Mobile self-organizing ad hoc networks (MANETs) can significantly enhance the capability to coordinate the emergency actions as well as monitor contaminated areas, explore unmanned space, inspect and control working e...
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ISBN:
(纸本)9781479967476
Mobile self-organizing ad hoc networks (MANETs) can significantly enhance the capability to coordinate the emergency actions as well as monitor contaminated areas, explore unmanned space, inspect and control working environments. The management of networks that can dynamically and freely organize into temporary topologies raises interesting problems, that are particularly challenging for networks formed by mobile devices. Due to the inherent complexity of these systems, the development of applications relying on mobile network nodes and wireless communication protocols would be greatly simplified by the use of specific tools for supporting testing and performance evaluation. Modeling and simulation are widely used in the design and development of wireless ad hoc networks. In this paper we model mobile ad hoc network (MANET) using discrete event systems methodology (DEVS) and describe the functionality and performance of the Java-based simulation tool for the performance evaluation of self-organizing and cooperative networks for emergency situation awareness. The simulator can provide a useful support for the verification of the design of a network system, employed communication protocols, control and coordination algorithms, allowing the user to display a step-by-step evolution of the network in a suitable graphical interface. The practical case studies are provided to illustrate the operation and performance of the presented software.
The variables in multidimensional systems are functions of more than one indeterminate, and such systems cannot be controlled by standard systems theory. This paper considers a subclass of these systems that operate o...
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The paper discusses how digital rights management (DRM) approach can be adapted to work in local or offline mode, without access to global central servers. The focus of this adaptation is not on protection of copyrigh...
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The paper discusses how digital rights management (DRM) approach can be adapted to work in local or offline mode, without access to global central servers. The focus of this adaptation is not on protection of copyright in a commercial content setting but on restricting access to non-public documents to legitimate users. Therefore we work under assumption that protection against illegal copying is out of scope - any copy of the content is legitimate and can be accessed as long as the user identity and access rights can be properly verified.
This paper addresses the problem of curb detection for ADAS or autonomous navigation in urban scenarios. The algorithm is based on clouds of 3D points. It is evaluated using 3D information from a pair of stereo camera...
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This paper addresses the problem of curb detection for ADAS or autonomous navigation in urban scenarios. The algorithm is based on clouds of 3D points. It is evaluated using 3D information from a pair of stereo cameras and a LIDAR. Curbs are detected based on road surface curvature. The curvature estimation requires a dense point cloud, therefore the density of the LIDAR cloud has been augmented using Iterative Closest Point (ICP) based on the previous scans. The proposed algorithm can deal with curbs of different curvature and heights, from as low as 3 cm, in a range up to 20 m (whenever that curbs are connected in the curvature image). The curb parameters are modeled using straight lines and compared to the ground-truth using the lateral error as the key parameter indicator. The ground-truth sequences were manually labeled on urban images from the KITTI dataset and made publicly available for the scientific community.
In this paper the problems of electric power systems creation and their further adaptation to modern software tools is considered. The discrete mathematical macromodels of electric power systems and approaches for the...
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In this paper the problems of electric power systems creation and their further adaptation to modern software tools is considered. The discrete mathematical macromodels of electric power systems and approaches for their creation based on experimental data are discussed.
This paper presents a novel point-cloud descriptor for robust and real-time tracking of multiple objects without any object knowledge. Following with the framework of incremental model-free multiple object tracking fr...
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This paper presents a novel point-cloud descriptor for robust and real-time tracking of multiple objects without any object knowledge. Following with the framework of incremental model-free multiple object tracking from our previous work [5][7][6], 6 DoF pose of each object is firstly estimated with input point-cloud data which is then segmented according to the estimated objects, and incremental model of each object is updated from the segmented point-clouds. Here, we propose Joint Color-Spatial Descriptor (JCSD) to enhance the robustness of the pose hypothesis evaluation to the point-cloud scene in the particle filtering framework. The method outperforms widely used point-to-point comparison methods, especially in the partially occluded scene, which is frequently happened in the dynamic object manipulation cases. By means of the robust descriptor, we achieved unsupervised multiple object segmentation accuracy higher than 99%. The model-free multiple object tracking was implemented by using a particle filtering with JCSD as a likelihood function. The robust likelihood function is implemented with GPU, thus facilitating real-time tracking of multiple objects.
This paper is concerned with the state estimation problem for a class of nonlinear discrete-time complex networks subject to false data injection attacks. By utilizing Bernoulli random binary distributed white sequenc...
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ISBN:
(纸本)9781467374439
This paper is concerned with the state estimation problem for a class of nonlinear discrete-time complex networks subject to false data injection attacks. By utilizing Bernoulli random binary distributed white sequences, the false data injection attack model is established to describe the characteristics of false data injection attacks applying to the complex networks under consideration. An estimator is designed to guarantee the ultimate boundedness of the estimation error in mean square. By employing stochastic analysis approach, suf cient conditions are derived for the existence of the desired estimators whose gains are parameterized by minimizing an upper bound of the output variance of the estimation errors. Finally, a numerical example is given to illustrate the effectiveness of the results.
Multi-hop localization is a common method which is suitable for large-scale application. However, it is usually influenced by the network anisotropy, leading to the instability of the localization performance. In orde...
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Multi-hop localization is a common method which is suitable for large-scale application. However, it is usually influenced by the network anisotropy, leading to the instability of the localization performance. In order to reduce the influence of the network anisotropy on the localization accuracy, this paper regards the localization as a regression forecasting process by constructing the mapping relationship between hop-counts and Euclidean distances among nodes. The method can effectively avoid the influence of anisotropy on localization, and it has low computation overhead and high localization accuracy without setting complex parameters. The simulation experimental results show that compared with the previous similar algorithms, the proposed algorithm can obtain a faster localization speed and a higher localization accuracy.
Considering that incremental localization is influenced by the heteroscedasticity problem caused by cumulative errors and the collinearity problem among nodes, this paper has proposed an incremental localization algor...
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ISBN:
(纸本)9781509018949
Considering that incremental localization is influenced by the heteroscedasticity problem caused by cumulative errors and the collinearity problem among nodes, this paper has proposed an incremental localization algorithm with consideration to cumulative error and collinearity problem. Using iteratively reweighted method, the algorithm reduces the influences of error accumulation and avoids collinearity problem between nodes with a regularized method. Simulation experiment results show that compared with the previous incremental localization algorithms the proposed algorithm can not only solve the problem of heteroscedasticity, but also obtain a localization solution with high accuracy. In addition, the method also takes into account the influence of collinearity on localization calculation in the process of locating, thus the method is suitable for different monitoring areas and has high adaptability.
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