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A Survey on MANETs: Architecture, Evolution, Applications, Security Issues and Solutions

10.11591/ijeecs.v12.i2.pp832-842
Burhan Ul Islam , Rashidah Funke Olanrewaju , Farhat Anwar , Athaur Rahman Najeeb , Mashkuri Yaacob
Mobile ad hoc networks or MANETs, also referred to as mobile mesh networks at times, are self-configuring networks of mobile devices that are joined using wireless channels. These represent convoluted distributed systems comprising of wireless mobile nodes which are free to move and self-organise dynamically into temporary and arbitrary, ad hoc topologies. This makes it possible for devices as well as people to internetwork seamlessly in such regions that have no communication infrastructure in place. Conventionally, the single communication networking application following the ad hoc concept had been tactical networks. Lately, new technologies have been introduced such as IEEE 802.11, Hyperlan and Bluetooth that are assisting in the deployment of commercial MANETs external to the military realm. Such topical evolutions infuse a new and rising interest in MANET research and development. This paper provides an overview of the dynamic domain of MANETs. It begins with the discussion on the evolution of MANETs followed by its significance in various fields. Besides, the MANETs have been analysed from the security perspective, particularly the work performed in the node misbehaviour paradigm has been elaborated.
Volume: 12
Issue: 2
Page: 832-842
Publish at: 2018-11-01

Data Mining Approach to Herbs Classification

10.11591/ijeecs.v12.i2.pp570-576
Adillah Dayana Ahmad Dali , Nurul Aswa Omar , Aida Mustapha
Herbs are one of the high-value products in Malaysia. The term ‘herbs’ has more than one definition. It is also demanding by multiple manifolds. Herbs are used in many sectors nowadays. The ability to identify variety herbs in the market is quite hard without the intervention of human experts. Unfortunately, human experts are prone to error. Herbs classification is able to assist human experts and at the same time minimizing the intervention. This research performs identification and classification of herbs based on image capture ad variety of classification algorithms such as an Artificial Neural Network (ANN), K-Nearest Neighbors (IBK), Decision Table (DT) and M5P Tree algorithms. The selected algorithms are implemented and evaluated to their relative performance and IBK is found to produce the highest quality outputs.
Volume: 12
Issue: 2
Page: 570-576
Publish at: 2018-11-01

The Information System Project Profiles among Universities in Indonesia

10.11591/ijeecs.v12.i2.pp865-872
A'ang Subiyakto , Abd. Rahman Ahlan , Mira Kartiwi , Nashrul Hakiem , Muhammad Qomarul Huda , Aries Susanto
Information system (IS) utilizations may have been one of the determinant factors for the survival requirement of the higher education sector in Indonesia. Despite the IS projects have been conducted since the 1980s in this country, but many studies indicated that success of the projects seems like in the low level. This survey study was purposed to know the status of the IS project success among universities by involving the internal stakeholders. The higher education database of the Ministry of Research, Technology, and Higher Education (MRTHE) and the Association of Computing and Informatics Institutions Indonesia (ASCII-I) in the year 2015 were used for selecting around 1.230 respondents based on a purposive random sampling. The blended questionnaires were then distributed with almost 298 (±24%) responses. This paper presents five project profiles, including the aim, IS strategic plan availability, internal team involvement, budget allocation, and the success level of the projects.
Volume: 12
Issue: 2
Page: 865-872
Publish at: 2018-11-01

Jatropha Curcas Disease Identification With Extreme Learning Machine

10.11591/ijeecs.v12.i2.pp883-888
Triando Hamonangan Saragih , Diny Melsye Nurul Fajri , Wayan Firdaus Mahmudy , Abdul Latief Abadi , Yusuf Priyo Anggodo
Jatropha is a plant that has many functions, but this plant can be attacked by various diseases. Expert systems can be applied in identifying so that can help both farmers and extension workers to identify the disease. one of method that can be used is Extreme Learning Machine. Extreme Learning Machine is a method of learning in Neural Network which has a one-time iteration concept in each process. In this study get a maximum accuracy of 66.67% with an average accuracy of 60.61%. This proves the identification using Extreme Learning Machine is better than the comparison method that has been done before.
Volume: 12
Issue: 2
Page: 883-888
Publish at: 2018-11-01

Game Theory for Resource Allocation in Heterogeneous Wireless Networks - A Review

10.11591/ijeecs.v12.i2.pp843-851
Farhat Anwar , Mosharrof H. Masud , Burhan Ul Islam , Rashidah Funke Olanrewaju , Suhaimi Abd Latif
In next-generation wireless networks, a user can be connected through Multi-Mode Device (MMD) to the multiple wireless networks in Heterogeneous Wireless Networks (HWN) considering several factors; including network technology, data service type, available bandwidth, Quality of Service (QoS), monetary cost, etc. To deal with all these multi attributes, game theory based models have been used to point out a better solution. This paper evaluates the techniques, methods, advantages, limitations of some game theory-based models for wireless resource allocation in HWN. Finally, it concludes that the Shapley Value method can be used for further research activities for its efficiency.
Volume: 12
Issue: 2
Page: 843-851
Publish at: 2018-11-01

YouTube Spam Comment Detection Using Support Vector Machine and K–Nearest Neighbor

10.11591/ijeecs.v12.i2.pp612-619
Aqliima Aziz , Cik Feresa Mohd Foozy , Palaniappan Shamala , Zurinah Suradi
Social networking such as YouTube, Facebook and others are very popular nowadays. The best thing about YouTube is user can subscribe also giving opinion on the comment section. However, this attract the spammer by spamming the comments on that videos. Thus, this study develop a YouTube detection framework by using Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN). There are five (5) phases involved in this research such as Data Collection, Pre-processing, Feature Selection, Classification and Detection. The experiments is done by using Weka and RapidMiner. The accuracy result of SVM and KNN by using both machine learning tools show good accuracy result. Others solution to avoid spam attack is trying not to click the link on comments to avoid any problems.
Volume: 12
Issue: 2
Page: 612-619
Publish at: 2018-11-01

Internet of Things Application in Monitoring Sick Building Syndrome

10.11591/ijeecs.v12.i2.pp505-512
Keshihakumar Kalaithasan , N. A. M. Radzi , H. Z. Abidin
Sick Building Syndrome (SBS) is a health condition whereby a patient is presented with either vague temporary symptoms such as fatigue, aches and sensitivity to odour or more significant temporary symptoms such as itchy eyes, skin rashes and nasal allergy when they are in a building. Numerous factors have been associated with SBS, but the lack of an accurate diagnosis for these symptoms make treatment more difficult, as risk of treating the patient with wrong diagnosis is relative when the cause root is not known. Thus, taking a preventive approach is a more viable solution to the problem. In this paper, a simple, mobile and cost efficient Internet of Things (IoT) based SBS system is proposed. The system is built using Raspberry Pi minicomputer that would then be integrated with an IoT middleware. The middleware would enable the user to monitor parameters that are to be tested; which are temperature, humidity, light, sound and dust. Three IoT middleware are used to evaluate which one works best for the SBS system proposed. The combination of recorded sensor data would then be used to determine whether or not the building is causing SBS to the occupant. The studies show that FavorIoT platform is the most suitable IoT platform to be used with the SBS system and the system has successfully identified whether or not a building is causing SBS.
Volume: 12
Issue: 2
Page: 505-512
Publish at: 2018-11-01

Non-Orthogonal Multiple Access Assisted Device-to-Device Communication: Best Helping Base Station Approach

10.11591/ijeecs.v12.i2.pp803-813
Huu-Phuc Dang , Minh-Sang Nguyen , Dinh-Thuan Do
It can be studied in this paper that a cooperative non-orthogonal multiple access (NOMA) helps device-to-device (D2D) communication system through base station (BS). In particular, we investigate BS selection scheme as a best channel condition for dedicated devices where a different data transmission demand on each device is resolved. The analysis on amplifying-and forward (AF) relay is proposed to evaluate system performance of the conventional cooperative NOMA scheme. Under the realistic assumption of perfect channel estimation, the achievable outage probability of both devices is investigated, and several impacts on system performance are presented. The mathematical formula in closed form related to probability has also been found. By implementing Monte-Carlo simulation, the simulation results confirm the accuracy of the derived analytical results. Also, the proposed D2D cooperative NOMA system introduces expected performance on reasonable selected parameters in the moderate signal to noise ratio (SNR) regime.
Volume: 12
Issue: 2
Page: 803-813
Publish at: 2018-11-01

Analysis Review on Public Key Cryptography Algorithms

10.11591/ijeecs.v12.i2.pp447-454
Jasmin Ilyani Ahmad , Roshidi Din , Mazida Ahmad
This paper presents several Public Key Cryptography (PKC) algorithms based on the perspective of researchers’ effort since it was invented in the last four decades. The categories of the algorithms had been analyzed which are Discrete Logarithm, Integer Factorization, Coding Theory, Elliptic Curve, Lattices, Digital Signature and Hybrid algorithms. This paper reviewed the previous schemes in different PKC algorithms. The aim of this paper is to present the comparative trends of PKC algorithms based on number of research for each algorithm in last four decades, the roadmap of PKC algorithms since they were invented and the most chosen algorithms among previous researchers. Finally, the strength and drawback of proposed schemes and algorithms also presented in this paper.
Volume: 12
Issue: 2
Page: 447-454
Publish at: 2018-11-01

Link Adaptation Algorithm for IEEE 802.11 Wireless Local Area Netwoks in Fading Channel

10.11591/ijeecs.v12.i2.pp677-684
Wan Norsyafizan W Muhamad , Suzi Seroja Sarnin , Azlina Idris , Aliya Syahira
This paper proposes an enhance version of the dual parameter based cost function algorithm for time-varying environments. For ideal channel condition, the proposed cost function algorithm adaptively change the transmission data rate according to traffic load condition. However, similar approach is not well-suited for time-varying environment. For time-varying condition, transmission of packet at lower transmission data rate is much preferred in order to achieve reliable transmission. In addition to that, packet transmission at lower data rate contributes to low energy consumption due to lower transmit power used to transmit packet. This paper presents a combination of cost function algorithm with adaptive rate/power. Adaptation of transmission data rate and transmit power for time varying condition are carried out based on received power values. An OPNET based simulation model was developed to analyses the performance of the propose algorithm. Simulation results show the proposed algorithm can significantly improve the energy efficiency of a WLAN while maintaining the network QoS in fading channel environments.
Volume: 12
Issue: 2
Page: 677-684
Publish at: 2018-11-01

Optimal Tuning of SVC-PI Controller using Whale Optimization Algorithm for Angle Stability Improvement

10.11591/ijeecs.v12.i2.pp620-624
N. A. M. Kamari , I. Musirin , Z. A. Hamid , A. A. Ibrahim
This paper proposed a new swarm-based optimization technique for tuning conventional proportional-integral (PI) controller parameters of a static var compensator (SVC) which controls a synchronous generator in a single machine infinite bus (SMIB) system. As one of the Flexible Alternating Current Transmission Systems (FACTS) devices, SVC is designed and implemented to improve the damping of a synchronous generator. In this study, two parameters of PI controller namely proportional gain, KP and integral gain, KI are tuned with a new optimization method called Whale Optimization Algorithm (WOA). This technique mimics the social behavior of humpback whales which is characterized by their bubble-net hunting strategy in order to enhance the quality of the solution. Validation with respect to damping ratio and eigenvalues determination confirmed that the proposed technique is more efficient than Evolutionary Programming (EP) and Artificial Immune System (AIS) in improving the angle stability of the system. Comparison between WOA, EP and AIS optimization techniques showed that the proposed computation approach gives better solution and faster computation time.
Volume: 12
Issue: 2
Page: 620-624
Publish at: 2018-11-01

Penetration Testing using Kali Linux: SQL Injection, XSS, Wordpres, and WPA2 Attacks

10.11591/ijeecs.v12.i2.pp729-737
Teddy Surya Gunawan , Muhammad Kasim Lim , Mira Kartiwi , Noreha Abdul Malik , Nanang Ismail
Nowadays, computers, smart phones, smart watches, printers, projectors, washing machines, fridges, and other mobile devices connected to Internet are exposed to various threats and exploits. Of the various attacks, SQL injection, cross site scripting, Wordpress, and WPA2 attack were the most popular security attacks and will be further investigated in this paper. Kali Linux provides a great platform and medium in learning various types of exploits and peneteration testing. All the simulated attack will be conducted using Kali Linux installed on virtual machine in a compuer with Intel Core i5 and 8 GB RAM, while the victim’s machine is the host computer which run Windows 10 version 1709. Results showed that the attacks launched both on web and firewall were conducted successfully.
Volume: 12
Issue: 2
Page: 729-737
Publish at: 2018-11-01

A Modular and Extensible Framework for Human Resource System

10.11591/ijeecs.v12.i2.pp641-647
Muhammad Zabir Abdul Halim , Marshima Mohd Rosli
Human Resource is an essential part for an organization and integration of technology would just enhance the effectiveness. One of the scope that Human Resource must take note is performance and productivity. However, there is still no infallible or highly reliable way to measure productivity and performance of an organization and its employees for current industrial Human Resource systems. There is still possible improvement that can be made to the system in the current time. The purpose of this study is to propose a modular and extensible framework of Human Resource systems that used to measure the performance and productivity of an organization. This study compares the different criteria of existing Human Resource systems to ensure the proposed framework would surpassed the current real world industrial system. The results of the review provide insights for important criteria in HR system to increase the accuracy of the performance review. This study also constructs Entity Relationship Diagram (ERD) to demonstrate the logical structure of the proposed framework. The ERD will form the foundation of the proposed framework to improve the Human Resource system for evaluating the productivity and performance of an organization.
Volume: 12
Issue: 2
Page: 641-647
Publish at: 2018-11-01

Prediction of Solar Radiation Intensity using Extreme Learning Machine

10.11591/ijeecs.v12.i2.pp691-698
Hadi Suyono , Hari Santoso , Rini Nur Hasanah , Unggul Wibawa , Ismail Musirin
The generated energy capacity at a solar power plant depends on the availability of solar radiation. In some regions, solar radiation is not always available throughout the day, or even week, depending on the weather and climate in the area. To be able to produce energy optimally throughout the year, the availability of solar radiation needs to be predicted based on the weather and climate behavior data. Many methods have been so far used to predict the availability of solar radiation, either by mathematical approach, statistical probability, or even artificial intelligence-based methods. This paper describes a method of predicting the availability of solar radiation using the Extreme Learning Machine (ELM) method. It is based on the artificial intelligence methods and known to have a good prediction accuracy. To measure the performance of the ELM method, a conventional forecasting method using the Multiple Linear Regression (MLR) method has been used as a comparison. The implementation of both the ELM and MLR methods has been tested using the solar radiation data of the Basel City, Switzerland, which are available to public. Five years of data have been divided into training data and testing data for 6 case-studies considered. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) have been used as the parameters to measure the prediction results based on the actual data analysis. The results show that the obtained average values of RMSE and MAE by using the ELM method respectively are 122.45 W/m2 and 84.04 W/m2, while using the MLR method they are 141.18 W/m2 and 104.87 W/m2 respectively. It means that the ELM method proved to perform better than the MLR method, giving 15.29% better value of RMSE parameter and 24.79% better value of MAE parameter.
Volume: 12
Issue: 2
Page: 691-698
Publish at: 2018-11-01

Components of Participatory Engagement within E-Learning Community

10.11591/ijeecs.v12.i2.pp556-561
Noor Hida Natrah Aziz , Haryani Haron , Afdallyna Fathiyah Harun
This paper explores the components of participatory engagement to improve learner’s engagement within the e-learning community. Data are gathered through observations to measuring the interactions of the learner’s. The measurement included the learners’ feedbacks through the interactions of learners and educators, learners and learners and learners and content. Follow-up interviews are conducted to get deeper insights into the interactions and overall learner’s engagement environment. Findings show that in order to promote learners’ engagement in e-learning components such as accessibility, mobility, active learning and collaboration must be present in an e-learning ecosystem.
Volume: 12
Issue: 2
Page: 556-561
Publish at: 2018-11-01
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