The blockchain and crypto currency has become one of the most essential components of a communication
network in the recent years. Through communication networking, we browse the internet, make VoIP phone
calls, have video conferences and check e-mails via computers. A lot of researches are being conducting to
address the blockchain and crypto currency challenges in communication networking and provide
corresponding solutions. In this paper, a diverse kind of novel research works in terms of mechanisms,
techniques, architectures, and frameworks have been proposed to provide possible solutions against the existing
challenges in the communication networking. Such novel research works involve thermal load capacity
techniques, intelligent sensing mechanism, secure cloud computing system communication algorithm for
wearable healthcare systems, sentiment analysis, optimized resources.
With the sustained and rapid development of new energy sources, the demand for electric energy is increasing
day by day. However, China’s energy distribution is not balanced, and the construction of transmission lines is
in a serious lag behind the improvement of generating capacity. So there is an urgent need to increase the
utilization of transmission capacity. The transmission capacity is mainly limited by the maximum allowable
operating temperature of conductor. At present, the evaluation of transmission capacity mostly adopts the static
thermal rating (STR) method under severe environment. Dynamic thermal rating (DTR) technique can
improve the utilization of transmission capacity to a certain extent. In this paper, the meteorological parameters
affecting the conductor temperature are analyzed with the IEEE standard thermal equivalent equation of
overhead transmission lines, and the real load capacity of 220 kV transmission line is calculated with 7-year
actual meteorological data in Weihai. Finally, the thermal load capacity of DTR relative to STR under given
confidence is analyzed. By identifying the key parameters that affect the thermal rating and analyzing the
relevant environmental parameters that affect the conductor temperature, this paper provides a theoretical basis
for the wind power grid integration and grid intelligence. The results show that the thermal load potential of
transmission lines can be effectively excavated by DTR, which provides a theoretical basis for improving the
absorptive capacity of power grid.
This work develops a monitoring system for the population with health concerns. A belt integrated with an onbody
circuit and sensors measures a wearer’s selected vital signals. The electrocardiogram sensors monitor heart
conditions and an accelerometer assesses the level of physical activity. Sensed signals are transmitted to the
circuit module through digital yarns and are forwarded to a mobile device via Bluetooth. An interactive
application, installed on the mobile device, is used to process the received signals and provide users with realtime
feedback about their status. Persuasive functions are designed and implemented in the interactive
application to encourage users’ physical activity. Two signal processing algorithms are developed to analyze the
data regarding heart and activity. A user study is conducted to evaluate the performance and usability of the
HEVC is the high efficiency video coding standard, which provides better coding efficiency contrasted with the
other video coding standard. But at the same time the computational complexity increases drastically. Thirtyfive
kinds of intra-prediction modes are defined in HEVC, while 9 kinds of intra prediction modes are defined
in H.264/AVC. This paper proposes a fast rough mode decision (RMD) algorithm which adopts the smoothness
of the up-reference pixels and the left-reference pixels to decrease the computational complexity. The three step
search method is implemented in RMD process. The experimental results compared with HM13.0 indicate that
the proposed algorithm can save 39.7% of the encoding time, while Bjontegaard delta bitrate (BDBR) is
increased slightly by 1.35% and Bjontegaard delta peak signal-to-noise ratio (BDPSNR) loss is negligible.
The blooming of social media has simulated interest in sentiment analysis. Sentiment analysis aims to
determine from a specific piece of content the overall attitude of its author in relation to a specific item, product,
brand, or service. In sentiment analysis, the focus is on the subjective sentences. Hence, in order to discover
and extract the subjective information from a given text, researchers have applied various methods in
computational linguistics, natural language processing, and text analysis. The aim of this paper is to provide an
in-depth up-to-date study of the sentiment analysis algorithms in order to familiarize with other works done in
the subject. The paper focuses on the main tasks and applications of sentiment analysis. State-of-the-art
algorithms, methodologies and techniques have been categorized and summarized to facilitate future research
in this field.
This paper proposes a system that can detect the data leakage pattern using a convolutional neural network
based on defining the behaviors of leaking data. In this case, the leakage detection scenario of data leakage is
composed of the patterns of occurrence of security logs by administration and related patterns between the
security logs that are analyzed by association relationship analysis. This proposed system then detects whether
the data is leaked through the convolutional neural network using an insider malicious behavior graph. Since
each graph is drawn according to the leakage detection scenario of a data leakage, the system can identify the
criminal insider along with the source of malicious behavior according to the results of the convolutional neural
network. The results of the performance experiment using a virtual scenario show that even if a new malicious
pattern that has not been previously defined is inputted into the data leakage detection system, it is possible to
determine whether the data has been leaked. In addition, as compared with other data leakage detection
systems, it can be seen that the proposed system is able to detect data leakage more flexibly.
Cloud computing is the concept of providing information technology services on the Internet, such as software,
hardware, networking, and storage. These services can be accessed anywhere at any time on a pay-per-use basis.
However, storing data on servers is a challenging aspect of cloud computing. This paper utilizes cryptography
and access control to ensure the confidentiality, integrity, and proper control of access to sensitive data. We
propose a model that can protect data in cloud computing. Our model is designed by using an enhanced RSA
encryption algorithm and a combination of role-based access control model with extensible access control
markup language (XACML) to facilitate security and allow data access. This paper proposes a model that uses
cryptography concepts to store data in cloud computing and allows data access through the access control
model with minimum time and cost for encryption and decryption.
We propose approaches for improving Bloom filter in terms of false positive probability and membership query
speed. To reduce the false positive probability, we propose special type of additional Bloom filters that are used
to handle false positives caused by the original Bloom filter. Implementing the proposed approach for a routing
table lookup, we show that our approach reduces the routing table lookup time by up to 28% compared to the
original Bloom filter by handling most false positives within the fast memory. We also introduce an approach
for improving the membership query speed. Taking the hash table-like approach while storing only values, the
proposed approach shows much faster membership query speed than the original Bloom filter (e.g., 34 times
faster with 10 subsets). Even compared to a hash table, our approach reduces the routing table lookup time by
up to 58%.
The Event-B design pattern is an excellent way to quickly develop a formal model of the system. Researchers
have proposed a number of Event-B design patterns, but they all lack formal behavior semantics. This makes
the analysis, verification, and simulation of the behavior of the Event-B model very difficult, especially for the
control-intensive systems. In this paper, we propose a novel method to transform the Event-B synchronous
control flow design pattern into the labeled transition system (LTS) behavior model. Then we map the design
pattern instantiation process of Event-B to the instantiation process of LTS model and get the LTS behavior
semantic model of Event-B model of a multi-level complex control system. Finally, we verify the linear temporal
logic behavior properties of the LTS model. The experimental results show that the analysis and simulation of
system behavior become easier and the verification of the behavior properties of the system become convenient
after the Event-B model is converted to the LTS model.
In wearable healthcare systems, sensor devices can be deployed in places around the human body such as the
stomach, back, arms, and legs. The sensors use tiny batteries, which have limited resources, and old sensor
batteries must be replaced with new batteries. It is difficult to deploy sensor devices directly into the human
body. Therefore, instead of replacing sensor batteries, increasing the lifetime of sensor devices is more efficient.
A transmission power control (TPC) algorithm is a representative technique to increase the lifetime of sensor
devices. Sensor devices using a TPC algorithm control their transmission power level (TPL) to reduce battery
energy consumption. The TPC algorithm operates on a closed-loop mechanism that consists of two parts, such
as sensor and sink devices. Most previous research considered only the sink part of devices in the closed-loop.
If we consider both the sensor and sink parts of a closed-loop mechanism, sensor devices reduce energy
consumption more than previous systems that only consider the sensor part. In this paper, we propose a new
approach to consider both the sensor and sink as part of a closed-loop mechanism for efficient energy
management of sensor devices. Our proposed approach judges the current channel condition based on the
values of various body sensors. If the current channel is not optimal, sensor devices maintain their current TPL
without communication to save the sensor’s batteries. Otherwise, they find an optimal TPL. To compare
performance with other TPC algorithms, we implemented a TPC algorithm and embedded it into sensor
devices. Our experimental results show that our new algorithm is better than other TPC algorithms, such as
linear, binary, hybrid, and ATPC.
Single-user spectrum sensing is susceptible to multipath effects, shadow effects, hidden terminals and other
unfavorable factors, leading to misjudgment of perceived results. In order to increase the detection accuracy
and reduce spectrum sensing cost, we propose an adaptive cooperative sensing strategy based on an estimated
signal-to-noise ratio (SNR). Which can adaptive select different sensing strategy during the local sensing phase.
When the estimated SNR is higher than the selection threshold, adaptive double threshold energy detector (ED)
is implemented, otherwise cyclostationary feature detector is performed. Due to the fact that only a better
sensing strategy is implemented in a period, the detection accuracy is improved under the condition of low SNR
with low complexity. The local sensing node transmits the perceived results through the control channel to the
fusion center (FC), and uses voting rule to make the hard decision. Thus the transmission bandwidth is
effectively saved. Simulation results show that the proposed scheme can effectively improve the system
detection probability, shorten the average sensing time, and has better robustness without largely increasing
the costs of sensing system.
With the rapid increase of information on the World Wide Web, finding useful information on the internet has
become a major problem. The recommendation system helps users make decisions in complex data areas where
the amount of data available is large. There are many methods that have been proposed in the recommender
system. Collaborative filtering is a popular method widely used in the recommendation system. However,
collaborative filtering methods still have some problems, namely cold-start problem. In this paper, we propose
a movie recommendation system by using social network analysis and collaborative filtering to solve this
problem associated with collaborative filtering methods. We applied personal propensity of users such as age,
gender, and occupation to make relationship matrix between users, and the relationship matrix is applied to
cluster user by using community detection based on edge betweenness centrality. Then the recommended
system will suggest movies which were previously interested by users in the group to new users. We show shown
that the proposed method is a very efficient method using mean absolute error.
The remote monitoring and warning system for dangerous chemicals is designed with the concept of the Cyber-
Physical System (CPS) in this paper. The real-time perception, dynamic control, and information service of
major hazards chemicals are realized in this CPS system. The CPS system architecture, the physical layer and
the applacation layer, are designed in this paper. The terminal node is mainly composed of the field collectors
which complete the data acquisition of sensors and video in the physical layers, and the use of application layer
makes CPS system safer and more reliable to monitor the hazardous chemicals. The cloud application layer
completes the risk identification and the prediction of the major hazard sources. The early intelligent warning
of the major dangerous chemicals is realized and the security risk images are given in the cloud application
layer. With the CPS technology, the remote network of hazardous chemicals has been completed, and a major
hazard monitoring and accident warning online system is formed. Through the experiment of the terminal
node, it can be proved that the terminal node can complete the mass data collection and classify. With this
experiment it can be obtained the CPS system is safe and effective. In order to verify feasible, the multi-risk
warning based on CPS is simulated, and results show that the system solves the problem of hazardous chemicals
enterprises safety management.
The purpose of this study was to collect and analyze personal bio data and social network services (SNS) data,
derive user preference and user life pattern, and propose intuitive and precise user modeling. This study not
only tried to conduct eye tracking experiments using various smart devices to be the ground of the
recommendation system considering the attribute of smart devices, but also derived classification preference
by analyzing eye tracking data of collected bio data and SNS data. In addition, this study intended to combine
and analyze preference of the common classification of the two types of data, derive final preference by each
smart device, and based on user life pattern extracted from final preference and collected bio data (amount of
activity, sleep), draw the similarity between users using Pearson correlation coefficient. Through derivation of
preference considering the attribute of smart devices, it could be found that users would be influenced by smart
devices. With user modeling using user behavior pattern, eye tracking, and user preference, this study tried to
contribute to the research on the recommendation system that should precisely reflect user tendency.
Supplier evaluation is of great significance in green supply chain management. Influenced by factors such as
economic globalization, sustainable development, a holistic index framework is difficult to establish in green
supply chain. Furthermore, the initial index values of candidate suppliers are often characterized by uncertainty
and incompleteness and the index weight is variable. To solve these problems, an index framework is established
after comprehensive consideration of the major factors. Then an adaptive weight D-S theory model is put
forward, and a fuzzy-rough-sets-AHP method is proposed to solve the adaptive weight in the index framework.
The case study and the comparison with TOPSIS show that the adaptive weight D-S theory model in this paper
is feasible and effective.
Orthogonal frequency division multiplexing (OFDM) is a system which is used to encode data using multiple
carriers instead of the traditional single carrier system. This method improves the spectral efficiency (optimum
use of bandwidth). It also lessens the effect of fading and intersymbol interference (ISI). In 1995, digital audio
broadcast (DAB) adopted OFDM as the first standard using OFDM. Later in 1997, it was adopted for digital
video broadcast (DVB). Currently, it has been adopted for WiMAX and LTE standards. In this project, a Verilog
design is employed to implement an OFDM transmitter (DAC block) and receiver (FFT and ADC block).
Generally, OFDM uses FFT and IFFT for modulation and demodulation. In this paper, 16-point FFT
decimation-in-frequency (DIF) with the radix-2 algorithm and direct summation method have been analyzed.
ADC and DAC in OFDM are used for conversion of the signal from analog to digital or vice-versa has also been
analyzed. All the designs are simulated using Verilog on ModelSim simulator. The result generated from the
FFT block after Verilog simulation has also been verified with MATLAB.
The traditional classification methods mostly assume that the data for class distribution is balanced, while
imbalanced data is widely found in the real world. So it is important to solve the problem of classification with
imbalanced data. In Mahalanobis-Taguchi system (MTS) algorithm, data classification model is constructed
with the reference space and measurement reference scale which is come from a single normal group, and thus
it is suitable to handle the imbalanced data problem. In this paper, an improved method of MTS-CBPSO is
constructed by introducing the chaotic mapping and binary particle swarm optimization algorithm instead of
orthogonal array and signal-to-noise ratio (SNR) to select the valid variables, in which G-means, F-measure,
dimensionality reduction are regarded as the classification optimization target. This proposed method is also
applied to the financial distress prediction of Chinese listed companies. Compared with the traditional MTS
and the common classification methods such as SVM, C4.5, k-NN, it is showed that the MTS-CBPSO method
has better result of prediction accuracy and dimensionality reduction.
In this study, we applied the long short-term memory (LSTM) model to classify the cryptocurrency price time
series. We collected historic cryptocurrency price time series data and preprocessed them in order to make
them clean for use as train and target data. After such preprocessing, the price time series data were
systematically encoded into the three-dimensional price tensor representing the past price changes of
cryptocurrencies. We also presented our LSTM model structure as well as how to use such price tensor as input
data of the LSTM model. In particular, a grid search-based k-fold cross-validation technique was applied to find
the most suitable LSTM model parameters. Lastly, through the comparison of the f1-score values, our study
showed that the LSTM model outperforms the gradient boosting model, a general machine learning model
known to have relatively good prediction performance, for the time series classification of the cryptocurrency
price trend. With the LSTM model, we got a performance improvement of about 7% compared to using the GB
In this paper, we present a certificate management platform for performance assessment during recruitment
using blockchain. Applicants are awarded certificates according to a predetermined level of progress based on
their performances. All certificates are stored on a recruitment management platform that serves as an
environment for storing and presenting all awarded certificates. The hashed information of all the certificates
are stored in the blockchain, and once stored, the contents cannot be tampered with. Therefore, anyone can
check the validity of the certificates using this blockchain. Our proposed platform will be useful for recruitment
and application management, career management, and personal history maintenance.
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)