A Survey of Localization in Wireless Sensor Network <s> Pattern Matching Method. <s> Positioning in indoor wireless environments is growing rapidly in importance and gains commercial interests in context-awareness applications. The essential challenge in localization is the severe fluctuation of receive signal strength (RSS) for the mobile client even at a fixed location. This work explores the major noisy source resulted from the multipath in an indoor wireless environment and presents an advanced positioning architecture to reduce the disturbance. Our contribution is to propose a novel approach to extract the robust signal feature from measured RSS which is provided by IEEE 802.11 MAC software so that the multipath effect can be mitigated efficiently. The dynamic multipath behavior, which can be modeled by a convolution operation in the time domain, can be transformed into an additive random variable in the logarithmic spectrum domain. That is, the convolution process becomes a linear and separable operation in the logarithmic spectrum domain and then can be effectively removed. To our best knowledge, this work is the first to enhance the robustness to a multipath fading condition, which is common in the environments of an indoor wireless LAN (WLAN) location fingerprinting system. Our approach is conceptually simple and easy to be implemented for practical applications. Neither a new hardware nor an extra sensor network installation is required. Both analytical simulation and experiments in a real WLAN environment demonstrate the usefulness of our approach to significant performance improvements. The numerical results show that the mean and the standard deviation of estimated error are reduced by 42% and 29%, respectively, as compared to the traditional maximum likelihood based approach. Moreover, the experimental results also show that fewer training samples are required to build the positioning models. This result can be attributed to that the location related information is effectively extracted by our algorithm. <s> BIB001 </s> A Survey of Localization in Wireless Sensor Network <s> Pattern Matching Method. <s> A model for predicting precision and accuracy performance of indoor fingerprint based positioning systems is very desirable for system designers as it helps estimate the probability of location selection before actual deployment. Such information can be used to tune the fingerprint database or improve the offline fingerprint collection phase. This paper presents a new analytical model that applies proximity graphs for approximating the probability distribution of error distance given a location fingerprint database using WLANs received signals, and its associated statistics. Simulations are used to validate the analytical model, which is found to produce results close to that from simulations. The model permits an analysis of the internal structure of location fingerprints. We employ the analysis of the fingerprint structure to identify and eliminate inefficient location fingerprints stored in the fingerprint database. Knowledge of where the inefficient fingerprints are can potentially be employed in a better location fingerprint collecting scheme from a grid system in the offline phase. <s> BIB002 </s> A Survey of Localization in Wireless Sensor Network <s> Pattern Matching Method. <s> ZigBee as IEEE 802.15.4 standard has been using for main research topic in the wireless sensor network (WSN) applications. The new method of radio frequency (RF) fingerprint-based technique for indoor localization is proposed. The received signal strength indicator (RSSI) is used as database values which correspond to the location of the sensor nodes. Fuzzy C-Means (FCM) clustering algorithm is applied as the experiment data cluster method. FCM algorithm is deployed to cluster the obtained feature vectors into several classes corresponding to the different amount of RSSI values. The results show that FCM can cluster the target node in a group of the fingerprint database. The location of target node is arranged in various forms to validate the accuracy of the clustering technique. Euclidean distance is used as the parameter to compare the similarity between fingerprint database and the target location. The results show that the new method is simple and effective method to reduce the complexity and to support the low power and to reduce the time using in the fingerprint-based localization technique. <s> BIB003 </s> A Survey of Localization in Wireless Sensor Network <s> Pattern Matching Method. <s> While wireless local area network-based indoor localization is attractive, the problems concerning how to capture the signal-propagating character in the complex dynamic environment and how to accommodate the receiver gain difference of different mobile devices are challenging. In this article, we solve these problems by modeling them as common mode noise and develop a localization algorithm based on a novel differential radio map approach. We propose a differential operation to improve the performance of the radio map module, where the location is estimated according to the difference of received signal strength (RSS) instead of RSS itself. The particle filter algorithm is adopted to realize the target localization and tracking task. Furthermore, to calculate the particle weight at arbitrary locations, we propose a local linearization technique to realize continuous interpolation of the radio map. The indoor experiment results demonstrate the effectiveness and robustness of our approach. <s> BIB004 </s> A Survey of Localization in Wireless Sensor Network <s> Pattern Matching Method. <s> The indoor localization is an actual problem because there are more and more application areas. New technical solutions are available, which have contributed to the indoor localization researches. In this work Fingerprint (FP) localization methodology applied in the experimental indoor environment is presented. The Wireless Sensor Network technology (WSN) is used in real environment, which provided the necessary measurement results to the FP localization. For the processing Received Signal Strength Indicator (RSSI) and for determining the position the neural network model is used. The RSSI values used for the learning of the neural network are preprocessed (mean, median, standard deviation) in order to increase the accuracy of the system. The type of the neural network is a feed-forward network. During obtain learning different algorithms were applied. The mean square error of Euclidean distance between calculated and real coordinates and the histogram of precision were used to determine the accuracy of the neural network. <s> BIB005
Pattern matching localization, also called map-based or Fingerprint algorithm, is one of the most viable solutions for range-free localization methods recently. The fingerprint localization involves two phases. During the first phase, the received signals at selected locations are recorded in an offline database called radio map. Then, the second phase, it works at the online state. The pattern matching algorithms are used to infer the location of unknown node by matching the current observed signal features to the prerecorded values on the map  BIB003 . Fang et al. BIB001 proposed a novel method to extract the feature of robust signals to efficiently mitigate the multipath effect. This method enhances the robustness under a multipath fading condition and is commonly used for the indoor environments. Swangmuang and Krishnamurthy BIB002 presented a new analytical model that applies proximity graphs for approximating the probability distribution of error distance, which recorded a location fingerprint database by using the received signals. In addition, there are many problems under the indoor positioning environment as following: how to capture the character of propagation signal in the complex dynamic environment and how to accommodate the receiver gain difference of different mobile devices and so on. Wang et al. BIB004 solved these problems by modeling them as common mode noise and then developed a location algorithm based on a novel differential radio map. Gogolak et al. BIB005 proposed fingerprint localization methodology based on neural network which is applied in the real experimental indoor environment. It provided the necessary measurement results to the fingerprint localization.