Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008368
Asma Z. Yamani, Khawlah Bajbaa, Reem Aljunaid
Cloud computing plays an important role in businesses' digital transformation as they offer easy-to-use services that save time and effort. Despite incredible features that are provided by cloud computing platforms, these platforms become the desirable target of attackers. This study aims to survey the literature for security threats related to web applications that have been developed using cloud computing services and then provide a set of recommendations to mitigate these threats. In this study, we first surveyed the literature for documented cases of threats faced while relying on cloud computing, then an online survey was sent to Computer Science students and web developers. The survey's questions were related to web threats whether they are aware of these threats or not and whether they have already applied any prevention measures for these threats. Then, a set of recommendations were provided that can help them to mitigate these threats. Finally, a tool was designed for generating security policies for the Broken Access Control threat for Firebase. Eighty-five responses were considered for this study. The average participants' awareness of all threats is 61 % despite 92% of participants having taken at least one security course. The main causes for neglecting to implement mitigation techniques was the lack of training and that developers are relying on the web services to provide security measures, then comes the process being time-consuming. The designed tool for mitigating Broken Access control showed promising results to ease the implementation of mitigation techniques. We conclude that due to the lack of awareness and negligence in implementing mitigation techniques, many present web apps may be compromised. Developing security tools for novice users, whenever possible, provides a solution for the main causes of the neglect to implement such measures and should be investigated further.
{"title":"Web Application Security Threats and Mitigation Strategies when Using Cloud Computing as Backend","authors":"Asma Z. Yamani, Khawlah Bajbaa, Reem Aljunaid","doi":"10.1109/CICN56167.2022.10008368","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008368","url":null,"abstract":"Cloud computing plays an important role in businesses' digital transformation as they offer easy-to-use services that save time and effort. Despite incredible features that are provided by cloud computing platforms, these platforms become the desirable target of attackers. This study aims to survey the literature for security threats related to web applications that have been developed using cloud computing services and then provide a set of recommendations to mitigate these threats. In this study, we first surveyed the literature for documented cases of threats faced while relying on cloud computing, then an online survey was sent to Computer Science students and web developers. The survey's questions were related to web threats whether they are aware of these threats or not and whether they have already applied any prevention measures for these threats. Then, a set of recommendations were provided that can help them to mitigate these threats. Finally, a tool was designed for generating security policies for the Broken Access Control threat for Firebase. Eighty-five responses were considered for this study. The average participants' awareness of all threats is 61 % despite 92% of participants having taken at least one security course. The main causes for neglecting to implement mitigation techniques was the lack of training and that developers are relying on the web services to provide security measures, then comes the process being time-consuming. The designed tool for mitigating Broken Access control showed promising results to ease the implementation of mitigation techniques. We conclude that due to the lack of awareness and negligence in implementing mitigation techniques, many present web apps may be compromised. Developing security tools for novice users, whenever possible, provides a solution for the main causes of the neglect to implement such measures and should be investigated further.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132968707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008279
Muhammad Noman Saeed, Ahmad Mufarreh Al Mufarreh, K. M. Noaman, Muhammad Arshad, Atiq Rafiq Shaikh
Higher education across the world during the COVID pandemic changes its knowledge delivery mode from on-campus studies to off-campus studies, i.e. E-Learning. The e-education provider must be competent in order to create a robust learning environment that can handle the difficulties facing teachers, students, and system administrators at this rapid pace of change. The system administrator needs to improve the network connectivity, bandwidth etc. for providing seamless connectivity for E-Learning alongside their campus network services. The challenge of providing smooth services for e-learning is sometimes hurdled the other network services for the campus and therefore the management and administrator suggest deploying the e-learning services on the cloud and setting apart the campus network services. This will solve the problem of available network limits can face by the institute due to the limited amount of hardware and bandwidth issues. Furthermore, the cloud deployment reduces the capital as well as the recurring cost of running the services. This paper will focus to address the problem defined above and providing Amazon Web Services (AWS) based cost-effective cloud architecture for OpenedX based learning solutions. This study is expected to demonstrate a technological solution for the process of implementing a cloud-based LMS.
{"title":"JUX - A Cloud Hosted Learning Management System Based on OpenedX","authors":"Muhammad Noman Saeed, Ahmad Mufarreh Al Mufarreh, K. M. Noaman, Muhammad Arshad, Atiq Rafiq Shaikh","doi":"10.1109/CICN56167.2022.10008279","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008279","url":null,"abstract":"Higher education across the world during the COVID pandemic changes its knowledge delivery mode from on-campus studies to off-campus studies, i.e. E-Learning. The e-education provider must be competent in order to create a robust learning environment that can handle the difficulties facing teachers, students, and system administrators at this rapid pace of change. The system administrator needs to improve the network connectivity, bandwidth etc. for providing seamless connectivity for E-Learning alongside their campus network services. The challenge of providing smooth services for e-learning is sometimes hurdled the other network services for the campus and therefore the management and administrator suggest deploying the e-learning services on the cloud and setting apart the campus network services. This will solve the problem of available network limits can face by the institute due to the limited amount of hardware and bandwidth issues. Furthermore, the cloud deployment reduces the capital as well as the recurring cost of running the services. This paper will focus to address the problem defined above and providing Amazon Web Services (AWS) based cost-effective cloud architecture for OpenedX based learning solutions. This study is expected to demonstrate a technological solution for the process of implementing a cloud-based LMS.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131223004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008346
Kanda Alamer, Abdulaziz Aldribi
One of the most rapidly spreading areas of infor-mation technology is cloud computing. However, this raises sig-nificant security issues that entice burglars. This paper presents a machine learning-based framework for intrusion classification for cloud computing networks. It offers new capabilities derived from cloud network flow. By dividing the flow into windows of time, a method known as the Riemann Chunking Scheme computes these features. After experimenting with this dataset, we have extracted 40 features that best describe the problem of anomaly classification and improve the accuracy of the study on multilayer perceptron for anomaly classification in cloud network traffic
{"title":"Intrusion Classification for Cloud Computing Network: A Step Towards an Intelligent Classification System","authors":"Kanda Alamer, Abdulaziz Aldribi","doi":"10.1109/CICN56167.2022.10008346","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008346","url":null,"abstract":"One of the most rapidly spreading areas of infor-mation technology is cloud computing. However, this raises sig-nificant security issues that entice burglars. This paper presents a machine learning-based framework for intrusion classification for cloud computing networks. It offers new capabilities derived from cloud network flow. By dividing the flow into windows of time, a method known as the Riemann Chunking Scheme computes these features. After experimenting with this dataset, we have extracted 40 features that best describe the problem of anomaly classification and improve the accuracy of the study on multilayer perceptron for anomaly classification in cloud network traffic","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130975920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008350
C. Peña, Ciro Rodríguez, Israel Arellano Romero
The proposal of a facial recognition system to increase security, through facial recognition with multiple utilities such as facilitating the access of people with adequate protection measures in times of Covid-19, as well as security when seeking to hide their identity. The methodology considers the use of tools such as Python and OpenCV, as well as models such as Eigen Faces, Fisher Faces, and LBPH Faces, as units of analysis are considered photographs and portions of the video that capture facial expressions that then their patterns are trained with facial recognition algorithms. The results obtained show that the LBPH Faces obtained confidence values lower than 70, with a 95% certainty of recognition and a shorter recognition time, improving the accuracy of facial recognition, also with the increase of the data was achieved to improve the accuracy of recognition as well as improve confidence regarding the safety of people.
{"title":"Processing of Images Based on Machine Learning to Avoid Unauthorized Entry","authors":"C. Peña, Ciro Rodríguez, Israel Arellano Romero","doi":"10.1109/CICN56167.2022.10008350","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008350","url":null,"abstract":"The proposal of a facial recognition system to increase security, through facial recognition with multiple utilities such as facilitating the access of people with adequate protection measures in times of Covid-19, as well as security when seeking to hide their identity. The methodology considers the use of tools such as Python and OpenCV, as well as models such as Eigen Faces, Fisher Faces, and LBPH Faces, as units of analysis are considered photographs and portions of the video that capture facial expressions that then their patterns are trained with facial recognition algorithms. The results obtained show that the LBPH Faces obtained confidence values lower than 70, with a 95% certainty of recognition and a shorter recognition time, improving the accuracy of facial recognition, also with the increase of the data was achieved to improve the accuracy of recognition as well as improve confidence regarding the safety of people.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133329232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008307
Hussah Albinali, Maha Alharbi, Randah Alharbi, M. Aljabri
With the exponential growth and advancement of the internet and network technologies, the number of active internet users is expected to exceed 5 billion worldwide by 2025. As a result, designing high performance multi-threaded web server capable of concurrently handling thousands of incoming requests per second is in high demand. To achieve this goal, it is essential for the multi-threaded web server to utilize synchronization algorithm to coordinate the simultaneous use of resources by clients; avoid data inconsistency and still provide timely responses. The aim of this study is to compare and evaluate the performance of different traditional and modern synchronization algorithms adopted by multi-threaded web servers. An empirical experiment is conducted that simulates a file synchronization problem in distributed systems to evaluate how the performance of a multi-threaded web server is affected by those synchronization algorithms. Mutex locks, Spinlocks, Semaphore, and WebR2sync are evaluated, and the performance of each synchronization algorithm is measured using three performance metrics: Synchronization Time, Failure Rate, and Scalability. Results show that Semaphore has the fastest synchronization time, WebR2sync has the lowest failure rate, and Mutex excels at scalability. Such findings help with the decision of which synchronization algorithms to utilize for multi-threaded web servers based on the requirements.
{"title":"Synchronization Techniques for Multi-threaded Web Server: A Comparative Study","authors":"Hussah Albinali, Maha Alharbi, Randah Alharbi, M. Aljabri","doi":"10.1109/CICN56167.2022.10008307","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008307","url":null,"abstract":"With the exponential growth and advancement of the internet and network technologies, the number of active internet users is expected to exceed 5 billion worldwide by 2025. As a result, designing high performance multi-threaded web server capable of concurrently handling thousands of incoming requests per second is in high demand. To achieve this goal, it is essential for the multi-threaded web server to utilize synchronization algorithm to coordinate the simultaneous use of resources by clients; avoid data inconsistency and still provide timely responses. The aim of this study is to compare and evaluate the performance of different traditional and modern synchronization algorithms adopted by multi-threaded web servers. An empirical experiment is conducted that simulates a file synchronization problem in distributed systems to evaluate how the performance of a multi-threaded web server is affected by those synchronization algorithms. Mutex locks, Spinlocks, Semaphore, and WebR2sync are evaluated, and the performance of each synchronization algorithm is measured using three performance metrics: Synchronization Time, Failure Rate, and Scalability. Results show that Semaphore has the fastest synchronization time, WebR2sync has the lowest failure rate, and Mutex excels at scalability. Such findings help with the decision of which synchronization algorithms to utilize for multi-threaded web servers based on the requirements.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132435452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008298
S. Mohammed
Many researchers use deep learning and technical indicators to forecast future stock prices. There are several hundred technical indicators and each one of them has a number of parameters. Finding the optimal combination of indicators with their optimal parameter values is very challenging. The aim of this work is to study if there is any benefit of feeding deep learning models with technical indicators instead of only feeding them with price and volume. After all, technical indicators are just functions of price and volume. Empirical studies done in this work using Saudi stocks show that deep learning models can benefit from technical indicators only if the right combination of technical indicators together with their right parameter values are used. The experimental results show that the right combination of technical indicators can improve the forecasting accuracy of deep learning modules. They also showed that using the wrong combination of indicators is worse than using no indicator even if they were assigned the best parameter values.
{"title":"The Validity of Using Technical Indicators When forecasting Stock Prices Using Deep Learning Models: Empirical Evidence Using Saudi Stocks","authors":"S. Mohammed","doi":"10.1109/CICN56167.2022.10008298","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008298","url":null,"abstract":"Many researchers use deep learning and technical indicators to forecast future stock prices. There are several hundred technical indicators and each one of them has a number of parameters. Finding the optimal combination of indicators with their optimal parameter values is very challenging. The aim of this work is to study if there is any benefit of feeding deep learning models with technical indicators instead of only feeding them with price and volume. After all, technical indicators are just functions of price and volume. Empirical studies done in this work using Saudi stocks show that deep learning models can benefit from technical indicators only if the right combination of technical indicators together with their right parameter values are used. The experimental results show that the right combination of technical indicators can improve the forecasting accuracy of deep learning modules. They also showed that using the wrong combination of indicators is worse than using no indicator even if they were assigned the best parameter values.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129852751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008380
Saiman Quazi, Sarhan M. Musa
Text classification is one of the important fields in Natural Language Processing (NLP). It assigns text documents into at least two categories in the domain by submitting and deriving a set of features to describe each document and to select the correct category for each one for a set of pre-defined tags or categories based on content. It is even used in several real-life applications such as engineering, science, and marketing and it can be quite effective in addressing problems with labeled data. There are certain Deep Learning (DL) algorithms that can be handy in categorizing text data such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes. This paper illustrates how the text in each document is reviewed and grouped into different sets through the above-mentioned techniques. That way, it will determine which method is best suited for higher accuracy and what possible problems the deep learning model faces using text classification and categorization so that new solutions can be invented to resolve these issues without interfering with the processes in the future.
{"title":"Text Classification and Categorization through Deep Learning","authors":"Saiman Quazi, Sarhan M. Musa","doi":"10.1109/CICN56167.2022.10008380","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008380","url":null,"abstract":"Text classification is one of the important fields in Natural Language Processing (NLP). It assigns text documents into at least two categories in the domain by submitting and deriving a set of features to describe each document and to select the correct category for each one for a set of pre-defined tags or categories based on content. It is even used in several real-life applications such as engineering, science, and marketing and it can be quite effective in addressing problems with labeled data. There are certain Deep Learning (DL) algorithms that can be handy in categorizing text data such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes. This paper illustrates how the text in each document is reviewed and grouped into different sets through the above-mentioned techniques. That way, it will determine which method is best suited for higher accuracy and what possible problems the deep learning model faces using text classification and categorization so that new solutions can be invented to resolve these issues without interfering with the processes in the future.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122361937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008295
Bin Zhang, Shejiao Hu
With the continuous development of social economy, intelligent acquisition terminal plays an important role in some remote and abusive environments. In this paper, an intelligent integrated acquisition terminal is proposed, which can be effectively applied to irrigation area information collection, earthquake disaster monitoring and other scenarios. The acquisition terminal (RTU) proposed in this paper adopts a design scheme combining GD32F407 embedded system and embedded industrial computer to complete data collection, processing, transmission, display, storage and intelligent control of equipment. The RTU acquisition base plate is designed by GD32F407 chip, including RS485, 4-20MA, SSI Gray code, relay control, Ethernet and other interfaces [1]. The RS485 interface uses Modbus Rtu protocol to request data from sensors connected to the outside. SSI Gray code interface can be connected to absolute value encoder to collect data, gray code interface carries on data transmission through 422 interface on hardware, and carries on data latching at corresponding edge through analog clock signal on software level. Modbus TCP protocol is used to exchange information between the acquisition baseboard and the industrial computer through Ethernet. The industrial computer runs the fast control configuration software, which can receive the transmitted data and realize intelligent control of external devices.
{"title":"Design and Implementation of Intelligent Acquisition Terminal Based on Modbus","authors":"Bin Zhang, Shejiao Hu","doi":"10.1109/CICN56167.2022.10008295","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008295","url":null,"abstract":"With the continuous development of social economy, intelligent acquisition terminal plays an important role in some remote and abusive environments. In this paper, an intelligent integrated acquisition terminal is proposed, which can be effectively applied to irrigation area information collection, earthquake disaster monitoring and other scenarios. The acquisition terminal (RTU) proposed in this paper adopts a design scheme combining GD32F407 embedded system and embedded industrial computer to complete data collection, processing, transmission, display, storage and intelligent control of equipment. The RTU acquisition base plate is designed by GD32F407 chip, including RS485, 4-20MA, SSI Gray code, relay control, Ethernet and other interfaces [1]. The RS485 interface uses Modbus Rtu protocol to request data from sensors connected to the outside. SSI Gray code interface can be connected to absolute value encoder to collect data, gray code interface carries on data transmission through 422 interface on hardware, and carries on data latching at corresponding edge through analog clock signal on software level. Modbus TCP protocol is used to exchange information between the acquisition baseboard and the industrial computer through Ethernet. The industrial computer runs the fast control configuration software, which can receive the transmitted data and realize intelligent control of external devices.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"475 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123053614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008253
Ali Almohammedi, A. Zerguine, M. Deriche, S. M. Sait
A new Diffusion Artificial Bee Colony (DABC) heuristic algorithm is developed to estimate system parameters in Wireless Sensor Networks (WSNs). The main contribution is the incorporation of ABC algorithm in the traditional Diffusion Least Mean Square (DLMS) algorithm which leads to a better Mean Square Error (MSE) performance. The DABC algorithm shows excellent convergence beyond the noise variance boundary. In the diffusion stage, each node shares the local best cost function and corresponding local best particle position to immediate neighboring nodes. The extensive simulations show that the proposed DABC approach achieves excellent MSE improvement (MSD deterioration) in comparison to existing DLMS algorithms.
{"title":"Artificial Bee Colony DLMS Beyond Mean Square Error Boundary in Ad-hoc WSN","authors":"Ali Almohammedi, A. Zerguine, M. Deriche, S. M. Sait","doi":"10.1109/CICN56167.2022.10008253","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008253","url":null,"abstract":"A new Diffusion Artificial Bee Colony (DABC) heuristic algorithm is developed to estimate system parameters in Wireless Sensor Networks (WSNs). The main contribution is the incorporation of ABC algorithm in the traditional Diffusion Least Mean Square (DLMS) algorithm which leads to a better Mean Square Error (MSE) performance. The DABC algorithm shows excellent convergence beyond the noise variance boundary. In the diffusion stage, each node shares the local best cost function and corresponding local best particle position to immediate neighboring nodes. The extensive simulations show that the proposed DABC approach achieves excellent MSE improvement (MSD deterioration) in comparison to existing DLMS algorithms.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128604245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008318
Hussein Abdel-jaber, J. AlKhateeb, Malak EL-Amir
Active Queue Management (AQM) algorithms mainly aim to control congested networks. Managing the congestion situation leads to offer better performance for the networks. Both Improved Gentle Random Early Detection (IGRED) and Improved RED (IM-RED) algorithms are compared in this paper. The comparison is conducted to find out which algorithm presents better performance between IM-RED and IGRED. The results demonstrate that IGRED provides more acceptable results for the following parameters: the mean queue length (mql), the average queueing delay (D) and the overflow packet loss probability (PLoss) during the occurrence of the congestion. According to this congestion, fewer packets where dropped using the IM-RED prior filling the buffer.
{"title":"Evaluation of the Performance for IM-RED and IGRED Algorithms using Discrete-time Queues","authors":"Hussein Abdel-jaber, J. AlKhateeb, Malak EL-Amir","doi":"10.1109/CICN56167.2022.10008318","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008318","url":null,"abstract":"Active Queue Management (AQM) algorithms mainly aim to control congested networks. Managing the congestion situation leads to offer better performance for the networks. Both Improved Gentle Random Early Detection (IGRED) and Improved RED (IM-RED) algorithms are compared in this paper. The comparison is conducted to find out which algorithm presents better performance between IM-RED and IGRED. The results demonstrate that IGRED provides more acceptable results for the following parameters: the mean queue length (mql), the average queueing delay (D) and the overflow packet loss probability (PLoss) during the occurrence of the congestion. According to this congestion, fewer packets where dropped using the IM-RED prior filling the buffer.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128788326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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