52,193 research outputs found

    A New Distributed Localization Method for Sensor Networks

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    This paper studies the problem of determining the sensor locations in a large sensor network using relative distance (range) measurements only. Our work follows from a seminal paper by Khan et al. [1] where a distributed algorithm, known as DILOC, for sensor localization is given using the barycentric coordinate. A main limitation of the DILOC algorithm is that all sensor nodes must be inside the convex hull of the anchor nodes. In this paper, we consider a general sensor network without the convex hull assumption, which incurs challenges in determining the sign pattern of the barycentric coordinate. A criterion is developed to address this issue based on available distance measurements. Also, a new distributed algorithm is proposed to guarantee the asymptotic localization of all localizable sensor nodes

    Passive source localization using power spectral analysis and decision fusion in wireless distributed sensor networks

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    Source localization is a challenging issue for multisensor multitarget detection, tracking and estimation problems in wireless distributed sensor networks. In this paper, a novel source localization method, called passive source localization using power spectral analysis and decision fusion in wireless distributed sensor networks is presented. This includes an energy decay model for acoustic signals. The new method is computationally efficient and requires less bandwidth compared with current methods by making localization decisions at individual nodes and performing decision fusion at the manager node. This eliminates the requirement of sophisticated synchronization. A simulation of the proposed method is performed using different numbers of sources and sensor nodes. Simulation results confirmed the improved performance of this method under ideal and noisy conditions

    Range-based localization for UWB sensor networks in realistic environments

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    The Non-Line of Sight (NLOS) problem is the major drawback for accurate localization within Ultra-Wideband (UWB) sensor networks. In this article, a comprehensive overview of the existing methods for localization in distributed UWB sensor networks under NLOS conditions is given and a new method is proposed. This method handles the NLOS problem by an NLOS node identification and mitigation approach through hypothesis test. It determines the NLOS nodes by comparing the mean square error of the range estimates with the variance of the estimated LOS ranges and handles the situation where less than three Line of Sight (LOS) nodes are available by using the statistics of an arrangement of circular traces. The performance of the proposed method has been compared with some other methods by means of computer simulation in a 2D area

    Multilevel B-Splines-Based Learning Approach for Sound Source Localization

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    © 2001-2012 IEEE. In this paper, a new learning approach for sound source localization is presented using ad hoc either synchronous or asynchronous distributed microphone networks based on the time differences of arrival (TDOA) estimation. It is first to propose a new concept in which the coordinates of a sound source location are defined as the functions of TDOAs, computing for each pair of microphone signals in the network. Then, given a set of pre-recorded sound measurements and their corresponding source locations, the multilevel B-splines-based learning model is proposed to be trained by the input of the known TDOAs and the output of the known coordinates of the sound source locations. For a new acoustic source, if its sound signals are recorded, the correspondingly computed TDOAs can be fed into the learned model to predict the location of the new source. Superiorities of the proposed method are to incorporate the acoustic characteristics of a targeted environment and even remaining uncertainty of TDOA estimations into the learning model before conducting its prediction and to be applicable for both synchronous or asynchronous distributed microphone sensor networks. The effectiveness of the proposed algorithm in terms of localization accuracy and computational cost in comparisons with the state-of-the-art methods was extensively validated on both synthetic simulation experiments as well as in three real-life environments

    EasyLoc: RSS-Based Localization Made Easy

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    AbstractLocalization based on Received Signal Strength (RSS) is a key method for locating objects in Wireless Sensor Networks (WSNs). However, current RSS-based methods are ineffective at both deployment and operation design levels since they (i.) usually require a labor-intensive pre-deployment profiling operations to map the RSS to either locations or distances and (ii.) rely on heavy processing operations. These two designs problems limit the possibility of implementing the localization technique on resources constrained sensor nodes and also restrict its scalability and practical use. In this paper, we tackle the challenge of devising a self-organizing and practical RSS-based localization technique that improves on previous approaches in terms of ease of deployment, ease of implementation while still providing a reasonable accuracy. To this end, we come up with a new solution, EasyLoc, a plug-and-play and distributed RSS-based localization method that requires zero pre-deployment configuration. The idea consists in exploiting the available distance information between anchors to derive an online and anchor-specific RSS-to-distance mapping. We show that, in addition to its simplicity, EasyLoc provides location errors of 90% less than 1m and an average error of 0.48m in small environment and 1.8m in large environment

    Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

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    Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

    A survey of localization in wireless sensor network

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    Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network
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