[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290191-105":59,"doc-detail-290191-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","advances-of-vehicular-ad-hoc-network-using-machine-learning-approach","Advances of vehicular ad hoc network using machine learning approach","","Vehicular ad hoc networks (VANETs) underpin intelligent transportation systems by enabling direct vehicle communications and links to roadside infrastructure, supporting safety and efficiency services. With VANETs generating large data volumes, machine learning methods become key to tackling complex tasks. This work provides a comprehensive literature review on machine learning applications in VANETs, highlighting challenges, emphasizing more accessible machine learning solutions, and outlining future research directions to advance VANET capabilities.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/advances-of-vehicular-ad-hoc-network-using-machine-learning-approach/290191/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/advances-of-vehicular-ad-hoc-network-using-machine-learning-approach/290191.png","ImageObject",300,407,{"name":92,"@type":93},"Himbo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What role do VANETs play in intelligent transportation systems?","Question",{"text":113,"@type":114},"VANETs enable seamless communication between vehicles and other entities, including roadside infrastructure, supporting a wide range of ITS services.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Why is machine learning useful for VANETs?",{"text":118,"@type":114},"Because VANETs generate and communicate large amounts of data, machine learning provides data-driven tools to address complex challenges across multiple goals.",{"name":120,"@type":111,"acceptedAnswer":121},"How can machine learning be applied in VANET development?",{"text":122,"@type":114},"Machine learning algorithms can be used for VANET testing and training: a model learns patterns from training data and is then used for prediction on new unseen data.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290191,1789640904,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":46,"preview_page_count":4},687197100911,"https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697","Advances of vehicular ad hoc network using machine learning  \napproach  \nSee Thian Meng1, Sumendra Yogarayan1, Siti Fatimah Abdul Razak1, Subarmaniam Kannan1,  \nAfizan Azman2  \n1Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia 2School of Computing, Faculty of Information and Technology, Taylors University , Subang Jaya, Malaysia  \nArticle history:  \nReceived Jun 21, 2023 Revised Jul 22, 2023 Accepted Aug 7, 2023  \nKeywords:  \nAlgorithm  \nIntelligent transportation systems  \nMachine learning Technique  \nVehicular ad hoc networks (VANETs) play a crucial role in intelligent transportation systems (ITS), enabling seamless communication between vehicles and other entities. VANETs provide a wide range of services, allowing vehicles to communicate with each other and with roadside infrastructure. With the increasing amount of data generated by VANETs, machine learning approaches have emerged as valuable tools to address complex challenges in this domain. This paper presents a comprehensive literature review on the application of machine learning in VANETs. The paper discusses the potential challenges and future research directions in the field, emphasizing the need for more accessible machine learning solutions for VANETs. This review emphasizes the significant role of machine learning approach in advancing the capabilities of VANETs and shaping the future of intelligent transportation systems.  \nVehicular ad hoc network  \nThis is an open access article under the CC BY-SA license.  \nCorresponding Author:  \nSumendra Yogarayan  \nFaculty of Information Science and Technology, Multimedia University Ayer Keroh Lama Street, 75450 Melaka, Malaysia  \nEmail: [sumendra@mmu.edu.my](sumendra@mmu.edu.my)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nVehicular ad hoc networks (VANETs) serve as a fundamental component of intelligent transportation systems (ITS), enabling seamless communication between vehicles and other entities [1], [2] . When compared to traditional ITS, the VANETs provides a wider range of services. This is because VANET can communicate not just with one another, but also with base stations located along the road. Network-based VANET’s communication capabilities between vehicle to vehicle (V2V), roadside infrastructure and other vehicles (V2I) are its main advantages. Utilising the communication platforms and protocols offered by the supporting infrastructure makes this possible [3]–[5] . VANETs have the potential to enhance safety, comfort, and overall road efficiency. VANETs consist of interconnected vehicles that can form networks even without prior knowledge of each other [6] . The contemporary systems for traffic control and road safety make extensive use of these networks. Through the exchange of basic safety messages (BSMs), for instance, crucial information like the present location, speed, and acceleration can be transmitted between automobiles, improving eyesight and awareness of the situation for the near surroundings [7], [8] .  \nFurthermore, VANETs enable vehicles to communicate safety and traffic-related information with each other while on the road. VANETs are distinguished by numerous nodes (vehicles) and constant movement. The security of VANETs is essential for establishing a reliable ITS that ensures safety. According to World Health Organisation (WHO), road accidents are ranked as the eighth major contributor to global fatalities [9], [10] . Next, VANETs are understood to be essential tools for preventing traffic accidents and  \nfatalities. Vehicles can transmit and receive data packets from roadside units (RSUs) within these networks in addition to exchanging data packets among themselves. Because VANETs generate and communicate such a significant amount of data, applying machine learning techniques is advantageous because they may be applied to a variety of goals [11]–[13] . Artificial intelligence (AI) refers to the ability of a computer or robot to perform tasks that ar","cbCaisGLnmkKoMxM","https://ap.wps.com/l/cbCaisGLnmkKoMxM","pdf",250371,"English","# INTRODUCTION\n# LITERATURE REVIEW","[{\"question\":\"What role do VANETs play in intelligent transportation systems?\",\"answer\":\"VANETs enable seamless communication between vehicles and other entities, including roadside infrastructure, supporting a wide range of ITS services.\"},{\"question\":\"Why is machine learning useful for VANETs?\",\"answer\":\"Because VANETs generate and communicate large amounts of data, machine learning provides data-driven tools to address complex challenges across multiple goals.\"},{\"question\":\"How can machine learning be applied in VANET development?\",\"answer\":\"Machine learning algorithms can be used for VANET testing and training: a model learns patterns from training data and is then used for prediction on new unseen data.\"}]","Advances of vehicular ad hoc network using machine learning approach | PDF"]