ISSN (Print): 2210-3279
ISSN (Online): 2210-3287
Volume 10, 8 Issues, 2020
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ISSN (Print): 2210-3279
ISSN (Online): 2210-3287
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Special Issue Submission
Blockchain Technologies for Internet of Things (IoT)
Guest Editor(s): Mohammad Tabrez Quasim, Mohammad Ayoub Khan, Prashant Johri
Submit Abstract via Email
It was a pleasure for me to submit my research work to be published by Bentham Science. You have got a professional team who always keeps me updated with my paper progress.
I wish you all the best with such a team, and keep it up.
13 Abstract Ahead of Print are available electronically
77 Articles Ahead of Print are available electronically
Internet of Things (IoT), Cloud, Big Data and AI-Machine Learning are topics of contemporary research interest. They
cover not only information and communication technology, but also all kinds of systems in our society, including business,
finance, industry, manufacture, management, and environment. IoT connects the physical world to the Internet and generates
big amount of data. Cloud computing environment facilitates the processing of large data and makes intelligent decisions based
on large data analyses and machine learning. This Thematic Special Issue on IoT, Cloud, Big Data and Machine Learning:
Recent Advances and Future Trends of the Journal International Journal of Sensors, Wireless Communications and Control
incorporate thirteen (13) articles identified with the field. A short review about the commitments for this Thematic Special
Issue is as follows:
1. P. Chandrashaker Reddy and A. Suresh Babu contribute an article entitled “An Enhanced Multiple Linear Regression Model
for Seasonal Rainfall Prediction”. The main goal of this project is early and proper rainfall forecasting, that helpful to people
who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in
their crop and water management using big data analytics which produces high in terms of profit and production for farmers.
In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model
(EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological
Department, Hyderabad) in 1901 to 2002 period.
2. Nguyen Thi Ngoc Anh, Nguyen Danh Tu, Vijender Kumar Solanki, Nguyen Linh Giang, Vu Hoai Thu, Luong Ngoc Son,
Nguyen Duc Loc, Vu Thanh Nam contribute an article entitled “Integrating Employee Value Model with Churn Prediction”.
The process of prediction integrating Churn, value of employee and machine learning are described detail in 6 steps. The
pros of integrating model gives the more necessary results for company than Churn prediction model but the cons is
complexity of model and algorithms and speed of computing. A case study of an organization with 1470 employee positions
is carried out to demonstrate the whole integrating churn predict, EVM and machine learning process. The accuracy of the
integrating model is high from 82% to 85%. Moreover, the some results of Churn and value employee are analyzed.
3. Charu Bhardwaj, Shruti Jain and Meenakshi Sood contribute an article entitled “Automated Diagnostic Hybrid Lesion
Detection System for Diabetic Retinopathy Abnormalities”. In this research paper, an automated lesion detection diagnostic
scheme has been proposed for early detection of retinal abnormalities of red and yellow pathological lesions. The algorithm
of the proposed Hybrid Lesion Detection (HLD) includes retinal image pre-processing, blood vessel extraction, optical disc
localization and detection stages for detecting the presence of diabetic retinopathy lesions. Automated diagnostic systems
assist the ophthalmologists practice manual lesion detection techniques which are tedious and time-consuming. Detailed
statistical analysis is performed on the extracted shape, intensity and GLCM features and the optimal features are selected to
classify DR abnormalities. Exhaustive statistical investigation of the proposed approach using visual and empirical analysis
resulted in 31 significant features. The results show that the HLD approach achieved good classification results in terms of
three statistical indices: accuracy, 98.9%; sensitivity, 97.8%; and specificity, 100% with significantly less complexity.
4. Anurag Satpathy, Ganapati Panda, Rajasekhar Gogula and Renu Sharma contribute an article entitled “Low Complexity
Adaptive Nonlinear Models for the Diagnosis of Periodontal Disease”. The paper addresses a specific clinical problem of
diagnosis of periodontal disease with an objective to develop and evaluate the performance of low complexity adaptive
nonlinear models (ANM) using nonlinear expansion schemes and describes the basic structure and development of ANMs in
detail. Diagnostic data pertaining to periodontal findings of teeth obtained from patients have been used as inputs to train and
validate the proposed models.
5. Sarat Chandra Nayak, Subhranginee Das and Mohd Dilsad Ansari contribute an article entitled “TLBO-FLN: Teachinglearning
Based Optimization of Functional Link Neural Networks for Stock Closing Price Prediction”. Stock closing price
prediction is enormously complicated. Artificial neural networks (ANN) are excellent approximation algorithms applied to
this area. Several nature-inspired evolutionary optimization techniques are proposed and used in the literature to search the
optimum parameters of ANN based forecasting models. However, most of them need fine-tuning of several control
parameters as well as algorithm specific parameters to achieve optimal performance. Improper tuning of such parameters
either leads toward additional computational cost or land at local optima. Teaching learning based optimization (TLBO) is a
newly proposed algorithm which does not necessitate any parameters specific to it. The intrinsic capability of functional link
artificial neural network (FLANN) to recognize the multifaceted nonlinear relationship present in the historical stock data
made it popular and got wide applications to stock market prediction. This article presents a hybrid model termed as teaching
learning based optimization of functional neural networks (TLBO-FLN) by combining the advantages of both TLBO and
6. Sonal Agrawal and Pradeep Tripathi contribute an article entitled “Intuitionistic Fuzzy Score Function based Multi-Criteria
Decision Making Method for Selection of Cloud Service Provider”. Cloud computing (CC) has received great attention from
the scholarly researchers and IT companies. CC is a standard that offers services through the Internet. The standard has been
manipulated by existing skills (such as collect, peer-to-peer and grid computing) and currently accepted by approximately all
major associations. Various associations like as Microsoft and Facebook have revealed momentous investments in CC and
currently offer services with top levels of reliability. The well-organized and precise evaluation of cloud-based
communication network is an essential step in assurance both the business constancy and the continuous open services.
7. Syed Rameem Zahra and Mohammad Ahsan Chishti an article entitled “A Collaborative Edge-Cloud Internet of Things
based Framework for Securing the Indian Healthcare System”. Today, 73 years after the independence and twenty years after
the turn of the century, “Health for All” which should have been accomplished by now, remains a far-fetched and an elusive
dream. Instead, the people of India are bequeathed a triple burden of disease: sustaining the weight of transmittable
infections, expanding burden of nontransferable illnesses, and a healthcare system not efficient enough to handle them both.
At present, India is home to one-third of the poor population around the world. After a high population growth rate,
unregulated and inefficient healthcare is the major cause for this abjection and poverty. The global position of India vis-à-vis
the health indicators like Infant Mortality Rate (IMR), Crude Birth Rate (CBR), Crude Death Rate (CDR) and life
expectancy is shocking, shameful and on a downward trend. The objective of this paper was to identify the major issues in
the Indian healthcare system and offer Internet of Things (IoT) based solutions.
8. Tausifa Jan Saleem and Mohammad Ahsan Chishti contribute an article entitled “Exploring the Applications of Machine
Learning in Healthcare”. The objective of the research is to help the researchers in this field to get a comprehensive overview
of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this
paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.
9. Cerene Mariam Abraham, M. Sudheep Elayidom and T. Santhanakrishnan contribute an article entitled “Big Data Analysis
for Trend Recognition using Machine Learning Techniques”. This paper performs big data analytics on the Indian derivative
market and identifies a trend with the help of interdisciplinary areas such as cloud computing, machine learning and
10. Mohammad Irfan Bala and Mohammad Ahsan Chishti contribute an article entitled “Comparative Analysis of Load
Balancing Algorithms for Cloud Computing in IoT”. This work focuses on multiple load balancing algorithms whose
performance has been analysed and compared under varying load conditions. Cloud computing is a widely adopted
computing paradigm and its importance has increased multi-folds in the recent past due to the inception of Internet of Things
11. Ravinder Ahuja, Vineet Maheshwari, Siddhant Manglik, Abiha Kazmi, Rishika Arora and Anuradha Gupta contribute an
article entitled “Malicious apps Identification in Android Devices Using Machine Learning Algorithms”. In this paper,
malicious apps detection system is implemented using machine learning algorithms. For this 330 permission based features
of 558 android applications are taken into consideration. The main motto of this work is to develop a model which can
effectively detect the malicious and benign apps. In this we have used six feature selection techniques which will extract
important features from 330 permission based features of 558 apps and further fourteen classification algorithms are applied
using Python language.
12. Mohammad Khalid Pandit, Roohie Naaz Mir and Mohammad Ahsan Chishti contribute an article entitled “Adaptive Deep
Neural Networks for the Internet of Things”. Deep neural networks have become the state of art technology for real world
classification tasks due to their ability to learn better feature representations at each layer. However, the added accuracy that
is associated with the deeper layers comes at a huge cost of computation, energy and added latency. The implementation of
such architectures in resource constraint IoT devices is computationally prohibitive due to its computational and memory
requirements. These factors are particularly severe in IoT domain. In this paper we propose adaptive deep neural network
(ADNN) which gets split across the compute hierarchical layers i.e. edge, fog and cloud with all splits having one or more
exit locations. At every location the data sample adaptively chooses to exit from the NN (based on confidence criteria) or get
fed into deeper layers housed across different compute layers.
13. Midde Veenkateswarlu Naik, D. Vasumathi and A.P. Siva Kumar contribute an article entitled “An Improved Intelligent
Approach to Enhance the Sentiment Classifier for Knowledge Discovery Using Machine Learning”. The objective of the
research issue sentiment classifier accuracy has been hiked with the help of Kernel-based Support Vector Machine (SVM)
based on parameter optimization is applied. The optimal feature selections to classify sentiment or opinion towards about
review documents have been determined with the help of particle swarm optimization approach. The proposed method
utilized three datasets to simulate the results such as airline sentiment data, weather sentiment data, and global warming data
that are freely available datasets.
We hope that the quality research work published in this special issue will be able to serve the concerned humanity, science
Welcome to the special issue on “Recent research in network security analytics” in
International Journal of Sensors, Wireless Communications and Control. With the ripeness
of network data and encroachment of security technologies, data analytics now-adays
plays a very significant role in our day-to-day lives. Internet is one of the leading
accomplishment of this epoch thus securing it is also a precedence.
International Journal of Sensors, Wireless Communications and Control is a peer reviewed
journal considers both theoretical and implementation based papers. Network
security and Data analytics is an important call for present society where information
technology and services pass through each facet of our lives. However, this is demanding to achieve, as technology
is changing at a rapid speed and our systems turn into ever more complex. We are gradually more dependent
upon such information and communications infrastructures, and the threats we face are organized and
exploit our dependency by the attackers or cyber criminals. Moreover, cyber space is considered as fifth battlefield
after land, air, water and space.
The aim of this issue is to provide insight mechanisms while handling data; provide conceptual understanding
of network security issues, challenges and mechanisms; develop basic skills of secure network architecture
and explain the theory behind the security of networks, analytics and different cryptographic algorithms.
It is to present the most recent challenges and developments in data analytics and networks. It also
provides a forum for researchers, practitioners and educators to present and discuss the most recent innovations,
trends, and concerns, practical challenges encountered and the solutions adopted in these fields. Original
research papers and state of the art reviews will be accepted. We anticipate that the special issue will open
new entrance for further research and technology improvements in this important area.
In this regard, the first article is devoted to investigate that current wireless networks are based on unicast
routing protocol derived from wired networks. The purpose of this paper is to implement and evaluate opportunistic
routing protocols in new generation’s wireless network. This is a comparative study between two opportunistic
protocols, which are ExOR (Extremely Opportunistic Routing protocol) and SOAR (Simple Opportunistic
Adaptive Routing protocol). The main goal of this survey is to show the benefits required by using
opportunistic approach to optimize the new generation’s wireless networks operations and implemented the
most used protocols under MATLAB framework .
The second article proposed a defense model for wireless sensor network against worms attack based on
the concept of epidemic theory. This model basically focused on mechanism which can be used for protection
of sensor network against malware attack. They derived an expression for basic reproduction number this
helps in study of worm dynamics and development of control mechanism. The stability of network depends
on the value of basic reproduction number explained the worm free and endemic equilibrium. They also find
the threshold value of communication radius and node density. Correlate the basic reproduction number and
threshold value of communication radius and study its impact in the design of wireless sensor network. Explain
the effect of quarantine and recovery on the infectious nodes with the variation of parameters and
showed that if the rate of recovery increases the number of infected nodes decreases. The quarantine class of
nodes helps in controlling of worm spread in the wireless sensor network. The proposed model is efficient in
comparison to existing model proved by simulation. This is a good idea for the development of an antivirus
for wireless sensor network against worm attack .
The third article focuses on the infrastructure less operating mechanism of MANETs is leading to their
popularity and extending their role in the operations of certain real-time applications as well as certain multi- media applications. For MANETs being able to support such applications, the prime requirement is to support
efficient routing as well as to incorporate efficient QOS mechanisms. This guides multiple research trends
towards MANET for categories like: QOS (Quality of Service), efficient Routing etc. The approach proposed
in this paper centers on the methods that can overcome the limitations faced by the Zone Routing Protocol
when used for large networks and improve QOS thus making the use of MANET in real time applications
feasible. The proffered scheme combines the advantages provided by aggregation of routes, introduce a central
entity and optimize QOS based network performance. The algorithm divides the zones into bigger zones
within the network and appoints a Zone Head node for each newly formed bigger zone which is a collection
of nodes bigger than a ZRP zone with Zone Head as the central administrator. It then aggregates the routes at
the Zone Head which works as a central entity and routes the traffic into the cluster by the help of Route aggregation
mechanism thus, enhancing ZRP performance .
In the ever evolving field of software engineering the quality and predictability of success for a project has
been of concern for Project Managers. The next article focused on this concern by proposing certain mathematical
models, designated as process performance models (PPMs), which enhance the process improvement
and improves predictability factor for a project success. Mathematical model selected can be like Queuing
Theory, Time Series, Fuzzy logic etc. based on problem specified and nature of industry data. This PPMs approach
can lead to solution for similar problems and avoids rebuilding solution every time.
The benefit which accrues with design of PPM is the ability to predict and tune the parameters affecting
performance of the project to achieve desired result. The building of these PPMs for various real life project
situations has not been attempted by industry on a large scale. In case a model does not achieve the performance
levels as specified by the project or baselines for similar projects, the model is rejected. In the real life
problem discussed, authors work out Success of a given project based on Bayesian solution for a given network
problem. Also, the authors demonstrate how to assess process capability. The solution shows that it is
possible to display a network problem with non-numeric data by a relationship among variables as parentchild
in a tree structure. The solution is based on developing conditional probability tables. PPMs can be developed
for different emerging application areas like Cloud computing, e-Governance, Application Service
Maintenance etc. and a library of models can be created by an IT company. The relevant model can be selected
by a Manager from this Library. The authors opine that in future, building PPMs may become a necessity
in High Maturity IT Organizations.
The last article explored that RF energy harvesting is getting popular in research community because it
provides useful electrical energy source for wireless sensor network applications. Its popularity is due to its
physically separated power transmitter and power receiver topology. Although the energy received using the
RF energy transfer has its own limitation of distance from the RF power transmitter but this technology provides
enough power and energy to run an ultra-low power electronics circuits. Ambient RF sources (e.g. BTS
towers, TV towers, Radio towers, Wi-Fi sources etc.) also motivate the researchers to develop RF energy
harvesters which easily match with RF sources and starts to convert RF energy into useful electrical energy.
In this article, multiple experiments have been performed over the RF energy harvesters on the base of number
of stages of multiplier, matching networks, and equivalent practical resistive load. Different stages of RF
energy harvester have been implemented and optimized. The optimization of the RF energy harvester has
been performed using the inductor and capacitor circuit used in matching network .The circuit is optimized
in terms of return power loss (S11) and total overall efficiency. In this article, the voltage multiplier is investigated
without inductor and capacitor matching network due to which maximum efficiency of 60% at 12dBm
is achieved at 9th stage of voltage multiplier and only 5% at -15dBm. Matching network with inductor (L) and
capacitor (C) has been developed to achieve better S11 and efficiency. The maximum S11 of -49dB achieved
for 3rd stage voltage multiplier while the efficiency at this value is less than 30% for -20dBm to 20dBm input
RF power range. The value of L and C for such case is 21.24nH and 6.9pF respectively. In the next experiment,
the efficiency is tried to get improved and the maximum efficiency of 3rd stage was 68% but this is
found that for this case the value of S11 is -2.4dB. For improved efficiency the value of L and C is 26.5nH
and 6.3pF, respectively. These results hold same theory for all stages of voltage multipliers. There is scope of
research by finding the value of efficiency and S11 by using fabricated modules of simulated RF energy har- vesters. The comparison with the simulated values may provide more authenticate results and new dimension
of designing of RF energy harvesters. The research helps the research community to design the more appropriate
RF energy harvester for their applications.
Finally, we would like to thank all the reviewers for their excellent work and the authors for their contribution.
We expect that International Journal of Sensors, Wireless Communications and Control will provide
the best platform for the authors and the readers, with a comprehensive overview of the most recent developments
in information management research.
In this special issue of “International Journal of Sensors, Wireless Communications and Control”, the
projection is to publish research contributions that significantly advance the state-of-the-art research, and
six research articles explore new designing techniques, methodologies, concepts and protocols.
Singh et al, presents a cloud based environment parameter monitoring system. Wireless Sensor Area
Network is developed using ZigBee and NodeMCU.
Mehta et al, analysed the benefits of Clustering when it comes to scalability and proposes an algorithm
using the clustering mechanism.
Sharma et al, examined the dataset PIDD (Pima Indian Diabetes dataset) for the model. Dataset was
trained offline using the predictive techniques SVM, Naive Bayes and K-NN. Performance analysis was
performed on the basis of response time and waiting time of a request.
Gerg et al, validated and analyzed the performance of our algorithm by using the CloudSim toolkit to
simulate the cloud environment.
Verma, focused on the second option to design an efficient RSA variant. An improved RSA variant is
designed by adding MultiPrime feature to Dual RSA on the decryption side to increase the decryption
Bazaz and Zafar, presented a technique of using GA based approach in cloud network for QOS optimization
of parameters like packet drop rate and hop count.
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