[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125508-en":3,"doc-seo-125508-105":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},125508,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Energy-Aware Optimization and Machine Learning Frameworks for Sustainable Cognitive Networks - Doctoral Thesis Abstract","This thesis presents a unified framework for enabling sustainable cognitive networks by integrating machine learning with energy-aware optimization. As networks evolve toward 5G and beyond, rising demands for performance, energy efficiency, and autonomous management require intelligent, scalable methods across multiple layers: data generation, infrastructure optimization, distributed learning, and predictive control. Data-centric techniques create synthetic 5G packet-level data and refactor urban measurements into machine learning-ready flow-level traces. Infrastructure placement is optimized using decomposition-based and heuristic methods to improve energy efficiency up to 14% while preserving QoS. Distributed learning introduces AFSL and AFSL+ to reduce convergence time and cut energy usage up to 55% with stable accuracy. Communication overhead is further reduced with Ada-AFSL dynamic compression. Finally, ST-SplitGNN and ST-SplitGNN+ support traffic prediction and uncertainty-aware resource allocation via spatio-temporal split learning.","Energy-Aware Optimization and Machine Learning Frameworks for Sustainable Cognitive Networks  \nJUNIOR MOMO ZIAZET  \nA THESIS  \nIN  \nTHE DEPARTMENT  \nOF  \nCOMPUTER SCIENCE AND SOFTWARE ENGINEERING  \nPRESENTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS  \nFOR THE DEGREE OF  \nDOCTOR OF PHILOSOPHY (COMPUTER SCIENCE) AT  \nCONCORDIA UNIVERSITY  \nMONTRAL, QUBEC, CANADA  \nAUGUST 2025  \n© JUNIOR MOMO ZIAZET, 2025  \nCONCORDIA UNIVERSITY  \nSCHOOL OF GRADUATE STUDIES  \nThis is to certify that the thesis prepared  \nBy: Mr. Junior Momo Ziazet  \nEntitled: Energy-Aware Optimization and Machine Learning Frameworks for Sustain  \nable Cognitive Networksand submitted in partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy (Computer Science)  \ncomplies with the regulations of this University and meets the accepted standards with respect to originality and quality.  \nSigned by the Final Examining Committee:  \n  Chair  \nDr. Catherine Mulligan  \n  External Examiner Dr. Guido Alberto Maier  \nDr. Mirco Ravanelli  Arm’s Length Examiner  \n  Examiner  \nDr. Tristan Glatard  \n  Examiner  \nDr. Yann-Gae¨l Gue´he´neuc  \nDr. Brigitte Jaumard  Supervisor  \nApproved by  Tse-Hsun (Peter) Chen  Graduate Program Director  \nAugust 11, 2025  Mourad Debbabi, Dean   \nGina Cody School of Engineering and Computer Science  \nAbstract  \nEnergy-Aware Optimization and Machine Learning Frameworks for Sustainable Cognitive Networks  \nJunior Momo Ziazet, Ph.D.  \nConcordia University, 2025  \nThis thesis presents a unified framework for enabling sustainable cognitive networks through the integration of machine learning and energy-aware optimization. As networks evolve toward 5G and beyond, growing demands in performance, energy efficiency, and autonomous management call for intelligent, scalable solutions. This work addresses these challenges through a holistic approach spanning four layers: data generation, infrastructure optimization, distributed learning, and predictive control.  \nTo enable AI-driven intelligence, techniques are developed at the data layer that leverage data-centric AI to generate high-quality synthetic 5G packet-level data and refactor real-world urban data into machine learning-ready, 5G-like flow-level traces. These methods mitigate data scarcity and heterogeneity, providing realistic and diverse data essential for robust network learning systems.  \nIn the infrastructure layer, the placement of disaggregated 5G components, including Distributed Units, Centralized Units, and User Plane Functions, is formulated as a large-scale optimization problem. The proposed decomposition-based and heuristic approaches improve energy efficiency by up to 14% while maintaining Quality of Service and responsiveness. Experiments in simulated 5G environments highlight the limitations of traditional peak-time-based planning.  \nFor distributed learning, AFSL (Asynchronous Federated-Split Learning) and its energy-aware variant, AFSL+, are proposed to address client heterogeneity, achieving convergence time reductions of up to 13% . These frameworks selectively engage participants, reducing energy consumption by up to 55% without sacrificing accuracy and stability. To minimize communication overhead, Ada-AFSL is introduced, a dynamic compression technique that adapts to real-time bandwidth fluctuations. In realistic 5G and IoT scenarios, it achieves up to 82% data reduction while preserving performance and enhancing generalization.  \nLastly, ST-SplitGNN and ST-SplitGNN+ are developed as spatio-temporal split learning models for accurate traffic prediction and uncertainty-aware resource allocation. Learning process is partitioned between local and centralized components: at the edge, temporal encoders capture node-specific traffic patterns, while a centralized Graph Neural Network with a learnable adjacency matrix models time-dependent internode dependencies. These models enable proactive scaling policies that align reliability with sustainability goals.  \n","cbCaiucoZCSXbpAK","https://ap.wps.com/l/cbCaiucoZCSXbpAK","pdf",29105540,1,235,"English","en",105,"# Abstract\n## Data generation and trace refactoring\n## Infrastructure optimization for disaggregated 5G\n## Distributed learning: AFSL, AFSL+\n## Communication compression: Ada-AFSL\n## Spatio-temporal split learning: ST-SplitGNN","[{\"question\":\"What is the central contribution of the thesis for sustainable cognitive networks?\",\"answer\":\"It proposes a unified architecture that combines machine learning with energy-aware optimization across data generation, infrastructure optimization, distributed learning, and predictive control.\"},{\"question\":\"How does the thesis address data scarcity and heterogeneity for network learning?\",\"answer\":\"It develops data-centric techniques to generate synthetic 5G packet-level data and to refactor real-world urban data into 5G-like flow-level traces suitable for machine learning.\"},{\"question\":\"Which distributed learning frameworks are proposed, and what energy benefits do they target?\",\"answer\":\"AFSL and its energy-aware variant AFSL+ address client heterogeneity, reducing convergence time and cutting energy consumption by up to 55% while maintaining accuracy and stability.\"}]","Energy-Aware Optimization and Machine Learning Frameworks for Sustainable Cognitive Networks - Doctoral Thesis Abstract | PDF",1785899450,592,{"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},"energy-aware-optimization-and-machine-learning-frameworks-for-sustainable-cognitive-networks-doctoral-thesis-abstract","",{"@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/energy-aware-optimization-and-machine-learning-frameworks-for-sustainable-cognitive-networks-doctoral-thesis-abstract/125508/",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","2026-08-05",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 is the central contribution of the thesis for sustainable cognitive networks?","Question",{"text":75,"@type":76},"It proposes a unified architecture that combines machine learning with energy-aware optimization across data generation, infrastructure optimization, distributed learning, and predictive control.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis address data scarcity and heterogeneity for network learning?",{"text":80,"@type":76},"It develops data-centric techniques to generate synthetic 5G packet-level data and to refactor real-world urban data into 5G-like flow-level traces suitable for machine learning.",{"name":82,"@type":73,"acceptedAnswer":83},"Which distributed learning frameworks are proposed, and what energy benefits do they target?",{"text":84,"@type":76},"AFSL and its energy-aware variant AFSL+ address client heterogeneity, reducing convergence time and cutting energy consumption by up to 55% while maintaining accuracy and stability.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]