• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.1043-1063
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• 2019

With delegating proxy to process data before outsourcing, data owners in restricted access could enjoy flexible and powerful cloud storage service for productivity, but still confront with data integrity breach. Identity-based data auditing as a critical technology, could address this security concern efficiently and eliminate complicated owners' public key certificates management issue. Recently, Yu et al. proposed an Identity-Based Public Auditing for Dynamic Outsourced Data with Proxy Processing (https://doi.org/10.3837/tiis.2017.10.019). It aims to offer identity-based, privacy-preserving and batch auditing for multiple owners' data on different clouds, while allowing proxy processing. In this article, we first demonstrate this scheme is insecure in the sense that malicious cloud could pass integrity auditing without original data. Additionally, clouds and owners are able to recover proxy's private key and thus impersonate it to forge tags for any data. Secondly, we propose an improved scheme with provable security in the random oracle model, to achieve desirable secure identity based privacy-preserving batch public auditing with proxy processing. Thirdly, based on theoretical analysis and performance simulation, our scheme shows better efficiency over existing identity-based auditing scheme with proxy processing on single owner and single cloud effort, which will benefit secure big data storage if extrapolating in real application.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1618-1637
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• 2018

By means of compressive sensing (CS) technique, this paper considers the collection of sensor data with spatiotemporal correlations in wireless sensor networks (WSNs). In energy-constrained WSNs, one-dimensional CS methods need a lot of data transmissions since they are less applicable in fully exploiting the spatiotemporal correlations, while the Kronecker CS (KCS) methods suffer performance degradations when the signal dimension increases. In this paper, an appropriate sensing matrix as well as an efficient sensing method is proposed to further reduce the data transmissions without the loss of the recovery performance. Different matrices for the temporal signal of each sensor node are separately designed. The corresponding energy-efficient data gathering method is presented, which only transmitting a subset of sensor readings to recover data of the entire WSN. Theoretical analysis indicates that the sensing structure could have the relatively small mutual coherence according to the selection of matrix. Compared with the existing spatiotemporal CS (CS-ST) method, the simulation results show that the proposed efficient measurement method could reduce data transmissions by about 25% with the similar recovery performance. In addition, compared with the conventional KCS method, for 95% successful recovery, the proposed sensing structure could improve the recovery performance by about 20%.

• Journal of Korea Multimedia Society
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• v.15 no.7
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• pp.891-902
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• 2012

Reversible data hiding scheme is a form of steganography in which the secret embedding data can be retrieved from a stego image for the purpose of identification, copyright protection and making a covert channel. The reversible data hiding should satisfy that not only are the distortions due to artifacts against the cover image invisible but also it has large embedding capacity as far as possible. In this paper, we propose a robust reversible data hiding scheme by exploiting the differences between a center pixel and its neighboring pixels in each sub-block of the image to embed secret data into extra space. Moreover, our scheme enhances the embedding capacity and can recover the embedded data from the stego image without causing any perceptible distortions to the cover image. Simulation results show that our proposed scheme has lower visible distortions in the stego image and provides robustness to geometrical image manipulations, such as rotation and cropping operations.

• Journal of the Optical Society of Korea
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• v.19 no.4
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• pp.390-396
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• 2015

We propose a partial occlusion removal method for computational integral imaging reconstruction (CIIR) based on the usage of the exemplar based inpainting technique. The proposed method is an improved version of the original linear inpainting based CIIR (LI-CIIR), which uses the inpainting technique to fill in the data missing region. The LI-CIIR shows good results for images which contain objects with smooth surfaces. However, if the object has a textured surface, the result of the LI-CIIR deteriorates, since the linear inpainting cannot recover the textured data in the data missing region well. In this work, we utilize the exemplar based inpainting to fill in the textured data in the data missing region. We call the proposed method the neighboring elemental image exemplar based inpainting (NEI-exemplar inpainting) method, since it uses sources from neighboring elemental images to fill in the data missing region. Furthermore, we also propose an automatic occluding region extraction method based on the use of the mutual constraint using depth estimation (MC-DE) and the level set based bimodal segmentation. Experimental results show the validity of the proposed system.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.10
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• pp.936-943
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• 2015

This paper presents arc/line segments-based Simultaneous Localization and Mapping (SLAM) by updating accumulated laser sensor data with a mobile robot moving in an unknown environment. For each scan, the sensor data in the set are stored by a small constant number of parameters that can recover the necessary information contained in the raw data of the group. The arc and line segments are then extracted according to different limit values, but based on the same parameters. If two segments, whether they are homogenous features or not, from two scans are matched successfully, the new segment is extracted from the union set with combined data information obtained by means of summing the equivalent parameters of these two sets, not combining the features directly. The covariance matrixes of the segments are also updated and calculated synchronously employing the same parameters. The experiment results obtained in an irregular indoor environment show the good performance of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.206-221
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• 2019

Source localization in three-dimensional (3-D) wireless sensor networks (WSNs) is becoming a major research focus. Due to the complicated air-ground environments in 3-D positioning, many of the traditional localization methods, such as received signal strength (RSS) may have relatively poor accuracy performance. Benefit from prior learning mechanisms, fingerprinting-based localization methods are less sensitive to complex conditions and can provide relatively accurate localization performance. However, fingerprinting-based methods require training data at each grid point for constructing the fingerprint database, the overhead of which is very high, particularly for 3-D localization. Also, some of measured data may be unavailable due to the interference of a complicated environment. In this paper, we propose an efficient kernel based 3-D localization algorithm via tensor completion. We first exploit the spatial correlation of the RSS data and demonstrate the low rank property of the RSS data matrix. Based on this, a new training scheme is proposed that uses tensor completion to recover the missing data of the fingerprint database. Finally, we propose a kernel based learning technique in the matching phase to improve the sensitivity and accuracy in the final source position estimation. Simulation results show that our new method can effectively eliminate the impairment caused by incomplete sensing data to improve the localization performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.310-313
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• 2022

In the pipeline for supplying purified water to each household, measurements such as flow meters and pressure meters are installed at important points to monitor in real time. The measured data acquired to the central control room through wireless or wired communication, but data may not be acquired due to intermittent communication failures. Since then, even if the communication network is restored, data during the failure period is not stored on the site, or even if it is stored, data cannot be automatically stored in the database. Low cost with universally installed in the field in order to address these data logging by developing a module is compatible with PLC, automatically would like to make sure that we can recover in the database.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.3
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• pp.79-90
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• 2015

Multipath routing technique is recognized as one of the effective approaches to improve the reliability of data forwarding. However, the traditional multipath routing focuses only on how many paths are needed to ensure a desired reliability. For this purpose, the protocols construct additional paths and thus cause significant energy consumption. These problems have motivated the study for the energy-efficient and reliable data forwarding. Thus, this paper proposes an energy-efficient concurrent multipath routing protocol with a small number of paths based on interaction between paths. The interaction between paths helps to reinforce the multipath reliability by making efficient use of resources. The protocol selects several nodes located in the radio overlapped area between a pair of paths as bridge nodes for the path-interaction. In order to operate the bridge node efficiently, when the transmission failure has detected by overhearing at each path, it performs recovery transmission to recover the path failure. Simulation results show that proposed protocol is superior to the existing multipath protocols in terms of energy consumption and delivery reliability.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.105-108
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• 1996

In this paper, we present an interpolation schema for image resolution enhancement using fuzzy logic. Proposed algorithm can recover both low and high frequency information in image data. In general, interpolation techniques are based on linear operators which are essentially details in the original image. In our fuzzy approach, the operator itself balances the strength of its sharpening and noise suppressing components according to the properties of the input image data. The proposed interpolation algorithm is performed in three step. First logic reasoning is applied to coarsely interpret the high frequency information. These results are combined to obtain the optical output. Using our approach, resolution of the original image can be applied to various kind of image processing topics such as image enhancement, subpixel edge detection, and filtering.

• Journal of information and communication convergence engineering
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• v.8 no.5
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• pp.549-553
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• 2010

Lim et al. proposed a Hybrid Group Key Management scheme for Hierarchical Self-Organizing Sensor Network in 2008 to provide a secure way to pass down the group key for cluster-based communication. This paper presents two practical attacks on the scheme proposed by Lim et al. by tampering sensor nodes of a cluster to recover necessary secret keys and by exploiting the IDS employed by the scheme. The first attack enables a long-term but slow data fabrication while other attack causes more severe DoS on the access to cluster sensor nodes.