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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 64 Documents
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Search results for , issue "Vol 30, No 2: May 2023" : 64 Documents clear
Distributed resource allocation model with presence of multiple jammer for underwater wireless sensor networks Sheetal Bagali; Ramakrishnan Sundaraguru
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp1002-1010

Abstract

Underwater-wireless sensor network (WSN) are prone to the jamming attacks; mainly in case of reactive jamming. Reactive jamming has emerged as one of the critical security threat for underwater-WSN; this occurs due to the reactive jammer capabilities of controlling and regulating jamming duration. Further reactive jammer possesses low detection probability and high vulnerability; moreover the existing model has been designed in consideration with terrestrial-WSN. Hence these models possesses limited capabilities of detecting the jamming and distinguish among uncorrupted and corrupted packets; also it fails to adapt with the dynamic environment. Furthermore co-operative mechanism of jamming model is presented for utilizing the resources in efficient way; however only few existing work has been carried out through the co-operative jamming detection; especially under presence of multiple jammer nodes. For overcoming research issues this paper presents distributed resource allocation (DRA) model adopting cross layer architecture under presence of multiple jammer. DRA algorithm is designed for allocating resource to jammer user in optimal manner. Experiment outcome shows the proposed DRA model achieves much better detection rate considering multi-jammer environment. Thus aid in achieving much better detection accuracy, packet drop, packet transmission and resource utilization performance.
A comprehensive survey on deep-learning based gait recognition for humans in the COVID-19 pandemic Md Shohel Sayeed; Ibrahim Bin Yusof; Mohd Fikri Azli bin Abdullah; Md Ahsanul Bari; Pa Pa Min
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp882-902

Abstract

Human gait recognition is a biometric technique that has been utilized for security purposes for the last decade. Gait recognition is an appealing biometric modality that aims to identify individuals based on the way they walk. The outbreak of the novel coronavirus (COVID-19), has spread across the world. The number of people infected with COVID-19 is rising rapidly throughout the world. Even though some vaccines for this pandemic have been developed to minimize the effects of COVID-19, deep learning-based gait recognition techniques have shown themselves to be an effective tool for identifying the individuals wearing face mask in COVID-19 pandemic. These techniques play an important part in reducing the rate of COVID-19 spreading throughout the world in the context of the COVID-19 pandemic. Deep learning methods are currently dominating the state-of-the-art in gait recognition and have fostered real-world applications. The main objective of this paper is to provide a comprehensive overview of recent advancements in gait recognition with deep learning, including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. The purpose of this discussion is to identify current challenges that need to be addressed as well as to suggest some directions for future research that could be explored.
Unmanned aerial vehicle: a review and future directions Mahmood A. Al-Shareeda; Murtaja Ali Saare; Selvakumar Manickam
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp778-786

Abstract

The use of unmanned aerial vehicles (UAVs) will be crucial in the next generation of wireless communications infrastructure. When compared to traditional ground-based solutions, it is expected that their use in a variety of communication-based applications will increase coverage and spectrum efficiency. In this paper, we provide a detailed review of all relevant research works as follows. This paper presents types of UAVs (e.g., wireless coverage, military, agriculture, medical applications, environment, and climate, and delivery and transportation), characteristics of UAVs (e.g., node density, altering system topology, node mobility, radio broadcasting mode, frequency band, localization, and power consumption and network lifetime), the application of UAVs (e.g., Multi-UAV cooperation, UAV-to-VANET collaborations, and UAV-to-ground tasks). Additionally, this paper reviews the routing protocols of UAVs (e.g., topology-based, position-based, heterogeneous, delay-tolerant networks (DTNs), swarm-Based, and cluster-based) and simulation tools (e.g., OMNeT++, AVENS, MATLAB, NS3, SUMO, and OPNET). The design and development of any new methods for UAVs may use this work as a guide and reference.
A survey on automatic engagement recognition methods: online and traditional classroom Ajitha Sukumaran; Arun Manoharan
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp1178-1191

Abstract

Student engagement in a learning environment is directly related to students’ perception and involvement of the educational activities in the class, along with their physical and mental health. This paper presents an extensive survey of the various automatic engagement detection approaches and algorithms based on computer vision, physiological and neurological signals analysis-based methods. The computer vision-based techniques depend on the traits captured by image sensors such as facial expressions, gesture and posture analysis, and gaze direction. The physiological and neurological signal based approach depends on the sensor data, like heart rate (HR), electroencephalogram (EEG), blood pressure (BP), and galvanic skin response (GSR). A brief analysis of the available datasets for Engagement Recognition and its features are also summarized. This study highlights a few commercially available wearables which provides the physiological signals that helps in student’s attentivity recognition. Our study reveal that the accuracy of engagement recognition system will increase if we increase the number of modalities used. In this survey, we intend to support the upcoming researchers as well as tutors of smart education set up by providing an overview of existing or proposed approaches of automatic engagement detection techniques in different scenarios.
New blender-based augmentation method with quantitative evaluation of CNNs for hand gesture recognition Huong-Giang Doan; Ngoc-Trung Nguyen
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp796-806

Abstract

In this study, we extensively analyze and evaluate the performance of recent deep neural networks (DNNs) for hand gesture recognition and static gestures in particular. To this end, we captured an unconstrained hand dataset with complex appearances, shapes, scales, backgrounds, and viewpoints. We then deployed some new trending convolution neuron networks (CNNs) for gesture classification. We arrived at three major conclusions: i) DenseNet121 architecture is the best recognition rate through almost evaluated red, green, blue (RGB) and augmentation datasets. Its performance is outstanding in most original works; ii) blender-based augmentation help to significantly increase 9% of accuracy, compared to the use of a RGB cues; iii) most CNNs can achieve impressive results at 97% accuracy when the training and testing datasets come from the same lab-based or constrained environment. Their performance is drastically reduced when dealing with gestures collected in unconstrained environments. In particular, we validated the best CNN on a new unconstrained dataset. We observed a significant reduction with an accuracy of only 74.55%. This performance can be improved up to 80.59% by strategies such as blender-based and/or GAN-based data augmentations to obtain an acceptable result of 83.17%. These findings contribute crucial factors and make fruitful recommendations for the development of a robust hand-based interface in practice
Blackhole attacks in internet of things networks: a review Noor Hisham Kamis; Warusia Yassin; Mohd Faizal Abdollah; Siti Fatimah Abdul Razak; Sumendra Yogarayan
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp1080-1090

Abstract

The internet of things (IoT) is one of data revolution area and is the following extraordinary mechanical jump after the internet. In terms of IoT, it is expected that electronic gadgets that are used on a regular basis would be connected to the current of the internet. IPv6 over low-power wireless personal area networks (6LoWPAN) is a one of IPv6 header pressure technology, and accordingly, it is vulnerable to attack. The IoT is a combination of devices with restricted resource assets like memory, battery power, and computational capability. To solve this, RPL or routing protocol for low power Lossy network is deploy by utilizing a distance vector scheme. One of denial of service (Dos) attack to RPL network is blackhole attack in which the assailant endeavors to become a parent by drawing in a critical volume of traffic to it and drop all packets. In this paper, we discuss research on numerous attacks and current protection methods, focusing on the blackhole attack. There is also discussion of challenge, open research issues and future perspectives in RPL security. Furthermore, research on blackhole attacks and specific detection technique proposed in the literature is also been presented.
Using support vector machine regression to reduce cloud security risks in developing countries Sanaa Hammad Dhahi; Estqlal Hammad Dhahi; Ban Jawad Khadhim; Shaymaa Taha Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp1159-1166

Abstract

The use of the cloud by governments throughout the world is being aggressively investigated to increase efficiency and reduce costs. The majority of cloud computing risk management programs prioritize addressing cloud security issues that government organizations may face when they choose to adopt cloud computing systems, but these programs lack evidence of security risks, and problems with using cloud computing in developing nations are uncommon, so they called for more research in this area. The objective of this paper is to use quantitative models namely Spearman's Rank correlation coefficient, simple regression, and support vector machine regression (SVMR) for estimating cloud security issues based on cloud control factors for improving the mitigation of cloud computing security issues based on control factors using intelligent models in a government organization. Identify the proper cloud control factors for every cloud security issue from estimation errors using a standard for performance measurement like mean square error (MSE) and root mean square error (RMSE), performance measurement to evaluate and validate proposed models. SVMR is an approach to enhance practices for cloud security platforms to mitigate risks and infrastructure for cloud adoption in developing countries in this paper.
Model of intention and actual use mobile learning in higher education institutions in Iraq Ayad Shihan Izkair; Muhammad Modi Lakulu
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp1250-1258

Abstract

Mobile learning (ML) is now involved in the creation of teaching and learning methods for higher education. However, this involvement is varied among countries. The objective of this research was to examine the factors that impact students' intentions to use ML in Iraqi higher education institutions (HEIs). Building on unified theory of acceptance and use of technology (UTAUT) and information system success model (ISSM), a conceptual model was developed. The population of this study are users in Iraqi univeristies. Using a stratified random sampling, a total of 323 responses were collected to examine the proposed hypotheses. The findings showed that variables of UTAUT such as effort expectancy (EE), social influence (SI), performance expectancy (PE), facilitating conditions (FC) as well as variables of ISSM such as satisfaction along with perceived enjoyment and self efficacy affected positively the intention to use ML (ITUML) which in turn affected actual use (AU). Gender and experience moderated the effect of PE, EE, and SI on ITUML. A model of ITUML among users in Iraqi HEI was developed. Decision makers are advised to focus on certain variables to enhance the usage of ML in HEI.
Energy aware optimized dynamic routing mechanism in wireless sensor networks Geeta Patil; Arvind Mallikarjun Bhavikatti
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp944-955

Abstract

A trade-off between energy efficiency and optimized routing is massively recommended for transmission efficiency enhancement in wireless sensor networks (WSNs). Therefore, in this paper, graph-based energy optimized dynamic routing (GEODR) mechanism is introduced to set up a balance between energy consumption minimization and throughput enhancement using a dynamic and optimized routing mechanism in WSNs. A clustering scheme is employed based on graph theory, and cluster boundaries are formed using distance vectors. Cluster head (CH) selection is performed based on residual energy, the distance between CHs, and the mobility of the sink node. Each cluster is scattered with multiple tiny nodes, and event monitoring is performed. A model for graph-based dynamic routing to transmit data packets, cluster and cluster boundary formation, and optimization of routing problems is discussed. The performance efficiency of the proposed GEODR mechanism is determined by taking 100 sensor nodes, and 20 nodes are selected as CHs in a sensor network, and several other network parameters are also considered. A massive improvement in energy is observed by using sink node mobility. Experimental results are obtained using the proposed GEODR mechanism in terms of data packet transmission, alive nodes, dead nodes, and residual energy and compared against classical routing mechanisms such as low energy adaptive clustering hierarchy (LEACH) and stable election protocol (SEP).
Low-cost portable throttle curve manipulator for smooth initial movement of an electric vehicle Dimas Adiputra; Pangestu Widodo; Aldo Juan Widodo; Yosefan Alfeus Bayuaji; Nadia Dinda Pratama Putri
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp681-689

Abstract

This research aims to develop a low-cost portable throttle curve manipulator for a smooth initial movement of an electric vehicle. The hardware is mostly made up of an Arduino and a pulse width modulation (PWM)-to- direct current (DC) converter, which can be easily installed in electric vehicle. The manipulator produces a throttle output curve based on the current throttle input. The suitable throttle output curve is investigated in two stages. First, the four throttle curve types are compared based on motor vibration change and total energy usage during initial movement. They are none, linear, exponential, and polynomial curve types with a delay of 1 s. Then, in the second stage, the delay is varied from 0.5 to 2.5 s. The result shows that the linear throttle curve output with a delay of 1 s produces is appropriate to refine the initial movement of an electric vehicle compared to the polynomial and exponential curve types. The brushless DC electric (BLDC) motor vibration change decreases from 148.75 Hz to 107.45 Hz and total energy usage decreases from 90.64 joules to 87.23 joules. Therefore, the research concludes that the low-cost portable throttle curve manipulator can be developed using a linear throttle output curve with a delay of 1 s.

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