WLAN Location Determination Systems Research Articles

The Horus location determination system

We present the design and implementation of the Horus WLAN location determination system. The design of the Horus system aims at satisfying two goals: high accuracy and low computational requirements. The Horus system identifies different causes for the wireless channel variations and addresses them to achieve its high accuracy. It uses location-clustering techniques to reduce the computational requirements of the algorithm. The lightweight Horus algorithm helps in supporting a larger number of users by running the algorithm at the clients. We discuss the different components of the Horus system and evaluate its performance on two testbeds. Our results show that the Horus system achieves its goal. It has an error of less than 0.6 meter on the average and its computational requirements are more than an order of magnitude better than other WLAN location determination systems. Moreover, the techniques developed in the context of the Horus system are general and can be applied to other WLAN location determination systems to enhance their accuracy. We also report lessons learned from experimenting with the Horus system and provide directions for future work.

Analysis of the Optimal Strategy for WLAN Location Determination Systems

This paper presents a general analysis for the performance of WLAN location determination systems. WLAN location determination systems estimate the users location using software-based techniques in a WLAN environment. We present an analytical method for calculating the average distance error and probability of error of WLAN location determination systems. These expressions are obtained with no assumptions regarding the distribution of signal strength or the probability of the user being at a specific location, which are usually taken to be in uniform distribution over all the possible locations in current WLAN location determination systems. We use these expressions to find the optimal strategy to estimate user location and to prove formally that probabilistic techniques give more accuracy than deterministic techniques, which has been taken for granted, without proof, for some time. The analytical results are validated through simulation experiments and we present the results of testing actual WLAN location determination systems in an experimental testbed. We also study the effect of the assumption that the user's position follows a uniform distribution over the set of possible locations on the accuracy of WLAN location determination systems. Knowing the probability distribution of the user's position can reduce the number of access points required to obtain a given accuracy. With a high density of access points, however, the performance of a WLAN location determination system is consistent under different probability distributions for the user's position, which can be used to reduce the energy consumed in the location determination algorithm and the time required to build the user profile.

Location-Clustering Techniques For Wlan Location Determination Systems

We present location clustering as a technique to significantly reduce the computational requirements of WLAN location determination systems. We provide two algorithms, joint clustering and incremental triangulation, and describe their trade-offs between computational cost and location determination accuracy. Both techniques reduce computational cost by more than an order of magnitude, allowing noncentralized implementation on mobile clients and enabling new context-aware applications. We present a performance comparison of the two techniques in an actual testbed implementation.

LOCATION-CLUSTERING TECHNIQUES FOR WLAN LOCATION DETERMINATION SYSTEMS

We present location clustering as a technique to significantly reduce the computational requirements of WLAN location determination systems. We provide two algorithms, joint clustering and incremental triangulation, and describe their trade-offs between computational cost and location determination accuracy. Both techniques reduce computational cost by more than an order of magnitude, allowing noncentralized implementation on mobile clients and enabling new context-aware applications. We present a performance comparison of the two techniques in an actual testbed implementation.