22 research outputs found

    Truth Discovery in Crowdsourced Detection of Spatial Events

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    ACKNOWLEDGMENTS This research is based upon work supported in part by the US ARL and UK Ministry of Defense under Agreement Number W911NF-06-3-0001, and by the NSF under award CNS-1213140. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views or represent the official policies of the NSF, the US ARL, the US Government, the UK Ministry of Defense or the UK Government. The US and UK Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.Peer reviewedPostprin

    Truth Discovery in Crowdsourced Detection of Spatial Events

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    Supporting reasoning with different types of evidence in intelligence analysis

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    The aim of intelligence analysis is to make sense of information that is often conflicting or incomplete, and to weigh competing hypotheses that may explain a situation. This imposes a high cognitive load on analysts, and there are few automated tools to aid them in their task. In this paper, we present an agent-based tool to help analysts in acquiring, evaluating and interpreting information in collaboration with others. Agents assist analysts in reasoning with different types of evidence to identify what happened and why, what is credible, and how to obtain further evidence. Argumentation schemes lie at the heart of the tool, and sense-making agents assist analysts in structuring evidence and identifying plausible hypotheses. A crowdsourcing agent is used to reason about structured information explicitly obtained from groups of contributors, and provenance is used to assess the credibility of hypotheses based on the origins of the supporting information

    Received Signal Strength-Based Wireless Localization via Semidefinite Programming: Noncooperative and Cooperative Schemes

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    The received signal strength (RSS)-based approach to wireless localization offers the advantage of low cost and easy implementability. To circumvent the nonconvexity of the conventional maximum likelihood (ML) estimator, in this paper, we propose convex estimators specifically for the RSS-based localization problems. Both noncooperative and cooperative schemes are considered. We start with the noncooperative RSS-based localization problem and derive a nonconvex estimator that approximates the ML estimator but has no logarithm in the residual. Next, we apply the semidefinite relaxation technique to the derived nonconvex estimator and develop a convex estimator. To further improve the estimation performance, we append the ML estimator to the convex estimator with the result by the convex estimator as the initial point. We then extend these techniques to the cooperative localization problem. The corresponding Cramer-Rao lower bounds (CRLB) are derived as performance benchmarks. Our proposed convex estimators comply well with the RSS measurement model, and simulation results clearly demonstrate their superior performance for RSS-based wireless localization

    GPS Localization Accuracy Improvement by Fusing Terrestrial TOA Measurements

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    This paper explores the use of terrestrial time of arrival (TOA) measurements to improve the initial Global Positioning System (GPS) location fix accuracy. First, we present a geometric approach when a GPS location fix and one TOA measurement are available. Then, a more general hybrid GPS/TOA method via the Weighted Least Square Estimator (WLSE) is proposed. To simplify the calculation, a closed-form solution based on the two-step Least Square approach is also designed. The Cramer-Rao Lower Bound (CRLB) is derived as a performance benchmark. Simulation results exhibit excellent performance of the proposed methods which attain the CRLB in different scenarios. The proposed methods work even if only one TOA measurement (in addition to a GPS location fix) is available and the corresponding accuracy improvement (compared with the initial GPS location fix) can be as much as 30%

    Parallel and streaming truth discovery in large-scale quantitative crowdsourcing

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    To enable reliable crowdsourcing applications, it is of great importance to develop algorithms that can automatically discover the truths from possibly noisy and conflicting claims provided by various information sources. In order to handle crowdsourcing applications involving big or streaming data, a desirable truth discovery algorithm should not only be effective, but also be scalable. However, with respect to quantitative crowdsourcing applications such as object counting and percentage annotation, existing truth discovery algorithms are not simultaneously effective and scalable. They either address truth discovery in categorical crowdsourcing or perform batch processing that does not scale. In this paper, we propose new parallel and streaming truth discovery algorithms for quantitative crowdsourcing applications. Through extensive experiments on real-world and synthetic datasets, we demonstrate that 1) both of them are quite effective, 2) the parallel algorithm can efficiently perform truth discovery on large datasets, and 3) the streaming algorithm processes data incrementally, and it can efficiently perform truth discovery both on large datasets and in data streams

    Received Signal Strength-Based Wireless Localization via Semidefinite Programming

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    Wireless localization has drawn significant attention over the past decade and the received signal strength (RSS) based localization scheme provides a low-cost, low-complexity and easy-implementation solution. When the statistics of the RSS measurement error is known, the Maximum Likelihood (ML) estimator is asymptotically optimal. However, due to the nature of the localization problem itself, the formed ML estimator is nonconvex, causing the search for the global minimum very difficult. In addition, its performance highly depends on the initial point provided if a local optimization method is applied to find the solution. To circumvent this problem, we apply the Semidefinite Programming (SDP) relaxation technique to the RSS-based localization problem. After reformulation and relaxation, we finally form a convex SDP estimator. A superior property of a convex estimator is that the solution is not affected by the initial point provided since any local minimum is also its global minimum. The Cramer-Rao Lower Bound (CRLB) is then derived as a benchmark for the performance comparison. Simulation results show that the proposed SDP estimator exhibit excellent performance in the RSS-based localization system and it is very suitable for the case when there are only very limited base stations hearable
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