A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Time Consumption. <s> The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy. <s> BIB001 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Time Consumption. <s> We estimate the location of a WLAN user based on radio signal strength measurements performed by the user's mobile terminal. In our approach the physical properties of the signal propagation are not taken into account directly. Instead the location estimation is regarded as a machine learning problem in which the task is to model how the signal strengths are distributed in different geographical areas based on a sample of measurements collected at several known locations. We present a probabilistic framework for solving the location estimation problem. In the empirical part of the paper we demonstrate the feasibility of this approach by reporting results of field tests in which a probabilistic location estimation method is validated in a real-world indoor environment. <s> BIB002 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Time Consumption. <s> We present a WLAN location determination technique, the Joint Clustering technique, that uses: (1) signal strength probability distributions to address the noisy wireless channel, and (2) clustering of locations to reduce the computational cost of searching the radio map. The Joint Clustering technique reduces computational cost by more than an order of magnitude, compared to the current state of the art techniques, allowing non-centralized implementation on mobile clients. Results from 802.11-equipped iPAQ implementations show that the new technique gives user location to within 7 feet with over 90% accuracy. <s> BIB003 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Time Consumption. <s> Context-aware computing is an emerging computing paradigm that can provide new or improved services by exploiting user context information. In this paper, we present a wireless-local-area-network-based (WLAN-based) indoor positioning technology. The wireless device deploys a position-determination model to gather location information from collected WLAN signals. A model-based signal distribution training scheme is proposed to trade off the accuracy of signal distribution and training workload. A tracking-assistant positioning algorithm is presented to employ knowledge of the area topology to assist the procedure of position determination. We have set up a positioning system at the IBM China Research Laboratory. Our experimental results indicate an accuracy of 2 m with a 90% probability for static devices and, for moving (walking) devices, an accuracy of 5 m with a 90% probability. Moreover, the complexity of the training procedure is greatly reduced compared with other positioning algorithms. <s> BIB004
Time consumption in this paper is a metric used for counting the time for generating a fingerprint database of a whole target area. There are two types of fingerprinting-based positioning algorithms, namely, deterministic and probabilistic algorithms, respectively. Conventional expert site survey of probabilistic fingerprinting needs enough samples to estimate the signal distribution of a grid. For instance, Youssef et al. collected 300 samples at each reference point to estimate a histogram-based joint distribution BIB003 . Each sample took one second, which means that 5 minutes is needed for generating a fingerprint of grid. The researchers from the Helsinki Institute for Information Technology and Ekahau BIB002 also collected 40 samples for each grid. Xiang et al. BIB004 and we  used a model-based signal-distribution training scheme to decrease down the number of training samples. The deterministic fingerprinting algorithm needs less samples; for instance, RADAR BIB001 combined four samples into one fingerprint. The time consumption of either probabilistic or deterministic solution is assessable and decided by the size of the target area, the density of grids, and the accuracy requirement. However, crowd sensing based fingerprint learning is an unpredicted process due to the uncertain crowd movement flow, which increases the time consumption of fingerprint database generation.