The Journal of Information Processing Systems
(JIPS) is the official international journal of the Korea Information Processing Society.
As information processing systems are progressing at a rapid pace, the Korea Information Processing Society is committed to providing researchers and other professionals
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all over the world to promote, share, and discuss all major research issues and developments in the field of information processing systems and other related fields.
ISSN: 1976-913X (Print), ISSN: 2092-805X (Online)
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Journal of Information Processing Systems, Vol. 15, No.1, 2019
Recently, big data and artificial intelligence (AI) based on communication systems have become one of the
hottest issues in the technology sector, and methods of analyzing big data using AI approaches are now
considered essential. This paper presents diverse paradigms to subjects which deal with diverse research areas, such as image segmentation, fingerprint matching, human tracking techniques, malware distribution networks, methods of intrusion detection, digital image watermarking, wireless sensor networks, probabilistic neural networks, query processing of encrypted data, the semantic web, decision-making, software engineering, and so on.
In order to improve the edge segmentation effect of the level set image segmentation and avoid the influence of
the initial contour on the level set method, a saliency level set image segmentation model based on local Renyi
entropy is proposed. Firstly, the saliency map of the original image is extracted by using saliency detection
algorithm. And the outline of the saliency map can be used to initialize the level set. Secondly, the local energy
and edge energy of the image are obtained by using local Renyi entropy and Canny operator respectively. At
the same time, new adaptive weight coefficient and boundary indication function are constructed. Finally, the
local binary fitting energy model (LBF) as an external energy term is introduced. In this paper, the contrast
experiments are implemented in different image database. The robustness of the proposed model for
segmentation of images with intensity inhomogeneity and complicated edges is verified.
This paper introduces an efficient fingerprint matching method based on multiple reference minutiae points.
First, we attempt to effectively align two fingerprints by employing multiple reference minutiae points.
However, the corresponding minutiae points between two fingerprints are ambiguous since a minutia of one
fingerprint can be a match to any minutia of the other fingerprint. Therefore, we introduce a novel method
based on linear classification concept to establish minutiae correspondences between two fingerprints. Each
minutiae correspondence represents a possible alignment. For each possible alignment, a matching score is
computed using minutiae and ridge orientation features and the maximum score is then selected to represent
the similarity of the two fingerprints. The proposed method is evaluated using fingerprint databases, FVC2002
and FVC2004. In addition, we compare our approach with two existing methods and find that our approach
outperforms them in term of matching accuracy, especially in the case of non-linear distorted fingerprints.
Furthermore, the experiments show that our method provides additional advantages in low quality fingerprint
images such as inaccurate position, missing minutiae, and spurious extracted minutiae.
A method for detecting foreign substances in mould based on scatter grams was presented to protect moulds
automatically during moulding production. This paper proposes a wavelet transform foreign detection method
based on Monte Carlo analysis mechanism to identify foreign objects in the tube. We use the Monte Carlo
method to evaluate the image, and obtain the width of the confidence interval by the deviation statistical gray
histogram to divide the image type. In order to stabilize the performance of the algorithm, the high-frequency
image and the low-frequency image are respectively drawn. By analyzing the position distribution of the pixel
gray in the two images, the suspected foreign object region is obtained. The experiments demonstrate the
effectiveness of our approach by evaluating the labeled data.
This study is concerned with the mechanism and structure of an optical microscope and an automatic multifocus
algorithm for automatically selecting sharp images from multiple foci of a cell. To obtain precise cell
images quickly, a z-axis actuator with a resolution of 0.1 ?m was designed to control an optical microscope
Moreover, a lighting control system was constructed to select the color and brightness of light that best suit the
object being viewed. Cell images are captured by the instrument and the sharpness of each image is determined
using Gaussian and Laplacian filters. Next, cubic spline interpolation and peak detection algorithms are applied
to automatically find the most vivid points among multiple images of a single object. A cancer cell imaging
experiment using propidium iodide staining confirmed that a sharp multipoint image can be obtained using
this microscope. The proposed system is expected to save time and effort required to extract suitable cell images
and increase the convenience of cell analysis.
Storage system often applies erasure codes to protect against disk failure and ensure system reliability and
availability. Liberation code that is a type of coding scheme has been widely used in many storage systems
because its encoding and modifying operations are efficient. However, it cannot effectively achieve fast recovery
from single disk failure in storage systems, and has great influence on recovery performance as well as response
time of client requests. To solve this problem, in this paper, we present HRSF, a Hybrid Recovery method for
solving Single disk Failure. We present the optimal algorithm to accelerate failure recovery process. Theoretical
analysis proves that our scheme consumes approximately 25% less amount of data read than the conventional
method. In the evaluation, we perform extensive experiments by setting different number of disks and chunk
sizes. The results show that HRSF outperforms conventional method in terms of the amount of data read and
failure recovery time.
Accurate detection, tracking and analysis of human movement using robots and other visual surveillance
systems is still a challenge. Efforts are on to make the system robust against constraints such as variation in
shape, size, pose and occlusion. Traditional methods of detection used the sliding window approach which
involved scanning of various sizes of windows across an image. This paper concentrates on employing a stateof-
the-art, hierarchical graph based method for segmentation. It has two stages: part level segmentation for
color-consistent segments and object level segmentation for category-consistent regions. The tracking phase is
achieved by employing SIFT keypoint descriptor based technique in a combined matching and tracking scheme
with validation phase. Localization of human region in each frame is performed by keypoints by casting votes
for the center of the human detected region. As it is difficult to avoid incorrect keypoints, a consensus-based
framework is used to detect voting behavior. The designed methodology is tested on the video sequences having
3 to 4 persons.
An innovative tone modeling framework based on deep neural networks in tone recognition was proposed in
this paper. In the framework, both the prosodic features and the articulatory features were firstly extracted as
the raw input data. Then, a 5-layer-deep deep belief network was presented to obtain high-level tone features.
Finally, support vector machine was trained to recognize tones. The 863-data corpus had been applied in
experiments, and the results show that the proposed method helped improve the recognition accuracy
significantly for all tone patterns. Meanwhile, the average tone recognition rate reached 83.03%, which is 8.61%
higher than that of the original method.
Malicious code distribution on the Internet is one of the most critical Internet-based threats and distribution
technology has evolved to bypass detection systems. As a new defense against the detection bypass technology
of malicious attackers, this study proposes the automated tracing of malicious websites in a malware
distribution network (MDN). The proposed technology extracts automated links and classifies websites into
malicious and normal websites based on link structure. Even if attackers use a new distribution technology,
website classification is possible as long as the connections are established through automated links. The use of
a real web-browser and proxy server enables an adequate response to attackers’ perception of analysis
environments and evasion technology and prevents analysis environments from being infected by malicious
code. The validity and accuracy of the proposed method for classification are verified using 20,000 links, 10,000
each from normal and malicious websites.
In this paper, an improved one-against-one support vector machine algorithm is used to classify multiple power
quality disturbances. To solve the problem of parameter selection, an improved particle swarm optimization
algorithm is proposed to optimize the parameters of the support vector machine. By proposing a new inertia
weight expression, the particle swarm optimization algorithm can effectively conduct a global search at the
outset and effectively search locally later in a study, which improves the overall classification accuracy. The
experimental results show that the improved particle swarm optimization method is more accurate than a grid
search algorithm optimization and other improved particle swarm optimizations with regard to its classification
of multiple power quality disturbances. Furthermore, the number of support vectors is reduced.
User-based and item-based approaches have been developed as the solutions of the movie recommendation
problem. However, the user-based approach is faced with the problem of sparsity, and the item-based approach
is faced with the problem of not reflecting users’ preferences. In order to solve these problems, there is a research
on the combination of the two methods using the concept of similarity. In reality, it is not free from the problem
of sparsity, since it has a lot of parameters to be calculated. In this study, we propose a combining method that
simplifies the combination equation of prior study. This method is relatively free from the problem of sparsity,
since it has less parameters to be calculated. Thus, it can get more accurate results by reflecting the users rating
to calculate the parameters. It is very fast to predict new movie ratings as well. In experiments for the proposed
method, the initial error is large, but the performance gets quickly stabilized after. In addition, it showed about
6% lower average error rate than the existing method using similarity.
Although the research of immune-based anomaly detection technology has made some progress, there are still
some defects which have not been solved, such as the loophole problem which leads to low detection rate and
high false alarm rate, the exponential relationship between training cost of mature detectors and size of selfantigens.
This paper proposed an intrusion detection method based on changes of antibody concentration in
immune response to improve and solve existing problems of immune based anomaly detection technology. The
method introduces blood relative and blood family to classify antibodies and antigens and simulate correlations
between antibodies and antigens. Then, the method establishes dynamic evolution models of antigens and
antibodies in intrusion detection. In addition, the method determines concentration changes of antibodies in
the immune system drawing the experience of cloud model, and divides the risk levels to guide immune
responses. Experimental results show that the method has better detection performance and adaptability than
With the recent advances of memory technologies, high-performance non-volatile memories such as nonvolatile
dual in-line memory module (NVDIMM) have begun to be used as an addition or an alternative to
server-side storages. When these memory bus-connected storages (MBSs) are installed over non-uniform
memory access (NUMA) servers, the distance between NUMA nodes and MBSs is one of the crucial factors
that influence file processing performance, because the access latency of a NUMA system varies depending on
its distance from the NUMA nodes. This paper presents the design and implementation of a high-performance
logical volume manager for MBSs, called MBS-LVM, when multiple MBSs are scattered over a NUMA server.
The MBS-LVM consolidates the address space of each MBS into a single global address space and dynamically
utilizes storage spaces such that each thread can access an MBS with the lowest latency possible. We
implemented the MBS-LVM in the Linux kernel and evaluated its performance by porting it over the tmpfs, a
memory-based file system widely used in Linux. The results of the benchmarking show that the write
performance of the tmpfs using MBS-LVM has been improved by up to twenty times against the original tmpfs
over a NUMA server with four nodes.
Accurate forest fires detection algorithms remain a challenging issue, because, some of the objects have the
same features with fire, which may result in high false alarms rate. This paper presents a new video-based, image
processing forest fires detection method, which consists of four stages. First, a background-subtraction
algorithm is applied to detect moving regions. Secondly, candidate fire regions are determined using CIE
L?a?b? color space. Thirdly, special wavelet analysis is used to differentiate between actual fire and fire-like
objects, because candidate regions may contain moving fire-like objects. Finally, support vector machine is used
to classify the region of interest to either real fire or non-fire. The final experimental results verify that the
proposed method effectively identifies the forest fires.
Transmitting visual information over a broadcasting network is not only prone to a copyright violation but also
is a forgery. Authenticating such information and protecting its authorship rights call for more advanced data
encoding. To this end, electronic watermarking is often adopted to embed inscriptive signature in imaging data.
Most existing watermarking methods while focusing on robustness against degradation remain lacking of
measurement against security loophole in which the encrypting scheme once discovered may be recreated by
an unauthorized party. This could reveal the underlying signature which may potentially be replaced or forged.
This paper therefore proposes a novel digital watermarking scheme in temporal-frequency domain. Unlike
other typical wavelet based watermarking, the proposed scheme employed the Lorenz chaotic map to specify
embedding positions. Effectively making this is not only a formidable method to decrypt but also a stronger
will against deterministic attacks. Simulation report herein highlights its strength to withstand spatial and
frequent adulterations, e.g., lossy compression, filtering, zooming and noise.
A production system is a management system that supports all activities to perform production operations at
the manufacturing site. From the point-of-view of a smart factory, smart manufacturing systems redesigned the
concept of onsite production systems to fit the entire system and its necessary functional composition. In this
study, we select the key functions needed to build a smart factory for a PCB line and propose a new six-step
model for the deployment of a smart manufacturing system by integrating essential functions. The smart
manufacturing system newly classified the production and operation tasks of PCB manufacturing and selected
necessary functions through requirement analysis and benchmarking of advanced companies. The selected
production operation tasks are mapped to the functions of the system and configured into seven modules, and
the optimal deployment model is presented to allow flexible responses to the characteristics of the tasks. These
methodologies are first presented in this study, and the proposed model was applied to the PCB line to confirm
that they had significant changes in the work method, qualitative effects, and quantitative effects. Typically, lead
time and WIP have reduced by about 50%.
During thermal power coal-fired boiler operation, it is very important to detect the pulverized coal
concentration in the air pipeline for the boiler combustion stability and economic security. Because the current
measurement methods used by power plants are often involved with large measurement errors and unable to
monitor the pulverized coal concentration in real-time, a new method is needed. In this paper, a new method
based on microwave circular waveguide is presented. High Frequency Electromagnetic Simulation (HFSS)
software was used to construct a simulation model for measuring pulverized coal concentration in power plant
pipeline. Theoretical analysis and simulation experiments were done to find the effective microwave emission
frequency, installation angle, the type of antenna probe, antenna installation distance and other important
parameters. Finally, field experiment in Jilin Thermal Power Plant proved that with selected parameters, the
measuring device accurately reflected the changes in the concentration of pulverized coal.
The nature of wireless transmission has made wireless sensor networks defenseless against various attacks. This
paper presents warning message counter method (WMC) to detect blackhole attack, grayhole attack and
sinkhole attack in wireless sensor networks. The objective of these attackers are, to draw the nearby network
traffic by false routing information and disrupt the network operation through dropping all the received packets
(blackhole attack), selectively dropping the received packets (grayhole and sinkhole attack) and modifying the
content of the packet (sinkhole attack). We have also attempted light weighted symmetric key cryptography to
find data modification by the sinkhole node. Simulation results shows that, WMC detects sinkhole attack,
blackhole attack and grayhole attack with less false positive 8% and less false negative 6%.
Currently, the crew working on a ship is required to carry a seafarer's book in most countries around the world,
including the Republic of Korea (ROK). Yet, many fishermen working in the international waters of the ROK
do not abide by this rule as the procedure of obtaining it is rather inconvenient or they do not understand the
necessity or the benefits of having it. Also, as the regulation of carrying the certificate has been strengthened, it
is important for them to avoid making a criminal record unintentionally. This study discusses the digitalization
of the seafarer’s book based on several security measures in addition to BLE Beacon-based positioning
technology, which can be useful for the e-Navigation. Normally, seamen’s certificates are recorded by the
captain, medical institution, or issuing authority and then kept in an onboard safe or a certificate cabinet. The
material of the certificates is a cloth that can withstand salinity as the certificate could be contaminated by mold.
In the past, the captains and their crews were uncooperative when the ROK’s maritime police tried to inspect
several ships simultaneously because of the time and cost involved. Thus, a system with which the maritime
police will be able to conveniently manage the crews is proposed.
The 2nd Journal of Information Processing Systems Awards
"Block-VN: A Distributed Blockchain Based Vehicular Network Architecture in Smart City"
Pradip Kumar Sharma, Seo Yeon Moon and Jong Hyuk Park (Seoul National University of Science and Technology, Korea)
Publication (Corresponding Author)
Chengyou Wang (Shangdong University, China)
Quorum-based algorithms are widely used for solving several problems in mobile ad hoc networks (MANET) and wireless sensor networks (WSN). Several quorum-based protocols are proposed for multi-hop ad hoc networks that each one has its pros and cons. Quorum-based protocol (QEC or QPS) is the first study in the asynchronous sleep scheduling protocols. At the time, most of the proposed protocols were non-adaptive ones. But nowadays, adaptive quorum-based protocols have gained increasing attention, because we need protocols which can change their quorum size adaptively with network conditions. In this paper, we first introduce the most popular quorum systems and explain quorum system properties and its performance criteria. Then, we present a comparative and comprehensive survey of the non-adaptive and adaptive quorum-based protocols which are subsequently discussed in depth. We also present the comparison of different quorum systems in terms of the expected quorum overlap size (EQOS) and active ratio. Finally, we summarize the pros and cons of current adaptive and non-adaptive quorum-based protocols.
The significant advances in information and communication technologies are changing the process of how information is accessed. The internet is a very important source of information and it influences the development of other media. Furthermore, the growth of digital content is a big problem for academic digital libraries, so that similar tools can be applied in this scope to provide users with access to the information. Given the importance of this, we have reviewed and analyzed several proposals that improve the processes of disseminating information in these university digital libraries and that promote access to information of interest. These proposals manage to adapt a user’s access to information according to his or her needs and preferences. As seen in the literature one of the techniques with the best results, is the application of recommender systems. These are tools whose objective is to evaluate and filter the vast amount of digital information that is accessible online in order to help users in their processes of accessing information. In particular, we are focused on the analysis of the fuzzy linguistic recommender systems (i.e., recommender systems that use fuzzy linguistic modeling tools to manage the user’s preferences and the uncertainty of the system in a qualitative way). Thus, in this work, we analyzed some proposals based on fuzzy linguistic recommender systems to help researchers, students, and teachers access resources of interest and thus, improve and complement the services provided by academic digital libraries.
Associative and bidirectional associative memories are examples of associative structures studied intensively in the literature. The underlying idea is to realize associative mapping so that the recall processes (one- directional and bidirectional ones) are realized with minimal recall errors. Associative and fuzzy associative memories have been studied in numerous areas yielding efficient applications for image recall and enhancements and fuzzy controllers, which can be regarded as one-directional associative memories. In this study, we revisit and augment the concept of associative memories by offering some new design insights where the corresponding mappings are realized on the basis of a related collection of landmarks (prototypes) over which an associative mapping becomes spanned. In light of the bidirectional character of mappings, we have developed an augmentation of the existing fuzzy clustering (fuzzy c-means, FCM) in the form of a so- called collaborative fuzzy clustering. Here, an interaction in the formation of prototypes is optimized so that the bidirectional recall errors can be minimized. Furthermore, we generalized the mapping into its granular version in which numeric prototypes that are formed through the clustering process are made granular so that the quality of the recall can be quantified. We propose several scenarios in which the allocation of information granularity is aimed at the optimization of the characteristics of recalled results (information granules) that are quantified in terms of coverage and specificity. We also introduce various architectural augmentations of the associative structures.
Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.
This survey paper explores the application of multimodal feedback in automated systems for motor learning. In this paper, we review the findings shown in recent studies in this field using rehabilitation and various motor training scenarios as context. We discuss popular feedback delivery and sensing mechanisms for motion capture and processing in terms of requirements, benefits, and limitations. The selection of modalities is presented via our having reviewed the best-practice approaches for each modality relative to motor task complexity with example implementations in recent work. We summarize the advantages and disadvantages of several approaches for integrating modalities in terms of fusion and frequency of feedback during motor tasks. Finally, we review the limitations of perceptual bandwidth and provide an evaluation of the information transfer for each modality.
The recent advent of increasingly affordable and powerful 3D scanning devices capable of capturing high resolution range data about real-world objects and environments has fueled research into effective 3D surface reconstruction techniques for rendering the raw point cloud data produced by many of these devices into a form that would make it usable in a variety of application domains. This paper, therefore, provides an overview of the existing literature on surface reconstruction from 3D point clouds. It explains some of the basic surface reconstruction concepts, describes the various factors used to evaluate surface reconstruction methods, highlights some commonly encountered issues in dealing with the raw 3D point cloud data and delineates the tradeoffs between data resolution/accuracy and processing speed. It also categorizes the various techniques for this task and briefly analyzes their empirical evaluation results demarcating their advantages and disadvantages. The paper concludes with a cross-comparison of methods which have been evaluated on the same benchmark data sets along with a discussion of the overall trends reported in the literature. The objective is to provide an overview of the state of the art on surface reconstruction from point cloud data in order to facilitate and inspire further research in this area.
Gene identification is at the center of genomic studies. Although the first phase of the Encyclopedia of DNA Elements (ENCODE) project has been claimed to be complete, the annotation of the functional elements is far from being so. Computational methods in gene identification continue to play important roles in this area and other relevant issues. So far, a lot of work has been performed on this area, and a plethora of computational methods and avenues have been developed. Many review papers have summarized these methods and other related work. However, most of them focus on the methodologies from a particular aspect or perspective. Different from these existing bodies of research, this paper aims to comprehensively summarize the mainstream computational methods in gene identification and tries to provide a short but concise technical reference for future studies. Moreover, this review sheds light on the emerging trends and cutting-edge techniques that are believed to be capable of leading the research on this field in the future.
In this paper we present some research results on computing intensive applications using modern high performance architectures and from the perspective of high computational needs. Computing intensive applications are an important family of applications in distributed computing domain. They have been object of study using different distributed computing paradigms and infrastructures. Such applications distinguish for their demanding needs for CPU computing, independently of the amount of data associated with the problem instance. Among computing intensive applications, there are applications based on simulations, aiming to maximize system resources for processing large computations for simulation. In this research work, we consider an application that simulates scheduling and resource allocation in a Grid computing system using Genetic Algorithms. In such application, a rather large number of simulations is needed to extract meaningful statistical results about the behavior of the simulation results. We study the performance of Oracle Grid Engine for such application running in a Cluster of high computing capacities. Several scenarios were generated to measure the response time and queuing time under different workloads and number of nodes in the cluster.
The accuracy of training-based activity recognition depends on the training procedure and the extent to which the training dataset comprehensively represents the activity and its varieties. Additionally, training incurs substantial cost and effort in the process of collecting training data. To address these limitations, we have developed a training-free activity recognition approach based on a fuzzy logic algorithm that utilizes a generic activity model and an associated activity semantic knowledge. The approach is validated through experimentation with real activity datasets. Results show that the fuzzy logic based algorithms exhibit comparable or better accuracy than other trainingbased approaches.
Recent technological advances provide the opportunity to use large amounts of multimedia data from a multitude of sensors with different modalities (e.g., video, text) for the detection and characterization of criminal activity. Their integration can compensate for sensor and modality deficiencies by using data from other available sensors and modalities. However, building such an integrated system at the scale of neighborhood and cities is challenging due to the large amount of data to be considered and the need to ensure a short response time to potential criminal activity. In this paper, we present a system that enables multi-modal data collection at scale and automates the detection of events of interest for the surveillance and reconnaissance of criminal activity. The proposed system showcases novel analytical tools that fuse multimedia data streams to automatically detect and identify specific criminal events and activities. More specifically, the system detects and analyzes series of incidents (an incident is an occurrence or artifact relevant to a criminal activity extracted from a single media stream) in the spatiotemporal domain to extract events (actual instances of criminal events) while cross-referencing multimodal media streams and incidents in time and space to provide a comprehensive view to a human operator while avoiding information overload. We present several case studies that demonstrate how the proposed system can provide law enforcement personnel with forensic and real time tools to identify and track potential criminal activity.
The confinement problem was first noted four decades ago. Since then, a huge amount of efforts have been spent on defining and mitigating the problem. The evolution of technologies from traditional operating systems to mobile and cloud computing brings about new security challenges. It is perhaps timely that we review the work that has been done. We discuss the foundational principles from classical works, as well as the efforts towards solving the confinement problem in three domains: operating systems, mobile computing, and cloud computing. While common issues exist across all three domains, unique challenges arise for each of them, which we discuss.
Since a social network by definition is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications, which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary; estimating a user"'"s interests typically involves non-stationary distributions. The consequent time varying nature of the distribution to be tracked imposes stringent constraints on the "unlearning” capabilities of the estimator used. Therefore, resorting to strong estimators that converge with a probability of 1 is inefficient since they rely on the assumption that the distribution of the user"'"s preferences is stationary. In this vein, we propose to use a family of stochastic-learning based Weak estimators for learning and tracking a user"'"s time varying interests. Experimental results demonstrate that our proposed paradigm outperforms some of the traditional legacy approaches that represent the state-of-the-art technology.
The most important criterion for achieving the maximum performance in a wireless mesh network (WMN) is to limit the interference within the network. For this purpose, especially in a multi-radio network, the best option is to use non-overlapping channels among different radios within the same interference range. Previous works that have considered non-overlapping channels in IEEE 802.11a as the basis for performance optimization, have considered the link quality across all channels to be uniform. In this paper, we present a measurement-based study of link quality across all channels in an IEEE 802.11a-based indoor WMN test bed. Our results show that the generalized assumption of uniform performance across all channels does not hold good in practice for an indoor environment and signal quality depends on the geometry around the me routers.
This paper describes different aspects of a typical RFID implementation. Section 1 provides a brief overview of the concept of Automatic Identification and compares the use of different technologies while Section 2 describes the basic components of a typical RFID system. Section 3 and Section 4 deal with the detailed specifications of RFID transponders and RFID interrogators respectively. Section 5 highlights different RFID standards and protocols and Section 6 enumerates the wide variety of applications where RFID systems are known to have made a positive improvement. Section 7 deals with privacy issues concerning the use of RFIDs and Section 8 describes common RFID system vulnerabilities. Section 9 covers a variety of RFID security issues, followed by a detailed listing of countermeasures and precautions in Section 10.
Granular Computing has emerged as a unified and coherent framework of designing, processing, and interpretation of information granules. Information granules are formalized within various frameworks such as sets (interval mathematics), fuzzy sets, rough sets, shadowed sets, probabilities (probability density functions), to name several the most visible approaches. In spite of the apparent diversity of the existing formalisms, there are some underlying commonalities articulated in terms of the fundamentals, algorithmic developments and ensuing application domains. In this study, we introduce two pivotal concepts: a principle of justifiable granularity and a method of an optimal information allocation where information granularity is regarded as an important design asset. We show that these two concepts are relevant to various formal setups of information granularity and offer constructs supporting the design of information granules and their processing. A suite of applied studies is focused on knowledge management in which case we identify several key categories of schemes present there.
In earlier days, most of the data carried on communication networks was textual data requiring limited bandwidth. With the rise of multimedia and network technologies, the bandwidth requirements of data have increased considerably. If a network link at any time is not able to meet the minimum bandwidth requirement of data, data transmission at that path becomes difficult, which leads to network congestion. This causes delay in data transmission and might also lead to packet drops in the network. The retransmission of these lost packets would aggravate the situation and jam the network. In this paper, we aim at providing a solution to the problem of network congestion in mobile ad hoc networks [1, 2] by designing a protocol that performs routing intelligently and minimizes the delay in data transmission. Our Objective is to move the traffic away from the shortest path obtained by a suitable shortest path calculation algorithm to a less congested path so as to minimize the number of packet drops during data transmission and to avoid unnecessary delay. For this we have proposed a protocol named as Congestion Aware Selection Of Path With Efficient Routing (CASPER). Here, a router runs the shortest path algorithm after pruning those links that violate a given set of constraints. The proposed protocol has been compared with two link state protocols namely, OSPF [3, 4] and OLSR [5, 6, 7, 8].The results achieved show that our protocol performs better in terms of network throughput and transmission delay in case of bulky data transmission.
Vehicular networks are a promising application of mobile ad hoc networks. In this paper, we introduce an efficient broadcast technique, called CB-S (Cell Broadcast for Streets), for vehicular networks with occlusions such as skyscrapers. In this environment, the road network is fragmented into cells such that nodes in a cell can communicate with any node within a two cell distance. Each mobile node is equipped with a GPS (Global Positioning System) unit and a map of the cells. The cell map has information about the cells including their identifier and the coordinates of the upper-right and lower-left corner of each cell. CB-S has the following desirable property. Broadcast of a message is performed by rebroadcasting the message from every other cell in the terrain. This characteristic allows CB-S to achieve an efficient performance. Our simulation results indicate that messages always reach all nodes in the wireless network. This perfect coverage is achieved with minimal overhead. That is, CB-S uses a low number of nodes to disseminate the data packets as quickly as probabilistically possible. This efficiency gives it the advantage of low delay. To show these benefits, we give simulations results to compare CB-S with four other broadcast techniques. In practice, CB-S can be used for information dissemination, or to reduce the high cost of destination discovery in routing protocols. By also specify the radius of affected zone, CB-S is also more efficient when broadcast to a subset of the nodes is desirable.
Cryptographic hash functions reduce inputs of arbitrary or very large length to a short string of fixed length. All hash function designs start from a compression function with fixed length inputs. The compression function itself is designed from scratch, or derived from a block cipher or a permutation. The most common procedure to extend the domain of a compression function in order to obtain a hash function is a simple linear iteration; however, some variants use multiple iterations or a tree structure that allows for parallelism. This paper presents a survey of 17 extenders in the literature. It considers the natural question whether these preserve the security properties of the compression function, and more in particular collision resistance, second preimage resistance, preimage resistance and the pseudo-random oracle property.
This paper proposes a novel reversible data hiding scheme based on a Vector Quantization (VQ) codebook. The proposed scheme uses the principle component analysis (PCA) algorithm to sort the codebook and to find two similar codewords of an image block. According to the secret to be embedded and the difference between those two similar codewords, the original image block is transformed into a difference number table. Finally, this table is compressed by entropy coding and sent to the receiver. The experimental results demonstrate that the proposed scheme can achieve greater hiding capacity, about five bits per index, with an acceptable bit rate. At the receiver end, after the compressed code has been decoded, the image can be recovered to a VQ compressed image.
The interconnection of mobile devices in urban environments can open up a lot of vistas for collaboration and content-based services. This will require setting up of a network in an urban environment which not only provides the necessary services to the user but also ensures that the network is secure and energy efficient. In this paper, we propose a secure, energy efficient dynamic routing protocol for heterogeneous wireless sensor networks in urban environments. A decision is made by every node based on various parameters like longevity, distance, battery power which measure the node and link quality to decide the next hop in the route. This ensures that the total load is distributed evenly while conserving the energy of battery-constrained nodes. The protocol also maintains a trusted population for each node through Dynamic Trust Factor (DTF) which ensures secure communication in the environment by gradually isolating the malicious nodes. The results obtained show that the proposed protocol when compared with another energy efficient protocol (MMBCR) and a widely accepted protocol (DSR) gives far better results in terms of energy efficiency. Similarly, it also outdoes a secure protocol (QDV) when it comes to detecting malicious nodes in the network.
The trend of Next Generation Networks’ (NGN) evolution is towards providing multiple and multimedia services to users through ubiquitous networks. The aim of IP Multimedia Subsystem (IMS) is to integrate mobile communication networks and computer networks. The IMS plays an important role in NGN services, which can be achieved by heterogeneous networks and different access technologies. IMS can be used to manage all service related issues such as Quality of Service (QoS), Charging, Access Control, User and Services Management. Nowadays, internet technology is changing with each passing day. New technologies yield new impact to IMS. In this paper, we perform a survey of IMS and discuss the different impacts of new technologies on IMS such as P2P, SCIM, Web Service and its security issues.
Due to the convergence of voice, data, and video, today’s telecom operators are facing the complexity of service and network management to offer differentiated value-added services that meet customer expectations. Without the operations support of well-developed Business Support System/Operations Support System (BSS/OSS), it is difficult to timely and effectively provide competitive services upon customer request. In this paper, a suite of NGOSS-based Telecom OSS (TOSS) is developed for the support of fulfillment and assurance operations of telecom services and IT services. Four OSS groups, TOSS-P (intelligent service provisioning), TOSS-N (integrated large-scale network management), TOSS-T (trouble handling and resolution), and TOSS-Q (end-to-end service quality management), are organized and integrated following the standard telecom operation processes (i.e., eTOM). We use IPTV and IP-VPN operation scenarios to show how these OSS groups co-work to support daily business operations with the benefits of cost reduction and revenue acceleration.
By providing ubiquitous Internet connectivity, wireless networks offer more convenient ways for users to surf the Internet. However, wireless networks encounter more technological challenges than wired networks, such as bandwidth, security problems, and handoff latency. Thus, this paper proposes new technologies to solve these problems. First, a Security Access Gateway (SAG) is proposed to solve the security issue. Originally, mobile terminals were unable to process high security calculations because of their low calculating power. SAG not only offers high calculating power to encrypt the encryption demand of SAG¡¯s domain, but also helps mobile terminals to establish a multiple safety tunnel to maintain a secure domain. Second, Robust Header Compression (RoHC) technology is adopted to increase the utilization of bandwidth. Instead of Access Point (AP), Access Gateway (AG) is used to deal with the packet header compression and de-compression from the wireless end. AG¡¯s high calculating power is able to reduce the load on AP. In the original architecture, AP has to deal with a large number of demands by header compression/de-compression from mobile terminals. Eventually, wireless networks must offer users ¡°Mobility¡± and ¡°Roaming¡±. For wireless networks to achieve ¡°Mobility¡± and ¡°Roaming,¡± we can use Mobile IPv6 (MIPv6) technology. Nevertheless, such technology might cause latency. Furthermore, how the security tunnel and header compression established before the handoff can be used by mobile terminals handoff will be another great challenge. Thus, this paper proposes to solve the problem by using Early Binding Updates (EBU) and Security Access Gateway (SAG) to offer a complete mechanism with low latency, low handoff mechanism calculation, and high security.
Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.
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The 2nd Journal of Information Processing Systems Awards
"Block-VN: A Distributed Blockchain Based Vehicular Network Architecture in Smart City"
Pradip Kumar Sharma, Seo Yeon Moon and Jong Hyuk Park (Seoul National University of Science and Technology, Korea)
Publication (Corresponding Author)
Chengyou Wang (Shangdong University, China)