No abstract

Reliable people counting is crucial to many urban applications. However, most existing people counting systems are sensor-based and can only work in some fixed gateways or checkpoints where sensors have been installed. This high dependence on the exact locations of sensors leads to low accuracy. To overcome these limitations, in this paper, we propose a smartphone-based people counting system, Wi-Counter, by leveraging the pervasive Wi-Fi infrastructure. To collect comprehensive Wi-Fi signals and people count information based on crowdsource, Wi-Counter first adopts a preprocessor to overcome the noisy, discrepant, and fragile data based on the Wiener filter and Newton interpolation. It then makes use of the designated five-layer neural network to learn the relation model between the Wi-Fi signals and the number of people. By analyzing the received Wi-Fi signals, Wi-Counter can estimate the number of people based on the resulting model. We have conducted experiments by implementing a prototype of Wicounter based on smartphones and evaluated the system in terms of accuracy and power consumption in an indoor testbed covering an area of 96 m 2 . Wi-Counter achieved a counting accuracy of up to 93% and exhibited reliable and robust performance resisting temporal environmental changes with negligible power usage.

No abstract

Wireless tracking analysis is useful for deploying the efficient indoor positioning system. Location Fingerprinting (LF) method uses a training dataset of Wi-Fi received signal strength (RSS) at different location to track the target. Fuzzy logic modeling can be applied to evaluate the behavior of wireless received signal strength (RSS). Previous analytical models based on LF are not sufficient for modeling spatial factors of wireless coverage. Spatial analytical model is useful for analysis of how the wireless infrastructure affecting the accuracy of positioning. The main concept of fuzzy logic is to reflect the reality of our world of experience, which is uncertain and fuzzy. In this paper, we develop a multi-layer fuzzy modeling for the wireless coverage in the huge and open area. Large scale site surveying has been used to collect RSS in 9.34 hectare campus area. The color fuzzy model allows us to visualize the spatial distribution of wireless RSS. Base on the fuzzy analytical model, we analyze the effect of existence of human's presence and large obstacle, the accuracy and efficiency of tracking system.