R. Vijayakumar, Manisha Mali, Sonali A. Patil, V. Gomathy, Harishchander Anandaram
{"title":"Analyzing the theoretical merits of Loxi load balancer for improving the efficiency of load balancing in 5G-edge IoT applications based on Kubernetes","authors":"R. Vijayakumar, Manisha Mali, Sonali A. Patil, V. Gomathy, Harishchander Anandaram","doi":"10.1002/itl2.563","DOIUrl":"https://doi.org/10.1002/itl2.563","url":null,"abstract":"<p>Load balancing, a critical aspect of cloud and cloud-based applications, is a major challenge that demands our attention. Due to the increasing dynamic workloads, load balancing becomes more important in the cloud. One of the hyperscale models that stands out for its ability to efficiently balance load by scaling the demands and allocating resources is the Loxi-Load-Balancer (LLB). This paper explores explicitly LLB's application in the context of 5G-Edge IoT applications based on Kubernetes. LLB's unique features, such as its open-source nature for cloud-native loads, its use of eBPF as the core engine to avoid adding additional software modules to configure the kernel, and its ability to change its services using the existing layers, set it apart from other load balancers. These features provide high security, observability, and networking. This paper delves into how LLB is used for load balancing in Kubernetes to increase speed and provide flexibility and customizable services. LLB automates all the internal and external administrations concerning monitoring, deployment, scaling, migration, routing, configuration, and resource allocation. This paper focused on developing an efficient resource allocation management system by load balancing using Loxi-Load-Balancer-extended Berkeley Packet Filter (LLB-eBPF). Detailed information about the LLB-eBPF-Kubernetes is given in this paper to help you understand the basics of LLB, eBPF, and Kubernetes.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143690252","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}
{"title":"Semantic sensor data integration for talent development via hybrid multi-objective evolutionary algorithm","authors":"Fang Luo, Ya-Juan Yang, Yu-Cheng Geng","doi":"10.1002/itl2.557","DOIUrl":"https://doi.org/10.1002/itl2.557","url":null,"abstract":"<p>In this work, we propose a new hybrid Multi-Objective Evolutionary Algorithm (hMOEA) specifically designed for semantic sensor data integration, targeting talent development within the burgeoning field of the Semantic Internet of Things (SIoT). Our approach synergizes the capabilities of Multi-Objective Particle Swarm Optimization and Genetic Algorithms to tackle the sophisticated challenges inherent in Sensor Ontology Matching (SOM). This innovative hMOEA framework is adapt at discerning precise semantic correlations among diverse ontologies, thereby facilitating seamless interoperability and enhancing the functionality of IoT applications. Central to our contributions are the development of an advanced multi-objective optimization model that underpins the SOM process, the implementation of the hMOEA framework which sets a new benchmark for accurate semantic sensor data integration, and the rigorous validation of hMOEA's superiority through extensive testing in varied real-world SOM scenarios. This research not only marks a significant advancement in SOM but also highlights the critical role of cutting-edge SOM methodologies in educational curricula, for example, the new business subject education proposed by China in recent years, aimed at equipping future professionals with the necessary skills to innovate and lead in the SIoT and SW domains.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/itl2.557","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Keerthan Simha.R, Raghavan H K, Akshatha Prabhu, Pallavi Joshi
{"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":"10.1002/itl2.555","url":null,"abstract":"<p>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.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"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, Yogita Deepak Sinkar, Shashikant V. Athawale
{"title":"A framework of survivability model virtualized wireless sensor networks for IOT-assisted wireless sensor network","authors":"Bere Sachin Sukhadeo, Sarika Dilip Dhurgude, Yogita Deepak Sinkar, Shashikant V. Athawale","doi":"10.1002/itl2.552","DOIUrl":"10.1002/itl2.552","url":null,"abstract":"<p>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.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"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}
{"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":"10.1002/itl2.560","url":null,"abstract":"<p>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.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"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
{"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":"10.1002/itl2.559","url":null,"abstract":"<p>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.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"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, Yogita Deepak Sinkar, Sarika Dilip Dhurgude, Shashikant V. Athawale
{"title":"Plant disease detection using machine learning techniques based on internet of things (IoT) sensor network","authors":"Bere Sachin Sukhadeo, Yogita Deepak Sinkar, Sarika Dilip Dhurgude, Shashikant V. Athawale","doi":"10.1002/itl2.546","DOIUrl":"10.1002/itl2.546","url":null,"abstract":"<p>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%.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"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}
{"title":"Integrating optical security management with optical-layer controller architecture for enhanced network security","authors":"Himanshi Babbar, Shalli Rani","doi":"10.1002/itl2.558","DOIUrl":"10.1002/itl2.558","url":null,"abstract":"<p>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.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"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}
{"title":"Efficient and Reliable Emergency Routing for Wireless Body Area Networks","authors":"Bhavana Alte, Amarsinh Vidhate","doi":"10.1002/itl2.551","DOIUrl":"https://doi.org/10.1002/itl2.551","url":null,"abstract":"<p>The development of Wireless Body Area Networks (WBAN) is currently in progress with the aim of facilitating communication among small body sensors. This data can be employed to offer prompt or timely aid to individuals who are not just in a critical state but also those who are unable to promptly commute to medical facilities due to physical limitations, congested roadways, or their place of residence. Another crucial requirement for WBAN is their capability to deliver quality of service for diverse forms of network traffic. Privacy and system security are crucial to protecting patient records throughout use and storage by healthcare providers. This study introduces a novel emergency data handling method for emergency healthcare communication networks. Furthermore, the proposed system provides Reliable Emergency Routing to ensure secure transfer of emergency and non-emergency WBAN data rewards are used to establish routes to securely transmit non-emergency and emergency data in highly exposed situations while minimizing data loss and delay. Simulations showed that the recommended approach outperformed current protocols in energy consumption, latency, packet delivery ratio throughput, and communication overhead.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143456076","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}
{"title":"EEERP-RL: Enhanced energy-efficient routing protocol based on reinforcement learning for wireless sensor network","authors":"Mohanad J. Jaber, Zahraa Jasim Jaber","doi":"10.1002/itl2.548","DOIUrl":"https://doi.org/10.1002/itl2.548","url":null,"abstract":"<p>Wireless Sensor Networks (WSN) efficiently monitors and record environmental conditions, transmitting this data to central locations via widely distributed, sensor nodes. One major challenge in WSN involves creating an energy-efficient routing protocol that minimizes energy consumption and extends the network's longevity. In this paper, we propose EEERP-RL, an enhanced energy-efficient QoS routing protocol for WSNs, based on reinforcement learning (RL). The proposed protocol has been compared with two other protocols to determine which one gives the best performance in the network OSPF and SDN-Q. There is an investigation of packet delivery ratios and delays (m), as well as the impact of alive nodes, dead nodes, and energy consumption. Based on simulation results, the proposed protocol outperforms as compared to existing protocol in terms of different network traffic loads and node mobility.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455841","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}