Pub Date : 2024-08-26DOI: 10.1109/TNSM.2024.3450014
Abid Yaqoob;Gabriel-Miro Muntean
Next-generation cellular networks strive to offer ubiquitous connectivity, enhanced transmission rates with increased capacity, and superior network coverage. However, they face significant challenges due to the growing demand for multimedia services across diverse devices. Adaptive multimedia streaming services are essential for achieving good viewer Quality of Experience (QoE) levels amidst these challenges. Yet, the existing adaptive video streaming solutions do not consider diverse QoE preferences or are limited to meeting specific QoE objectives. This paper presents FReD-ViQ, a Fuzzy Reinforcement Learning-Driven Adaptive Streaming Solution for Improved Video QoE that combines the strengths of fuzzy logic and advanced Deep Reinforcement Learning (DRL) mechanisms to deliver exceptional, individually tailored user experiences. FReD-ViQ is a sophisticated streaming solution that leverages efficient membership function modelling to achieve a more finely-grained representation of both input and output spaces. This advanced representation is augmented by a set of fuzzy rules that govern the decision-making process. In addition to its fuzzy logic capabilities, FReD-ViQ incorporates a novel DRL algorithm based on Dueling Double Deep Q-Network (Dueling DDQN), noisy networks, and prioritized experience replay (PER) techniques. This innovative fusion enables effective modelling of uncertain network dynamics and high-dimensional state spaces while optimizing exploration-exploitation trade-offs in adaptive streaming environments. Extensive performance evaluations in real-world simulation settings demonstrate that FReD-ViQ effectively surpasses existing solutions across multiple QoE models, yielding average improvements of 23.10% (Linear QoE), 23.97% (Log QoE), and 33.42% (HD QoE).
{"title":"FReD-ViQ: Fuzzy Reinforcement Learning Driven Adaptive Streaming Solution for Improved Video Quality of Experience","authors":"Abid Yaqoob;Gabriel-Miro Muntean","doi":"10.1109/TNSM.2024.3450014","DOIUrl":"10.1109/TNSM.2024.3450014","url":null,"abstract":"Next-generation cellular networks strive to offer ubiquitous connectivity, enhanced transmission rates with increased capacity, and superior network coverage. However, they face significant challenges due to the growing demand for multimedia services across diverse devices. Adaptive multimedia streaming services are essential for achieving good viewer Quality of Experience (QoE) levels amidst these challenges. Yet, the existing adaptive video streaming solutions do not consider diverse QoE preferences or are limited to meeting specific QoE objectives. This paper presents FReD-ViQ, a Fuzzy Reinforcement Learning-Driven Adaptive Streaming Solution for Improved Video QoE that combines the strengths of fuzzy logic and advanced Deep Reinforcement Learning (DRL) mechanisms to deliver exceptional, individually tailored user experiences. FReD-ViQ is a sophisticated streaming solution that leverages efficient membership function modelling to achieve a more finely-grained representation of both input and output spaces. This advanced representation is augmented by a set of fuzzy rules that govern the decision-making process. In addition to its fuzzy logic capabilities, FReD-ViQ incorporates a novel DRL algorithm based on Dueling Double Deep Q-Network (Dueling DDQN), noisy networks, and prioritized experience replay (PER) techniques. This innovative fusion enables effective modelling of uncertain network dynamics and high-dimensional state spaces while optimizing exploration-exploitation trade-offs in adaptive streaming environments. Extensive performance evaluations in real-world simulation settings demonstrate that FReD-ViQ effectively surpasses existing solutions across multiple QoE models, yielding average improvements of 23.10% (Linear QoE), 23.97% (Log QoE), and 33.42% (HD QoE).","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5532-5547"},"PeriodicalIF":4.7,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10648983","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1109/tnsm.2024.3449575
Hui Wang, Zhenyu Yang, Ming Li, Xiaowei Zhang, Yanlan Hu, Donghui Hu
{"title":"CoSIS: A Secure, Scalability, Decentralized Blockchain via Complexity Theory","authors":"Hui Wang, Zhenyu Yang, Ming Li, Xiaowei Zhang, Yanlan Hu, Donghui Hu","doi":"10.1109/tnsm.2024.3449575","DOIUrl":"https://doi.org/10.1109/tnsm.2024.3449575","url":null,"abstract":"","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"33 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"FAPM: A Fake Amplification Phenomenon Monitor to Filter DRDoS Attacks With P4 Data Plane","authors":"Dan Tang, Xiaocai Wang, Keqin Li, Chao Yin, Wei Liang, Jiliang Zhang","doi":"10.1109/tnsm.2024.3449889","DOIUrl":"https://doi.org/10.1109/tnsm.2024.3449889","url":null,"abstract":"","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"7 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Facilitated by the widespread adoption of Internet of Things (IoT) networks, Location-based services (LBS) have emerged as a new type of services, requiring a high quality of service (QoS) and to provide access to all devices within predefined zones of interest. This is made possible via specific IoT Networks architectures based on the Software Defined Network paradigm. To address the challenge of unstable IoT networks management, where devices can move, appear, or vanish unpredictably, we propose a novel architecture based on a selection process of dominant devices acting as gateways, ensuring continuity of service. We investigate two selection processes, respectively based on Connected Dominating Sets and Deep Q-Network techniques. The objective of this method is to optimize energy consumption while providing high QoS and extending network access to offline devices within predefined zones of interest. In order to evaluate the performance of the proposed architecture with different selection processes, we conducted experiments using emulation tools allowing communication mode demand generations. The metrics used were the proportion of dominant devices, the energy consumption savings, the quality of service and the network extension to offline devices. Ultimately, we present a recommendation concerning the selection process based on the needs of the system.
{"title":"Managing a Resilient Multitier Architecture for Unstable IoT Networks in Location Based-Services","authors":"Aurélien Chambon;Abderrezak Rachedi;Abderrahim Sahli;Ahmed Mebarki","doi":"10.1109/TNSM.2024.3449044","DOIUrl":"10.1109/TNSM.2024.3449044","url":null,"abstract":"Facilitated by the widespread adoption of Internet of Things (IoT) networks, Location-based services (LBS) have emerged as a new type of services, requiring a high quality of service (QoS) and to provide access to all devices within predefined zones of interest. This is made possible via specific IoT Networks architectures based on the Software Defined Network paradigm. To address the challenge of unstable IoT networks management, where devices can move, appear, or vanish unpredictably, we propose a novel architecture based on a selection process of dominant devices acting as gateways, ensuring continuity of service. We investigate two selection processes, respectively based on Connected Dominating Sets and Deep Q-Network techniques. The objective of this method is to optimize energy consumption while providing high QoS and extending network access to offline devices within predefined zones of interest. In order to evaluate the performance of the proposed architecture with different selection processes, we conducted experiments using emulation tools allowing communication mode demand generations. The metrics used were the proportion of dominant devices, the energy consumption savings, the quality of service and the network extension to offline devices. Ultimately, we present a recommendation concerning the selection process based on the needs of the system.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5304-5320"},"PeriodicalIF":4.7,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-22DOI: 10.1109/tnsm.2024.3447789
Sushmit Bhattacharjee, Konstantinos Alexandris, Thomas Bauschert
{"title":"Multi-Domain TSN Orchestration & Management for Large-Scale Industrial Networks","authors":"Sushmit Bhattacharjee, Konstantinos Alexandris, Thomas Bauschert","doi":"10.1109/tnsm.2024.3447789","DOIUrl":"https://doi.org/10.1109/tnsm.2024.3447789","url":null,"abstract":"","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"46 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"IoTDL2AIDS: Towards IoT-Based System Architecture Supporting Distributed LSTM Learning for Adaptive IDS on UAS","authors":"Amar Rasheed, Mohamed Baza, Gautam Srivastava, Narashimha Karpoor, Cihan Varol","doi":"10.1109/tnsm.2024.3448312","DOIUrl":"https://doi.org/10.1109/tnsm.2024.3448312","url":null,"abstract":"","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"8 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Packet Loss in Real-Time Communications: Can ML Tame its Unpredictable Nature?","authors":"Tailai Song, Gianluca Perna, Paolo Garza, Michela Meo, Maurizio Matteo Munafò","doi":"10.1109/tnsm.2024.3442616","DOIUrl":"https://doi.org/10.1109/tnsm.2024.3442616","url":null,"abstract":"","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"13 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"SAC-PP: Jointly Optimizing Privacy Protection and Computation Offloading for Mobile Edge Computing","authors":"Shigen Shen, Xuanbin Hao, Zhengjun Gao, Guowen Wu, Yizhou Shen, Hong Zhang, Qiying Cao, Shui Yu","doi":"10.1109/tnsm.2024.3447753","DOIUrl":"https://doi.org/10.1109/tnsm.2024.3447753","url":null,"abstract":"","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"84 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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