Internet of Things (IoT) technology has been recently utilized in diverse fields. Smart city is one of the IoT application domains with a lot of research topics and which is operated by integrated IoT applications. In this paper, diverse kinds of solutions, processes, and frameworks to address the existing challenges in information technology are introduced. Such solutions involve various future track topics including blockchain, security, steganography, optimization, machine learning, smart system, and so on. In the subsequent paragraphs, we describe each topic in a summarized way in terms of the existing challenges and their solutions. Specifically, this paper introduced 18 novel and enhanced research studies from different countries in the world. We present diverse kinds of paradigms to subjects that tackle diverse kinds of research areas such as IoT and Smart City, and so on.
An individual’s health data is very sensitive and private. Such data are usually stored on a private or community owned cloud, where access is not restricted to the owners of that cloud. Anyone within the cloud can access this data. This data may not be read only and multiple parties can make to it. Thus, any unauthorized modification of health-related data will lead to incorrect diagnosis and mistreatment. However, we cannot restrict semipublic access to this data. Existing security mechanisms in e-health systems are competent in dealing with the issues associated with these systems but only up to a certain extent. The indigenous technologies need to be complemented with current and future technologies. We have put forward a method to complement such technologies by incorporating the concept of blockchain to ensure the integrity of data as well as its provenance.
In order to ensure second-order multi-agent systems (MAS) realizing consensus more quickly in a limited time, a new protocol is proposed. In this new protocol, the gradient algorithm of the overall cost function is introduced in the original protocol to enhance the connection between adjacent agents and improve the moving speed of each agent in the MAS. Utilizing Lyapunov stability theory, graph theory and homogeneity theory, sufficient conditions and detailed proof for achieving a finite-time consensus of the MAS are given. Finally, MAS with three following agents and one leading agent is simulated. Moreover, the simulation results indicated that this new protocol could make the system more stable, more robust and convergence faster when compared with other protocols.
In order to protect secret digital documents against vulnerabilities while communicating, steganography
algorithms are applied. It protects a digital file from unauthorized access by hiding the entire content. Pixelvalue-
difference being a method from spatial domain steganography utilizes the difference gap between
neighbor pixels to fulfill the same. The proposed approach is a block-wise embedding process where blocks of
variable size are chosen from the cover image, therefore, a stream of secret digital contents is hidden. Least
significant bit (LSB) substitution method is applied as an adaptive mechanism and optimal pixel adjustment
process (OPAP) is used to minimize the error rate. The proposed application succeeds to maintain good hiding
capacity and better signal-to-noise ratio when compared against other existing methods. Any means of digital
communication specially e-Governance applications could be highly benefited from this approach.
The synchronization scheme based on moving average is robust and suitable for the same rule to be adopted in
embedding watermark and synchronization code, but the imperceptibility and search efficiency is seldom
reported. The study aims to improve the original scheme for robust audio watermarking. Firstly, the survival of
the algorithm from desynchronization attacks is improved. Secondly, the scheme is improved in inaudibility.
Objective difference grade (ODG) of the marked audio is significantly changed. Thirdly, the imperceptibility of
the scheme is analyzed and the derived result is close to experimental result. Fourthly, the selection of parameters
is optimized based on experimental data. Fifthly, the search efficiency of the scheme is compared with those of
other synchronization code schemes. The experimental results show that the proposed watermarking scheme
allows the high audio quality and is robust to common attacks such as additive white Gaussian noise,
requantization, resampling, low-pass filtering, random cropping, MP3 compression, jitter attack, and time scale
modification. Moreover, the algorithm has the high search efficiency and low false alarm rate.
The growth of telemedicine-based wireless communication for images—magnetic resonance imaging (MRI)
and computed tomography (CT)—leads to the necessity of learning the concept of image compression. Over
the years, the transform based and spatial based compression techniques have attracted many types of
researches and achieve better results at the cost of high computational complexity. In order to overcome this,
the optimization techniques are considered with the existing image compression techniques. However, it fails
to preserve the original content of the diagnostic information and cause artifacts at high compression ratio.
In this paper, the concept of histogram based multilevel thresholding (HMT) using entropy is appended with
the optimization algorithm to compress the medical images effectively. However, the method becomes time
consuming during the measurement of the randomness from the image pixel group and not suitable for
medical applications. Hence, an attempt has been made in this paper to develop an HMT based image
compression by utilizing the opposition based improved harmony search algorithm (OIHSA) as an
optimization technique along with the entropy. Further, the enhancement of the significant information
present in the medical images are improved by the proper selection of entropy and the number of thresholds
chosen to reconstruct the compressed image.
The prediction of the sum of container is very important in the field of container transport. Many influencing
factors can affect the prediction results. These factors are usually composed of many variables, whose
composition is often very complex. In this paper, we use gray relational analysis to set up a proper forecast
index system for the prediction of the sum of containers in foreign trade. To address the issue of the low
accuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factors
and other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP)
neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalized
by the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residual
correction calculation for the results based on the preliminary data. The results of practical examples show that
the overall relative error of the combined prediction model is no more than 1.5%, which is less than the relative
error of the single prediction models. It is hoped that the research can provide a useful reference for the
prediction of the sum of container and related studies.
This study analyzed changes in sociality and democratic-citizenship among elementary school students in the
information class and the science class at the Science Education Institute for the Gifted, who were divided into
an experimental group and a control group. The experimental group engaged in the Learning Together (LT)
cooperative form of learning for which the remix function of Scratch, an educational programming language,
was applied, while the control group was given general instructor-led lessons. Members in the experimental
group were able to modify processes during projects through the usage of the remix function, thereby actively
participating in the projects and eventually generating team-based results. The post-class t-tests showed a
greater degree of improvements in sociality and democratic citizenship for the experimental group that was
offered the remix-function-based cooperative learning than the control group. Statistically significant
differences were present between two groups particularly in “cooperative spirit” sub-domain of sociality and
the “community” and “responsibility” sub-domains of democratic citizenship.
Dynamic thermal rating of the overhead transmission lines is affected by many uncertain factors. The ambient
temperature, wind speed and wind direction are the main sources of uncertainty. Measurement uncertainty is
an important parameter to evaluate the reliability of measurement results. This paper presents the uncertainty
analysis based on Monte Carlo. On the basis of establishing the mathematical model and setting the probability
density function of the input parameter value, the probability density function of the output value is determined
by probability distribution random sampling. Through the calculation and analysis of the transient thermal
balance equation and the steady- state thermal balance equation, the steady-state current carrying capacity, the
transient current carrying capacity, the standard uncertainty and the probability distribution of the minimum
and maximum values of the conductor under 95% confidence interval are obtained. The simulation results
indicate that Monte Carlo method can decrease the computational complexity, speed up the calculation, and
increase the validity and reliability of the uncertainty evaluation.
Surveillance cameras have installed in many places because security and safety is becoming important in
modern society. Through surveillance cameras installed, we can deal with troubles and prevent accidents.
However, watching surveillance videos and judging the accidental situations is very labor-intensive. So now,
the need for research to analyze surveillance videos is growing. This study proposes an algorithm to track
multiple persons using SURF and background subtraction. While the SURF algorithm, as a person-tracking
algorithm, is robust to scaling, rotating and different viewpoints, SURF makes tracking errors with sudden
changes in videos. To resolve such tracking errors, we combined SURF with a background subtraction
algorithm and showed that the proposed approach increased the tracking accuracy. In addition, the background
subtraction algorithm can detect persons in videos, and SURF can initialize tracking targets with these detected
persons, and thus the proposed algorithm can automatically detect the enter/exit of persons.
For clustering large-scale data, which cannot be loaded into memory entirely, incremental clustering algorithms
are very popular. Usually, these algorithms only concern the within-cluster compactness and ignore the
between-cluster separation. In this paper, we propose two incremental fuzzy compactness and separation (FCS)
clustering algorithms, Single-Pass FCS (SPFCS) and Online FCS (OFCS), based on a fuzzy scatter matrix.
Firstly, we introduce two incremental clustering methods called single-pass and online fuzzy C-means
algorithms. Then, we combine these two methods separately with the weighted fuzzy C-means algorithm, so
that they can be applied to the FCS algorithm. Afterwards, we optimize the within-cluster matrix and betweencluster
matrix simultaneously to obtain the minimum within-cluster distance and maximum between-cluster
distance. Finally, large-scale datasets can be well clustered within limited memory. We implemented experiments
on some artificial datasets and real datasets separately. And experimental results show that, compared with
SPFCM and OFCM, our SPFCS and OFCS are more robust to the value of fuzzy index m and noise.
This paper presents an optimal implementation of a Daubechies-based pipelined discrete wavelet packet
transform (DWPT) processor using finite impulse response (FIR) filter banks. The feed-forward pipelined (FFP)
architecture is exploited for implementation of the DWPT on the field-programmable gate array (FPGA). The
proposed DWPT is based on an efficient transpose form structure, thereby reducing its computational complexity
by half of the system. Moreover, the efficiency of the design is further improved by using a canonical-signed
digit-based binary expression (CSDBE) and advanced functional sharing (AFS) methods. In this work, the AFS
technique is proposed to optimize the convolution of FIR filter banks for DWPT decomposition, which reduces
the hardware resource utilization by not requiring any embedded digital signal processing (DSP) blocks. The
proposed AFS and CSDBE-based DWPT system is embedded on the Virtex-7 FPGA board for testing. The
proposed design is implemented as an intellectual property (IP) logic core that can easily be integrated into DSP
systems for sub-band analysis. The achieved results conclude that the proposed method is very efficient in
improving hardware resource utilization while maintaining accuracy of the result of DWPT.
The transmission capacity of transmission lines is affected by environmental parameters such as ambient
temperature, wind speed, wind direction and so on. The environmental parameters can be measured by the
installed measuring devices. However, it is impossible to install the environmental measuring devices
throughout the line, especially considering economic cost of power grid. Taking into account the limited
number of measuring devices and the distribution characteristics of environment parameters and transmission
lines, this paper first studies the environmental parameter estimating method of inverse distance weighted
interpolation and ordinary Kriging interpolation. Dynamic thermal rating of transmission lines based on IEEE
standard and CIGRE standard thermal equivalent equation is researched and the key parameters that affect the
load capacity of overhead lines is identified. Finally, the distributed thermal rating of transmission line is
realized by using the data obtained from China meteorological data network. The cost of the environmental
measurement device is reduced, and the accuracy of dynamic rating is improved.
Three-dimensional (3D) human pose reconstruction from single-view image is a difficult and challenging topic.
Existing approaches mostly process frame-by-frame independently while inter-frames are highly correlated in
a sequence. In contrast, we introduce a novel spatial-temporal 3D human pose reconstruction framework that
leverages both intra and inter-frame relationships in consecutive 2D pose sequences. Orthogonal matching
pursuit (OMP) algorithm, pre-trained pose-angle limits and temporal models have been implemented. Several
quantitative comparisons between our proposed framework and recent works have been studied on CMU
motion capture dataset and Vietnamese traditional dance sequences. Our framework outperforms others by
10% lower of Euclidean reconstruction error and more robust against Gaussian noise. Additionally, it is also
important to mention that our reconstructed 3D pose sequences are more natural and smoother than others.
Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linear
measurements. Various studies about DCS have been carried out recently. In many practical applications, there
is no prior information except for standard sparsity on signals. The typical example is the sparse signals have
block-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usually
unavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptive
orthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. In
contrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMP
resorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, which
consists of forward selection and backward removal stages in each iteration. An advantage of this method is
that perfect reconstruction performance can be achieved without prior information on the block-sparsity
structure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.
A hybrid kernel function of support vector machine is proposed to improve the classification performance of
power quality disturbances. The kernel function mathematical model of support vector machine directly affects
the classification performance. Different types of kernel functions have different generalization ability and
learning ability. The single kernel function cannot have better ability both in learning and generalization. To
overcome this problem, we propose a hybrid kernel function that is composed of two single kernel functions to
improve both the ability in generation and learning. In simulations, we respectively used the single and multiple
power quality disturbances to test classification performance of support vector machine algorithm with the
proposed hybrid kernel function. Compared with other support vector machine algorithms, the improved
support vector machine algorithm has better performance for the classification of power quality signals with
single and multiple disturbances.
Artificial bee colony algorithm is a strong global search algorithm which exhibits excellent exploration ability.
The conventional ABC algorithm adopts employed bees, onlooker bees and scouts to cooperate with each other.
However, its one dimension and greedy search strategy causes slow convergence speed. To enhance its
performance, in this paper, we abandon the greedy selection method and propose an artificial bee colony
algorithm with special division and intellective search (ABCIS). For the purpose of higher food source research
efficiency, different search strategies are adopted with different employed bees and onlooker bees. Experimental
results on a series of benchmarks algorithms demonstrate its effectiveness.
The Internet of Things (IoT) is one of the main enablers for situation awareness needed in accomplishing smart
cities. IoT devices, especially for monitoring purposes, have stringent timing requirements which may not be
met by cloud computing. This deficiency of cloud computing can be overcome by fog computing for which fog
nodes are placed close to IoT devices. Because of low capabilities of fog nodes compared to cloud data centers,
fog nodes may not be deployed with all the services required by IoT devices. Thus, in this article, we focus on
the issue of fog service placement and present the recent research trends in this issue. Most of the literature on
fog service placement deals with determining an appropriate fog node satisfying the various requirements like
delay from the perspective of one or more service requests. In this article, we aim to effectively place fog services
in accordance with the pre-obtained service demands, which may have been collected during the prior time
interval, instead of on-demand service placement for one or more service requests. The concept of the logical
fog network is newly presented for the sake of the scalability of fog service placement in a large-scale smart city.
The logical fog network is formed in a tree topology rooted at the cloud data center. Based on the logical fog
network, a service placement approach is proposed so that services can be placed on fog nodes in a resourceeffective
SMART home is one of the most popular applications of Internet-of-Things (IoT) technologies, which is
expanding in terms of range of applications. SMART home technology provides convenience at home by
connecting household appliances to a single network, control, and management. However, many general home
appliances do not support the network functions yet; hence, enjoying such convenient technology could be
difficult, and it could be expensive in the beginning to build the framework. In addition, even though products
with SMART home technologies are purchased, the control systems could differ from device to device. Thus,
in this paper, we propose a SMART home framework, called an S-mote that can operate all the IoT functions
in a single application by adding an infrared or radio frequency module to general home appliances. The
proposed framework is analyzed using four types of performance tests by five evaluators. The results of the
experiment show that the SMART home environment was implemented successfully and that it functions
appropriately, without any operational issues, with various home appliances, including the latest IoT devices,
and even those equipped with an infrared or radio frequency module.
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)