Numerous sensor nodes spread out across the surveillance regionform the Wireless-Sensor Network (WSN), a smart, self-organizing network. Since the lumpscan typically only be motorized by batteries, creating a WSN while maintaining an optimal energybalance and extending the network's lifetime is the biggest issue.A novel network architecture that integrates nanotechnology with sensor networks isknown as a Wireless-NanoSensor-Network(WNSN). A new area of focus in research is intra-bodyiWNSNs,which are WNSNs with promising potential applications in biomedicine, damage detection,and intra-body health monitoring. We suggest an energy-balance-clustering-routing protocol(EBCR) for iSN nodes that have limited energy storage, short communication range, and low computationand processing capabilities. The protocol uses a novel hierarchical clustering approach tolessen the communication burden on nano-nodes.Cluster nano-nodes can use one-hop routing to send data directly to the Cluster-Head(CH)nodes, and the CH-nodes can utilize multi-hop routing to send data to the nano control node. In addition,selecting the next hop node to minimize energy usage while guaranteeing successful datapacket delivery involves balancing distance and channel capacity. The protocol's strengths in energyefficiency, network-lifetime extension, and data-packet transmission success rate were highlightedby the simulation results.It is clear that the EBCR protocol is a viable option for iWNSNs' routing system.
{"title":"An Energy-Balance Clustering Routing Protocol for Intra-Body Wireless Nanosensor Networks","authors":"Santhosh S, Vamshi Krishna B, Lakshmi Prasad Mudarakola, Saptarshi Mukherjee, Mannava Yesubabu, Vikas Sharma","doi":"10.2174/0122103279318474240705104252","DOIUrl":"https://doi.org/10.2174/0122103279318474240705104252","url":null,"abstract":"\u0000\u0000Numerous sensor nodes spread out across the surveillance region\u0000form the Wireless-Sensor Network (WSN), a smart, self-organizing network. Since the lumps\u0000can typically only be motorized by batteries, creating a WSN while maintaining an optimal energy\u0000balance and extending the network's lifetime is the biggest issue.\u0000\u0000\u0000\u0000A novel network architecture that integrates nanotechnology with sensor networks is\u0000known as a Wireless-NanoSensor-Network(WNSN). A new area of focus in research is intra-bodyiWNSNs,\u0000which are WNSNs with promising potential applications in biomedicine, damage detection,\u0000and intra-body health monitoring. We suggest an energy-balance-clustering-routing protocol\u0000(EBCR) for iSN nodes that have limited energy storage, short communication range, and low computation\u0000and processing capabilities. The protocol uses a novel hierarchical clustering approach to\u0000lessen the communication burden on nano-nodes.\u0000\u0000\u0000\u0000Cluster nano-nodes can use one-hop routing to send data directly to the Cluster-Head(CH)\u0000nodes, and the CH-nodes can utilize multi-hop routing to send data to the nano control node. In addition,\u0000selecting the next hop node to minimize energy usage while guaranteeing successful data\u0000packet delivery involves balancing distance and channel capacity. The protocol's strengths in energy\u0000efficiency, network-lifetime extension, and data-packet transmission success rate were highlighted\u0000by the simulation results.\u0000\u0000\u0000\u0000It is clear that the EBCR protocol is a viable option for iWNSNs' routing system.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"107 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821544","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}
The paper focuses on enhancing the performance of 5G wireless mobilecommunication systems. Furthermore, it addresses the increasing demand for high data rates, improved channel capacity, and spectrum efficiency outlined by the 3rd Generation Partnership Project (3GPP) protocol.To develop an innovative Non-Orthogonal Multiple Access (NOMA)-based channel estimation (CE) model aimed at improving the performance of 5G wireless mobile communicationsystemsA proportionate recursive least squares (PRLS) algorithm is utilized for estimating thecharacteristics of practical Rayleigh fading channels. The applicability of the PRLS algorithm is investigated in Lambertian channels for indoor broadband communication systems such as power linecommunication (PLC) and visual light communication (VLC) systems.The assessment of evaluation metrics, including mean square error (MSE), bit error rate(BER), spectral efficiency (SE), energy efficiency (EE), capacity, and data rate, have been analysed. Faster convergence and higher accuracy compared to existing state-of-the-art approacheshave been demonstrated.The NOMA-based channel estimation model presents significant promise in enhancing the performance of 5G wireless communication systems. The demands for higher data rates andimproved spectral efficiency as per 3GPP standards have been addressed.
{"title":"Non-orthogonal Multiple Access (NOMA) Channel Estimation for Mobile\u0000& PLC-VLC Based Broadband Communication System","authors":"Manidipa Sarkar, Ankit Nayak, Sarita Nanda, Suprava Patnaik","doi":"10.2174/0122103279310677240606101233","DOIUrl":"https://doi.org/10.2174/0122103279310677240606101233","url":null,"abstract":"\u0000\u0000The paper focuses on enhancing the performance of 5G wireless mobile\u0000communication systems. Furthermore, it addresses the increasing demand for high data rates, improved channel capacity, and spectrum efficiency outlined by the 3rd Generation Partnership Project (3GPP) protocol.\u0000\u0000\u0000\u0000To develop an innovative Non-Orthogonal Multiple Access (NOMA)-based channel estimation (CE) model aimed at improving the performance of 5G wireless mobile communication\u0000systems\u0000\u0000\u0000\u0000A proportionate recursive least squares (PRLS) algorithm is utilized for estimating the\u0000characteristics of practical Rayleigh fading channels. The applicability of the PRLS algorithm is investigated in Lambertian channels for indoor broadband communication systems such as power line\u0000communication (PLC) and visual light communication (VLC) systems.\u0000\u0000\u0000\u0000The assessment of evaluation metrics, including mean square error (MSE), bit error rate\u0000(BER), spectral efficiency (SE), energy efficiency (EE), capacity, and data rate, have been analysed. Faster convergence and higher accuracy compared to existing state-of-the-art approaches\u0000have been demonstrated.\u0000\u0000\u0000\u0000The NOMA-based channel estimation model presents significant promise in enhancing the performance of 5G wireless communication systems. The demands for higher data rates and\u0000improved spectral efficiency as per 3GPP standards have been addressed.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"120 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141820810","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 : 2024-07-19DOI: 10.2174/0122103279305872240702112248
Amit Sharma, J. Amutharaj, N. S. Ram, M. Narender, S. Rajesh, M. Tiwari, K. P. Yuvaraj, Mangala Shetty
The research investigates the utility of cosine similarity as an innovativerecommendation system designed to assist individuals in making financial choices tailored to theirunique preferences and objectives. It embarks on an extensive analysis of diverse datasets encompassing a wide array of financial products, including investment portfolios, credit card offerings, insurance plans, personal loan options, and car loan packages. Each dataset undergoes meticulous feature extraction and preprocessing to optimize the accuracy of the cosine similarity model.The research then applies cosine similarity to calculate the similarity scores between individual financial products, thereby producing personalized recommendations. These recommendations are predicated on a comprehensive spectrum of input variables. The outcomes of these casestudies demonstrate the potency of cosine similarity as a foundation for the development of tailoredfinancial guidance systems. Such recommendations empower individuals to make informed decisions that are intrinsically aligned with their distinctive financial aspirations.Ridge and lasso regression algorithms are deployed to develop predictive models for assessing investment preferences and evaluating potential investment returns.The study highlights the necessity for financial institutions and advisory platforms toinvest in data quality and algorithmic sophistication to enhance the efficacy and accuracy of thesefinancial recommendations.
{"title":"Optimizing Financial Decision Support Systems with Machine LearningDriven Recommendations","authors":"Amit Sharma, J. Amutharaj, N. S. Ram, M. Narender, S. Rajesh, M. Tiwari, K. P. Yuvaraj, Mangala Shetty","doi":"10.2174/0122103279305872240702112248","DOIUrl":"https://doi.org/10.2174/0122103279305872240702112248","url":null,"abstract":"\u0000\u0000The research investigates the utility of cosine similarity as an innovative\u0000recommendation system designed to assist individuals in making financial choices tailored to their\u0000unique preferences and objectives. It embarks on an extensive analysis of diverse datasets encompassing a wide array of financial products, including investment portfolios, credit card offerings, insurance plans, personal loan options, and car loan packages. Each dataset undergoes meticulous feature extraction and preprocessing to optimize the accuracy of the cosine similarity model.\u0000\u0000\u0000\u0000The research then applies cosine similarity to calculate the similarity scores between individual financial products, thereby producing personalized recommendations. These recommendations are predicated on a comprehensive spectrum of input variables. The outcomes of these case\u0000studies demonstrate the potency of cosine similarity as a foundation for the development of tailored\u0000financial guidance systems. Such recommendations empower individuals to make informed decisions that are intrinsically aligned with their distinctive financial aspirations.\u0000\u0000\u0000\u0000Ridge and lasso regression algorithms are deployed to develop predictive models for assessing investment preferences and evaluating potential investment returns.\u0000\u0000\u0000\u0000The study highlights the necessity for financial institutions and advisory platforms to\u0000invest in data quality and algorithmic sophistication to enhance the efficacy and accuracy of these\u0000financial recommendations.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"114 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821379","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 : 2024-07-19DOI: 10.2174/0122103279312138240625052021
P. Hemalatha, J. Lavanya
In the realm of Big Data Analytics, ensuring the fairness of data-driven decisionmaking processes is imperative. This abstract introduces the Learning Embedded Fairness Interpretation (LEFI) Model, a novel approach designed to uncover and address data fairness functional requirements with an exceptional accuracy rate of 97%. The model harnesses advanced data mappingand classification analysis techniques, employing Explainable-AI (xAI) for transparent insights into fairness within large datasetsThe LEFI Model excels in navigating diverse datasets by mapping data elements to discern patterns contributing to biases. Through systematic classification analysis, LEFI identifies potential sources of unfairness, achieving an accuracy rate of 97% in discerning and addressing theseissues. This high accuracy empowers data analysts and stakeholders with confidence in the model'sassessments, facilitating informed and reliable decision-making. Crucially, the LEFI Model's implementation in Python leverages the power of this versatile programming language. The Pythonimplementation seamlessly integrates advanced mapping, classification analysis, and xAI to provide a robust and efficient solution for achieving data fairness in Big Data Analytics.This implementation ensures accessibility and ease of adoption for organizations aimingto embed fairness into their data-driven processes. The LEFI Model, with its 97% accuracy, exemplifies a comprehensive solution for data fairness in Big Data Analytics. Moreover, by combiningadvanced technologies and implementing them in Python, LEFI stands as a reliable framework fororganizations committed to ethical data usage.The model not only contributes to the ongoing dialogue on fairness but also sets anew standard for accuracy and transparency in the analytics pipeline, advocating for a more equitable future in the realm of Big Data Analytics.
{"title":"Unveiling Data Fairness Functional Requirements in Big Data Analytics\u0000Through Data Mapping and Classification Analysis","authors":"P. Hemalatha, J. Lavanya","doi":"10.2174/0122103279312138240625052021","DOIUrl":"https://doi.org/10.2174/0122103279312138240625052021","url":null,"abstract":"\u0000\u0000In the realm of Big Data Analytics, ensuring the fairness of data-driven decisionmaking processes is imperative. This abstract introduces the Learning Embedded Fairness Interpretation (LEFI) Model, a novel approach designed to uncover and address data fairness functional requirements with an exceptional accuracy rate of 97%. The model harnesses advanced data mapping\u0000and classification analysis techniques, employing Explainable-AI (xAI) for transparent insights into fairness within large datasets\u0000\u0000\u0000\u0000The LEFI Model excels in navigating diverse datasets by mapping data elements to discern patterns contributing to biases. Through systematic classification analysis, LEFI identifies potential sources of unfairness, achieving an accuracy rate of 97% in discerning and addressing these\u0000issues. This high accuracy empowers data analysts and stakeholders with confidence in the model's\u0000assessments, facilitating informed and reliable decision-making. Crucially, the LEFI Model's implementation in Python leverages the power of this versatile programming language. The Python\u0000implementation seamlessly integrates advanced mapping, classification analysis, and xAI to provide a robust and efficient solution for achieving data fairness in Big Data Analytics.\u0000\u0000\u0000\u0000This implementation ensures accessibility and ease of adoption for organizations aiming\u0000to embed fairness into their data-driven processes. The LEFI Model, with its 97% accuracy, exemplifies a comprehensive solution for data fairness in Big Data Analytics. Moreover, by combining\u0000advanced technologies and implementing them in Python, LEFI stands as a reliable framework for\u0000organizations committed to ethical data usage.\u0000\u0000\u0000\u0000The model not only contributes to the ongoing dialogue on fairness but also sets a\u0000new standard for accuracy and transparency in the analytics pipeline, advocating for a more equitable future in the realm of Big Data Analytics.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"116 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822115","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 : 2024-07-05DOI: 10.2174/0122103279319738240618201043
S. U, Lakshmi Prasad Mudarakola, Raguru Jaya Krishna, B. Prasanthi, Dastagiraiah C, I. Tayubi
The integration of communication tools has allowed for effectivedecision-making in smart cities and Internet-of-Things (IoT). One major issue that people whocommute to cities everyday encounter is traffic congestion. Thanks to the progress and backing ofICTs, transportation solutions have been designed and implemented, leading to the development ofITSs and the provision of numerous innovative services.These services include ones that guarantee safety, provide drivers withuseful information, enable greater street movement, and avoid congestion, among many others. Inindustrialized nations, traffic data is collected by specialized sensors that can anticipate future patterns.Commuters are kept informed of any traffic updates through the Internet. When there is littleor no physical infrastructure and Internet connection, these methods become unworkable. Internetaccess is still a problem in rural regions, and there are no roadside units in underdeveloped nations.This article presents an architecture for smart cities' intelligent vehicular networks that can impromptuaccept data from nearby vehicles in real time and use it to choose routes. As embeddeddevices in vehicles, we utilized Android-based smartphones with Wi-Fi Direct capabilities. To setup our smart transportation system, we utilized a vehicular ad hoc network.Data was collected and processed using separate methods between two major cities in adeveloping nation. Resource utilization, transmission delay, packet loss, and total trip time weremeasured against several fixed- and dynamic-route-selection algorithms to assess the framework'sperformance.When equated to a conventional fixed-route-selection procedure, our results reveal a33.3% reduction in trip times.
通信工具的集成使智慧城市和物联网（IoT）中的决策变得更加有效。每天往返城市的人们都会遇到的一个主要问题就是交通拥堵。这些服务包括保障安全、为驾驶员提供有用信息、提高街道通行能力和避免拥堵等。在工业化国家，交通数据是由能够预测未来模式的专业传感器收集的。当几乎没有或根本没有实体基础设施和互联网连接时，这些方法就变得行不通了。互联网接入在农村地区仍然是个问题，不发达国家也没有路边装置。本文介绍了智慧城市智能车载网络的架构，该架构可实时从附近车辆临时接收数据，并利用这些数据选择路线。作为车辆的嵌入式设备，我们使用了具有 Wi-Fi Direct 功能的安卓智能手机。为了建立我们的智能交通系统，我们使用了一个车载 ad hoc 网络。我们使用不同的方法收集和处理了发展中国家两个主要城市之间的数据。为了评估该框架的性能，我们对几种固定路线和动态路线选择算法的资源利用率、传输延迟、数据包丢失和总行程时间进行了测量。
{"title":"An Intelligent Transport System Using Vehicular Network for Smart Cities","authors":"S. U, Lakshmi Prasad Mudarakola, Raguru Jaya Krishna, B. Prasanthi, Dastagiraiah C, I. Tayubi","doi":"10.2174/0122103279319738240618201043","DOIUrl":"https://doi.org/10.2174/0122103279319738240618201043","url":null,"abstract":"\u0000\u0000The integration of communication tools has allowed for effective\u0000decision-making in smart cities and Internet-of-Things (IoT). One major issue that people who\u0000commute to cities everyday encounter is traffic congestion. Thanks to the progress and backing of\u0000ICTs, transportation solutions have been designed and implemented, leading to the development of\u0000ITSs and the provision of numerous innovative services.\u0000\u0000\u0000\u0000These services include ones that guarantee safety, provide drivers with\u0000useful information, enable greater street movement, and avoid congestion, among many others. In\u0000industrialized nations, traffic data is collected by specialized sensors that can anticipate future patterns.\u0000Commuters are kept informed of any traffic updates through the Internet. When there is little\u0000or no physical infrastructure and Internet connection, these methods become unworkable. Internet\u0000access is still a problem in rural regions, and there are no roadside units in underdeveloped nations.\u0000This article presents an architecture for smart cities' intelligent vehicular networks that can impromptu\u0000accept data from nearby vehicles in real time and use it to choose routes. As embedded\u0000devices in vehicles, we utilized Android-based smartphones with Wi-Fi Direct capabilities. To set\u0000up our smart transportation system, we utilized a vehicular ad hoc network.\u0000\u0000\u0000\u0000Data was collected and processed using separate methods between two major cities in a\u0000developing nation. Resource utilization, transmission delay, packet loss, and total trip time were\u0000measured against several fixed- and dynamic-route-selection algorithms to assess the framework's\u0000performance.\u0000\u0000\u0000\u0000When equated to a conventional fixed-route-selection procedure, our results reveal a\u000033.3% reduction in trip times.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141674707","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 : 2024-04-16DOI: 10.2174/0122103279287315240327115754
Nagaraju Thandu, Murugeswari R
Visually impaired people face numerous challenges when itcomes to indoor navigation. While outdoor navigation benefits from advancements in GPS and related technologies, indoor spaces present intricate, complex, and often less accessible environmentsfor those with visual impairments.In response to these challenges, we propose an innovative approachto enhance indoor navigation for individuals with visual impairments, leveraging the power of anAI chatbot. Our AI chatbot employs cutting-edge artificial intelligence techniques to provide realtime assistance and guidance, facilitating independent navigation within intricate indoor settings.By harnessing natural language processing technologies, the chatbot engages in intuitive interactions with users, comprehending their queries and offering detailed instructions for efficient indoornavigation. The main goal of this research is to enhance the independence of people with visualimpairments by offering them a reliable and easily accessible tool.This tool, driven by our Volcano Eruption Optimization Network, promises to significantly enhance the independence and overall indoor navigation experience for visually impaired people, ultimately fostering a greater sense of autonomy in navigating complex indoorspaces.Self-Attention-Based Multimodality Convolutional Volcano Eruption optimizationOptimizing Weight Parameters with Volcano Eruption-Based Optimization (VEO)our AI chatbot-based approach presents a promising solution to the pressing issue of indoor navigation for individuals with visual impairments. We have successfully harnessed cutting-edge artificial intelligence techniques, including natural language processing and computer vision, to empower visually impaired users with real-time assistance and guidance within complex indoor environments.none
{"title":"Enhancing Indoor Navigation for Visually Impaired Individuals with an\u0000AI Chatbot Utilizing VEO Optimized Nodes and Natural Language\u0000Processing","authors":"Nagaraju Thandu, Murugeswari R","doi":"10.2174/0122103279287315240327115754","DOIUrl":"https://doi.org/10.2174/0122103279287315240327115754","url":null,"abstract":"\u0000\u0000Visually impaired people face numerous challenges when it\u0000comes to indoor navigation. While outdoor navigation benefits from advancements in GPS and related technologies, indoor spaces present intricate, complex, and often less accessible environments\u0000for those with visual impairments.\u0000\u0000\u0000\u0000In response to these challenges, we propose an innovative approach\u0000to enhance indoor navigation for individuals with visual impairments, leveraging the power of an\u0000AI chatbot. Our AI chatbot employs cutting-edge artificial intelligence techniques to provide realtime assistance and guidance, facilitating independent navigation within intricate indoor settings.\u0000By harnessing natural language processing technologies, the chatbot engages in intuitive interactions with users, comprehending their queries and offering detailed instructions for efficient indoor\u0000navigation. The main goal of this research is to enhance the independence of people with visual\u0000impairments by offering them a reliable and easily accessible tool.\u0000\u0000\u0000\u0000This tool, driven by our Volcano Eruption Optimization Network, promises to significantly enhance the independence and overall indoor navigation experience for visually impaired people, ultimately fostering a greater sense of autonomy in navigating complex indoor\u0000spaces.\u0000\u0000\u0000\u0000Self-Attention-Based Multimodality Convolutional Volcano Eruption optimization\u0000\u0000\u0000\u0000Optimizing Weight Parameters with Volcano Eruption-Based Optimization (VEO)\u0000\u0000\u0000\u0000our AI chatbot-based approach presents a promising solution to the pressing issue of indoor navigation for individuals with visual impairments. We have successfully harnessed cutting-edge artificial intelligence techniques, including natural language processing and computer vision, to empower visually impaired users with real-time assistance and guidance within complex indoor environments.\u0000\u0000\u0000\u0000none\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140697642","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 : 2024-04-08DOI: 10.2174/0122103279297225240329042445
Ramasamy Mariappan
To design a Low Power Wide Area Network technology to provide long-range connectivity for IIoT applications.The evolution of Long Range Wide Area Networks ( LoRaWAN) is a potential candidate for next generation networks for managing the massive number of devices in the Industrial Internet of Things (IIoT).To design a Low Power Wide Area Network technology to provide long-range connectivity for IIoT applications.In addition to implement LoRaWAN, this research work deploys the proposed LoRaWAN into the 5G communication technology to achieve the massive IIOT use cases.The deployment of this hybrid LORA network has demonstrated its long range, low power, stability, flexibility, and low deployment cost through extensive performance evaluation carried out.This paper concludes the feasibility of deploying LoRaWAN technology for the future generation IIOT applications.No Applicable
{"title":"Design and Implementation of Long Range Wide Area Networks for\u0000Future Industrial IoT Applications","authors":"Ramasamy Mariappan","doi":"10.2174/0122103279297225240329042445","DOIUrl":"https://doi.org/10.2174/0122103279297225240329042445","url":null,"abstract":"\u0000\u0000To design a Low Power Wide Area Network technology to provide long-range connectivity for IIoT applications.\u0000\u0000\u0000\u0000The evolution of Long Range Wide Area Networks ( LoRaWAN) is a potential candidate for next generation networks for managing the massive number of devices in the Industrial Internet of Things (IIoT).\u0000\u0000\u0000\u0000To design a Low Power Wide Area Network technology to provide long-range connectivity for IIoT applications.\u0000\u0000\u0000\u0000In addition to implement LoRaWAN, this research work deploys the proposed LoRaWAN into the 5G communication technology to achieve the massive IIOT use cases.\u0000\u0000\u0000\u0000The deployment of this hybrid LORA network has demonstrated its long range, low power, stability, flexibility, and low deployment cost through extensive performance evaluation carried out.\u0000\u0000\u0000\u0000This paper concludes the feasibility of deploying LoRaWAN technology for the future generation IIOT applications.\u0000\u0000\u0000\u0000No Applicable\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"55 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140730081","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 : 2024-03-04DOI: 10.2174/0122103279285078240212063010
Martin Victor K, I. Jebadurai, G. Paulraj
The Internet of Things offers ubiquitous automation ofthings and makes human life easier. Sensors are deployed in the connected environment that sensethe medium and actuate the control system without human intervention. However, the tiny connecteddevices are prone to severe security attacks. As the Internet of Things has become evident ineveryday life, it is very important that we secure the system for efficient functioning.This paper proposes a secure federated learning-based protocol for mitigating BH attacksin the network.The experimental result proves that the intelligent network detects BH attacks and segregatesthe nodes to improve the efficiency of the network. The proposed techniques show improvedaccuracy in the presence of malicious nodes.The performance is also evaluated by varying the attack frequency time.
{"title":"Federated Learning-Based Black Hole Prevention in the Internet of Things\u0000Environment","authors":"Martin Victor K, I. Jebadurai, G. Paulraj","doi":"10.2174/0122103279285078240212063010","DOIUrl":"https://doi.org/10.2174/0122103279285078240212063010","url":null,"abstract":"\u0000\u0000The Internet of Things offers ubiquitous automation of\u0000things and makes human life easier. Sensors are deployed in the connected environment that sense\u0000the medium and actuate the control system without human intervention. However, the tiny connected\u0000devices are prone to severe security attacks. As the Internet of Things has become evident in\u0000everyday life, it is very important that we secure the system for efficient functioning.\u0000\u0000\u0000\u0000This paper proposes a secure federated learning-based protocol for mitigating BH attacks\u0000in the network.\u0000\u0000\u0000\u0000The experimental result proves that the intelligent network detects BH attacks and segregates\u0000the nodes to improve the efficiency of the network. The proposed techniques show improved\u0000accuracy in the presence of malicious nodes.\u0000\u0000\u0000\u0000The performance is also evaluated by varying the attack frequency time.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"59 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140080124","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 : 2024-02-21DOI: 10.2174/0122103279276389240129091937
Sheela S, S. M. D. Kumar
With recent improvements in fog computing, it is now feasible to offerfaster response time and better service delivery quality; however, the impending challenge is toplace the fog nodes within the environment optimally. A review of existing literature showcasesthat consideration of joint problems such as fog node placement and resource management are lessreported. Irrespective of different available methodologies, it is noted that a learning scheme facilitatesbetter capability to incorporate intelligence in the network device, which can act as an enablingtechnique for superior operation of fog nodes.The prime objective of the study isto introduce simplified and novel computational modelling toward the optimal placement of fognodes with improved resource allocation mechanisms concerning bandwidth.Implementedin Python, the proposed scheme performs predictive operations using the Deep Deterministic PolicyGradient (DDPG) method. Markov modelling is used to frame the model. OpenAI Gym library isused for environment modelling, bridging communication between the environment and the learningagent.Quantitative results indicate that the proposed scheme performs better than existingschemes by approximately 30%.The prime innovative approach introduced is theimplementation of a reinforcement learning algorithm with a Markov chain towards enriching thepredictive analytical capabilities of the controller system with faster service relaying operations a.a This article is an extension of our paper entitled “Computational Framework for Node Placementand Bandwidth Optimization in Dynamic Fog Computing Environments" presented at INDICON-2022, CUSAT, 24-27 November 2022.
随着雾计算技术的不断进步，现在可以提供更快的响应时间和更好的服务交付质量；然而，迫在眉睫的挑战是如何在环境中以最佳方式放置雾节点。对现有文献的回顾表明，对雾节点放置和资源管理等联合问题的考虑报道较少。本研究的主要目标是引入简化的新型计算建模，通过改进带宽方面的资源分配机制实现雾节点的优化放置。模型框架采用马尔可夫模型。定量结果表明，拟议方案的性能比现有方案高出约 30%。本文是我们在 2022 年 11 月 24 日至 27 日于美国加州大学伯克利分校举行的 INDICON-2022 大会上发表的论文 "Computational Framework for Node Placementand Bandwidth Optimization in Dynamic Fog Computing Environments "的延伸。
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Pub Date : 2024-01-31DOI: 10.2174/0122103279285271240112052931
Prasanna Dubey, R. Upadhyay, Uma Rathore Bhatt, Vijaylaxmi S. Bhat
Centralized Radio Access Network (C-RAN) is the most promising networkarchitecture for next-generation communication networks. It meets the need for flexibility onfronthaul as well as large bandwidth on backhaul of the network. All along, scheduling is very importantfor the transmission of information in an organized manner. C-RAN has not been studiedwith the scheduling domain strategies yet in the literature.So, in this work, packet transmission duration, overall transmission time, wait time, andfairness index parameters have been calculated and analysed for C-RAN architecture for two differentscheduling domains. The total transmission cycle time parameter is calculated for the threeupper functional split options of C-RAN. The overall transmission time is a parameter calculatedfor the entire uplink channel.To implement the network scenario, extensive scripting is done on MATLAB Editor forsingle scheduling domain (SSD) and multi-scheduling domain (MSD) for three higher functionalsplit options of C-RAN. The data traffic generated in the network is considered random.A closer examination of results reveals the advantages and disadvantages of both algorithms,as well as trade-offs between them.The results provide the pros and cons of the two strategies as mentioned in the article.For quicker data transmission, SSD should be preferred whereas MSD should be preferredif multiple users want to access resources simultaneously. Lower functional split options ofC-RAN require less transmission cycle time. The MSD technique is fairer than SSD.
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