Location is one of the basic information required for underwater optical wireless sensor networks (UOWSNs) for different purposes, such as relating the sensing measurements with precise sensor positions, enabling efficient geographic routing techniques, and sustaining link connectivity between the nodes. Even though various 2-D UOWSNs’ localization methods have been proposed in the past, the directive nature of optical wireless communications and 3-D deployment of sensors require to develop 3-D underwater localization methods. Additionally, the localization accuracy of the network strongly depends on the placement of the anchors. Therefore, we propose a robust 3-D localization method for partially connected UOWSNs, which can accommodate the outliers and optimize the placement of the anchors to improve the localization accuracy. The proposed method formulates the problem of missing pairwise distances and outliers as an optimization problem, which is solved through half quadratic minimization. Furthermore, analysis is provided to optimally place the anchors in the network, which improves the localization accuracy. The problem of optimal anchor placement is formulated as a combination of Fisher information matrices for the sensor nodes where the condition of D-optimality is satisfied. The numerical results indicate that the proposed method outperforms the literature substantially in the presence of outliers.
Outliers can significantly contaminate the ranging measurements, and thus lead to large localization error. A number of outlier detection techniques have been presented in @cite_11 for data mining, predictive modeling, cluster analysis, and wireless sensor networks. Recently, outlier rejection methods have been developed for multilateration based iterative localization @cite_19 . However, in sparse networks, each node may not be directly connected to the anchors and thus cannot perform multilateration for localization. Hence, networks such as UOWSNs with sparsity and hostile underwater environment (absorption, scattering, geometrical losses, turbulence, and air bubbles) require to develop robust localization scheme to encounter the outliers produced by all these phenomena. Therefore, in this paper, we have developed a novel 3D localization algorithm which is not only robust to the ranging errors but also accounts for such outliers.
@cite_19 @cite_11
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In this paper, a received signal strength (RSS) based localization technique is investigated for underwater optical wireless sensor networks (UOWSNs) where optical noise sources (e.g., sunlight, background, thermal, and dark current) and channel impairments of seawater (e.g., absorption, scattering, and turbulence) pose significant challenges. Hence, we propose a localization technique that works on the noisy ranging measurements embedded in a higher dimensional space and localize the sensor network in a low dimensional space. Once the neighborhood information is measured, a weighted network graph is constructed, which contains the one-hop neighbor distance estimations. A novel approach is developed to complete the missing distances in the kernel matrix. The output of the proposed technique is fused with Helmert transformation to refine the final location estimation with the help of anchors. The simulation results show that the root means square positioning error (RMSPE) of the proposed technique is more robust and accurate compared to baseline and manifold regularization. Common techniques in structure from motion do not explicitly handle foreground occlusions and disocclusions, leading to several trajectories of a single 3D point. Hence, different discontinued trajectories induce a set of (more inaccurate) 3D points instead of a single 3D point, so that it is highly desirable to enforce long continuous trajectories which automatically bridge occlusions after a re-identification step. The solution proposed in this paper is to connect features in the current image to trajectories which discontinued earlier during the tracking. This is done using a correspondence analysis which is designed for wide baselines and an outlier elimination strategy using the epipolar geometry. The reference to the 3D object points can be used as a new constraint in the bundle adjustment. The feature localization is done using the SIFT detector extended by a Gaussian approximation of the gradient image signal. This technique provides the robustness of SIFT coupled with increased localization accuracy. Our results show that the reconstruction can be drastically improved and the drift is reduced, especially in sequences with occlusions resulting from foreground objects. In scenarios with large occlusions, the new approach leads to reliable and accurate results while a standard reference method fails. A major problem in wireless sensor network localization is erroneous local geometric realizations in some parts of the network due to the sensitivity to certain distance measurement errors, which may in turn affect the reliability of the localization of the whole or a major portion of the sensor network. This phenomenon is well-described using the notion of "flip ambiguity" in rigid graph theory. In a recent study by the coauthors, an initial formal geometric analysis of flip ambiguity problems has been provided. The ultimate aim of that study was to quantify the likelihood of flip ambiguities in arbitrary sensor neighborhood geometries. In this paper we propose a more general robustness criterion to detect flip ambiguities in arbitrary sensor neighborhood geometries in planar sensor networks. This criterion enhances the recent study by the coauthors by removing the assumptions of accurately knowing some inter- sensor distances. The established robustness criterion is found to be useful in two aspects: (a) Analyzing the effects of flip ambiguity and (b) Enhancing the reliability of the location estimates of the prevailing localization algorithms by incorporating this robustness criterion to eliminate neighborhoods with flip ambiguity from being included in the localization process. This paper focuses on the principled study of distance reconstruction for distance-based node localization. We address an important issue in node localization by showing that a highly incomplete set of inter-node distance measurements obtained in ad-hoc node deployments carries sufficient information for the accurate reconstruction of the missing distances, even in the presence of noise and sensor node failures. We provide an efficient and provably accurate algorithm for this reconstruction, and we show that the resulting error is bounded, decreasing at a rate that is inversely proportional to radicn, the square root of the number of nodes in the region of deployment. Although this result is applicable to many localization schemes, in this paper we illustrate its use in conjunction with the popular multidimensional scaling algorithm. Our analysis reveals valuable insights and key factors to consider during the sensor network setup phase, to improve the quality of the position estimates