[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119834-en":3,"doc-seo-119834-105":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},119834,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Dynamic Resource Allocation in Industrial Internet of Things (IIoT) using Machine Learning Approaches","Dynamic resource allocation addresses computational limits in Industrial IoT devices, where processor capacity and battery constraints require efficient execution of computation-intensive, continuously generated tasks. The work proposes a collaborative joint offloading and scheduling approach: industrial IoT tasks are modeled as a directed acyclic graph, offloading is formulated as an optimization problem considering processor resources and energy, and dynamic resource allocation assigns edge-cloud computing resources. A collaborative q-learning based scheme allocates resources for execution, outperforming conventional baselines in response time, delay, and energy consumption.","Dynamic Resource Allocation in Industrial Internet of Things (IIoT) using Machine Learning Approaches  \nPankaj Singh Sisodiya1, Dr. Vijay Bhandari2  \n1Research Scholar, Department of Computer Science & Engineering  \n2Associate Professor, Department of Computer Science & Engineering  \n1,2Madhyanchal Professional University, Bhopal, India.  \n[sisodiya.pankaj90@gmail.com](sisodiya.pankaj90@gmail.com), [bhandarivijay314@gmail.com](bhandarivijay314@gmail.com)  \nAbstract: In today's era of rapid smart equipment development and the Industrial Revolution, the application scenarios for Internet of Things (IoT) technology are expanding widely. The combination of IoT and industrial manufacturing systems gives rise to the Industrial IoT (IIoT) . However, due to resource limitations such as computational units and battery capacity in IIoT devices (IIEs), it is crucial to execute computationally intensive tasks efficiently. The dynamic and continuous generation of tasks poses a significant challenge to managing the limited resources in the IIoT environment. This paper proposes a collaborative approach for optimal offloading and resource allocation of highly sensitive industrial IoT tasks. Firstly, the computation-intensive IIoT tasks are transformed into a directed acyclic graph. Then, task offloading is treated as an optimization problem, taking into account the models of processor resources and energy consumption for the offloading scheme. Lastly, a dynamic resource allocation approach is introduced to allocate computing resources to the edge-cloud server for the execution of computation-intensive tasks. The proposed joint offloading and scheduling (JOS) algorithm creates its DAG and prepare a offloading queue. This queue is designed using collaborative q-learning based reinforcement learning and allocate optimal resources to the JOS for execution of tasks present in offloading queue. For this machine learning approach is used to predict and allocate resources. The paper compares conventional and machine learning-based resource allocation methods. The machine learning approach performs better in terms of response time, delay, and energy consumption. The proposed algorithm shows that energy usage increases with task size, and response time increases with the number of users. Among the algorithms compared, JOS has the lowest waiting time, followed by DQN, while Q-learning performs the worst. Based on these findings, the paper recommends adopting the machine learning approach, specifically the JOS algorithm, for joint offloading and resource allocation.  \nKeywords: Internet of Things (IoT), Industrial IoT, Task Offloading, Resource Allocation, Optimal, Machine Learning.  \nI. Introduction  \nEvery day, your business generates a vast amount of data as it undergoes digitization across its entire value chain, from procurement to production to delivery and service. This digital transformation connects various aspects of your organization, and advancements in digital technologies such as autonomous plant operations, efficient electrical drives, and controls further support these efforts [1][2] . The term \"digitalization\" in business refers to the innovative use of digital technologies to fundamentally change the business model, unlock new revenue streams, and provide value-added services or products to customers. As a result, Industry 4.0 is revolutionizing how companies manufacture, enhance, and distribute their products [3][4] . Manufacturers are integrating emerging technologies like the Internet of Things (IoT), cloud computing, analytics, and artificial intelligence (AI) and machine learning into their production facilities and overall operations. The significance of digitalization in business became evident during the pandemic, as digitally transformed businesses experienced less disruption and recovered more quickly compared to those that had not embraced digitalization [5] . Industry 4.0, combined with the Industrial Internet of Things (IIo","cbCaisM9S48KdcFa","https://ap.wps.com/l/cbCaisM9S48KdcFa","pdf",470874,1,11,"English","en",105,"# Introduction\n## Industry 4.0 and Industrial IoT context\n## Challenges of resource allocation and delays\n## Dynamic resource allocation as a solution\n# Proposed Collaborative Offloading and Resource Allocation (JOS)\n## DAG modeling of tasks\n## Optimization-based offloading considering energy\n## Collaborative q-learning based dynamic allocation\n# Performance Comparison and Results\n## Response time, delay, and energy consumption\n## Algorithm ranking and recommendations","[{\"question\":\"Why is dynamic resource allocation necessary in Industrial IoT environments?\",\"answer\":\"Industrial IoT devices have limited computational units and battery capacity, and tasks arrive dynamically. Without efficient dynamic allocation, time-critical applications may suffer unacceptable processing delays.\"},{\"question\":\"How does the proposed approach model and schedule Industrial IoT tasks?\",\"answer\":\"Computation-intensive tasks are transformed into a directed acyclic graph, and task offloading is treated as an optimization problem that accounts for processor resources and energy consumption. A dynamic resource allocation method then assigns computing resources to the edge-cloud server for execution.\"},{\"question\":\"How does the machine learning-based resource allocation compare with conventional methods?\",\"answer\":\"The machine learning approach yields better performance in response time, delay, and energy consumption. Energy usage increases with task size, and response time increases with the number of users, with JOS showing the lowest waiting time among compared methods.\"}]","Dynamic Resource Allocation in Industrial Internet of Things (IIoT) using Machine Learning Approaches | PDF",1785726551,28,{"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},"dynamic-resource-allocation-in-industrial-internet-of-things-iiot-using-machine-learning-approaches","",{"@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/dynamic-resource-allocation-in-industrial-internet-of-things-iiot-using-machine-learning-approaches/119834/",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","2026-08-03",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},"Why is dynamic resource allocation necessary in Industrial IoT environments?","Question",{"text":75,"@type":76},"Industrial IoT devices have limited computational units and battery capacity, and tasks arrive dynamically. Without efficient dynamic allocation, time-critical applications may suffer unacceptable processing delays.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed approach model and schedule Industrial IoT tasks?",{"text":80,"@type":76},"Computation-intensive tasks are transformed into a directed acyclic graph, and task offloading is treated as an optimization problem that accounts for processor resources and energy consumption. A dynamic resource allocation method then assigns computing resources to the edge-cloud server for execution.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the machine learning-based resource allocation compare with conventional methods?",{"text":84,"@type":76},"The machine learning approach yields better performance in response time, delay, and energy consumption. 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