Network Engineering Research Articles

Comparison of software defined networking with traditional networking using NS2 simulator

The Software Defined Network (SDN) is an emerging concept in network engineering that entails the decoupling of the control plane and data plane. The data plane is executed on discrete nodes, while a centralised control plane is accountable for overseeing network-wide functionalities. In conventional networks, the integrated planes are incorporated into the network suite, and the transmission is determined by the route. In this work, the traditional way of routing using a communication plane is compared with routing using an SDN controller. The present study employs the NS2 simulator to evaluate the quality of service performance under high background traffic conditions. In the event of network congestion, it is imperative to utilize the available bandwidth to its fullest extent in order to increase the rate of bit transmission per second. The results of the traditional network and SDN network based on various parameters like Ambiguous collision, Delay, Jitter, Availability and Latency are compared in this paper. The results show that the SDN network's performance is better with concerning time.

Neural network models selection scheme for health mobile app development

Mobile healthcare application (mHealth app) assists the frontline health worker in providing necessary health services to the patient. Unfortunately, existing mHealth apps continue to have accuracy issues and limited number of disease detection systems. Thus, an intelligent disease diagnostics system may help medical staff as well as people in poor communities in rural areas. This study proposes a scheme for simultaneously selecting the best neural network models for intelligent disease detection systems on mobile devices. To find the best models for a given dataset, the proposed scheme employs neural network models capable of evolving altered neural network architectures. Eight neural network models are developed simultaneously and then implemented on the Android Studio platform. Mobile health applications use pre-trained neural network models to provide users with disease prediction results. The performance of the mobile application is measured against the existing available datasets. The trained neural network engines perform admirably, detecting 7 out of 8 diseases with high accuracy ranging from 86.00% to 100% and a low detection accuracy of 63%. The detection times vary from 0.01 to 0.057 seconds. The developed mHealth app may be used by health workers and patients to improve resource-poor community health services and patients' healthcare quality.

ESP Need Analysis of Computer and Network Engineering in Vocational High School

A vocational school is an educational institution that focuses on providing students with practical skills and training for specific careers or professions. Unfortunately, the English lessons given to vocational schools still follow the same English as English in general high school which does not support their needs in their specific major resulted in gaps in English skill for vocational students. The gaps between secondary schools teaching English and vocational schools in terms of resources, curriculum, and even infrastructure. Examining the ESP of computer and network engineering in vocational high school was the aim of this study. As research tools for this study, a questionnaire about needs analysis, analysis of textbooks, and analysis of interviews were used. The findings indicated that in vocational schools, ESP teaching and learning based on the school program is needed by both students and teachers. The poor value of English language proficiency, particularly ESP in vocational schools, is due to a number of factors, including a lack of ESP for computer and network engineering starting from the content, such as textbooks, suitable school facilities, and even the competence of teachers to teach ESP.

Analysis of the Performance of IoT Networks in Acoustic Environment by using LZW Data Compression Technique

The Internet of Things (IoT) has experienced phenomenal growth, opening up a wide range of applications in many settings. Due to the properties of sound propagation, IoT networks operating in acoustic environments in particular present special difficulties. Data compression techniques can be used to minimize overhead and maximize resource utilization in these networks to increase performance. The performance of IoT networks in acoustic environments is examined in this study, with a focus on routing overhead, throughput, and typical end-to-end delay. Lempel-Ziv-Welch (LZW) data compression is used to reduce data size and boost communication effectiveness. Three well-known protocols—MQTT, CoAP, and Machine-to-Machine (M2M)—are assessed in relation to acoustic Internet of Things networks. To mimic different acoustic conditions and collect performance metrics, a thorough experimental setup is used. Different network topologies, data speeds, and compression settings are used in the studies to determine how they affect the performance metrics. According to the analysis's findings, all three protocols' routing overhead is greatly decreased by the LZW data compression approach, which enhances network scalability and lowers energy usage. Additionally, the compressed data size has a positive impact on network throughput, allowing for effective data transmission in acoustic contexts with limited resources. Additionally, using LZW compression is seen to minimize the average end-to-end delay, improving real-time communication applications. This study advances knowledge of IoT networks operating in acoustic environments and the effects of data reduction methods on their functionality. The results offer useful information for network engineers and system designers to optimize the performance of IoT networks in similar situations. Additionally, a comparison of the MQTT, CoAP, and M2M protocols' suitability for acoustic IoT deployments is provided, assisting in the choice of protocol for particular application needs.

Assessment of SDN Controllers in Wireless Environment Using a Multi-Criteria Technique

Software-defined network (SDN) technology can offer wireless networks the advantages of simplified control and network management. This SDN subdomain technology is called the software-defined wireless network (SDWN). In this study, the performance of four controllers in an SDWN environment is assessed, since the controller is the most significant component of the entire network. Using the Mininet-WiFi platform, the performance of each controller is evaluated in terms of throughput, latency, jitter, and packet loss. Moreover, a multi-criteria evaluation is introduced and applied to provide a fair comparison between SDWNs. This study provides an appropriate configuration of SDWNs that is useful for network engineering and can be used for SDWNs performance optimization.

Analysis of Social Network Data Mining for Security Intelligence Privacy Machine Learning

The Modern communication on the Internet platform is most responsive through social media. Social media has changed and is still reshaping how we share our thoughts and emotions in communication. It has introduced a constant real-time communication pattern that was before unheard of. Young and old, organizations, governmental agencies, professional associations, etc., all have social media accounts that they use exclusively for communication with other users. Social media also acts as a powerful network engine that connects users regardless of where they are in the world. The development of global communication will greatly benefit from the availability of this new communication platform in the future. Consequently, there is a pressing need to research usage trends. Therefore, it is vital to investigate social media platform usage trends in order to develop automated systems that intelligence services can use to help avert national security incidents. Through the use of social media data mining, this research study suggests an automated machine learning model that can improve speedy response to crises involving national and International security.

New targets of TetR-type regulator SLCG_2919 for controlling lincomycin biosynthesis in Streptomyces lincolnensis.

The transcription factor (TF)-mediated regulatory network controlling lincomycin production in Streptomyces lincolnensis is yet to be fully elucidated despite several types of associated TFs having been reported. SLCG_2919, a tetracycline repressor (TetR)-type regulator, was the first TF to be characterized outside the lincomycin biosynthetic cluster to directly suppress the lincomycin biosynthesis in S. lincolnensis. In this study, improved genomic systematic evolution of ligands by exponential enrichment (gSELEX), an in vitro technique, was adopted to capture additional SLCG_2919-targeted sequences harboring the promoter regions of SLCG_6675, SLCG_4123-4124, SLCG_6579, and SLCG_0139-0140. The four DNA fragments were confirmed by electrophoretic mobility shift assays (EMSAs). Reverse-transcription quantitative polymerase chain reaction (RT-qPCR) showed that the corresponding target genes SLCG_6675 (anthranilate synthase), SLCG_0139 (LysR family transcriptional regulator), SLCG_0140 (beta-lactamase), SLCG_6579 (cytochrome P450), SLCG_4123 (bifunctional DNA primase/polymerase), and SLCG_4124 (magnesium or magnesium-dependent protein phosphatase) in ΔSLCGL_2919 were differentially increased by 3.3-, 4.2-, 3.2-, 2.5-, 4.6-, and 2.2-fold relative to those in the parental strain S. lincolnensis LCGL. Furthermore, the individual inactivation of these target genes in LCGL reduced the lincomycin yield to varying degrees. This investigation expands on the known DNA targets of SLCG_2919 to control lincomycin production and lays the foundation for improving industrial lincomycin yields via genetic engineering of this regulatory network.

INTRODUCTION OF DEVICES AND ASSEMBLING COMPUTERS TO STUDENTS

SMK Swasta Methodist Tanjung Morawa is a private school under the auspices of the Kasih Imanuel Indonesia Methodist Foundation, which was established in 2008. SMK Swasta Methodist Tanjung Morawa has various majors, one of which is Network and Computer Engineering (TKJ). Assembling computer equipment is one of the subjects that can help students become more familiar with computer equipment and know how to fix it and of course this lesson is already a lesson that is in accordance with the majors of SMK Swasta Methodist Tanjung Morawa students, namely Computer Network Engineering (TKJ). The students have studied theoretically related to assembling computer equipment, therefore, the Faculty of Informatics, Universitas Mikroskil offers activities in the form of training in assembling computer equipment to improve students' ability to have good knowledge in computer equipment and how to assemble it. This training activity lasted for 2 days and was carried out in the accounting laboratory at Universitas Mikroskil. During this training activity the students were given pre-test questions, materials and case studies, post-tests and final feedback.

Adaptive Search-and-Training for Robust and Efficient Network Pruning.

Both network pruning and neural architecture search (NAS) can be interpreted as techniques to automate the design and optimization of artificial neural networks. In this paper, we challenge the conventional wisdom of training before pruning by proposing a joint search-and-training approach to learn a compact network directly from scratch. Using pruning as a search strategy, we advocate three new insights for network engineering: 1) to formulate adaptive search as a cold start strategy to find a compact subnetwork on the coarse scale; and 2) to automatically learn the threshold for network pruning; 3) to offer flexibility to choose between efficiency and robustness. More specifically, we propose an adaptive search algorithm in the cold start by exploiting the randomness and flexibility of filter pruning. The weights associated with the network filters will be updated by ThreshNet, a flexible coarse-to-fine pruning method inspired by reinforcement learning. In addition, we introduce a robust pruning strategy leveraging the technique of knowledge distillation through a teacher-student network. Extensive experiments on ResNet and VGGNet have shown that our proposed method can achieve a better balance in terms of efficiency and accuracy and notable advantages over current state-of-the-art pruning methods in several popular datasets, including CIFAR10, CIFAR100, and ImageNet. The code associate with this paper is available at: https://see.xidian.edu.cn/faculty/wsdong/Projects/AST-NP.htm.

ANALISIS BIAYA SATUAN OPERASIONAL NON PERSONALIA BERBASIS STANDAR KEBUTUHAN KOMPETENSI DI SMK

This research is a qualitative research with a Descriptive Observational Case Studies approach which aims to obtain the factual non-personnel operational unit cost average required for the Competency of Computer and Network Engineering Skills at SMK Negeri 2 Bone. The research samples were school principals, deputy principals, BOS treasurers, administrative staff, heads of computer and network engineering competency skills computer and network engineering (TKJ) skills competency teachers at SMK Negeri 2 Bone. Data collection techniques through interviews, observation, questionnaire techniques and documentation. The data analysis technique used is qualitative analysis of flowing or interactive analysis models. This model consists of three components of analysis, namely data reduction, data presentation, and drawing conclusions or verification. The results of the study show that the average non-personnel operational unit cost in fact required for the Computer and Network Engineering Skills Competency at SMK Negeri 2 Bone in 2023 is Rp. 168.409,300, - using the current year's calculations and the number of students is 103 people spread across Class X, Class XI and Class XII.

The non-specific lethal complex regulates genes and pathways genetically linked to Parkinson's disease.

Genetic variants conferring risks for Parkinson's disease have been highlighted through genome-wide association studies, yet exploration of their specific disease mechanisms is lacking. Two Parkinson's disease candidate genes, KAT8 and KANSL1, identified through genome-wide studies and a PINK1-mitophagy screen, encode part of the histone acetylating non-specific lethal complex. This complex localizes to the nucleus, where it plays a role in transcriptional activation, and to mitochondria, where it has been suggested to have a role in mitochondrial transcription. In this study, we sought to identify whether the non-specific lethal complex has potential regulatory relationships with other genes associated with Parkinson's disease in human brain. Correlation in the expression of non-specific lethal genes and Parkinson's disease-associated genes was investigated in primary gene co-expression networks using publicly-available transcriptomic data from multiple brain regions (provided by the Genotype-Tissue Expression Consortium and UK Brain Expression Consortium), whilst secondary networks were used to examine cell type specificity. Reverse engineering of gene regulatory networks generated regulons of the complex, which were tested for heritability using stratified linkage disequilibrium score regression. Prioritized gene targets were then validated in vitro using a QuantiGene multiplex assay and publicly-available chromatin immunoprecipitation-sequencing data. Significant clustering of non-specific lethal genes was revealed alongside Parkinson's disease-associated genes in frontal cortex primary co-expression modules, amongst other brain regions. Both primary and secondary co-expression modules containing these genes were enriched for mainly neuronal cell types. Regulons of the complex contained Parkinson's disease-associated genes and were enriched for biological pathways genetically linked to disease. When examined in a neuroblastoma cell line, 41% of prioritized gene targets showed significant changes in mRNA expression following KANSL1 or KAT8 perturbation. KANSL1 and H4K8 chromatin immunoprecipitation-sequencing data demonstrated non-specific lethal complex activity at many of these genes. In conclusion, genes encoding the non-specific lethal complex are highly correlated with and regulate genes associated with Parkinson's disease. Overall, these findings reveal a potentially wider role for this protein complex in regulating genes and pathways implicated in Parkinson's disease.

CPU frequency scheduling of real-time applications on embedded devices with temporal encoding-based deep reinforcement learning

Small devices are frequently used in IoT and smart-city applications to perform periodic dedicated tasks with soft deadlines. This work focuses on developing methods to derive efficient power-management methods for periodic tasks on small devices. We first study the limitations of the existing Linux built-in methods used in small devices. We illustrate three typical workload/system patterns that are challenging to manage with Linux’s built-in solutions. We develop a reinforcement-learning-based technique with temporal encoding to derive an effective DVFS governor even with the presence of the three system patterns. The derived governor uses only one performance counter, the same as the built-in Linux mechanism, and does not require an explicit task model for the workload. We implemented a prototype system on the Nvidia Jetson Nano Board and experimented with it with six applications, including two self-designed and four benchmark applications. Under different deadline constraints, our approach can quickly derive a DVFS governor that can adapt to performance requirements and outperform the built-in Linux approach in energy saving. On Mibench workloads, with performance slack ranging from 0.04 s to 0.4 s, the proposed method can save 3%–11% more energy compared to Ondemand. AudioReg and FaceReg applications tested have 5%–14% energy-saving improvement. We have open-sourced the implementation of our in-kernel quantized neural network engine. The codebase can be found at: https://github.com/coladog/tinyagent.

Identification of the novel FOXP3-dependent Treg cell transcription factor MEOX1 by high-dimensional analysis of human CD4+ T cells.

CD4+ T cells play a central role in the adaptive immune response through their capacity to activate, support and control other immune cells. Although these cells have become the focus of intense research, a comprehensive understanding of the underlying regulatory networks that orchestrate CD4+ T cell function and activation is still incomplete. Here, we analyzed a large transcriptomic dataset consisting of 48 different human CD4+ T cell conditions. By performing reverse network engineering, we identified six common denominators of CD4+ T cell functionality (CREB1, E2F3, AHR, STAT1, NFAT5 and NFATC3). Moreover, we also analyzed condition-specific genes which led us to the identification of the transcription factor MEOX1 in Treg cells. Expression of MEOX1 was comparable to FOXP3 in Treg cells and can be upregulated by IL-2. Epigenetic analyses revealed a permissive epigenetic landscape for MEOX1 solely in Treg cells. Knockdown of MEOX1 in Treg cells revealed a profound impact on downstream gene expression programs and Treg cell suppressive capacity. These findings in the context of CD4+ T cells contribute to a better understanding of the transcriptional networks and biological mechanisms controlling CD4+ T cell functionality, which opens new avenues for future therapeutic strategies.

Design and Implementation of a Vlsm Simulator

Variable-Length Subnet Masks (VLSM), often referred to as "subnetting a subnet", is used to maximize addressing efficiency. The network administrator is able to use a long mask on networks with few hosts, and a short mask on subnets with many hosts. This addressing scheme allows growth and does not involve wasting addresses. VLSM gives a way of subnetting a network with minimal loses of IP addresses for a specific range. Unfortunately, the network administrator has to perform several mathematical steps (or use charts) to get the required results from VLSM. In this paper, a simple graph simulator is proposed (using Visual Basic 6.0 Language) to perform all the required mathematical steps and to display the obtained required information (the subnet ID, broadcast ID, usable addresses for sub networks and others). The simulator also includes the ability to draw a suggested network topology that matches the entries. The implementation of the simulation required only very few entries (IP, prefix and number of subnets).This simulator is useful for students, instructors, and network engineers to analysis and design a VLSM network by providing all required information in simple, fast and easy steps. Moreover, the software draws a full detailed suggested network topology which is considered a helpful tool for the network administrator that he should have.

VOCATIONAL HIGH SCHOOL STUDENTS' ABILITY TO SOLVE WORD PROBLEMS IN THE TOPIC OF LINEAR EQUATIONS SYSTEM WITH TWO VARIABLES

Problem-solving is a mathematical skill that students need to develop to solve challenges in everyday life. However, this ability has yet to be fully mastered by students. Therefore, this study analyzes the problem-solving skills of ten-grade private Vocational High School students in Sragen, Central Java, Indonesia, particularly in the Linear Equations System with Two Variables (LESTV). This study used a descriptive qualitative method involving 25 Computer and Network Engineering students (CNE). The problem-solving indicators used are understanding the problem, devising a plan, carrying out the plan, and looking back. This study grouped students into three levels based on the results of the Mid Semester Assessment (MSA) of students' initial abilities, namely low, medium, and high initial ability levels. Based on the analysis, this study indicates that students with low initial ability levels are less able to understand problems and the other stages. Students with a moderate level of initial ability can meet the indicators of understanding the problem and devising a plan. Students with a high level of initial ability fulfill all problem-solving indicators. Students have been able to understand problems, devise a plan, carry out the plan, and look back. Students with a high level of initial ability can analyze and conclude the results of the answers correctly, accompanied by re-examining the solutions they have obtained.

CORELLATION BETWEEN KNOWLEDGE LEVEL WITH HIV/AIDS PREVENTION BEHAVIOR AT YADIKA 8 JATIMULYA VOCATIONAL SCHOOL IN 2022

Background: The HIV/AIDS epidemic is a problem in Indonesia which is the 5th most at risk of HIV/AIDS in Asia, reports of HIV cases have increased every year since it was first reported in 1987, the most spike in increase was in 2016 compared to 2015 namely of 10,315 cases. Researcher's Objective: To find out the relationship between knowledge level and HIV/AIDS prevention behavior in Yadika 8 Jatimulya Vocational High School in 2022 Research Method: This research method is quantitative by using a cross sectional design. The population in this study were 110 teenagers at SMK Yadika 8 Jatimulya consisting of grades 10, 11 and 12 majoring in Accounting, Administration and Computer and Network Engineering. by using simple random sampling technique. Researcher results: Based on the results of research conducted by researchers at SMK Yadika 8 Jatimulya, the results of data processing using SPSS obtained Chi Square statistical test results, namely the Asymp Significance value (0.000) < value (0.05), this indicates that H0 is rejected. this shows that H0 is rejected. Thus, it can be concluded that there is a relationship between the level of knowledge and HIV/AIDS prevention behavior at SMK Yadika 8 Jatimulya in 2022. Conclusion: There is a relationship between the level of knowledge with HIV/AIDS prevention behavior in SMK Yadika 8 Jatimulya.

A DDoS Detection Method Based on Feature Engineering and Machine Learning in Software-Defined Networks.

Distributed denial-of-service (DDoS) attacks pose a significant cybersecurity threat to software-defined networks (SDNs). This paper proposes a feature-engineering- and machine-learning-based approach to detect DDoS attacks in SDNs. First, the CSE-CIC-IDS2018 dataset was cleaned and normalized, and the optimal feature subset was found using an improved binary grey wolf optimization algorithm. Next, the optimal feature subset was trained and tested in Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), Decision Tree, and XGBoost machine learning algorithms, from which the best classifier was selected for DDoS attack detection and deployed in the SDN controller. The results show that RF performs best when compared across several performance metrics (e.g., accuracy, precision, recall, F1 and AUC values). We also explore the comparison between different models and algorithms. The results show that our proposed method performed the best and can effectively detect and identify DDoS attacks in SDNs, providing a new idea and solution for the security of SDNs.

Pelaksanan Ujian Kompetensi IT dalam meningkatkan kualitas lulusan di SMKN Suak Tapeh Banyuasin

Vocational High School (SMK) is a formal education unit that organizes vocational education where the orientation produces graduate students who are ready to work. One area of expertise that is in great demand by students is computer and network engineering (TKJ), where this area of expertise teaches how to make students skilled and competent in the field of computer and network engineering. In this era of digital transformation, computer networks are part of human needs so there are many job opportunities in this field. The purpose of this service and socialization is to increase the understanding and knowledge of SMK students in the field of TKJ in conducting education and preparing themselves to enter the world of work. The partners of this service activity are students of SMKN Suak Tapeh Banyuasin where this activity is carried out in the Computer Laboratory Computer and Network Engineering Study Program. While the method of activity used in this service is planning, directing, guiding and teaching network practicum and conducting competency exams that must be followed before completing education at school. Through this service activity occurs increasing understanding from SMK students majoring in TKJ regarding the areas of expertise they take so as to improve the quality of graduates of SMK N Suak Tapeh and also as a provision to face the world of work. In this era of digital transformation, computer networks are part of human needs so there are many job opportunities in this field. The purpose of this service and socialization is to increase the understanding and knowledge of SMK students in the field of TKJ in conducting education and preparing themselves to enter the world of work. These service partners are students of SMKN Suak Tapeh Banyuasin

EMPIRICAL OBSERVATION OF WIRELESS NETWORK PERFORMANCE FOR TRANSMISSION CONTROL PROTOCOL (TCP) UPSTREAM AND DOWNSTREAM FOR DIFFERENT USERS SCENARIOS

The increase in demand for proper and fast access to network infrastructure around the University environment by staff, students, and visitors in the quest to carry out daily tasks or keeping up with trends/innovations around the globe is a daily experience resulting in some challenges. But these challenges are not unexpected. The access to network infrastructure around the environment encourages proper development. This paper presents an empirical observation of transmission control protocol upstream throughput (TCPupT) and downstream throughput (TCPdownT) performance for an IEEE 802.11g wireless local area network (WLAN) installed at a corridor space of University of Nigeria, Nsukka campus. This work measured the TCP upstream and downstream throughput values for single, double, and multiple users (several Users) accessing an installed WLAN access point. The paper adopted basic WLAN system setup approach while observing the TCP performance in relation to SNR. Data collected for each environment were statistically analyzed, equations and results were also presented. The observed TCP throughput values vary as users increase thereby affecting the observed round-trip time (RTT). The generated equations were also used to predict TCP throughput which would aid network engineers in the deployment of IEEE 802.11g WLAN systems especially around the university environment. It is now clear that the generated model equations can be used for predicting the expected performance of IEEE 802.11g WLAN systems during deployment. This work gives WLAN engineers, researchers and users an apparatus to rapidly estimate the TCP upstream and downstream throughput, by monitoring the received SNR for IEEE 802.11g systems.

DEVELOPMENT OF GUIDELINES FOR PREPARING HOTS QUESTION SCRIPTS FOR VOCATIONAL SCHOOLS MAJORING IN COMPUTER AND NETWORK ENGINEERING

The purpose of this research is to extend a guideline for creating High Order Thinking Skill (HOTS) questions or test for Computer and Network Engineering majors at the Vocational High School in Salatiga City. Research and development (R & D) approach is used in this research. This research uses the five steps of the Borg and Gall (1983) model: research and information gathering, data collection, product design, design validation, and design revision. All vocational school teachers majoring in Computer Engineering and Networks in Salatiga City were participated in this research. Data collection techniques were carried out by document studies, interviews, and questionnaires. Data analysis was carried out by qualitative analysis. The results of the research were in the form of extending a guideline for creating HOTS questions and test for Computer and Network Engineering teachers at Vocational High Schools in Salatiga City. Based on limited trials, this guideline received a score of 3,68 out of a maximum score of 4,00. That is what makes a book great. So, it can be concluded that this guideline can be used for Computer and network engineering teachers outside Salatiga City.