[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119931-en":3,"doc-seo-119931-105":29,"detail-sidebar-cat-0-en-105":89},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},119931,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning-Enabled Communication Approach for the Internet of Medical Things - A Kruskal Algorithm Based Reliable Routing Scheme","The Internet of Medical Things (IoMT) leverages wearable devices to automate healthcare processes, while machine learning enables smarter decision-making in transmission and routing. Existing frame broadcasting methods often suffer from congestion, energy inefficiency, and excessive load, degrading reliability. A machine learning-enabled shortest-path identification scheme is presented to reduce communication overheads and improve end-to-end performance. Using Kruskal’s algorithm, an optimal route between wearable devices is selected and evaluated with additional metrics. Each device maintains a supplementary path for failures or heavy traffic, while routing tables are updated in parallel, replacing paths when better alternatives emerge. Tests in an IoMT smart healthcare environment show lower end-to-end delay and packet loss (17% and 23%) and higher average throughput (about 21%) compared with existing schemes.","Machine Learning-Enabled Communication Approach for the Internet of Medical  \nThings  \nABSTRACT  \nThe Internet of Medical Things (IoMT) is mainly concerned with the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically, whereas machine learning approaches enable these smart systems to make informed decisions. Generally, broadcasting is used for the transmission of frames, whereas congestion, energy efficiency, and excessive load are among the common issues associated with existing approaches. In this paper, a machine learning-enabled shortest path identification scheme is presented to ensure reliable transmission of frames, especially with the minimum possible communication overheads in the IoMT network. For this purpose, the proposed scheme utilises a well-known technique, i.e., Kruskal’s algorithm, to find an optimal path from source to destination wearable devices. Additionally, other evaluation metrics are used to find a reliable and shortest possible communication path between the two interested parties. Apart from that, every device is bound to hold a supplementary path, preferably a second optimised path, for situations where the current communication path is no longer available, either due to device failure or heavy traffic. Furthermore, the machine learning approach helps enable these devices to update their routing tables simultaneously, and an optimal path could be replaced ifa better one is available. The proposed mechanism has been tested using a smart environment developed for the healthcare domain using IoMT networks. Simulation results show that the proposed machine learning-oriented approach performs better than existing approaches where the proposed scheme has achieved the minimum possible ratios, i.e., 17% and 23%, in terms of end-to-end delay and packet losses, respectively. Moreover, the proposed scheme has achieved an approximately 21% improvement in the average throughput compared to the existing schemes.","cbCair1V7f4i0Iyv","https://ap.wps.com/l/cbCair1V7f4i0Iyv","pdf",41333,1,"English","en",105,"# Abstract\n# Problem Context: Broadcasting Limitations in IoMT\n# Proposed Method: ML-Enabled Shortest Path Identification\n## Path Optimization with Kruskal’s Algorithm\n## Supplementary Backup Path for Failures or Congestion\n## Parallel Routing Table Updates and Path Replacement\n# Evaluation Setup and Metrics\n## End-to-End Delay and Packet Loss\n## Average Throughput Improvement\n# Experimental Results and Comparison","[{\"question\":\"What problem does the IoMT routing approach address?\",\"answer\":\"It targets unreliable frame transmission caused by congestion, energy inefficiency, and excessive load in existing broadcasting-based methods.\"},{\"question\":\"How does the proposed scheme compute reliable communication paths?\",\"answer\":\"It uses a machine learning-enabled shortest-path identification method where Kruskal’s algorithm helps find an optimal route between source and destination wearable devices, supported by additional evaluation metrics.\"},{\"question\":\"Why does each device maintain a supplementary path?\",\"answer\":\"To ensure continuity when the current path becomes unavailable due to device failure or heavy traffic, enabling rapid fallback to a second optimized route.\"}]","Machine Learning-Enabled Communication Approach for the Internet of Medical Things - A Kruskal Algorithm Based Reliable Routing Scheme | PDF",1785727058,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":27},"machine-learning-enabled-communication-approach-for-the-internet-of-medical-things-a-kruskal-algorithm-based-reliable-routing-scheme","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/machine-learning-enabled-communication-approach-for-the-internet-of-medical-things-a-kruskal-algorithm-based-reliable-routing-scheme/119931/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What problem does the IoMT routing approach address?","Question",{"text":73,"@type":74},"It targets unreliable frame transmission caused by congestion, energy inefficiency, and excessive load in existing broadcasting-based methods.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How does the proposed scheme compute reliable communication paths?",{"text":78,"@type":74},"It uses a machine learning-enabled shortest-path identification method where Kruskal’s algorithm helps find an optimal route between source and destination wearable devices, supported by additional evaluation metrics.",{"name":80,"@type":71,"acceptedAnswer":81},"Why does each device maintain a supplementary path?",{"text":82,"@type":74},"To ensure continuity when the current path becomes unavailable due to device failure or heavy traffic, enabling rapid fallback to a second optimized route.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":90},[91,95,99,103,108,113,118,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":92,"show_sort_weight":93,"slug":94},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":96,"show_sort_weight":97,"slug":98},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":100,"show_sort_weight":101,"slug":102},"Exam",70,"exam",{"id":104,"doc_module":4,"doc_module_name":45,"category_name":105,"show_sort_weight":106,"slug":107},5,"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":45,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":45,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":104,"slug":136},19,"General","general"]