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
with the academic information and resources they need to keep abreast with ongoing developments. The JIPS aims to be a premier source that enables researchers and professionals
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)
[Nov. 8, 2019] Call for papers about JIPS Survey Papers Awards scheduled in 2019 are registered. Please refer here for details.
Journal of Information Processing Systems, Vol. 15, No.5, 2019
In recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human
capabilities in various fields such as face recognition for security, weather prediction, and so on. Various
learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to
increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news
spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate moviebased
recommendation systems. In this paper, various algorithms are presented to mitigate these issues in
different systems and services. Convolutional neural network algorithms are used for detecting fake news in
Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased
accuracy in personalized recommendation-based services stock selection and volatility delay in stock
prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.
Recently, concomitant with a surge in numbers of Internet of Things (IoT) devices with various sensors, mobile
crowdsensing (MCS) has provided a new business model for IoT. For example, a person can share road traffic
pictures taken with their smartphone via a cloud computing system and the MCS data can provide benefits to
other consumers. In this service model, to encourage people to actively engage in sensing activities and to
voluntarily share their sensing data, providing appropriate incentives is very important. However, the sensing
data from personal devices can be sensitive to privacy, and thus the privacy issue can suppress data sharing.
Therefore, the development of an appropriate privacy protection system is essential for successful MCS. In this
study, we address this problem due to the conflicting objectives of privacy preservation and incentive payment.
We propose a privacy-preserving mechanism that protects identity and location privacy of sensing users
through an on-demand incentive payment and group signatures methods. Subsequently, we apply the proposed
mechanism to one example of MCS—an intelligent parking system—and demonstrate the feasibility and
efficiency of our mechanism through emulation.
In view of the deficiencies of existing weighted similarity indexes, a hierarchical clustering method initializeexpand-
merge (IEM) is proposed based on the similarity of common neighbors for community discovery in
weighted networks. Firstly, the similarity of the node pair is defined based on the attributes of their common
neighbors. Secondly, the most closely related nodes are fast clustered according to their similarity to form initial
communities and expand the communities. Finally, communities are merged through maximizing the
modularity so as to optimize division results. Experiments are carried out on many weighted networks, which
have verified the effectiveness of the proposed algorithm. And results show that IEM is superior to weighted
common neighbor (CN), weighted Adamic-Adar (AA) and weighted resources allocation (RA) when using
the weighted modularity as evaluation index. Moreover, the proposed algorithm can achieve more reasonable
community division for weighted networks compared with cluster-recluster-merge-algorithm (CRMA)
Signal complexity is one point of view to analyze the biological signal. It arises as a result of the physiological
signal produced by biological systems. Signal complexity can be used as a method in extracting the feature for
a biological signal to differentiate a pathological signal from a normal signal. In this research, Hjorth
descriptors, one of the signal complexity measurement techniques, were measured on signal sub-band as the
features for lung sounds classification. Lung sound signal was decomposed using two wavelet analyses: discrete
wavelet transform (DWT) and wavelet packet decomposition (WPD). Meanwhile, multi-layer perceptron and
N-fold cross-validation were used in the classification stage. Using DWT, the highest accuracy was obtained at
97.98%, while using WPD, the highest one was found at 98.99%. This result was found better than the multiscale
Hjorth descriptor as in previous studies.
Camera calibration is an important part of machine vision and close-range photogrammetry. Since current
calibration methods fail to obtain ideal internal and external camera parameters with limited computing
resources on mobile terminals efficiently, this paper proposes an improved fast camera calibration method for
mobile terminals. Based on traditional camera calibration method, the new method introduces two-order radial
distortion and tangential distortion models to establish the camera model with nonlinear distortion items.
Meanwhile, the nonlinear least square L-M algorithm is used to optimize parameters iteration, the new method
can quickly obtain high-precise internal and external camera parameters. The experimental results show that
the new method improves the efficiency and precision of camera calibration. Terminals simulation experiment
on PC indicates that the time consuming of parameter iteration reduced from 0.220 seconds to 0.063 seconds
(0.234 seconds on mobile terminals) and the average reprojection error reduced from 0.25 pixel to 0.15 pixel.
Therefore, the new method is an ideal mobile terminals camera calibration method which can expand the
application range of 3D reconstruction and close-range photogrammetry technology on mobile terminals.
In this paper, we present an ontology-based approach to labeling influential topics of scientific articles. First, to
look for influential topics from scientific article, topic modeling is performed, and then social network analysis
is applied to the selected topic models. Abstracts of research papers related to data mining published over the
20 years from 1995 to 2015 are collected and analyzed in this research. Second, to interpret and to explain
selected influential topics, the UniDM ontology is constructed from Wikipedia and serves as concept
hierarchies of topic models. Our experimental results show that the subjects of data management and queries
are identified in the most interrelated topic among other topics, which is followed by that of recommender
systems and text mining. Also, the subjects of recommender systems and context-aware systems belong to the
most influential topic, and the subject of k-nearest neighbor classifier belongs to the closest topic to other topics.
The proposed framework provides a general model for interpreting topics in topic models, which plays an
important role in overcoming ambiguous and arbitrary interpretation of topics in topic modeling.
Non-local means (NLM) algorithm is an effective and successful denoising method, but it is computationally
heavy. To deal with this obstacle, we propose a novel NLM algorithm with fuzzy metric (FM-NLM) for image
denoising in this paper. A new feature metric of visual features with fuzzy metric is utilized to measure the
similarity between image pixels in the presence of Gaussian noise. Similarity measures of luminance and
structure information are calculated using a fuzzy metric. A smooth kernel is constructed with the proposed
fuzzy metric instead of the Gaussian weighted L2 norm kernel. The fuzzy metric and smooth kernel
computationally simplify the NLM algorithm and avoid the filter parameters. Meanwhile, the proposed FMNLM
using visual structure preferably preserves the original undistorted image structures. The performance of
the improved method is visually and quantitatively comparable with or better than that of the current state-ofthe-
art NLM-based denoising algorithms.
With the wide spread of Social Network Services (SNS), fake news—which is a way of disguising false information
as legitimate media—has become a big social issue. This paper proposes a deep learning architecture for
detecting fake news that is written in Korean. Previous works proposed appropriate fake news detection models
for English, but Korean has two issues that cannot apply existing models: Korean can be expressed in shorter
sentences than English even with the same meaning; therefore, it is difficult to operate a deep neural network
because of the feature scarcity for deep learning. Difficulty in semantic analysis due to morpheme ambiguity.
We worked to resolve these issues by implementing a system using various convolutional neural network-based
deep learning architectures and “Fasttext” which is a word-embedding model learned by syllable unit. After
training and testing its implementation, we could achieve meaningful accuracy for classification of the body
and context discrepancies, but the accuracy was low for classification of the headline and body discrepancies.
Characteristics of fruit tree canopies are important target information for adjusting the pesticide application
rate in variable rate spraying in orchards. Therefore, the target detection of the canopy characteristics is very
important. In this study, a canopy volume measurement method for peach trees was presented and a variable
rate spraying system based on canopy volume measurement was developed using the ultrasonic sensing, one of
the most effective target detection method. Ten ultrasonic sensors and two flow control units were mounted on
the orchard air-assisted sprayer. The ultrasonic sensors were used to detect the canopy diameters and the flow
controls were used to modify the flow rate of the nozzles in real time. Two treatments were established: a
constant application rate of 300 Lha-1 was set as the control treatment for the comparison with the variable rate
application at a 0.095 Lm-3 canopy. The tracer deposition at different parts of peach trees and the tracer losses
to the ground (between rows and within rows) were analyzed in detail under constant rate and variable rate
application. The results showed that there were no significant differences between two treatments in the liquid
distribution and the capability to reach the inner parts of the crop canopies.
Today, most approaches used in the recommendation system provide correct data prediction similar to the data
that users need. The method that researchers are paying attention and apply as a model in the recommendation
system is the communities’ detection in the big social network. The outputted result of this approach is effective
in improving the exactness. Therefore, in this paper, the personalized movie recommendation system that
combines data mining for the k-clique method is proposed as the best exactness data to the users. The proposed
approach was compared with the existing approaches like k-clique, collaborative filtering, and collaborative
filtering using k-nearest neighbor. The outputted result guarantees that the proposed method gives significant
exactness data compared to the existing approach. In the experiment, the MovieLens data were used as practice
and test data.
In recent years, the problem of data drifted of the smart grid due to manual operation has been widely studied
by researchers in the related domain areas. It has become an important research topic to effectively and reliably
find the reasonable data needed in the Supervisory Control and Data Acquisition (SCADA) system has become
an important research topic. This paper analyzes the data composition of the smart grid, and explains the power
model in two smart grid applications, followed by an analysis on the application of each parameter in densitybased
spatial clustering of applications with noise (DBSCAN) algorithm. Then a comparison is carried out for
the processing effects of the boxplot method, probability weight analysis method and DBSCAN clustering
algorithm on the big data driven power grid. According to the comparison results, the performance of the
DBSCAN algorithm outperforming other methods in processing effect. The experimental verification shows
that the DBSCAN clustering algorithm can effectively screen the power grid data, thereby significantly
improving the accuracy and reliability of the calculation result of the main grid’s theoretical line loss.
This study proposes a deep neural network model based on an encoder–decoder structure for visual dialogs.
Ongoing linguistic understanding of the dialog history and context is important to generate correct answers to
questions in visual dialogs followed by questions and answers regarding images. Nevertheless, in many cases, a
visual understanding that can identify scenes or object attributes contained in images is beneficial. Hence, in
the proposed model, by employing a separate person detector and an attribute recognizer in addition to visual
features extracted from the entire input image at the encoding stage using a convolutional neural network, we
emphasize attributes, such as gender, age, and dress concept of the people in the corresponding image and use
them to generate answers. The results of the experiments conducted using VisDial v0.9, a large benchmark
dataset, confirmed that the proposed model performed well.
We design an ingenious view-pooling method named learning-based multiple pooling fusion (LMPF), and
apply it to multi-view convolutional neural network (MVCNN) for 3D model classification or retrieval. By this
means, multi-view feature maps projected from a 3D model can be compiled as a simple and effective feature
descriptor. The LMPF method fuses the max pooling method and the mean pooling method by learning a set
of optimal weights. Compared with the hand-crafted approaches such as max pooling and mean pooling, the
LMPF method can decrease the information loss effectively because of its “learning” ability. Experiments on
ModelNet40 dataset and McGill dataset are presented and the results verify that LMPF can outperform those
previous methods to a great extent.
Recently, artificial intelligence techniques have been widely used in the computer science field, such as the
Internet of Things, big data, cloud computing, and mobile computing. In particular, resource management is
of utmost importance for maintaining the quality of services, service-level agreements, and the availability of
the system. In this paper, we review and analyze various ways to meet the requirements of cloud resource
management based on artificial intelligence. We divide cloud resource management techniques based on
artificial intelligence into three categories: fog computing systems, edge-cloud systems, and intelligent cloud
computing systems. The aim of the paper is to propose an intelligent resource management scheme that
manages mobile resources by monitoring devices' statuses and predicting their future stability based on one of
the artificial intelligence techniques. We explore how our proposed resource management scheme can be
extended to various cloud-based systems.
Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to
have a direct influence on the stock markets globally. Given that the stock price data often contain both linear
and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The
autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however,
it provides an accurate and effective way to process autocorrelation and non-stationary data in time series
forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As
a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price
of the Shanghai composite index and Shenzhen component index.
Developing methods to search over an encrypted database (EDB) have received a lot of attention in the last few
years. Among them, order-revealing encryption (OREnc) and order-preserving encryption (OPEnc) are the
core parts in the case of range queries. Recently, some ideally-secure OPEnc schemes whose ciphertexts reveal
no additional information beyond the order of the underlying plaintexts have been proposed. However, these
schemes either require a large round complexity or a large persistent client-side storage of size O(n) where n
denotes the number of encrypted items stored in EDB. In this work, we propose a new construction of an
efficient OPEnc scheme based on an OREnc scheme. Security of our construction inherits the security of the
underlying OREnc scheme. Moreover, we also show that the construction of a non-interactive ideally-secure
OPEnc scheme with a constant client-side storage is theoretically possible from our construction.
This study proposes a novel video traffic flow detection method based on machine vision technology. The threeframe
difference method, which is one kind of a motion evaluation method, is used to establish initial
background image, and then a statistical scoring strategy is chosen to update background image in real time.
Finally, the background difference method is used for detecting the moving objects. Meanwhile, a simple but
effective shadow elimination method is introduced to improve the accuracy of the detection for moving objects.
Furthermore, the study also proposes a vehicle matching and tracking strategy by combining characteristics,
such as vehicle’s location information, color information and fractal dimension information. Experimental
results show that this detection method could quickly and effectively detect various traffic flow parameters,
laying a solid foundation for enhancing the degree of automation for traffic management.
This paper presents a two-dimensional attention-based long short-memory (2D-ALSTM) model for stock
index prediction, incorporating input attention and temporal attention mechanisms for weighting of important
stocks and important time steps, respectively. The proposed model is designed to overcome the long-term
dependency, stock selection, and stock volatility delay problems that negatively affect existing models. The 2DALSTM
model is validated in a comparative experiment involving the two attention-based models multi-input
LSTM (MI-LSTM) and dual-stage attention-based recurrent neural network (DARNN), with real stock data
being used for training and evaluation. The model achieves superior performance compared to MI-LSTM and
DARNN for stock index prediction on a KOSPI100 dataset.
As current algorithms unable to perform effective fusion processing of unknown complex radar signals lacking
database, and the result is unstable, this paper presents a multi-level fusion processing algorithm for complex
radar signals based on evidence theory as a solution to this problem. Specifically, the real-time database is
initially established, accompanied by similarity model based on parameter type, and then similarity matrix is
calculated. D-S evidence theory is subsequently applied to exercise fusion processing on the similarity of
parameters concerning each signal and the trust value concerning target framework of each signal in order. The
signals are ultimately combined and perfected. The results of simulation experiment reveal that the proposed
algorithm can exert favorable effect on the fusion of unknown complex radar signals, with higher efficiency and
less time, maintaining stable processing even of considerable samples.
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)