S. C. Mak

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.

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.

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

Localization systems for indoor areas using the existing wireless local area network (WLAN) infrastructure have been suggested recently. However, the current systems are not satisfactory. Common localization approaches suffer from inaccurate position tracking due to signal fluctuations in the wireless LAN. Newton Trust-Region method makes use of the convergence factor of a trajectory to eliminate the noise from the signal strength. In this paper, we apply a Newton Trust-Region (TR) algorithm to trajectory estimation based on the traditional Location Fingerprinting (localization) approach. Newton Trust-Region method optimize the Location Fingerprinting approach iteratively because each point in a trajectory normally falls into a region and have the same convergence in direction. Our experimental analysis shows that Newton Trust-Region method enhances the traditional localization approach with 15% fewer access points and is 20% more effective to achieve accurate localization. The proposed Newton Trust-Region method leads to substantially more accurate and robust localization system.

Cognitive Location-Aware Information Retrieval by Agent-Based Semantic Matching

This paper proposes semantic TFIDF, an agent-based system for retrieving location-aware information that makes use of semantic information in the data to develop smaller training sets, thereby improving the speed of retrieval while maintaining or even improving accuracy. This proposed method first assigns intelligent agents to gathering location-aware data, which they then classify, match, and organize to find a best match for a user query. This is done using semantic graphs in the WordNet English dictionary. Experiments will compare the proposed system with three other commonly used systems and show that it is significantly faster and more accurate.

Resource-Effective and Accurate WLAN Infrastructure Design and Localization Using a Cell-Structure Framework

Large scale WLAN infrastructures contain thousands of access points (APs) that are often deployed in an ad-hoc, empirical and non-optimal configurations. This unstructured approach leads to poor resource utilization and poor localization due to signal overlap and black spots. In this paper, we propose three structured approaches to WLAN infrastructure deployment to achieve high localization accuracy and optimal coverage. We propose, analyze and discuss triangular, square and hexagonal distributions. Our results show that the ad-hoc deployment of APs is less effective than any of the three structured approaches. Overall, the hexagonal approach is the most effective for localization operations. But surprisingly, when looking at the square distribution, the center part of localization accuracy is 30% higher than the other two structured approaches. In contrast, the ad-hoc distribution requires 50% more APs than hexagonal distribution to achieve effective localization. Our proposed structured approaches allow designers to achieve optimal localization in a cost-effective, resources-effective and accurate manner.

Cognitive location-aware information retrieval by agent-based semantic matching

Agents operating in both wired and wireless networks find and retrieve location-aware information. Agents in our system are required to endow with the full range of cognitive abilities, including perception, use of natural language, learning and the ability to understand the user query. The speed and accuracy of retrieval and the usefulness of the retrieved data depends on a number of factors including constant or frequent changes in its content or status, the effects of environmental factors such as the weather and traffic and the techniques that are used to categorize the relevance of the retrieved data. In this paper, we propose semantic TFIDF, an agent-based system for retrieving location-aware information that can improves the speed of retrieval while maintaining or even improving the accuracy by making use of semantic information in the data to develop smaller training sets. In our method, intelligent agents first gather location-aware data and then, using semantic graphs in the WordNet English dictionary, they classify, match and organize the information to find a best match for a user query. Our experiments compared our proposed system with three other commonly used systems and showed it to be significantly faster and more accurate.

Using fuzzy color maps to increase the positioning accuracy in poor Wi-Fi coverage regions

Recently, in the context of IEEE 802.11b/g network protocols, Wi-Fi radio channels been proposed to estimate the location of a smart mobile device. We can locate Wi-Fi-enabled devices by applying location-sensing techniques. However, the positioning accuracy depends greatly on the Wi-Fi signal coverage. The positioning accuracy due to poor Wi-Fi signal coverage has not been investigated systematically in the current research on Wi-Fi location awareness. Our previous work provide a location threshold of 1.82m on average. However, when a person enters in a poor Wi-Fi coverage region, the positioning accuracy drops dramatically. In this paper, we extend our previous work and create a fuzzy color map to visualize the distribution of Wi-Fi signal: red represents strong signals and blue represents weak signals. Then we make use of the proposed map by selecting the best candidates of AP to increase the positioning accuracy in the poor Wi-Fi coverage region. Our experiment result shows that we can reduce the distance error significantly by 25% in a poor Wi-Fi coverage environment and locate a person within 1.75m in average. The proposed method leads to substantially more accurate and robust localization system.