[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124274-en":3,"doc-seo-124274-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124274,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","A Machine Learning-based Approach for Detecting Communication Failures in Internet of Things Networks","Industrial internet of things systems rely on reliable, deadline-driven exchange of high-quality video and large sensing datasets among IIoT devices. In mmWave bands at 28 and 60 GHz, directional links become vulnerable to deafness, where a receiver cannot capture transmissions due to beam misalignment or blockage. This paper introduces an ML-based communication failure identification method that classifies failures as deafness or interference using device state parameters, and proposes ML-DMAC to improve throughput and shorten deafness duration, showing about 31% higher aggregate throughput and 88% less deafness time.","A machine learning-based approach for detecting communication failures in internet of things networks  \nRatna Kumari Vemuri1, Job Prasanth Kumar Chinta Kunta2, Pavan Madduru3, Perumal Senthilraja4,  \nYallapragada Ravi Raju5, Yamini Kodali6  \n1Department of Electronics and Communication Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada, India 2Lead Solution Data Architect, The Automobile Association, London, United Kingdom  \n3Director, Kinesics Meets Ltd, Newbury, United Kingdom  \n4Department of Computer Science and Engineering (AI & ML), B V Raju Institute of technology, Narsapur, India 5Department of Computer Science and Technology, Madanapalle Institute of Technology & Science, Madanapalle, India 6Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India  \nArticle history:  \nReceived May 11, 2024 Revised Jan 3, 2025 Accepted Jan 27, 2025  \nKeywords:  \nDeafness problems Deep learning DMAC  \nInternet of things Machine learning  \nCorresponding Author:  \nIn industrial systems, the exchange of massive content, such as high-quality video and large sensing data, among industrial internet of things devices (IIoTDs) is essential, often under strict deadlines. Utilizing millimeter-wave (mmWave) frequencies at 28 and 60 GHz can meet the requirements of industrial internet of things (IIoT) by offering high data rates. However, in the mmWave band, the use of directional antennas is imperative due to the short wavelength, rendering directional links susceptible to adverse effects like deafness problems, where a communicating node fails to receive signals from other transmitting nodes. To mitigate the deafness problem, this paper proposes a machine learning-based communication failure identification scheme for reliable device-to-device (D2D) communication in the mmWave band. The proposed scheme determines the type of network failure (deafness/interference) based on the IIoTD's state parameters. Furthermore, we introduce machine learning based directional medium access control (ML-DMAC) to enhance throughput and minimize the duration of deafness in D2D communication. Performance evaluations demonstrate that the proposed ML-DMAC outperforms existing schemes, achieving approximately 31% higher aggregate throughput and an 88% reduction in deafness duration.  \nThis is an open access article under the CC BY-SA license.  \nPerumal Senthilraja  \nDepartment of Computer Science and Engineering (AI & ML), B V Raju Institute of Technology Narsapur, Medak, Telangana, India  \nEmail: [senthilrajamtech@gmail.com](senthilrajamtech@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe industrial internet of things (IIoT) revolutionizes industrial processes by seamlessly combining physical machinery with digital technologies to enhance operations and productivity. These environments necessitate the smooth transmission of significant quantities of data, encompassing high-definition video feeds and sensor data, for the purposes of process monitoring, predictive maintenance, and quality control. IIoT applications require advanced communication technologies that can provide high throughput, low latency, and dependability [1] . Millimeter-wave (mmWave) communication appears to be a promising solution for meeting the bandwidth requirements of IIoT. mmWave communication at 28 and 60 GHz exhibits much higher data transmission speeds compared to microwave communication. mmWave communication poses significant challenges in industrial settings where reliability is of utmost importance.  \nEmploying directional antennas is challenging due to the tiny wavelength of mmWave communications. Directional antennas enhance spatial reuse and minimize interference, but they might potentially result in signal loss when a communication node is unable to receive signals from other transmitting nodes due tomisalignment or obstruction [2] . The issue of deafness in directed IIoT networks needs to be resolv","cbCaii9CZwvpHIm9","https://ap.wps.com/l/cbCaii9CZwvpHIm9","pdf",768954,1,9,"English","en",105,"# Introduction\n## Challenges of mmWave in IIoT\n## Proposed ML-based communication failure identification\n## ML-DMAC for reliable D2D communication","[{\"question\":\"What communication failure does the paper focus on in mmWave IIoT networks?\",\"answer\":\"The paper focuses on the deafness problem in directional D2D communication, along with identifying interference as another failure type.\"},{\"question\":\"How does the proposed method identify the type of network failure?\",\"answer\":\"It uses machine learning to analyze IIoTD state parameters and classify failures as deafness or interference.\"},{\"question\":\"What is ML-DMAC and what improvement does it deliver?\",\"answer\":\"ML-DMAC is a directional MAC protocol that incorporates machine learning to enhance D2D throughput and reduce the duration of deafness. Reported results show approximately 31% higher aggregate throughput and an 88% reduction in deafness duration.\"}]","A Machine Learning-based Approach for Detecting Communication Failures in Internet of Things Networks | PDF",1785821321,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-based-approach-for-detecting-communication-failures-in-internet-of-things-networks","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-based-approach-for-detecting-communication-failures-in-internet-of-things-networks/124274/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What communication failure does the paper focus on in mmWave IIoT networks?","Question",{"text":75,"@type":76},"The paper focuses on the deafness problem in directional D2D communication, along with identifying interference as another failure type.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method identify the type of network failure?",{"text":80,"@type":76},"It uses machine learning to analyze IIoTD state parameters and classify failures as deafness or interference.",{"name":82,"@type":73,"acceptedAnswer":83},"What is ML-DMAC and what improvement does it deliver?",{"text":84,"@type":76},"ML-DMAC is a directional MAC protocol that incorporates machine learning to enhance D2D throughput and reduce the duration of deafness. Reported results show approximately 31% higher aggregate throughput and an 88% reduction in deafness duration.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]