Keerthan Simha.R, Raghavan H K, Akshatha Prabhu, Pallavi Joshi
Multi‐Factor Authentication (MFA) strengthens digital security by necessitating users to verify their identity. It uses various authentication methods like adding an extra layer of protection beyond conventional passwords. Proposed method introduces a novel MFA system that integrates multiple authentication layers, starting with two phase Graphical password with the traditional email‐password and progressing to facial recognition using Convolutional Neural Networks (CNN) and Quick response (QR) code authentication. To prove the robustness of our method, we are considering some test cases and few performance metrics like delay, accuracy, etc. The results are derived for False positive rates, complexity. The success rate is observed to be more than 93% for the proposed model.
{"title":"Beyond passwords: A multi‐factor authentication approach for robust digital security","authors":"Keerthan Simha.R, Raghavan H K, Akshatha Prabhu, Pallavi Joshi","doi":"10.1002/itl2.555","DOIUrl":"https://doi.org/10.1002/itl2.555","url":null,"abstract":"Multi‐Factor Authentication (MFA) strengthens digital security by necessitating users to verify their identity. It uses various authentication methods like adding an extra layer of protection beyond conventional passwords. Proposed method introduces a novel MFA system that integrates multiple authentication layers, starting with two phase Graphical password with the traditional email‐password and progressing to facial recognition using Convolutional Neural Networks (CNN) and Quick response (QR) code authentication. To prove the robustness of our method, we are considering some test cases and few performance metrics like delay, accuracy, etc. The results are derived for False positive rates, complexity. The success rate is observed to be more than 93% for the proposed model.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141830414","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}
Bere Sachin Sukhadeo, Sarika Dilip Dhurgude, Y. Sinkar, Shashikant V. Athawale
Using traditional non‐virtualized Wireless Sensor Networks (WSNs) efficiently is difficult due to the embedded applications, which make the sensor nodes inaccessible to other applications. The proposed study considered both the node‐level and network‐level virtualization of wireless sensor networks to examine dynamic virtual network embedding. WSNs can leverage their shared sensing capabilities through network virtualization. Infrastructure providers earn more revenue by mapping more virtual network embedding (VNE) onto their substrate networks. VNE must therefore improve its acceptance ratio. The proposed RLE‐SVNE is demonstrated to be more efficient than state‐of‐the‐art in respect to acceptance, recovery, failure recovery delay, and revenue cost through simulation results. It compares the RLF‐SVNE method with C‐SVNE and N‐SVNE to demonstrate its superiority.
{"title":"A framework of survivability model virtualized wireless sensor networks for IOT‐assisted wireless sensor network","authors":"Bere Sachin Sukhadeo, Sarika Dilip Dhurgude, Y. Sinkar, Shashikant V. Athawale","doi":"10.1002/itl2.552","DOIUrl":"https://doi.org/10.1002/itl2.552","url":null,"abstract":"Using traditional non‐virtualized Wireless Sensor Networks (WSNs) efficiently is difficult due to the embedded applications, which make the sensor nodes inaccessible to other applications. The proposed study considered both the node‐level and network‐level virtualization of wireless sensor networks to examine dynamic virtual network embedding. WSNs can leverage their shared sensing capabilities through network virtualization. Infrastructure providers earn more revenue by mapping more virtual network embedding (VNE) onto their substrate networks. VNE must therefore improve its acceptance ratio. The proposed RLE‐SVNE is demonstrated to be more efficient than state‐of‐the‐art in respect to acceptance, recovery, failure recovery delay, and revenue cost through simulation results. It compares the RLF‐SVNE method with C‐SVNE and N‐SVNE to demonstrate its superiority.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141644713","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}
With the rapid development of the Internet of Things and 5G technology, smart university gymnasiums have become more and more important. However, it has become increasingly difficult for university gymnasium management, especially to detect abnormal behavior with dense crowds under limited venue space. To handle this issue, this paper designs an Artificial Intelligence Internet of Things (AIoT) abnormal behavior detection system which consists of the 5G camera, 5G transmission network and cloud platform. The 5G camera captures and transmits the video to the cloud platform by exploiting the 5G wireless sensor network. In the cloud platform, a hybrid variational autoencoder backbone which exploits the pre‐trained VGG16 and Transformer model is deployed to detect abnormal behaviors. Moreover, by introducing adversarial training mechanisms, the robustness of the proposed model is effectively improved. The experimental results on our self‐built gymnasium abnormal behavior dataset show that the proposed model can correctly identify most of the abnormal behaviors in the gymnasium compared to other models.
{"title":"Abnormal behavior monitoring enhanced smart university stadium under the background of “Internet plus”","authors":"Yan Li, Xiao Meng, Xiaochen Zhang","doi":"10.1002/itl2.560","DOIUrl":"https://doi.org/10.1002/itl2.560","url":null,"abstract":"With the rapid development of the Internet of Things and 5G technology, smart university gymnasiums have become more and more important. However, it has become increasingly difficult for university gymnasium management, especially to detect abnormal behavior with dense crowds under limited venue space. To handle this issue, this paper designs an Artificial Intelligence Internet of Things (AIoT) abnormal behavior detection system which consists of the 5G camera, 5G transmission network and cloud platform. The 5G camera captures and transmits the video to the cloud platform by exploiting the 5G wireless sensor network. In the cloud platform, a hybrid variational autoencoder backbone which exploits the pre‐trained VGG16 and Transformer model is deployed to detect abnormal behaviors. Moreover, by introducing adversarial training mechanisms, the robustness of the proposed model is effectively improved. The experimental results on our self‐built gymnasium abnormal behavior dataset show that the proposed model can correctly identify most of the abnormal behaviors in the gymnasium compared to other models.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141667033","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}
C. Annadurai, I. Nelson, K. Nirmala Devi, G. Thavasi Raja
In the era of 6G networks, Multiple Input Multiple Output (MIMO) technology offers unprecedented opportunities for high‐throughput and low‐latency communication. Existing communication frameworks, however, have difficulty optimizing both energy efficiency and reliability at the same time. In most cases, conventional routing protocols fail to meet the needs of MIMO systems, making them inefficient and prone to reliability problems due to their inability to dynamically adapt to different network conditions. This research addresses the intricate interplay between energy efficiency and reliability within the context of 6G networks with MIMO. The motivation for this research arises from the imperative to unlock the full potential of 6G networks with MIMO for achieving energy‐efficient and reliable communication. With the advancement of communication technology, seamless connectivity, minimal energy consumption, and robust reliability become increasingly critical. Currently, solutions cannot adapt dynamically to the diverse and dynamic conditions of a 6G environment. Through this research, we aim to bridge this gap, enhancing 6G network performance and sustainability with unprecedented gains in energy efficiency and reliability. We have developed the Dynamic Multipath Routing (DMR) algorithm by harnessing the advanced features of MIMO technology. The DMR algorithm strategically chooses paths to minimize the effects of fading, interference, and channel impairments, creating a resilient communication network. This improvement is essential for meeting the demanding connectivity needs of various 6G applications, covering ultra‐reliable low‐latency communication and massive machine‐type communication.
在 6G 网络时代，多输入多输出（MIMO）技术为高吞吐量和低延迟通信提供了前所未有的机遇。然而，现有的通信框架难以同时优化能效和可靠性。在大多数情况下，传统的路由协议无法满足 MIMO 系统的需求，使其效率低下，并且由于无法动态适应不同的网络条件而容易出现可靠性问题。本研究探讨了采用 MIMO 的 6G 网络中能效与可靠性之间错综复杂的相互作用。这项研究的动机来自于充分释放采用 MIMO 的 6G 网络的潜力，以实现高能效和高可靠性通信的迫切需要。随着通信技术的发展，无缝连接、最低能耗和稳健可靠性变得越来越重要。目前，解决方案无法动态适应 6G 环境中的各种动态条件。通过这项研究，我们旨在弥合这一差距，以前所未有的能效和可靠性提升来增强 6G 网络的性能和可持续性。我们利用多输入多输出（MIMO）技术的先进特性，开发了动态多路径路由（DMR）算法。DMR 算法战略性地选择路径，最大限度地减少衰减、干扰和信道损伤的影响，从而创建一个弹性通信网络。这一改进对于满足各种 6G 应用（包括超可靠低延迟通信和大规模机器型通信）的连接需求至关重要。
{"title":"Dynamic multipath routing for energy‐efficient and reliable communication in 6G networks with MIMO","authors":"C. Annadurai, I. Nelson, K. Nirmala Devi, G. Thavasi Raja","doi":"10.1002/itl2.559","DOIUrl":"https://doi.org/10.1002/itl2.559","url":null,"abstract":"In the era of 6G networks, Multiple Input Multiple Output (MIMO) technology offers unprecedented opportunities for high‐throughput and low‐latency communication. Existing communication frameworks, however, have difficulty optimizing both energy efficiency and reliability at the same time. In most cases, conventional routing protocols fail to meet the needs of MIMO systems, making them inefficient and prone to reliability problems due to their inability to dynamically adapt to different network conditions. This research addresses the intricate interplay between energy efficiency and reliability within the context of 6G networks with MIMO. The motivation for this research arises from the imperative to unlock the full potential of 6G networks with MIMO for achieving energy‐efficient and reliable communication. With the advancement of communication technology, seamless connectivity, minimal energy consumption, and robust reliability become increasingly critical. Currently, solutions cannot adapt dynamically to the diverse and dynamic conditions of a 6G environment. Through this research, we aim to bridge this gap, enhancing 6G network performance and sustainability with unprecedented gains in energy efficiency and reliability. We have developed the Dynamic Multipath Routing (DMR) algorithm by harnessing the advanced features of MIMO technology. The DMR algorithm strategically chooses paths to minimize the effects of fading, interference, and channel impairments, creating a resilient communication network. This improvement is essential for meeting the demanding connectivity needs of various 6G applications, covering ultra‐reliable low‐latency communication and massive machine‐type communication.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141670645","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}
Bere Sachin Sukhadeo, Y. Sinkar, Sarika Dilip Dhurgude, Shashikant V. Athawale
In recent years, smart agriculture has grown rapidly. A crop disease is generally caused by pests, insects, or pathogens and reduces the productivity of the crop by adversely affecting its yield. There is a severe loss of crops across the country due to various crop diseases, and one reason is not being able to detect the disease in its early stages keeps them from finding a solution. An Internet of Things (IOT) sensor network is used to detect and classify diseases in leaves in this paper. Precision agriculture uses machine learning techniques to increase crop growth, control the cultivation process, and enhance crop productivity with less human involvement. IOT sensor networks are being used in precision agriculture using machine learning techniques. A result of the proposed method shows an overall accuracy of 88%.
{"title":"Plant disease detection using machine learning techniques based on internet of things (IoT) sensor network","authors":"Bere Sachin Sukhadeo, Y. Sinkar, Sarika Dilip Dhurgude, Shashikant V. Athawale","doi":"10.1002/itl2.546","DOIUrl":"https://doi.org/10.1002/itl2.546","url":null,"abstract":"In recent years, smart agriculture has grown rapidly. A crop disease is generally caused by pests, insects, or pathogens and reduces the productivity of the crop by adversely affecting its yield. There is a severe loss of crops across the country due to various crop diseases, and one reason is not being able to detect the disease in its early stages keeps them from finding a solution. An Internet of Things (IOT) sensor network is used to detect and classify diseases in leaves in this paper. Precision agriculture uses machine learning techniques to increase crop growth, control the cultivation process, and enhance crop productivity with less human involvement. IOT sensor networks are being used in precision agriculture using machine learning techniques. A result of the proposed method shows an overall accuracy of 88%.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141714422","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}
To guarantee the availability, confidentiality, and integrity of data transferred over optical channels—especially in the context of fifth‐generation (5G) communication infrastructure—optical network security management is essential. This paper provides an overview of security management for optical networks, emphasizing the importance of this practice in today's communication infrastructure and the difficulties presented by ever changing cyberthreats. The architecture of optical security management is shown, with special attention to how well it integrates with current optical‐layer controllers and how it facilitates the coordination of security operations among optical networks. The study also looks at use cases for optical network security management in 5G networks, such as safe data transfer, defense against cyberattacks, maintaining privacy in 5G apps, network slicing security, and resistance to physical assaults. Such instances highlight the adaptability and significance of optical network security management in bolstering 5G networks' security, privacy, and resilience across a range of businesses and applications.
{"title":"Integrating optical security management with optical‐layer controller architecture for enhanced network security","authors":"Himanshi Babbar, S. Rani","doi":"10.1002/itl2.558","DOIUrl":"https://doi.org/10.1002/itl2.558","url":null,"abstract":"To guarantee the availability, confidentiality, and integrity of data transferred over optical channels—especially in the context of fifth‐generation (5G) communication infrastructure—optical network security management is essential. This paper provides an overview of security management for optical networks, emphasizing the importance of this practice in today's communication infrastructure and the difficulties presented by ever changing cyberthreats. The architecture of optical security management is shown, with special attention to how well it integrates with current optical‐layer controllers and how it facilitates the coordination of security operations among optical networks. The study also looks at use cases for optical network security management in 5G networks, such as safe data transfer, defense against cyberattacks, maintaining privacy in 5G apps, network slicing security, and resistance to physical assaults. Such instances highlight the adaptability and significance of optical network security management in bolstering 5G networks' security, privacy, and resilience across a range of businesses and applications.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141696805","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}
Intelligent sensing plays a crucial role in making vehicles safe and trouble‐free. The purpose of this paper is to introduce Vehicular Sensor Networks (VSNs) in a vehicular IoT‐based smart city paradigm, focusing on security. Furthermore, we discuss the robustness and reliability of VSN. In this design, Ad hoc On‐Demand Distance Vector (AODV) routing‐based Internet of Vehicles is integrated with a privacy‐aware secure ant colony optimization for smart cities in which suspicious vehicles are prevented from disseminating messages. IoV real‐time communication emphasizes data security. A comparison of experimental results shows that the proposed approach outperforms existing approaches. Smart city communication networks can be optimized using the proposed model.
{"title":"An efficient security and privacy approach for internet of vehicles in vehicular networks for smart cities","authors":"Elham Kariri","doi":"10.1002/itl2.554","DOIUrl":"https://doi.org/10.1002/itl2.554","url":null,"abstract":"Intelligent sensing plays a crucial role in making vehicles safe and trouble‐free. The purpose of this paper is to introduce Vehicular Sensor Networks (VSNs) in a vehicular IoT‐based smart city paradigm, focusing on security. Furthermore, we discuss the robustness and reliability of VSN. In this design, Ad hoc On‐Demand Distance Vector (AODV) routing‐based Internet of Vehicles is integrated with a privacy‐aware secure ant colony optimization for smart cities in which suspicious vehicles are prevented from disseminating messages. IoV real‐time communication emphasizes data security. A comparison of experimental results shows that the proposed approach outperforms existing approaches. Smart city communication networks can be optimized using the proposed model.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141353691","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}
S. A. Sahaaya Arul Mary, H. Anwar Basha, G. Mohanraj, R. Kiruthikaa, N. Saranya
Internet of Things (IoT) becomes a prominent sensing paradigm between the devices. Its evolution in the global digital increases extensively in various domains. For IoT application's sensors are the primary source for generating data. These collected data are subject to the identification and detection of outliers/anomalies. The massive volume of data generation makes anomaly detection a complex and challenging task. The anomalies affect the data accuracy and data quality. In this paper, the k‐NN classifier is proposed for enhancing classification accuracy. K‐NN follows a non‐parametric strategy and is one of the known classification algorithms. In the proposed system, k‐NN is utilized to perform classification or regression with estimations of their k nearest neighbors. The proposed system consists of three major processes such as data preprocessing, classification, visualization. This study explores the utilization of 5G connectivity and cloud computing infrastructure for outlier detection in IoT data streams. Leveraging the K‐Nearest Neighbors (KNN) classifier, our methodology focuses on efficiently identifying anomalies in IoT data. By integrating 5G connectivity for real‐time data transmission and cloud‐based machine learning for scalable analysis, we demonstrate a robust framework for outlier detection in IoT environments. The Experimental work with the proposed method is carried out using training and observation is tabulated with respective classes. As a result, on the three metrics, the proposed k‐NN proves its efficiency is far better than the others, with an average of 98.4% of accuracy.
{"title":"Leveraging 5G and cloud computing for outlier detection in IoT environments: A KNN approach","authors":"S. A. Sahaaya Arul Mary, H. Anwar Basha, G. Mohanraj, R. Kiruthikaa, N. Saranya","doi":"10.1002/itl2.550","DOIUrl":"https://doi.org/10.1002/itl2.550","url":null,"abstract":"Internet of Things (IoT) becomes a prominent sensing paradigm between the devices. Its evolution in the global digital increases extensively in various domains. For IoT application's sensors are the primary source for generating data. These collected data are subject to the identification and detection of outliers/anomalies. The massive volume of data generation makes anomaly detection a complex and challenging task. The anomalies affect the data accuracy and data quality. In this paper, the k‐NN classifier is proposed for enhancing classification accuracy. K‐NN follows a non‐parametric strategy and is one of the known classification algorithms. In the proposed system, k‐NN is utilized to perform classification or regression with estimations of their k nearest neighbors. The proposed system consists of three major processes such as data preprocessing, classification, visualization. This study explores the utilization of 5G connectivity and cloud computing infrastructure for outlier detection in IoT data streams. Leveraging the K‐Nearest Neighbors (KNN) classifier, our methodology focuses on efficiently identifying anomalies in IoT data. By integrating 5G connectivity for real‐time data transmission and cloud‐based machine learning for scalable analysis, we demonstrate a robust framework for outlier detection in IoT environments. The Experimental work with the proposed method is carried out using training and observation is tabulated with respective classes. As a result, on the three metrics, the proposed k‐NN proves its efficiency is far better than the others, with an average of 98.4% of accuracy.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141351287","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}
L. Srinivasan, Humaira Nishat, S. Shargunam, Deepak Kumar Nayak, K. Janani
In this research, we optimize live video broadcast performance by incorporating advanced technologies such as 5G, the Internet of Things (IoT), and cloud computing. Our approach utilizes the Random Forest classifier to categorize data, achieving a 99% precision rate. A comparative study demonstrates that our proposed technique outperforms RCNN and Mask‐RCNN methods in optimizing video streaming efficacy. We show that our method efficiently enhances video streaming quality by integrating machine learning technologies. The combination of 5G, IoT, and cloud computing creates a robust environment for delivering optimized Live video streaming to users. This research underscores the importance of leveraging cutting‐edge technology to address optimization challenges in modern video streaming systems, focusing on the real‐time optimization of video streams in contemporary technological environments.
{"title":"Optimizing live video streaming: Integrating 5G, IoT, and cloud computing with machine learning","authors":"L. Srinivasan, Humaira Nishat, S. Shargunam, Deepak Kumar Nayak, K. Janani","doi":"10.1002/itl2.556","DOIUrl":"https://doi.org/10.1002/itl2.556","url":null,"abstract":"In this research, we optimize live video broadcast performance by incorporating advanced technologies such as 5G, the Internet of Things (IoT), and cloud computing. Our approach utilizes the Random Forest classifier to categorize data, achieving a 99% precision rate. A comparative study demonstrates that our proposed technique outperforms RCNN and Mask‐RCNN methods in optimizing video streaming efficacy. We show that our method efficiently enhances video streaming quality by integrating machine learning technologies. The combination of 5G, IoT, and cloud computing creates a robust environment for delivering optimized Live video streaming to users. This research underscores the importance of leveraging cutting‐edge technology to address optimization challenges in modern video streaming systems, focusing on the real‐time optimization of video streams in contemporary technological environments.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141375194","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}
Energy efficiency is critical for prolonging the survivability of wireless sensor networks (WSNs), and clustering algorithms play a significant role in achieving this goal. An application‐specific wireless sensor network requires adapted methods and techniques to meet its requirements. A vast amount of research has been done on optimizing energy consumption and enhancing network lifetime of sensor nodes. To increase the lifetime of WSNs, we present and evaluate an energy‐efficient clustering algorithm based on distributed fuzzy logic (EECADFL). High reliability, low error rates during clustering, and its ability to perform well in large‐scale networks with many nodes are some of the main benefits of this method. In wireless sensor networks, simulation results showed that the scheme provided better lifetime performance while limiting dead nodes and improving cluster head selection.
{"title":"Energy‐efficient clustering algorithm using distributed fuzzy‐logic to prolong the survivability of wireless sensor networks","authors":"Lulwah M. Alkwai, Kusum Yadav","doi":"10.1002/itl2.549","DOIUrl":"https://doi.org/10.1002/itl2.549","url":null,"abstract":"Energy efficiency is critical for prolonging the survivability of wireless sensor networks (WSNs), and clustering algorithms play a significant role in achieving this goal. An application‐specific wireless sensor network requires adapted methods and techniques to meet its requirements. A vast amount of research has been done on optimizing energy consumption and enhancing network lifetime of sensor nodes. To increase the lifetime of WSNs, we present and evaluate an energy‐efficient clustering algorithm based on distributed fuzzy logic (EECADFL). High reliability, low error rates during clustering, and its ability to perform well in large‐scale networks with many nodes are some of the main benefits of this method. In wireless sensor networks, simulation results showed that the scheme provided better lifetime performance while limiting dead nodes and improving cluster head selection.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141383080","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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