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Transcoding-Enabled Edge Caching Strategy Optimization: A Dual-Timescale Meta-Learning Based Stackelberg Game Approach

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IEEE Internet of Things Journal 2025年

作者： Wang, Dan Zhu, Keke Song, Bin Yu, F. Richard Xidian University State Key Laboratory of Integrated Services Networks 710071 China Carleton University Department of Systems and Computer Engineering Ottawa Canada

The explosive growth in video services has significantly strained current mobile network infrastructure, leading to spectrum scarcity, backhaul congestion, and degraded quality of experience. While edge caching has emerged as a promising solution to address these challenges and deliver seamless video playback experience, multi-version edge caching for heterogeneous clients remains challenging due to varying client requirements and network conditions. This paper proposes a transcoding enabled edge caching framework for mobile edge-cloud computing networks. Specifically, we combine video transcoding with edge caching to support both "direct cache hits" and "soft cache hits" for reducing transmission latency. We model this joint caching and resource allocation problem as a Stackelberg game to minimize video transmission latency. To solve this problem, we develop a novel dual timescale Model Agnostic Meta-Learning (MAML) based Stackelberg game (DTMSG) optimization approach that determines the delay-optimal Stackelberg equilibrium (SE) and accelerates convergence. Simulation results demonstrate that our DTMSG optimization algorithm efficiently converges to the SE point, maximizing the utility function of the MVNO and BSs while reducing the average video transmission delay. © 2014 IEEE.

关键词： Mobile edge computing

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Elastic Spectrum Sensing: An Adaptive Sensing Method for Non-Terrestrial Communication under Highly Dynamic Channels

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IEEE Transactions on Wireless Communications 2025年第5期24卷 3868-3881页

作者： Xu, Tianheng Xu, Yinjun Zhou, Ting Zhang, Haijun Hu, Honglin Leung, Victor C.M. Chinese Academy of Sciences Shanghai Advanced Research Institute Shanghai201210 China Shanghai Frontier lnnovation Research Institute Shanghai201100 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China Shanghai University School of Microelectronics Shanghai200444 China University of Science and Technology Beijing Institute of Artificial Intelligence Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Beijing100083 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

With the rapid development of space technology, the role of satellite communications has become progressively significant. Non-terrestrial communication is deemed a critical scenario in the sixth generation (6G) communication systems, which showcases seamless connectivity, minimal geographic constraints and substantial communication capacity. Simultaneously, satellites and terminals spring up, spatial density increases, which further emphasizes the scarcity of spectrum resources. Consequently, to improve spectrum utilization for non-terrestrial communication is a significant concern. Spectrum sensing, which allows dynamic resource reuse, plays an important role in 6G. However, high mobility in non-terrestrial scenarios poses great challenges, such as fast time-varying channels, Doppler effect, etc., which seriously affect sensing accuracy and can not well support optimal spectrum utilization. Motivated by such circumstances, this paper proposes an elastic sensing method for the downlink non-terrestrial communication scenario. Firstly, we design the system architecture and sensing workflow. To overcome the negative effects raised by high mobility, we propose the elastic sensing criterion and multi-area dividing scheme for the sensing zone. Thresholds affected by the elastic sensing are derived for different areas. Finally, the numerical results show that from -10 dB to -5 dB, the proposed method can improve the total performance by an average of 28.3% while stabilizing the false alarm probability around 0.1 typical level and demonstrating higher constancy compared with traditional technologies from -10 dB to -5 dB. © 2002-2012 IEEE.

关键词： Satellite communication systems

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Control of Master-Slave DC Microgrid with Optical Fiber Communication Link 15

Control of Master-Slave DC Microgrid with Optical Fiber Comm...

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15th International Renewable Energy Congress, IREC 2025

作者： Alfergani, Asma Shaltami, Farhat M. Alfaitor, Khalid Ateea Khalil, Ashraf Mohamed, Faisal University of Benghazi Electrical and Electronics Engineering Department Benghazi Libya College of Electrical and Electronics Technology - Benghazi Benghazi Libya University of Benghazi Faculty of Information Technology Department of Computer Networks and Communications Benghazi Libya Dtu Engineering Technology Technical University of Denmark Kongens LyngbyDK-2750 Denmark Authority of Natural Science Research and Technology Tripoli Libya

ISBN: (纸本)9798331516772

In this paper, a model for the master-slave DC Microgrid over a fiber optic link is proposed. The delay margin is calculated according to Lyapunov-Krasovskii functional using the LMI toolbox in Matlab with the binary iteration algorithm. The optical fiber link is implemented using optisystem simulator. The optical fiber link is set to transmit the digitalized reference current from master controller to the slave controller. The link tested at 100Mbps, 1Gbps and 2.5Gbps data rate values and several optical fiber lengths and wavelength of 1550nm and 1400nm. The results show that the performance of the link is sufficient at received power more than -30dBm for all data rate values at 1550nm. While at 1400nm, the attenuation rate is very high. Therefore, the link must be 10km to achieve acceptable performance. This wavelength is used in this simulation to avoid the C band and O band, which used for telecommunication purposes. © 2025 IEEE.

关键词： MATLAB

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Eco-Friendly IoT: Leveraging Energy Harvesting for a Sustainable Future

IEEE Sensors Reviews

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IEEE Sensors Reviews 2025年 2卷 32-75页

作者： Bardia Safaei Mana Peiravian Mahdi Siamaki Department of Computer Engineering Sharif University of Technology Tehran Iran Reliable and Durable IoT Applications and Networks (RADIAN) Research Laboratory Sharif University of Technology Tehran Iran

Internet of Things (IoT) has revolutionized various industries with interconnected smart embedded devices featuring sensors and actuators. However, providing a sustainable power supply for these devices is a significant hurdle. The finite lifespan of traditional batteries necessitates frequent replacements, leading to increased maintenance costs, downtime, and environmental concerns associated with battery disposal. Energy harvesting offers an admirable alternative by converting ambient energy into usable electrical energy, enabling IoT devices to operate autonomously and sustainably, reducing maintenance needs, and enhancing reliability. Despite their diversity, energy harvesters face common limitations, such as variable energy availability, limited power output, and high initial costs. Recognizing these limitations is essential for informed decisions regarding their use in sustainable IoT applications and their communication performance. Due to the lack of a comprehensive and inclusive study in this context, this article gives an extensive survey on energy-harvesting technologies and their applications in IoT. By drawing insights from over 400 recent publications, this article first introduces a taxonomy by categorizing the existing technologies based on their energy sources into five general domains, i.e., photovoltaic, vibration and kinetic, radio frequency, thermoelectric, and chemical and biological. Then, it expands this categorization into 11 subdomains and delves into the working principles of every technology. Besides surveying potential IoT applications, this comprehensive study covers simulation-facilitating mathematical models, pros, cons, and commercial-off-the-shelf modules for each harvesting technology. It also includes evaluative simulations to analyze their output power behavior, focusing on their impact on wireless communication performance. Finally, by discussing the existing research trends, the survey aims to provide informed decision making a

关键词： Internet of Things Energy harvesting Batteries Reliability Costs Surveys Mathematical models Reviews Market research Maintenance

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Enhancing Quality of Service in IoT-WSN through Edge-Enabled Multi-Objective Optimization

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IEEE Transactions on Consumer Electronics 2025年

作者： Singh, Shailendra Pratap Kumar, Naween Kumar, Gyanendra Balusamy, Balamurugan Bashir, Ali Kashif Al Dabel, Maryam M. Department of Computer Science and Engineering MMMUT UP Gorakhpur India SCSET Bennett University Greater Noida India Department of IoT and Intelligent Systems Manipal University Jaipur Jaipur India India Networks and Security Manchester Metropolitan University United Kingdom Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia

The demand for real-time, high-quality services (QoS) is increasing with the proliferation of the resource-constrained nature of edge devices that facilitate the Internet of Things (IoT) and wireless sensor network (WSN) applications. Several existing multi-objective algorithms, such as MOPSO, Elitism MOGA, MODE, and others, are capable of balancing exploration and exploitation;they assist in efficient QoS management for WSN-IoT applications, address resource limitations, and align with the objectives of the applications. However, they suffer from showing robustness in the solution and efficient convergence rates on benchmark functions impacting overall QoS. This paper proposes a multi-objective optimization and edge-intelligent adaptation-based strategy to address QoS management issues, jointly optimize several competing objectives, like energy and latency, and maximize localization and coverage rates while considering the limitations of edge devices. The proposed work uses a novel Grey-wolf optimizer (GWO) Algorithm with an innovative bird-edge-computing adaptation approach to analyze the complex connections between input parameters, edge resources, and QoS indicators to generate Pareto-optimal solutions. The evaluation of the proposed edge intelligence technique with IoT applications demonstrates its effectiveness compared to conventional heuristic-based approaches. This approach enhances the QoS in IoT applications and improves resource utilization and scalability in edge computing environments. © 2025 IEEE.

关键词： Quality of service

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Quantum and Data Signals in a Single Fiber - Multiplexing by Smart Use of High-Grade Filters

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IEEE Communications Letters 2025年第5期29卷 908-912页

作者： Radil, Jan Slapak, Martin Vohnout, Rudolf Sima, Jaromir Novak, Tomas Havlis, Ondrej Vojtech, Josef Department of Optical Networks Cesnet a.l.e Generála Píky 430/26 Prague160 00 Czech Republic University of South Bohemia in České Budějovice Faculty of Science Department of Computer Science Branišovská 1760 České Budějovice370 05 Czech Republic Czech Technical University in Prague Faculty of Nuclear Sciences and Physical Engineering Department of Laser Physics and Photonics Trojanova 13 Prague120 01 Czech Republic

In this paper, we present the latest findings in coexistence of two rather different classes of optical signals in one fiber - classical data signals and quantum signals. The most significant and notable difference between the two is the optical power, which is several orders (7 or more) of magnitude distinct from each other. There are several approaches to address this issue - in this paper, at a glance, the realistic and practical approach, which relies on the selection of high-grade optical filters, was chosen. Rigorous, however affordable, optical filtering is required to avoid any possible crosstalk, not only from the classical data signals but also from the spontaneous nonlinear Raman scattering effect. In our experimental verification, it has been demonstrated that the selection of high-grade transmission components is a crucial element in the success of multiplexing strong and weak optical signals in a single fiber. In real-world applications, this approach significantly reduces infrastructure costs by eliminating the need for additional fiber. © 2025 IEEE.

关键词： Raman scattering

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Careful Resume: Design and Analysis with Picoquic over Satellite Paths

Careful Resume: Design and Analysis with Picoquic over Satel...

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Advanced Satellite Mobile Systems (ASMS)

作者： Matthias Hofstätter Jörg Deutschmann Raffaello Secchi Gorry Fairhurst Reinhard German Computer Networks and Communication Systems Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Engineering University of Aberdeen UK

ISBN: (数字)9798331522353

ISBN: (纸本)9798331522384

Slow Start increases the congestion window by doubling each round trip until congestion is detected. As a result, reaching optimal speed typically takes several round trip times, especially important in high bandwidth-delay product networks. Geostationary satellite communications now use Performance Enhancing Proxies to accelerate the growth of the congestion window. However, these are ineffective for connections with encrypted headers or UDP traffic. We propose a new method, Careful Resume, that enables a rapid ramp-up of congestion control by reusing previous parameters. We implement Careful Resume in Picoquic and run performance tests using an emulated geostationary satellite path, three real geostationary satellite networks, the low Earth orbit satellite megaconstellation Starlink, and a cellular 5G network. Results confirm that Careful Resume accelerates the ramp-up process, reducing total transmission time. Even for connections with lower bandwidth-delay products, the network congestion point can be reached much more quickly. Benefits are particularly pronounced for paths with high bandwidth-delay products and medium-sized transfers such as web traffic.

关键词： Transport protocols Satellite constellations 5G mobile communication Space communications Multimedia systems Conferences Low earth orbit satellites Signal processing Cryptography Geostationary satellites

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Energy-Efficient Distributed Learning for NOMA-Based Unmanned Aerial Agent-assisted MEC networks

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IEEE Internet of Things Journal 2025年

作者： Consul, Prakhar Budhiraja, Ishan Garg, Deepak Kumar, Neeraj Rodrigues, Joel J. P. C. Almuhaideb, Abdullah IILM University School of Computer Science and Engineering Department of Computing and Security Greater Noida India Bennett University School of Computer Science Engineering and Technology Greater Noida India SR University School of Artificial Intelligence and Computer Science Telangana Warangal India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Punjab Patiala India Imam Abdulrahman Bin Faisal University Department of Networks and Communications College of Computer Science and Information Technology Saudi Arabia PI Teresina Brazil

The Internet of Things (IoT) has become a revolutionary concept that connects various devices and systems to enable smooth communication and data exchange. In this vast network, unmanned aerial agents (UAA)-assisted mobile edge computing (MEC) communication plays a crucial role in facilitating direct interaction between edge devices. This aspect of IoT goes beyond traditional interactions between humans and machines. It creates a dynamic environment where devices collaborate autonomously, share information, and perform tasks. UAA-assisted MEC network offers several benefits such as supports short range communication, reduced delay, improved scalability, and enhanced energy efficiency. Furthermore, for the purpose of enhancing the widespread interconnection and exceptionally dependable minimal delay in the fifth generation (5G) and beyond network, the utilization of non-orthogonal multiple access (NOMA) can be considered. Within this context, the impact of federated learning (FL) on NOMA-based UAV-assisted MEC network in wirelesspowered communication networks is examined. Initially, the transmitters extract energy from the radio frequency signals emitted by the MEC server. Subsequently, the transmitters utilize NOMA to establish communication with the receivers by utilizing the stored harvested energy. The formulation of a stochastic optimization problem is proposed with the aim of improving energy consumption (EC) and minimizing delay. Results indicate that the proposed scheme exhibit superior accuracy compared to baseline schemes, achieving an accuracy 98.37% after 59 communication rounds. The FL is employed to attain the objective and accelerate the local training data across the UAA-assisted MEC network. © 2025 IEEE.

关键词： Resource allocation

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Mission-Driven Resource Scheduling in Satellite-Terrestrial networks: From Perspective of Collaboration and Reconfiguration

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IEEE Transactions on Communications 2025年

作者： Zhou, Di Sheng, Min Bao, Chenxi Wang, Yixin Li, Jiandong Han, Zhu Xidian University State Key Laboratory of Integrated Service Networks Shaanxi Xi'an710071 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Satellite-terrestrial networks (STNs) are emerging as a promising solution for provisioning comprehensive services, such as the Internet of Remote Things (IoRT) and remote sensing, within the realm of 6G wireless networks. Nonetheless, resource failures and the exigencies of diverse mission urgencies exacerbate the intricacies of resource scheduling in STNs, thus impeding the effective alignment of distinct mission requirements with dynamic resources. In light of these challenges, we first mathematically formulate the complex resource scheduling problem in STNs as a stochastic optimization paradigm, endeavoring to maximize the number of successfully accomplished missions. Subsequently, we conceptualize the resource evolution to delineate scheduling dynamics, encompassing potential contingencies of resource discontinuities. Next, we propose an innovative hierarchical deep learning-based mission-driven resource scheduling (HDL-MDRS) algorithm, aimed at optimizing resource collaboration and reconfiguration to amplify network performance within the dynamic ambits characterized by resource disruptions. The HDL-MDRS algorithm achieves a coarse-grained alignment of diverse mission requirements with multidimensional resources. It enhances overall mission fulfillment and network resource utilization efficiency through fine-grained collaboration and reconfiguration among satellites, both within and across different clusters. Notably, the simulation findings substantiate the effectiveness of the HDL-MDRS algorithm, effectively ensuring the requirements of different types of missions in case of the unforeseen resource failures, orchestrated through efficient resource collaboration and on-demand reconfiguration. © 2025 IEEE.

关键词： Stochastic systems

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Enhancing malware detection performance: leveraging K-Nearest Neighbors with Firefly Optimization Algorithm

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Multimedia Tools and Applications 2025年第12期84卷 10071-10094页

作者： Al Saaidah, Adeeb Abualhaj, Mosleh M. Shambour, Qusai Y. Abu-Shareha, Ahmad Adel Abualigah, Laith Al-Khatib, Sumaya N. Alraba’nah, Yousef H Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Software Engineering Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon College of Engineering Yuan Ze University Taoyuan32003 Taiwan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

Malware detection plays a crucial role in ensuring robust cybersecurity amidst the ever-evolving cyber threats. This research paper delves into the realm of machine learning (ML) algorithms for malware detection, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm, utilizing tailored parameter settings and the Firefly Optimization Algorithm (FOA). The study leverages the MalMem-2022 dataset to assess the efficacy of KNN and KNN with FOA in malware detection. The impact of parameter tuning and feature selection on classification is elucidated by comparing the performance of both approaches. Encouragingly, the results reveal promising advancements in one of the multiclass classification scenarios when employing KNN with FOA, yielding noteworthy enhancements in Accuracy, Recall, Precision, Matthews Correlation Coefficient, TNR, and F1-score by 2.65%, 2.65%, 2.47%, 3.59%, 0.17%, and 2.61%, respectively. These findings accentuate the significance of optimizing KNN’s parameters and implementing FOA for feature selection, culminating in heightened accuracy in malware detection. The symbiotic fusion of KNN and FOA exhibits remarkable effectiveness in augmenting the classification model’s performance, thus offering valuable insights for bolstering cybersecurity measures. Adopting ML algorithms, particularly KNN with specific parameter settings and FOA, presents a promising avenue for enhancing malware detection capabilities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nearest neighbor search

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