Youness Manzali, Mohamed El far, M. Chahhou, Mohammed Elmohajir
Abstract Decision trees are among the most popular classifiers in machine learning, artificial intelligence, and pattern recognition because they are accurate and easy to interpret. During the tree construction, a node containing too few observations (weak node) could still get split, and then the resulted split is unreliable and statistically has no value. Many existing machine-learning methods can resolve this issue, such as pruning, which removes the tree’s non-meaningful parts. This paper deals with the weak nodes differently; we introduce a new algorithm Enhancing Weak Nodes in Decision Tree (EWNDT), which reinforces them by increasing their data from other similar tree nodes. We called the data augmentation a virtual merging because we temporarily recalculate the best splitting attribute and the best threshold in the weak node. We have used two approaches to defining the similarity between two nodes. The experimental results are verified using benchmark datasets from the UCI machine-learning repository. The results indicate that the EWNDT algorithm gives a good performance.
{"title":"Enhancing Weak Nodes in Decision Tree Algorithm Using Data Augmentation","authors":"Youness Manzali, Mohamed El far, M. Chahhou, Mohammed Elmohajir","doi":"10.2478/cait-2022-0016","DOIUrl":"https://doi.org/10.2478/cait-2022-0016","url":null,"abstract":"Abstract Decision trees are among the most popular classifiers in machine learning, artificial intelligence, and pattern recognition because they are accurate and easy to interpret. During the tree construction, a node containing too few observations (weak node) could still get split, and then the resulted split is unreliable and statistically has no value. Many existing machine-learning methods can resolve this issue, such as pruning, which removes the tree’s non-meaningful parts. This paper deals with the weak nodes differently; we introduce a new algorithm Enhancing Weak Nodes in Decision Tree (EWNDT), which reinforces them by increasing their data from other similar tree nodes. We called the data augmentation a virtual merging because we temporarily recalculate the best splitting attribute and the best threshold in the weak node. We have used two approaches to defining the similarity between two nodes. The experimental results are verified using benchmark datasets from the UCI machine-learning repository. The results indicate that the EWNDT algorithm gives a good performance.","PeriodicalId":45562,"journal":{"name":"Cybernetics and Information Technologies","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44457956","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}
Abstract An effective password cracking detection system is the honeyword system. The Honeyword method attempts to increase the security of hashed passwords by making password cracking easier to detect. Each user in the system has many honeywords in the password database. If the attacker logs in using a honeyword, a quiet alert trigger indicates that the password database has been hacked. Many honeyword generation methods have been proposed, they have a weakness in generating process, do not support all honeyword properties, and have many honeyword issues. This article proposes a novel method to generate honeyword using the meerkat clan intelligence algorithm, a metaheuristic swarm intelligence algorithm. The proposed generation methods will improve the honeyword generating process, enhance the honeyword properties, and solve the issues of previous methods. This work will show some previous generation methods, explain the proposed method, discuss the experimental results and compare the new one with the prior ones.
{"title":"A Proposal for Honeyword Generation via Meerkat Clan Algorithm","authors":"Yasser A. Yasser, A. Sadiq, Wasim Alhamdani","doi":"10.2478/cait-2022-0003","DOIUrl":"https://doi.org/10.2478/cait-2022-0003","url":null,"abstract":"Abstract An effective password cracking detection system is the honeyword system. The Honeyword method attempts to increase the security of hashed passwords by making password cracking easier to detect. Each user in the system has many honeywords in the password database. If the attacker logs in using a honeyword, a quiet alert trigger indicates that the password database has been hacked. Many honeyword generation methods have been proposed, they have a weakness in generating process, do not support all honeyword properties, and have many honeyword issues. This article proposes a novel method to generate honeyword using the meerkat clan intelligence algorithm, a metaheuristic swarm intelligence algorithm. The proposed generation methods will improve the honeyword generating process, enhance the honeyword properties, and solve the issues of previous methods. This work will show some previous generation methods, explain the proposed method, discuss the experimental results and compare the new one with the prior ones.","PeriodicalId":45562,"journal":{"name":"Cybernetics and Information Technologies","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48001864","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}
Abstract Many routing algorithms proposed for IoT are based on modifications on RPL objective functions and trickle algorithms. However, there is a lack of an in-depth study to examine the impact of mobility on routing protocols based on MRHOF and OF0 algorithms. This paper examines the impact of group mobility on these algorithms, also examines their ability in distributing the load and the impact of varying traffic with the aid of simulations using the well-known Cooja simulator. The two algorithms exhibit similar performance for various metrics for low traffic rates and low mobility speed. However, when the traffic rate becomes relatively high, OF0 performance merits appear, in terms of throughput, packet load deviation, power deviation, and CPU power deviation. The mobility with higher speeds helps MRHOF to enhance its throughput and load deviation. The mobility allowed MRHOF to demonstrate better packets load deviation.
{"title":"Data Fusion and the Impact of Group Mobility on Load Distribution on MRHOF and OF0","authors":"Raad S. Al-Qassas, Malik Qasaimeh","doi":"10.2478/cait-2022-0005","DOIUrl":"https://doi.org/10.2478/cait-2022-0005","url":null,"abstract":"Abstract Many routing algorithms proposed for IoT are based on modifications on RPL objective functions and trickle algorithms. However, there is a lack of an in-depth study to examine the impact of mobility on routing protocols based on MRHOF and OF0 algorithms. This paper examines the impact of group mobility on these algorithms, also examines their ability in distributing the load and the impact of varying traffic with the aid of simulations using the well-known Cooja simulator. The two algorithms exhibit similar performance for various metrics for low traffic rates and low mobility speed. However, when the traffic rate becomes relatively high, OF0 performance merits appear, in terms of throughput, packet load deviation, power deviation, and CPU power deviation. The mobility with higher speeds helps MRHOF to enhance its throughput and load deviation. The mobility allowed MRHOF to demonstrate better packets load deviation.","PeriodicalId":45562,"journal":{"name":"Cybernetics and Information Technologies","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47567044","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}
Abstract The article is focused on the problem of early prediction of students’ learning failures with the purpose of their possible prevention by timely introducing supportive measures. We propose an approach to designing a predictive model for an academic course or module taught in a blended learning format. We introduce certain requirements to predictive models concerning their applicability to the educational process such as interpretability, actionability, and adaptability to a course design. We test three types of classifiers meeting these requirements and choose the one that provides best performance starting from the early stages of the semester, and therefore provides various opportunities to timely support at-risk students. Our empirical studies confirm that the proposed approach is promising for the development of an early warning system in a higher education institution. Such systems can positively influence student retention rates and enhance learning and teaching experience for a long term.
{"title":"Early Student-at-Risk Detection by Current Learning Performance and Learning Behavior Indicators","authors":"T. A. Kustitskaya, A. A. Kytmanov, M. Noskov","doi":"10.2478/cait-2022-0008","DOIUrl":"https://doi.org/10.2478/cait-2022-0008","url":null,"abstract":"Abstract The article is focused on the problem of early prediction of students’ learning failures with the purpose of their possible prevention by timely introducing supportive measures. We propose an approach to designing a predictive model for an academic course or module taught in a blended learning format. We introduce certain requirements to predictive models concerning their applicability to the educational process such as interpretability, actionability, and adaptability to a course design. We test three types of classifiers meeting these requirements and choose the one that provides best performance starting from the early stages of the semester, and therefore provides various opportunities to timely support at-risk students. Our empirical studies confirm that the proposed approach is promising for the development of an early warning system in a higher education institution. Such systems can positively influence student retention rates and enhance learning and teaching experience for a long term.","PeriodicalId":45562,"journal":{"name":"Cybernetics and Information Technologies","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49335322","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}
Abstract Identifying similar objects is one of the most challenging tasks in computer vision image recognition. The following musical instruments will be recognized in this study: French horn, harp, recorder, bassoon, cello, clarinet, erhu, guitar saxophone, trumpet, and violin. Numerous musical instruments are identical in size, form, and sound. Further, our works combine Resnet 50 with Spatial Pyramid Pooling (SPP) to identify musical instruments that are similar to one another. Next, the Resnet 50 and Resnet 50 SPP model evaluation performance includes the Floating-Point Operations (FLOPS), detection time, mAP, and IoU. Our work can increase the detection performance of musical instruments similar to one another. The method we propose, Resnet 50 SPP, shows the highest average accuracy of 84.64% compared to the results of previous studies.
{"title":"Combination of Resnet and Spatial Pyramid Pooling for Musical Instrument Identification","authors":"Christine Dewi, Rung-Ching Chen","doi":"10.2478/cait-2022-0007","DOIUrl":"https://doi.org/10.2478/cait-2022-0007","url":null,"abstract":"Abstract Identifying similar objects is one of the most challenging tasks in computer vision image recognition. The following musical instruments will be recognized in this study: French horn, harp, recorder, bassoon, cello, clarinet, erhu, guitar saxophone, trumpet, and violin. Numerous musical instruments are identical in size, form, and sound. Further, our works combine Resnet 50 with Spatial Pyramid Pooling (SPP) to identify musical instruments that are similar to one another. Next, the Resnet 50 and Resnet 50 SPP model evaluation performance includes the Floating-Point Operations (FLOPS), detection time, mAP, and IoU. Our work can increase the detection performance of musical instruments similar to one another. The method we propose, Resnet 50 SPP, shows the highest average accuracy of 84.64% compared to the results of previous studies.","PeriodicalId":45562,"journal":{"name":"Cybernetics and Information Technologies","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42432861","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}
Abstract Lack of personalization, rating sparsity, and cold start are commonly seen in e-Learning based recommender systems. The proposed work here suggests a personalized fused recommendation framework for e-Learning. The framework consists of a two-fold approach to generate recommendations. Firstly, it attempts to find the neighbourhood of similar learners based on certain learner characteristics by applying a user-based collaborative filtering approach. Secondly, it generates a matrix of ratings given by the learners. The outcome of the first stage is merged with the second stage to generate recommendations for the learner. Learner characteristics, namely knowledge level, learning style, and learner preference, have been considered to bring in the personalization factor on the recommendations. As the stochastic gradient approach predicts the learner-course rating matrix, it helps overcome the rating sparsity and cold-start issues. The fused model is compared with traditional stand-alone methods and shows performance improvement.
{"title":"Hy-MOM: Hybrid Recommender System Framework Using Memory-Based and Model-Based Collaborative Filtering Framework","authors":"G. George, Anisha M. Lal","doi":"10.2478/cait-2022-0009","DOIUrl":"https://doi.org/10.2478/cait-2022-0009","url":null,"abstract":"Abstract Lack of personalization, rating sparsity, and cold start are commonly seen in e-Learning based recommender systems. The proposed work here suggests a personalized fused recommendation framework for e-Learning. The framework consists of a two-fold approach to generate recommendations. Firstly, it attempts to find the neighbourhood of similar learners based on certain learner characteristics by applying a user-based collaborative filtering approach. Secondly, it generates a matrix of ratings given by the learners. The outcome of the first stage is merged with the second stage to generate recommendations for the learner. Learner characteristics, namely knowledge level, learning style, and learner preference, have been considered to bring in the personalization factor on the recommendations. As the stochastic gradient approach predicts the learner-course rating matrix, it helps overcome the rating sparsity and cold-start issues. The fused model is compared with traditional stand-alone methods and shows performance improvement.","PeriodicalId":45562,"journal":{"name":"Cybernetics and Information Technologies","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46459125","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}
Mousa Tayseer Jafar, Mohammad Al-Fawa'reh, Malek Barhoush, Mohammad H. Alshira'H
Abstract Public health responses to the COVID-19 pandemic since March 2020 have led to lockdowns and social distancing in most countries around the world, with a shift from the traditional work environment to virtual one. Employees have been encouraged to work from home where possible to slow down the viral infection. The massive increase in the volume of professional activities executed online has posed a new context for cybercrime, with the increase in the number of emails and phishing websites. Phishing attacks have been broadened and extended through years of pandemics COVID-19. This paper presents a novel approach for detecting phishing Uniform Resource Locators (URLs) applying the Gated Recurrent Unit (GRU), a fast and highly accurate phishing classifier system. Comparative analysis of the GRU classification system indicates better accuracy (98.30%) than other classifier systems.
{"title":"Enhancеd Analysis Approach to Detect Phishing Attacks During COVID-19 Crisis","authors":"Mousa Tayseer Jafar, Mohammad Al-Fawa'reh, Malek Barhoush, Mohammad H. Alshira'H","doi":"10.2478/cait-2022-0004","DOIUrl":"https://doi.org/10.2478/cait-2022-0004","url":null,"abstract":"Abstract Public health responses to the COVID-19 pandemic since March 2020 have led to lockdowns and social distancing in most countries around the world, with a shift from the traditional work environment to virtual one. Employees have been encouraged to work from home where possible to slow down the viral infection. The massive increase in the volume of professional activities executed online has posed a new context for cybercrime, with the increase in the number of emails and phishing websites. Phishing attacks have been broadened and extended through years of pandemics COVID-19. This paper presents a novel approach for detecting phishing Uniform Resource Locators (URLs) applying the Gated Recurrent Unit (GRU), a fast and highly accurate phishing classifier system. Comparative analysis of the GRU classification system indicates better accuracy (98.30%) than other classifier systems.","PeriodicalId":45562,"journal":{"name":"Cybernetics and Information Technologies","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44403421","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}
Abstract The purpose of this paper is to propose an approach to blockchain-enabled supply-chain model for a smart crop production framework. The defined tasks are: (1) analysis of blockchain ecosystem as a network of stakeholders and as an infrastructure of technical and logical elements; (2) definition of a supply-chain model; (3) design of blockchain reference infrastructure; (4) description of blockchain information channels with smart contracts basic functionalities. The results presented include: а supply-chain model facilitating seeds certification process, monitoring and supervision of the grain process, provenance and as optional interactions with regulatory bodies, logistics and financial services; the three level blockchain reference infrastructure and a blockchain-enabled supply-chain supporting five information channels with nine participants and smart contracts. An account management user application tool, the general descriptions of smart contract basic functionalities and a selected parts of one smart contract code are provided as examples.
{"title":"Blockchain-Enabled Supply-Chain in Crop Production Framework","authors":"I. Radeva, I. Popchev","doi":"10.2478/cait-2022-0010","DOIUrl":"https://doi.org/10.2478/cait-2022-0010","url":null,"abstract":"Abstract The purpose of this paper is to propose an approach to blockchain-enabled supply-chain model for a smart crop production framework. The defined tasks are: (1) analysis of blockchain ecosystem as a network of stakeholders and as an infrastructure of technical and logical elements; (2) definition of a supply-chain model; (3) design of blockchain reference infrastructure; (4) description of blockchain information channels with smart contracts basic functionalities. The results presented include: а supply-chain model facilitating seeds certification process, monitoring and supervision of the grain process, provenance and as optional interactions with regulatory bodies, logistics and financial services; the three level blockchain reference infrastructure and a blockchain-enabled supply-chain supporting five information channels with nine participants and smart contracts. An account management user application tool, the general descriptions of smart contract basic functionalities and a selected parts of one smart contract code are provided as examples.","PeriodicalId":45562,"journal":{"name":"Cybernetics and Information Technologies","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47462515","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}
Abstract Shoplifting is a troubling and pervasive aspect of consumers, causing great losses to retailers. It is the theft of goods from the stores/shops, usually by hiding the store item either in the pocket or in carrier bag and leaving without any payment. Revenue loss is the most direct financial effect of shoplifting. Therefore, this article introduces an Expert Shoplifting Activity Recognition (ESAR) system to reduce shoplifting incidents in stores/shops. The system being proposed seamlessly examines each frame in video footage and alerts security personnel when shoplifting occurs. It uses dual-stream convolutional neural network to extract appearance and salient motion features in the video sequences. Here, optical flow and gradient components are used to extract salient motion features related to shoplifting movement in the video sequence. Long Short Term Memory (LSTM) based deep learner is modeled to learn the extracted features in the time domain for distinguishing person actions (i.e., normal and shoplifting). Analyzing the model behavior for diverse modeling environments is an added contribution of this paper. A synthesized shoplifting dataset is used here for experimentations. The experimental outcomes show that the proposed approach attains better consequences up to 90.26% detection accuracy compared to the other prevalent approaches.
{"title":"ESAR, An Expert Shoplifting Activity Recognition System","authors":"Mohd. Aquib Ansari, D. Singh","doi":"10.2478/cait-2022-0012","DOIUrl":"https://doi.org/10.2478/cait-2022-0012","url":null,"abstract":"Abstract Shoplifting is a troubling and pervasive aspect of consumers, causing great losses to retailers. It is the theft of goods from the stores/shops, usually by hiding the store item either in the pocket or in carrier bag and leaving without any payment. Revenue loss is the most direct financial effect of shoplifting. Therefore, this article introduces an Expert Shoplifting Activity Recognition (ESAR) system to reduce shoplifting incidents in stores/shops. The system being proposed seamlessly examines each frame in video footage and alerts security personnel when shoplifting occurs. It uses dual-stream convolutional neural network to extract appearance and salient motion features in the video sequences. Here, optical flow and gradient components are used to extract salient motion features related to shoplifting movement in the video sequence. Long Short Term Memory (LSTM) based deep learner is modeled to learn the extracted features in the time domain for distinguishing person actions (i.e., normal and shoplifting). Analyzing the model behavior for diverse modeling environments is an added contribution of this paper. A synthesized shoplifting dataset is used here for experimentations. The experimental outcomes show that the proposed approach attains better consequences up to 90.26% detection accuracy compared to the other prevalent approaches.","PeriodicalId":45562,"journal":{"name":"Cybernetics and Information Technologies","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47463371","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}
Abstract Wireless sensor networks are an enthralling field of study with numerous applications. A Wireless Sensor Network (WSN) is used to monitor real-time scenarios such as weather, temperature, humidity, and military surveillance. A WSN is composed of several sensor nodes that are responsible for sensing, aggregating, and transmitting data in the system, in which it has been deployed. These sensors are powered by small batteries because they are small. Managing power consumption and extending network life is a common challenge in WSNs. Data transmission is a critical process in a WSN that consumes the majority of the network’s resources. Since the cluster heads in the network are in charge of data transmission, they require more energy. We need to know where these CHs are deployed in order to calculate how much energy they use. The deployment of a WSN can be either static or random. Although most researchers focus on random deployment, this paper applies the proposed Deterministic Centroid algorithm for static deployment. Based on the coverage of the deployment area, this algorithm places the sensors in a predetermined location. The simulation results show how this algorithm generates balanced clusters, improves coverage, and saves energy.
{"title":"Deterministic Centroid Localization for Improving Energy Efficiency in Wireless Sensor Networks","authors":"Sneha Vijayan, Nagarajan Munusamy","doi":"10.2478/cait-2022-0002","DOIUrl":"https://doi.org/10.2478/cait-2022-0002","url":null,"abstract":"Abstract Wireless sensor networks are an enthralling field of study with numerous applications. A Wireless Sensor Network (WSN) is used to monitor real-time scenarios such as weather, temperature, humidity, and military surveillance. A WSN is composed of several sensor nodes that are responsible for sensing, aggregating, and transmitting data in the system, in which it has been deployed. These sensors are powered by small batteries because they are small. Managing power consumption and extending network life is a common challenge in WSNs. Data transmission is a critical process in a WSN that consumes the majority of the network’s resources. Since the cluster heads in the network are in charge of data transmission, they require more energy. We need to know where these CHs are deployed in order to calculate how much energy they use. The deployment of a WSN can be either static or random. Although most researchers focus on random deployment, this paper applies the proposed Deterministic Centroid algorithm for static deployment. Based on the coverage of the deployment area, this algorithm places the sensors in a predetermined location. The simulation results show how this algorithm generates balanced clusters, improves coverage, and saves energy.","PeriodicalId":45562,"journal":{"name":"Cybernetics and Information Technologies","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46006469","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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