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Accurate node localization in Wireless Sensor Networks (WSNs) is crucial for applications like environmental monitoring and military surveillance. Traditional localization methods, such as trilateration, often struggle with accuracy due to signal attenuation and environmental obstructions, leading to significant localization errors in practical scenarios. This paper aims to address the limitations of traditional localization methods by developing and evaluating a hybrid localization method that enhances accuracy and robustness in node localization within WSNs. The proposed method integrates the Bounding Box approach with the Harmony Search optimization algorithm, resulting in the Bounding Box Harmony Search (BBHS) method. The BBHS method utilizes the initial geometric constraints provided by the Bounding Box approach and refines these estimates using the global optimization capabilities of the Harmony Search algorithm. Simulation results, obtained using a custom-developed WSN localization simulator, demonstrate that the BBHS method significantly reduces localization errors compared to traditional trilateration and the standalone Bounding Box method. The BBHS method consistently provides enhanced accuracy and robustness across varying network conditions, highlighting its effectiveness in practical deployment scenarios. The advancements presented in this paper suggest that hybrid methods like BBHS represent a significant step forward in WSN localization technologies. By combining geometric constraints with optimization processes, the BBHS method overcomes the drawbacks of earlier techniques, paving the way for more reliable and resilient WSN operations.

Underwater wireless sensor networks (UWSNs) play an increasingly important role in monitoring the marine environment. The research of sensor node localization is one of the biggest concerns in UWSNs, but there are still many formidable obstacles to overcome, including localization accuracy, localization rate, and other issues. In contrast to localization accuracy, however, few studies focus on increasing the node localization rate. Therefore, this paper proposes a maximum localization rate (MLR) algorithm for 3D large-scale UWSNs. In view of the number of anchor nodes around the unknown node, the MLR algorithm designs different positioning strategies, such as the triangular cosine method and the dynamic position-assisted localization method. The MLR algorithm also adopts the regular tetrahedron network topology and the collinear judgment mechanism to guarantee localization accuracy. The simulation results demonstrate that excluding isolated nodes, the MLR algorithm can achieve the maximum localization rate while maintaining a certain level of positioning accuracy at the same time.

Position awareness is very important for many sensor network applications. However, the use of Global Positioning System receivers to every sensor node is very costly. Therefore, anchor based localization techniques are proposed. The lack of anchors in some Wireless Sensor Networks lead to the appearance of multi-hop localization, which permits to localize nodes even if they are far from anchors. One of the well-known multi-hop localization algorithms is the Distance Vector-Hop algorithm (DV-Hop). Although its simplicity, DV-Hop presents some deficiencies in terms of localization accuracy. Therefore, to deal with this issue, we propose in this paper an improvement of DV-Hop algorithm, called Regularized Least Square DV-Hop Localization Algorithm for multi-hop wireless sensors networks. The proposed solution improves the location accuracy of sensor nodes within their sensing field in both isotropic and anisotropic networks. We used the double Least Square localization method and the statistical filtering optimization strategy, which is the Regularized Least Square method. Simulation results prove that the proposed algorithm outperforms the original DV-Hop algorithm with up to 60%, as well as other related works, in terms of localization accuracy.

An object detector based on convolutional neural network (CNN) has been widely used in the field of computer vision because of its simplicity and efficiency. The average accuracy of CNN model detection results in the object detector is greatly affected by the loss function. The precision of the localization algorithm in the loss function is the main factor affecting the result. Based on the complete intersection over union (CIoU) loss function, an improved penalty function is proposed to improve the localization accuracy. Specifically, the algorithm more comprehensively considers matching bounding boxes between prediction with ground truth, using the proportional relationship of the aspect ratio from both bounding boxes. Under the same aspect ratio of the two bounding boxes, the influence factors of the prediction box on localization accuracy were considered. In this way, the function of the penalty function is strengthened, and localization accuracy of the network model improved. This loss function is called Improved CIoU (ICIoU). Experiments on the Udacity, PASCAL VOC, and MS COCO datasets have demonstrated the effectiveness of ICIoU in improving localization accuracy of network models by using the one-stage object detector YOLOv4. Compared with CIoU, the proposed ICIoU improved average precision (AP) by 0.57% and AP75 by 0.12% on Udacity, AP by 0.26% and AP75 by 1.28% on PASCAL VOC, and AP by 0.06% and AP75 by 0.65% on MS COCO.

Localization is necessary for applications such as warning systems, where the sensed data along with the location of occurrence is essential for decision making in Wireless Sensor Networks (WSNs). Equipping each sensor node with a global positioning system (GPS) unit is infeasible, as it increases both the power consumption and cost of the deployed network. A more practical approach is to employ a single or a few GPS-equipped mobile anchor nodes (MANs). These MANs localize the unknown sensor nodes by broadcasting their current locations periodically while moving in the area of interest. In a realistic environment, obstacles may be present in the deployment area that obstruct the radio connectivity between unknown sensors and the MANs. In this paper, we propose a mechanism to handle irregular obstacles in the path of various existing static trajectories. Also, we have considered an accurate and reliable channel model to provide a realistic evaluation of the proposed method. The results are compared on the basis of four different localization techniques, namely, accuracy priority trilateration (APT), time priority trilateration (TPT), weighted centroid localization (WCL) and weight-compensated weighted centroid localization (WCWCL). Experimental and simulation results show that the proposed method effectively handles regular and irregular obstacles in the deployed environment. [ABSTRACT FROM AUTHOR]

With the continuous development of wireless network technology, more and more mobile devices are connected to the network, and the location information of these devices is becoming one of the important basis for analyzing other geographic data in the net. Location is an important problem in spatial information sensing, and the theoretical basis of location is the free space fading feature of wireless signals. In this paper, we proposes WiLocWare, which is a middle ware to understand and modeling the performance of the state-of-the-art and to be proposed algorithms. We evaluated and characterized the correlation between localization accuracy and networks parameters such as signal propagation model, the coverage of wireless radios, the distributions of wireless devices and the density of the anchor nodes. The experimental results show the localization accuracies under different wireless signatures, WiLocWare is also scalable for the performance evaluation for the to be proposed algorithms.

Localization accuracy is a fundamental requirement for Simultaneous Localization and Mapping (SLAM) systems. Traditional visual SLAM (vSLAM) schemes are usually based upon the assumption of static environments, so they do not perform well in dynamic environments. While a number of vSLAM frameworks have been reported for dynamic environments, the localization accuracy is usually unsatisfactory. In this article, we present a novel motion detection and segmentation method using Red Green Blue-Depth (RGB-D) data to improve the localization accuracy of feature-based RGB-D SLAM in dynamic environments. To overcome the problem due to undersegmentation generated by the semantic segmentation network, a mask inpainting method is developed to ensure the completeness of object segmentation. In the meantime, an optical flow-based motion detection method is proposed to detect dynamic objects from moving cameras, allowing robust detection by removing irrelevant information. Experiments performed on the public Technical University of Munich (TUM) RGB-D data set show that the presented scheme outperforms the state-of-art RGB-D SLAM systems in terms of trajectory accuracy, improving the localization accuracy of RGB-D SLAM in dynamic environments. [ABSTRACT FROM AUTHOR]

Deep learning has recently shown great potentials in learning powerful features for visual tracking. However, most deep learning-based trackers neglect localization accuracy in the evaluation process of candidates. What's more, they usually over-rely on the discriminative features in a single frame in the training process. Consequently, they may fail when the discriminative features are occluded or changed in the tracking phase. In this paper, we propose a novel localization-aware meta tracker (LMT) guided with adversarial features to address the above issues. First of all, we design a novel intersection over union guided method to effectively balance the problem of classification and localization accuracy. To further improve the robustness of our classifier, we creatively use adversarial features during offline training phase. Those adversarial features can effectively guide the classifier in learning how to better deal with the situation where the discriminative features are occluded or changed. Finally, benefiting from meta learning, our algorithm only needs to perform one iterative update on the first frame and it can perform well on the tracking sceneries. The extensive experiments demonstrate the outstanding performance of our LMT compared with the state-of-the-art trackers on three benchmarks: OTB-2015, VOT-2016, and VOT-2018.

Global Positioning System (GPS) localization has been attracting attention recently in various areas, including intelligent transportation systems (ITSs), navigation systems, road tolling, smart parking, and collision avoidance. Although, various approaches for improving localization accuracy have been reported in the literature, there is still a need for more efficient and more effective measures that can ascribe some level of accuracy to the localization process. These measures will enable localization systems to manage the localization process and resources to achieve the highest accuracy possible and to mitigate the impact of inadequate accuracy on the target application. The localization accuracy of any GPS system depends heavily on both the technique it uses to compute locations and the measurement conditions in its surroundings. However, while localization techniques have recently started to demonstrate significant improvement in localization performance, they continue to be severely impacted by the measurement conditions in their environment. Indeed, the impact of the measurement conditions on the localization accuracy in itself is an ill-conditioned problem due to the incongruent nature of the measurement process. This paper proposes a scheme to address localization accuracy estimation. This scheme involves two steps, namely, measurement condition disambiguation and enhanced location accuracy classification. Real-life comparative experiments are presented to demonstrate the efficacy of the proposed scheme in classifying GPS localization accuracy under various measurement conditions. [ABSTRACT FROM PUBLISHER]

Brain source localization accuracy in magnetoencephalography (MEG) requires accuracy in both digitizing anatomical landmarks and coregistering to anatomical magnetic resonance images (MRI). We compared the source localization accuracy and MEG-MRI coregistration accuracy of two head digitization systems—a laser scanner and the current standard electromagnetic digitization system (Polhemus)—using a calibrated phantom and human data. When compared using the calibrated phantom, surface and source localization accuracy for data acquired with the laser scanner improved over the Polhemus by 141% and 132%, respectively. Laser scan digitization reduced MEG source localization error by 1.38 mm on average. In human participants, a laser scan of the face generated a 1000-fold more points per unit time than the Polhemus head digitization. An automated surface-matching algorithm improved the accuracy of MEG-MRI coregistration over the equivalent manual procedure. Simulations showed that the laser scan coverage could be reduced to an area around the eyes only while maintaining coregistration accuracy, suggesting that acquisition time can be substantially reduced. Our results show that the laser scanner can both reduce setup time and improve localization accuracy, in comparison to the Polhemus digitization system. [ABSTRACT FROM PUBLISHER]

Wireless access points (APs) divide a plane into small areas where their coverage ranges overlap. A mobile device can be located within a particular small overlapped area based on the unique set of APs covering the device. We formally define an Optimal Loc-deployment problem for both coverage and area localization. Our objective is to deploy a minimum number of APs that provide full communication coverage while achieving the ability to locate a mobile device within a certain area no larger than a given accuracy parameter. We obtain a formula that precisely determines the optimal solution for more than half of the accuracy values. For the rest of the accuracy values, we propose an algorithm that will return an approximation whose difference to the optimal solution is less than \epsilon for any \epsilon > 0. Finally, we conduct extensive numerical evaluation and real experiments to validate our proposed solutions. [ABSTRACT FROM PUBLISHER]

We describe systematic measurements carried out with single and double GEM detectors with printed circuit readout. The maximum safe operating gain has been measured at increasing radiation flux and under exposure to heavily ionizing tracks. Detection efficiency, localization accuracy and cluster size have been measured in a minimum ionizing particle beam. With a suitably configured readout electrode, fast, two-dimensional localization of radiation is demonstrated [ABSTRACT FROM PUBLISHER]