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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 9,138 Documents
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Energy efficient routing protocol for enhancing the network lifetime in wireless sensor network Hiremath, Veeresh; Kerur, Sidlingappa; Gudnavar, Anand
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp944-957

Abstract

Wireless sensor networks (WSNs) confront significant challenges related to battery capacity, as sensor nodes operate on limited energy resources. To address this issue, low energy adaptive clustering hierarchy (LEACH) protocol is commonly employed for power management in WSNs. LEACH is commonly used for power management. Here, sensing region is divided into clusters and sectors, placing a gateway node at the center to minimize energy consumption during data transmission. It employs one-hop, two-hop, or three-hop pathways based on node proximity to the base station (BS) to optimize energy usage. Network performance is assessed using rounds, throughput, and energy usage. MATLAB simulations compare the proposed approach with dual layer LEACH (DL-LEACH) and LEACH, showing significant improvements in network lifetime. The proposed scheme outperforms LEACH by 515% and 347% for 20% and 50% node depletion, respectively. Compared to DL-LEACH, it extends network lifetime by 27% and 59% under similar scenarios. Sectoring, clustering, and multi-hop communication reduce energy consumption, enhancing network lifetime and addressing WSN challenges effectively.
An enhanced multi-objective artificial bee colony algorithm with non-dominated sorting strategy Hamid Bouali; Bachir Benhala; Mohammed Guerbaoui
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1736-1747

Abstract

This paper presents an improved metaheuristic technique inspired by the foundational concepts of the artificial bee colony (ABC) algorithm adapted to deal with multi-objective optimization challenges. Our approach combines the main ideas of ABC with a non-dominated sorting strategy including aspects of Pareto dominance, crowding distance, and greedy selection method. Furthermore, the chosen non-dominated solutions are archived in a repository with a static size. The presented approach, multi-objective artificial bee colony (MOABC), is compared to other state-of-the-art algorithms including the non-dominated sorting genetic algorithm II (NSGA II) and the multi-objective particle swarm optimization (MOPSO). MOABC and selected algorithms from the literature are applied to five zitzler-deb-thiele (ZDT) Multi-objective benchmark functions. Then three key metrics are employed for performance evaluations: generational distance (GD), spread (SP), and hypervolume (HV). The simulation results suggest that the proposed method is competitive and presents an effective choice for tackling multi-objective optimization problems.
Forecasting water quality through machine learning and hyperparameter optimization Elvin Elvin; Antoni Wibowo
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp496-506

Abstract

Forecasting water quality through machine learning and hyperparameter optimization is a research endeavor aimed at enhancing the water quality prediction process. The primary goal of this study is to employ various machine learning algorithms for water quality prediction and to refine existing models from previous research. The paper encompasses a comprehensive literature review of previous water quality prediction studies and introduces novel theoretical insights. The research employs a classic machine learning problem-solving approach, predominantly utilizing the extreme gradient boost (XGBoost) algorithm. Additionally, it evaluates other machine learning algorithms, including the random forest (RF) classifier, decision tree (DT) classifier, adaptive boosting (AdaBoost) classifier, support vector machine (SVM), Naïve Bayes, and extra tree classifier for comparison. The evaluation process utilizes a classification report, providing insights into the precision, recall, f1-score, and accuracy of each machine learning model. Notably, the XGBoost model exhibits superior performance, achieving an impressive 97.06% accuracy. Precision stands at 94.22%, recall at 81.5%, and F1-score at 87.4%. These results represent a significant advancement over prior water quality prediction models, emphasizing the potential of machine learning and hyperparameter optimization to enhance water quality forecasting in environmental monitoring.
Dual image watermarking based on NSST-LWT-DCT for color image Avivah, Siti Nur; Ernawan, Ferda; Mat Raffei, Anis Farihan
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp907-915

Abstract

Advanced internet technology allows unauthorized individuals to modify and distribute digital images. Image watermarking is a popular solution for copyright protection and ensuring digital security. This research presents an embedding scheme with a set of conditions using non-subsampled Shearlet transform (NSST), lifting wavelet transform (LWT), and discrete cosine transform (DCT). Red and green channels are employed for the embedding process. The red channel is converted by NSST-LWT. The low-frequency area (LL) frequency is then split into small blocks of 8×8, each partition block is then transformed by DCT. The DCT coefficient of (3,4), (5,2), (5,3), (3,5), called matrix M1, and (2,5), (4,3), (6,2), (4,4), called matrix M2 are selected for singular value decomposition (SVD) process. With a set of conditions, the watermark bits are incorporated into those singular values. The green channel is cropped to get the center image before splitting into 4×4 pixels. The block components are then selected based on the least entropy value for the embedding regions. The selected blocks are then computed using LWT-SVD. A set of conditions for U(1,1) and U(2,1) are used to incorporate the watermark logo. The experimental findings reveal that the suggested scheme achieves high imperceptibility and resilience under various evaluating attacks with an average peak signal-to-noise ratio (PSNR) and correlation value (NC) values are up to 43.89 dB and 0.96, respectively.
Hybrid total variance void-based noise removal in infrared images Kamaleswari Pandurangan; Krishnaraj Nagappan
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1705-1714

Abstract

An artifact known as an image is what makes the depiction of a thing or a person feasible. An image is a representation of visual perception and has a physical appearance that is analogous to that of the subject being portrayed. In situations when there is insufficient illumination, such as at night or when there is a lot of background noise, the use of infrared imagery can help improve the accuracy of object detection. Infrared images are able to account for a wide variety of noises, including those that are the result of sensor faults, lens distortion, software artifacts, blur, and other problems. It is difficult to do qualitative and quantitative analysis on thermal images due to the significant levels of noise that are present in these images. Eliminating noise in an infrared image by employing the total variance void (TVV) denoising technique while preserving the integrity of the image’s boundaries and texture. Denoising thermal images make use of a technique that is both efficient and reliable thanks to an integrated algorithm that combines TV denoising and Noise2Void (N2V). Strengths of the two methods, it is possible to produce denoised images of superior quality with improved retention of edge and texture detail.
Design and development of arduino-based automation home system using the internet of things Sunday Adeola Ajagbe; Oyetunde Adeoye Adeaga; Oluwaseyi Omotayo Alabi; Adewale Bashir Ikotun; Musa A. Akintunde; Matthew O. Adigun
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp767-776

Abstract

The home automation system described in this paper is low-cost, dependable, and versatile. It uses an Arduino microcontroller and Bluetooth internet protocol (IP) connectivity to allow authorized users to remotely access and control devices. The suggested system employs the internet of things (IoT), which is server-independent, to manage human-desired appliances ranging from industrial machinery to consumer products. In this project, we have taken a Bluetooth module that is programmed through an Arduino Nano to control various devices auto-switching of mechanical devices and monitoring of water level within a range of 130 m using an Android application. This is done to show the effectiveness and viability of this system. Each bulb was switched on/off remotely using a mobile phone successfully. The operation of the water pump attached to the source bucket were controlled from the phone while in manual mode and controlled by an ultrasonic sensor while in automatic mode. It enables remote control of a number of devices, including lights and pumps, and decision-making based on sensor feedback.
Extracting geo-references from social media text using bi-long short term memory networks Mangal, Dharmendra; Makwana, Hemant
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1263-1270

Abstract

The social media data provides great source of information about global and local events, with millions of users. More precisely, the fact that brief messages are practical and are highly popular. Many recent studies have been motivated to estimate the location of the events identified by tracking posts in social media text messages. It might be difficult to extract location data and estimate the location of an event while maintaining a sufficient level of situation awareness, particularly in disaster situations like fires or traffic accidents. In this presented work we proposed an approach to identify geo-references in the text messages. We used bi-directional long short term memory (LSTM) neural networks to extract location information in the text messages. The results show that applying Bi-LSTM on dataset gives high level accuracy after fine-tuning (up to 10 epochs). The testing results show that accuracy achieved is 0.98 and 0.076 loss value. This proves that the proposed methodology is better than the previous conditional random field (CRF)-based approaches.
Comparing Leach protocol and its descendants on transferring scalar data Bennani, Mohamed Taj; Zbakh, Abdelali; El Far, Mohamed; Lamrini, Mohamed; El Hichami, Outman; El Fahssi, Khalid; Satori, Hassan
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp255-262

Abstract

In the last years, The CMOS was developed and miniaturized rapidly, which, made sensors very fast, small and accurate. Hence, the creation of wireless sensor network (WSN) which are a network of nodes that exchange the data between them until it reaches the sink (base station). It is responsible for treating the data and transfer them to other servers linked to the internet for further treatment or storage. Therefore, everything related to WSN is a big topic of research for scientific community, especially transferring scalar data. In fact, many factors enter into account when it comes to send data like a radio, range of transmission, energy consumption and routing protocol. Routing protocols are very important in transferring data. They also have a big impact on energy consumption by nodes. Many categories of routing protocols exist: planning and level routing. Each type has its strength and weakness points. So, using a routing protocol in high-density environments is very challenging in energy consumption and data delivery. In addition, since level routing protocols like Leach are known for their energy efficiency. We choose three level routing protocol (Leach, MLD-Leach and MRE-Leach) to put them in a harsh environment to test their energy consumption and data transferring. We found that MLD-Leach has better energy consumption and data delivery.
An Intrusion Detection System against RPL-based Routing Attacks for IoT Networks Manjula Hebbaka Shivanajappa; Roopa Maidanahalli Seetharamaiah; Bharath Viswaraju Sai; Arunalatha Jakkanahally Siddegowda; Venugopal Kuppanna Rajuk
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1324-1335

Abstract

The significant improvement in the Internet, Internet of Things (IoT), communication, and cloud computing have created considerable challenges in providing security for data and devices.  In IoT networks, “Routing Protocol for Low power and Lossy networks”- (RPL) is a communication protocol that enables devices to exchange information and communicate with limited resources like low processing capabilities, less memory and energy. Through the Internet, unauthorised users can access RPL-based IoT networks, making these networks susceptible to routing attacks. Therefore, it is crucial to design Intrusion Detection System-(IDS) to address attacks from IoT communication devices. In this paper, we have proposed GCNConv, a Graph Neural Network (GNN) method that allows capturing the edge and node features of a graph to identify routing attacks. The proposed   system   has experimented on the RADAR dataset and experimental findings proved that, our approach performs well compared to state-of-the-art method with reference to precision, F1-score, accuracy and recall.
A new approach to solve the problem of partial shading in a photovoltaic system Abdessamad, Benlafkih; El Idrissi Mohamed, Chafik; Hadjoudja, Abdelkader; El Moujahid, Yassine; El Maliki, Anas; Othmane, Echarradi; Mounir, Fahoume
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1298-1308

Abstract

This paper introduces a novel global maximum power point (GMPP) tracking method that addresses the challenges of efficiency and power quality degradation in photovoltaic (PV) systems caused by inadequate tracking of the GMPP. The proposed approach employs a cuckoo search algorithm with proportional, integral, and derivative (CSPID). A bio-inspired optimization technique, to effectively track the GMPP under varying weather conditions. To demonstrate its effectiveness, the CSPID algorithm is comprehensively evaluated against two well-established methods, particle swarm optimization (PSO), and cuckoo search algorithm traditional (CSA). The evaluation includes three different scenarios with gradual changes in irradiance and temperature, these tests show the ability of the algorithm to handle the condition of partial shading. The results reveal that the CSPID method achieves an average tracking time of 0.098s and an average tracking efficiency of 99.62%, thereby significantly improving the efficiency and quality of photovoltaic energy production.

Page 808 of 914 | Total Record : 9138

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