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.
"One major challenge for existing WiFi based IPSs is how to reduce this computational load while preserve the localization accuracy. Some existing approaches to address this issue are leveraging feature extraction methods , , which aim to extract the most valuable feature components for localization. However, the large variation of WiFi RSS restricts the improvement of these approaches. "
[Show abstract][Hide abstract] ABSTRACT: WiFi based Indoor Positioning System (IPS) has become the most popular and practical system to provide Location Based Service (LBS) in indoor environments due to the availability of massive existing WiFi network infrastructures in buildings. WiFi based IPSs leverage received signal strengths (RSSs) from large numbers of WiFi access points (APs) and estimate the location of clients. Proper AP selection methods are required to select a subset of APs that provide useful information for localization in order to reduce the computational load and preserve or even enhance the localization accuracy of the entire IPS. Existing approaches select and measure the discriminative ability of APs individually during the offline phase without the consideration of the dependence among them. In this paper, based on mutual information of APs, we present online mutual information (OnlineMI), a novel AP selection strategy that measures the collective discriminative ability among APs. Furthermore, the proposed AP selection process is conducted online to adapt various environmental dynamics. Weighted k nearest neighbor (WKNN) approach is further employed as the localization algorithm after the OnlineMI AP selection strategy. Extensive experiments are carried out, and performance evaluation and comparison with existing methods demonstrates the superiority of the proposed OnlineMI-WKNN approach.
2015 IEEE International Conference on Automation Science and Engineering (CASE), Gothenburg, Sweden; 08/2015
"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
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