This paper presents a projection-based location system in an indoor wireless local area network (WLAN) environment. Our algorithm projects the received signal strength (RSS) onto a discriminative space such that the information of all access points (APs) is more efficiently utilized. The projection is determined by multiple discriminant analysis (MDA), thereby guaranteeing maximal discriminative information involved in the positioning system. The study conducts a series of experiments on the effects of our approach in a realistic indoor environment. The results show that not only is the positioning accuracy significantly improved, but the system cost, including the computation and data collection, is also greatly reduced at the same time. This is because our approach extracts only useful information for positioning, whereas the redundant noise is discarded to avoid the problem of overfitting and unnecessary calculations. Compared with prior works, this technique can produce a more graceful balance between the positioning accuracy and the computational complexity for the resource-weak clients.
"In other words, using scenarios with lots of APs, some of them do not add new location information and can even introduce error in the system. In these cases, some mechanisms must be implemented to determine which AP is " good " or " bad " neighbor . Therefore, APs' distribution is a vital part of the system and the development of an algorithm capable of best APs' distribution determination must be considered, without compromising the network coverage and quality of service. "
[Show abstract][Hide abstract] ABSTRACT: This paper describes a wireless location and occupancy system used to create house and user energetic profile systems. Both procedures will be used to raise consumers' energy awareness in PV efficient building systems. Home energy consumption must be improved in order to evolve towards Net Zero Energy Buildings. Only the combine use of home PV grid-connected power systems with the active and responsible citizen's engagement in addressing the carbon and energy reduction challenge will allow homes and buildings to become Net Zero Energy Buildings. The developed system is composed by two subsystems. The first one (designated as WiLOS) deals with the problem of users' location inside a building, while the second (designated as HUEPS) uses the energy consumption of the building and the location information to create the users' energetic profiles. Both subsystems are described and experimental data is presented in order to show the effectiveness of the proposed methodology.
2nd International Conference on Renewable Energy Research and Applications, Madrid, Spain; 10/2013
"One of the most popular mapping function is the probabilistic models ,  which regards positioning as a regression problem  by l = R r=1 l r · p(X|l r ), where X is the online measured RSS, l r represents the r-th reference location, p(l r |X) indicates the likelihood of location l r given the observation X, R is the number of reference locations and l represents the estimated result. Additionally, some projectionbased approaches constructs the mappping in different signal spaces , . In the kernel method, the likelihood value is assigned to a kernel function around each of the observations in the training data as p( "
[Show abstract][Hide abstract] ABSTRACT: Abstract-This study focuses on indoor localization in Wireless Local Area Networks (WLANs). We investigate the unequal contribution of each access point (AP) on location estimation. The main contribution is two parts. First, a novel mechanism is proposed to measure the degrees of the AP importance. The importance of each AP is quantified by the signal discrimination between distinct locations. We utilize such numerical relevancies to select important APs for positioning. Second, the importance is further embedded into our positioning system. We provide a weighted kernel function where the effect of APs is differentiated. That is, the larger weights are assigned to the more important APs. Moreover, we develop a quasi-entropy function to avoid an abrupt change on the weights. Our positioning system is developed in a real-worldWLAN environment, where the realistic measurement of receive signal strength (RSS) is collected. Experimental results show that the positioning accuracy is significantly improved by taking the different importance into consideration.
Global Telecommunications Conference (GLOBECOM 2010), 2010 IEEE; 01/2011
[Show abstract][Hide abstract] ABSTRACT: Projection techniques have been used in Wi-Fi location fingerprinting systems to improve positioning accuracy. However, environmental dynamics present challenges to projection design. Furthermore, current projection-optimization techniques used in positioning, such as principal component analysis (PCA) and multiple discriminant analysis (MDA), have both advantages and limitations. This paper proposes a dynamic hybrid projection (DHP) technique for improved Wi-Fi localization, in which the projection is dynamically determined by simultaneously exploiting the complementary advantages of PCA and MDA while avoiding their unfavorable properties. The main contribution of this work is twofold: First, this study provides a novel formulation of a hybrid projection, which embeds the discriminative power into PCA and compensates for the two numerical problems of MDA in a unified framework. Second, DHP dynamically adjusts the hybrid mechanism with additional information, regarding the online-input region. That is, the proposed projection is input dependent, whereas traditional projections are fixed after training. This study applies the proposed algorithm to location fingerprinting in a realistic indoor Wi-Fi environment. On-site experimental results demonstrate that DHP outperforms static projection schemes, reducing the 50th and 67th percentile localization errors by 24.73%-30% and 18.18%-19.51%, respectively, compared with PCA and MDA.
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