Eddie C. L. Chan

Using Wi-Fi Signal Strength to Localize in Wireless Sensor Networks

Wireless sensor network (WSN) is widely used in many applications such as localization and real-time tracking system. Previous researches commonly suffer the line-of-sight (LOS) problem and dependence on contrast of the background light intensity. Location Fingerprinting (LF) method uses a training dataset of received signal strength (RSS) at different location to track the target. The drawbacks of LF method are needed to have extensive training dataset surveying and highly affected by the changing of internal building infrastructure. In this paper, a sensor-based LF method will be implemented to replace extensive site-surveying. Using a Kalman Filter tracks multiple points to characterize a trajectory. Our experimental result shows that the effectiveness of our method leads to have more accurate and effective tracking system.

Wireless Tracking Analysis in Location Fingerprinting

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 multilayer 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 humans presence and large obstacle, the accuracy and efficiency of tracking system.

Orientation-based Wi-Fi positioning on the google Nexus One

While localization systems for indoor areas using the existing wireless local area network (WLAN) infrastructure have recently been proposed, wireless LAN localization approaches suffer from a number of significant drawbacks. To begin with, there is inaccurate position tracking due to the orientation of the mobile device and signal fluctuation. In this paper, we apply an orientation filter and a Newton Trust Region (TR) algorithm to eliminate the noisy location estimation. We implement the localization algorithm on the Nexus One which is a Wi-Fi enabled device with a digital compass. The average error distance is only 1.82m. We achieve 90% precision within 2.45m. The proposed method leads to substantially more accurate and robust localization system.

ShopProfiler: Profiling shops with crowdsourcing data

Sensing data from mobile phones provide us exciting and profitable applications. Recent research focuses on sensing indoor environment, but suffers from inaccuracy because of the limited reachability of human traces or requires human intervention to perform sophisticated tasks. In this paper, we present ShopProfiler, a shop profiling system on crowdsourcing data. First, we extract customer movement patterns from traces. Second, we improve accuracy of building floor plan by adopting a gradient-based approach and then localize shops through WiFi heat map. Third, we categorize shops by designing an SVM classifier in shop space to support multi-label classification. Finally, we infer brand name from SSID by applying string similarity measurement. Based on over five thousand traces in three big malls in two different countries, we conclude that ShopProfiler achieves better accuracy in building refined floor plan, and characterizes shops in terms of location, category and name with little human intervention.

Cognitive garment design interface using user behavior tree model

An effective user interface helps to hinge on ideas and imagination from fashion designers and most importantly express their artworks with their flair. Shape, material, color, movement and flow — all these qualities give a piece of clothing its uniqueness, and the designer uses drawings to communicate his intentions. Sketches of various views of the garment provide the preliminary clues needed for bulk manufacturing. However, it is very difficult to develop a common user interface platform even as intuitive as sketching interface, since different designers have different senses and habits to work on their drawings. In this paper, we focus on this sketching issues and propose a user behavior tree (UBT) model that helps to return the corresponding shapes according to the preference of user. Also, in the front-tier, we provide a 3D user interface for editing the clothing panels, adjusting the sewing lines and simulating the garment design. Experiment results show the effectiveness and efficiency of the proposed model.

Effect of channel interference on indoor wireless local area network positioning

Localization systems for indoor areas have recently been suggested that make use of existing wireless local area network (WLAN) infrastructure and location fingerprinting approach. However, most existing research work ignores channel interference between wireless infrastructures and this could affect accurate and precise positioning. A better understanding of the properties of channel interference could assist in improving the positioning accuracy while saving significant amounts of resources in the location-aware infrastructure. This paper investigates to what extent the positioning accuracy is affected by channel interference between access points. Two sets of experiments compare how the positioning accuracy is affected in three different channel assignment schemes: ad-hoc, sequential, and orthogonal data is analyzed to understand what features of channel interference affect positioning accuracy. The results show that choosing an appropriate channel assignment scheme could make localization 10% more accurate and reduce the number of access points that are required by 15%. The experimental analysis also indicates that the channel interference usually obeys a right-skewed distribution and positioning accuracy is heavily dependent on channel interference between access points (APs).

Wi-Fi Positioning Based on Fourier Descriptors

Location Fingerprinting (LF) is a common Wi-Fi positioning method, which locates a device by accessing a pre-recorded database containing the location fingerprint (i.e., the received signal strengths and coordinates). Most LF methods use the absolute received signal strength (RSS) to estimate the location. There are two drawbacks for using the absolute RSS. First, the absolute RSS in a time interval may not be representable of the IEEE 802.11 signal, as the signal may fluctuate. Second, a manual error-pone calibration is needed across different mobile platform. In this paper, we proposed to use Fourier descriptors in LF. We first transform the IEEE 802.11b Wi-Fi signal into a Fourier domain. Then, the Fourier descriptors are used to estimate the location by applying the K-Nearest Neighbor algorithm. Our experimental results show that the effectiveness of LF methods based on Fourier descriptors lead to substantially more accurate and robust localization.

Wi-Counter: Smartphone-Based People Counter Using Crowdsourced Wi-Fi Signal Data

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 Wi-counter based on smartphones and evaluated the system in terms of accuracy and power consumption in an indoor testbed covering an area of 96 m

Partial matching of garment panel shapes with dynamic sketching design

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Using the Newton Trust-Region Method to Localize in WLAN Environment

. Wi-Counter achieved a counting accuracy of up to 93% and exhibited reliable and robust performance resisting temporal environmental changes with negligible power usage.