M.A. Youssef∗ ,∗ ∗ and A. Agrawala∗∗
Optimal WLAN positioning strategy, WLAN location determination, WLAN simulation 1 An earlier version of this paper appeared in the 2004 Com- munication Networks and Distributed Systems Modelling and Simulation Conference. ∗ Alexandria University, Egypt ∗∗ Department of Computer Science, University of Maryland, College Park, Maryland 20742; e-mail: {moustafa, agrawala}@ cs.umd.edu Recommended by Prof. Azim Houshyar
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.
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