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Tang Y.,Changzhou Key Laboratory of Sensor Networks and Environmental Sensing | Shen Y.,Changzhou Key Laboratory of Sensor Networks and Environmental Sensing | Jiang A.,Changzhou Key Laboratory of Sensor Networks and Environmental Sensing | Xu N.,Changzhou Key Laboratory of Sensor Networks and Environmental Sensing | And 2 more authors.
Proceedings - IEEE International Symposium on Circuits and Systems | Year: 2013

Sparse representation theory has been well developed in recent years. In this paper, we consider an image denoising problem which can be efficiently solved under the framework of the sparse representation theory. The traditional image denoising methods based on the sparse representation seldom take into account the special structure of the data. As an attempt to overcome such problem, the Graph regularized K-means singular value decomposition (Graph K-SVD) algorithm is proposed with the manifold learning. The local geometrical structure of the image is considered in the sparse optimization model with the graph Laplacian. This manifold-based optimization problem is well solved in the framework of the traditional K-SVD algorithm. Since the novel strategy adds a graph regularizer to the sparse representation model in order to emphasize the correlations among image blocks, the Graph K-SVD can achieve better denoising performance than the traditional K-SVD. © 2013 IEEE. Source


Han G.,Hohai University | Han G.,Changzhou Key Laboratory of Sensor Networks and Environmental Sensing | Zhang C.,Hohai University | Shu L.,Guangdong University of Petrochemical Technology | And 2 more authors.
International Journal of Distributed Sensor Networks | Year: 2013

Node deployment is one of the fundamental tasks for underwater acoustic sensor networks (UASNs) where the deployment strategy supports many fundamental network services, such as network topology control, routing, and boundary detection. Due to the complex deployment environment in three-dimensional (3D) space and unique characteristics of underwater acoustic channel, many factors need to be considered specifically during the deployment of UASNs. Thus, deployment issues in UASNs are significantly different from those of wireless sensor networks (WSNs). Node deployment for UASNs is an attractive research topic upon which a large number of algorithms have been proposed recently. This paper seeks to provide an overview of the most recent advances of deployment algorithms in UASNs while pointing out the open issues. In this paper, the deployment algorithms are classified into three categories based on the mobility of sensor nodes, namely, (I) static deployment, (II) self-adjustment deployment, and (III) movement-assisted deployment. The differences of the representative algorithms in aspects of sensor node types, computation complexity, energy consumption, deployment objectives, and so forth, are discussed and investigated in detail. © 2013 Guangjie Han et al. Source


Han G.,Hohai University | Han G.,Changzhou Key Laboratory of Sensor Networks and Environmental Sensing | Jiang J.,Hohai University | Shu L.,Osaka University | And 2 more authors.
Computer Journal | Year: 2013

Accurately locating unknown nodes is a critical issue in the study of wireless sensor networks (WSNs). Many localization approaches have been proposed based on anchor nodes, which are assumed to know their locations by manual placement or additional equipments such as global positioning system. However, none of these approaches can work properly under the adversarial scenario. In this paper, we propose a novel scheme called two-step secure localization (TSSL) stand against many typical malicious attacks, e.g. wormhole attack and location spoofing attack. TSSL detects malicious nodes step by step. First, anchor nodes collaborate with each other to identify suspicious nodes by checking their coordinates, identities and time of sending information. Then, by using a modified mesh generation scheme, malicious nodes are isolated and the WSN is divided into areas with different trust grades. Finally, a novel localization algorithm based on the arrival time difference of localization information is adopted to calculate locations of unknown nodes. Simulation results show that the TSSL detects malicious nodes effectively and the localization algorithm accomplishes localization with high localization accuracy. © 2012 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved. Source


Han G.,Hohai University | Han G.,Changzhou Key Laboratory of Sensor Networks and Environmental Sensing | Jiang J.,Hohai University | Jiang J.,Changzhou Key Laboratory of Sensor Networks and Environmental Sensing | And 3 more authors.
Sensors | Year: 2012

In Underwater Wireless Sensor Networks (UWSNs), localization is one of most important technologies since it plays a critical role in many applications. Motivated by widespread adoption of localization, in this paper, we present a comprehensive survey of localization algorithms. First, we classify localization algorithms into three categories based on sensor nodes' mobility: stationary localization algorithms, mobile localization algorithms and hybrid localization algorithms. Moreover, we compare the localization algorithms in detail and analyze future research directions of localization algorithms in UWSNs. © 2012 by the authors. Source


Zhu C.,Hohai University | Zhu C.,Changzhou Key Laboratory of Sensor Networks and Environmental Sensing | Zheng C.,Hohai University | Shu L.,Osaka University | And 2 more authors.
Journal of Network and Computer Applications | Year: 2012

A wireless sensor network (WSN) is composed of a group of small power-constrained nodes with functions of sensing and communication, which can be scattered over a vast region for the purpose of detecting or monitoring some special events. The first challenge encountered in WSNs is how to cover a monitoring region perfectly. Coverage and connectivity are two of the most fundamental issues in WSNs, which have a great impact on the performance of WSNs. Optimized deployment strategy, sleep scheduling mechanism, and coverage radius cannot only reduce cost, but also extend the network lifetime. In this paper, we classify the coverage problem from different angles, describe the evaluation metrics of coverage control algorithms, analyze the relationship between coverage and connectivity, compare typical simulation tools, and discuss research challenges and existing problems in this area. © 2011 Elsevier Ltd. All rights reserved. Source

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