering data must be developed. In this study, scattering intensity ratios for three wavelengths are derived via simulations of light scattering by particles. An upper cumulative sum is defined for the three wavelengths, and an index for the start of particle inflow is extracted. In addition, valid intervals are extracted based on the scattering intensity ratios and the moving variance of adjacent wavelengths, and the properties of each particle are defined using the extracted indexes. For verification, a data acquisition device that can obtain data using the three selected wavelengths (470, 525, and 850 nm) from two sensors is designed. Five types of fire sources and non-fire alarm sources are selected and used in a test chamber designed to generate particle data. After applying the algorithm, the data in the valid data intervals can be used to derive a sample mean scattering intensity ratio that is more constant than that of the overall data or the data processed using the CUSUM index. In addition, the fire sources have a higher sample mean scattering intensity ratio than water vapor, which is a non-fire alarm source. The scattering intensity ratios for smoke particles can be extracted in real time via a comparison with experimental results obtained from the selected sensors.
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