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Secured node detection technique based on artificial neural network for wireless sensor network

10.11591/ijece.v11i1.pp536-544
Bassam Hasan , Sameer Alani , Mohammed Ayad Saad
The wireless sensor network is becoming the most popular network in the last recent years as it can measure the environmental conditions and send them to process purposes. Many vital challenges face the deployment of WSNs such as energy consumption and security issues. Various attacks could be subjects against WSNs and cause damage either in the stability of communication or in the destruction of the sensitive data. Thus, the demands of intrusion detection-based energy-efficient techniques rise dramatically as the network deployment becomes vast and complicated. Qualnet simulation is used to measure the performance of the networks. This paper aims to optimize the energy-based intrusion detection technique using the artificial neural network by using MATLAB Simulink. The results show how the optimized method based on the biological nervous systems improves intrusion detection in WSN. In addition to that, the unsecured nodes are affected the network performance negatively and trouble its behavior. The regress analysis for both methods detects the variations when all nodes are secured and when some are unsecured. Thus, Node detection based on packet delivery ratio and energy consumption could efficiently be implemented in an artificial neural network.
Volume: 11
Issue: 1
Page: 536-544
Publish at: 2021-02-01

Content based video retrieval using discrete cosine transform

10.11591/ijeecs.v21.i2.pp839-845
Sumaya Hamad , Ahmeed Suliman Farhan , Doaa Yaseen Khudhur
A content based video retrieval (CBVR)framework is built in this paper.  One of the essential features of video retrieval process and CBVR is a color value. The discrete cosine transform (DCT) is used to extract a query video features to compare with the video features stored in our database. Average result of 0.6475 was obtained by using the DCT after implementing it to the database we created and collected, and on all categories. This technique was applied on our database of video, Check 100 database videos, 5 videos in each category.
Volume: 21
Issue: 2
Page: 839-845
Publish at: 2021-02-01

IoT based implemented comparison analysis of two well-known network platforms for smart home automation

10.11591/ijece.v11i1.pp442-450
Sameer Alani , Sarmad Nozad Mahmood , Sarah Zaeead Attaallah , Haneen Sameer Mhmood , Zeena Abdulsattar Khudhur , Azzam Amer Dhannoon
The developments of the internet of things (IoT) technologies fascinated the universe and provided great opportunities to introduce these innovations in smart house networks. Smart home automation is highly required these days. Smart home automation is a collection of electronic devices connected to monitor and control in the market home appliance remotely. However, it is still needed to design a friendly and reliable system since the system mainly depends on the devices used and the environment of the network. NETPI and BLYNK are IoT frameworks used for hardware-agnostic with smartphones, websites, private clouds, system security, data mining, and deep learning. The results confirmed that NETPI provides flexibility to deal with several NODEMCU controllers in a single control framework. The proposed system shows its applicability in monitoring and controlling home appliances remotely.
Volume: 11
Issue: 1
Page: 442-450
Publish at: 2021-02-01

Maximum power point tracking techniques for photovoltaic systems: a comparative study

10.11591/ijece.v11i1.pp57-73
M. A. Abo-Sennah , M. A. El-Dabah , Ahmed El-Biomey Mansour
Photovoltaic systems (PV) are one of the most important renewable energy resources (RER). It has limited energy efficiency leading to increasing the number of PV units required for certain input power i.e. to higher initial cost. To overcome this problem, maximum power point tracking (MPPT) controllers are used. This work introduces a comparative study of seven MPPT classical, artificial intelligence (AI), and bio-inspired (BI) techniques: perturb and observe (P&O), modified perturb and observe (M-P&O), incremental conductance (INC), fuzzy logic controller (FLC), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and cuckoo search (CS). Under the same climatic conditions, a comparison between these techniques in view of some criteria’s: efficiencies, tracking response, implementation cost, and others, will be performed. Simulation results, obtained using MATLAB/SIMULINK program, show that the MPPT techniques improve the lowest efficiency resulted without control. ANFIS is the highest efficiency, but it requires more sensors. CS and ANN produce the best performance, but CS provided significant advantages over others in view of low implementation cost, and fast computing time. P&O has the highest oscillation, but this drawback is eliminated using M-P&O. FLC has the longest computing time due to software complexity, but INC has the longest tracking time.
Volume: 11
Issue: 1
Page: 57-73
Publish at: 2021-02-01

Planning multi-terminal direct current grids based graphs theory

10.11591/ijece.v11i1.pp37-46
Mario A. Rios , Fredy A. Acero
Transmission expansion planning in AC power systems is well known and employs a variety of optimization techniques and methodologies that have been used in recent years. By contrast, the planning of HVDC systems is a new matter for the interconnection of large power systems, and the interconnection of renewable sources in power systems. Although the HVDC systems has evolved, the first implementations were made considering only the needs of transmission of large quantities of power to be connected to the bulk AC power system. However, for the future development of HVDC systems, meshed or not, each AC system must be flexible to allow the expansion of these for future conditions. Hence, a first step for planning HVDC grids is the planning and development of multi-terminal direct current (MTDC) systems which will be later transformed in a meshed system. This paper presented a methodology that use graph theory for planning MTDC grids and for the selection of connection buses of the MTDC to an existing HVAC transmission system. The proposed methodology was applied to the Colombian case, where the obtained results permit to migrate the system from a single HVDC line to a MTDC grid.
Volume: 11
Issue: 1
Page: 37-46
Publish at: 2021-02-01

Optimized architecture for SNOW 3G

10.11591/ijece.v11i1.pp545-557
N. B. Hulle , Prathiba B. , Sarika R. Khope , K. Anuradha , Yogini Borole , D. Kotambkar
SNOW 3G is a synchronous, word-oriented stream cipher used by the 3GPP standards as a confidentiality and integrity algorithms. It is used as first set in long term evolution (LTE) and as a second set in universal mobile telecommunications system (UMTS) networks. The cipher uses 128-bit key and 128 bit IV to produce 32-bit ciphertext. The paper presents two techniques for performance enhancement. The first technique uses novel CLA architecture to minimize the propagation delay of the 232 modulo adders. The second technique uses novel architecture for S-box to minimize the chip area. The presented work uses VHDL language for coding. The same is implemented on the FPGA device Virtex xc5vfx100e manufactured by Xilinx. The presented architecture achieved a maximum frequency of 254.9 MHz and throughput of 7.2235 Gbps.
Volume: 11
Issue: 1
Page: 545-557
Publish at: 2021-02-01

Contactless digital tachometer using microcontroller

10.11591/ijece.v11i1.pp293-299
R. Palanisamy , S. Vidyasagar , V. Kalyanasundaram , R. Sridhar
Tachometer is a device that used for counting or for the measuring purpose of the number of revolutions (that is the total number rotations made by the device in unit of measuring time) of an object in unit time. It is expressed in the unit of RPS or RPM, the model uses a set of infrared transducer receiver to count the RPM pulses, and the Arduino microcontroller is used for the implementation of the project. The individual pulses are counted by the microcontroller to give the final output of the RPM.
Volume: 11
Issue: 1
Page: 293-299
Publish at: 2021-02-01

Improved feature exctraction process to detect seizure using CHBMIT-dataset

10.11591/ijece.v11i1.pp827-843
Raveendra Kumar T. H. , C. K. Narayanappa
One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.
Volume: 11
Issue: 1
Page: 827-843
Publish at: 2021-02-01

APMorph: finite-state transducer for Amazigh pronominal morphology

10.11591/ijece.v11i1.pp699-706
Rachid Ammari , Ahbib Zenkoua
Our work aims to present an amazigh pronominal morphological analyzer (APMorph) based on xerox’s finite-state transducer (XFST). Our system revolves around a large lexicon named “APlex” including the affixed pronoun to the noun and to the verb and the characteristics relating to each lemma. A set of rules are added to define the inflectional behavior and morphosyntactic links of each entry as well as the relationship between the different lexical units. The implementation and the evaluation of our approach will be detailed within this article. The use of XFST remains a relevant choice in the sense that this platform allows both analysis and generation. The robustness of our system makes it able to be integrated in other applications of natural language processing (NLP) especially spellchecking, machine translation, and machine learning. This paper presents a continuation of our previous works on the automatic processing of Amazigh nouns and verbs.
Volume: 11
Issue: 1
Page: 699-706
Publish at: 2021-02-01

Knowledge discovery from gene expression dataset using bagging lasso decision tree

10.11591/ijeecs.v21.i2.pp1151-1159
Umu Sa'adah , Masithoh Yessi Rochayani , Ani Budi Astuti
Classifying high-dimensional data are a challenging task in data mining. Gene expression data is a type of high-dimensional data that has thousands of features. The study was proposing a method to extract knowledge from high-dimensional gene expression data by selecting features and classifying. Lasso was used for selecting features and the classification and regression tree (CART) algorithm was used to construct the decision tree model. To examine the stability of the lasso decision tree, we performed bootstrap aggregating (Bagging) with 50 replications. The gene expression data used was an ovarian tumor dataset that has 1,545 observations, 10,935 gene features, and binary class. The findings of this research showed that the lasso decision tree could produce an interpretable model that theoretically correct and had an accuracy of 89.32%. Meanwhile, the model obtained from the majority vote gave an accuracy of 90.29% which showed an increase in accuracy of 1% from the single lasso decision tree model. The slightly increasing accuracy shows that the lasso decision tree classifier is stable.
Volume: 21
Issue: 2
Page: 1151-1159
Publish at: 2021-02-01

Resumption of virtual machines after adaptive deduplication of virtual machine images in live migration

10.11591/ijece.v11i1.pp654-663
Naga Malleswari T. Y. J. , Senthil Kumar T. , JothiKumar C.
In cloud computing, load balancing, energy utilization are the critical problems solved by virtual machine (VM) migration. Live migration is the live movement of VMs from an overloaded/underloaded physical machine to a suitable one. During this process, transferring large disk image files take more time, hence more migration and down time. In the proposed adaptive deduplication, based on the image file size, the file undergoes both fixed, variable length deduplication processes. The significance of this paper is resumption of VMs with reunited deduplicated disk image files. The performance measured by calculating the percentage reduction of VM image size after deduplication, the time taken to migrate the deduplicated file and the time taken for each VM to resume after the migration. The results show that 83%, 89.76% reduction overall image size and migration time respectively. For a deduplication ratio of 92%, it takes an overall time of 3.52 minutes, 7% reduction in resumption time, compared with the time taken for the total QCOW2 files with original size. For VMDK files the resumption time reduced by a maximum 17% (7.63 mins) compared with that of for original files.
Volume: 11
Issue: 1
Page: 654-663
Publish at: 2021-02-01

An image encryption algorithm with a novel chaotic coupled mapped lattice and chaotic image scrambling technique

10.11591/ijeecs.v21.i2.pp1103-1112
Behrang Chaboki , Ali Shakiba
In this paper, we build a novel chaotic coupled lattice mapping with positive Lyapunov exponent, and introduce a novel chaotic image scrambling mechanism. Then, we propose a chaotic image encryption algorithm which uses the introduced chaotic coupled lattice mapping to apply permutation by iteratively applying the introduced chaotic image scrambling mechanism, and diffusing the pixel values. We use a sorting approach rather than quantizing the chaotic floating-point values to construct the diffusion matrix. We also study the security of the proposed algorithm concerning several security measures including brute-force attack, differential attack, key sensitivity, and statistical attacks. Moreover, the proposed algorithm is robust against data loss and noise attacks.
Volume: 21
Issue: 2
Page: 1103-1112
Publish at: 2021-02-01

Fingerprint positioning of users devices in long term evolution cellular network using K nearest neighbour algorithm

10.11591/ijece.v11i1.pp528-535
Mohammed J. Alhasan , Sarmad Muneer Abdulhussein , Ali H. K. Khwayyir
The rapid exponential growth in wireless technologies and the need for public safety has led to increasing demand for location-based services. Terrestrial cellular networks can offer acceptable position estimation for users that can meet the statutory requirements set by the Federal Communications Commission in case of network-based positioning, for safety regulations. In this study, the proposed radio frequency pattern matching (RFPM) method is implemented and tested to determine a user’s location effectively. The RFPM method has been tested and validated in two different environment. The evaluations show remarkable results especially in the Micro cell scenario, at 67% of positioning error 15m and at 90% 31.78m for Micro cell scenario, with results of 75.66m at 67% and 141.4m at 90% for Macro cell scenario.
Volume: 11
Issue: 1
Page: 528-535
Publish at: 2021-02-01

A hybrid of CNN and LSTM methods for securing web application against cross-site scripting attack

10.11591/ijeecs.v21.i2.pp1022-1029
Raed Waheed Kadhim , Methaq Talib Gaata
Cross-site scripting (XSS) is today one of the biggest threatthat could targeting the Web application. Based on study published by the open web applications security project (OWASP), XSS vulnerability has been present among the TOP 10 Web application vulnerabilities.Still,an important security-related issue remains how to effectively protect web applications from XSS attacks.In first part of this paper, a method for detecting XSS attack was proposed by combining convolutional neural network (CNN) with long short term memories (LSTM), Initially, pre-processing was applied to XSS Data Set by decoding, generalization and tokanization, and then word2vec was applied to convert words into word vectors in XSS payloads. And then we use the combination CNN with LSTM to train and test word vectors to produce a model that can be used in a web application. Based on the obtaned results, it is observed that the proposed model achevied an excellent result with accuracy of 99.4%.
Volume: 21
Issue: 2
Page: 1022-1029
Publish at: 2021-02-01

Design of a model reference adaptive PID control algorithm for a tank system

10.11591/ijece.v11i1.pp300-318
Yohan Darcy Mfoumboulou
This paper describes the design of an adaptive controller based on model reference adaptive PID control (MRAPIDC) to stabilize a two-tank process when large variations of parameters and external disturbances affect the closed-loop system. To achieve that, an innovative structure of the adaptive PID controller is defined, an additional PI is designed to make sure that the reference model produces stable output signals and three adaptive gains are included to guarantee stability and robustness of the closed-loop system. Then, the performance of the model reference adaptive PID controller on the behaviour of the closed-loop system is compared to a PI controller designed on MATLAB when both closed-loop systems are under various conditions. The results demonstrate that the MRAPIDC performs significantly better than the conventional PI controller.
Volume: 11
Issue: 1
Page: 300-318
Publish at: 2021-02-01
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