Computer Networks最新文献

{"title":"A hybrid priority-aware genetic algorithm and opposition-based learning for scheduling IoT tasks in green fog computing","authors":"Rezvan Salimi ,&nbsp;Sadoon Azizi ,&nbsp;Javad Dogani","doi":"10.1016/j.comnet.2025.111349","DOIUrl":"10.1016/j.comnet.2025.111349","url":null,"abstract":"<div><div>With the rapid growth of Internet of Things (IoT) devices, efficient task scheduling in fog computing systems has become crucial to ensure optimal resource utilization. In addition, the increasing demand for eco-friendly solutions has led to the emergence of green fog computing, which aims to leverage renewable energy sources to power fog nodes. Difficulties such as the diverse requirements of IoT tasks, the distributed and heterogeneous nature of fog nodes, and the fluctuations of renewable energy sources have made the task scheduling problem increasingly complex and pose significant challenges. To address these issues, in this paper, we first present a mixed-integer nonlinear programming (MINLP) model with the objective of minimizing the total system cost, which consists of brown energy consumption, deadline violation time, and monetary cost. To provide an effective and efficient solution for the model, we then propose <em>PGA-OBL</em>, a hybrid algorithm that combines the priority-aware genetic algorithm with an opposition-based learning strategy. The proposed algorithm is implemented in Python and evaluated through various experiments, comparing its performance with a standard genetic algorithm, a priority-aware semi-greedy approach, and a green energy-aware algorithm. The results confirm that PGA-OBL achieves significantly better convergence than the standard genetic algorithm. Additionally, it reduces the total system cost by approximately 6.2% to 20.8% compared to competing approaches.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"267 ","pages":"Article 111349"},"PeriodicalIF":4.4,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071567","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":"BASTION: Beyond automated service and security orchestration for next-generation networks","authors":"José Manuel Bernabé Murcia ,&nbsp;Alejandro M. Zarca ,&nbsp;Antonio Skármeta","doi":"10.1016/j.comnet.2025.111352","DOIUrl":"10.1016/j.comnet.2025.111352","url":null,"abstract":"<div><div>The adoption of 5G technology and beyond introduces advanced capabilities, such as dynamic resource coordination and allocation tailored to specific service and security requirements. To achieve efficient security and network management, service automation and orchestration are essential. This paper presents BASTION, a ZSM-aligned framework for enhanced service and security (meta) orchestration. By leveraging an intent-based, policy-driven approach, it enables the orchestration and enforcement of service and security policies across B5G infrastructures, dynamically adapting to real-time infrastructure conditions. While meta-orchestration capabilities focus on selecting the most suitable orchestration algorithm based on the system’s current status and the received requirements, orchestration capabilities primarily determine what, where, when, and how to enforce services and security policies. Additionally, the modular design and implementation allow for the seamless integration of new security capabilities through plugins, drivers, and managers. This advancement represents a significant step towards building resilient, adaptable, and secure B5G networks capable of meeting the complex demands of modern network environments. The implementation details showcase the full range of capabilities offered by the BASTION framework, highlighting its effectiveness through successful European and national projects. Furthermore, the performance evaluation section provides a comprehensive analysis of orchestration efficiency, breaking down execution times across different phases. In particular, BASTION demonstrates exceptional performance, achieving decision times as low as 1.3 ms and deploying services and security policies, including fully operational dynamic VNFs in less than 30 s, underscoring its ability to deliver fast, scalable, and efficient orchestration in complex environments.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"267 ","pages":"Article 111352"},"PeriodicalIF":4.4,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088854","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":"Power control and task offloading strategies for high-density wireless body area networks based on deep reinforcement learning","authors":"Yang Liao ,&nbsp;Huayang Zhou ,&nbsp;Chengfeng Leng ,&nbsp;Zhenlang Su ,&nbsp;Tuanfa Qin","doi":"10.1016/j.comnet.2025.111351","DOIUrl":"10.1016/j.comnet.2025.111351","url":null,"abstract":"<div><div>With the widespread application of Wireless Body Area Network (WBAN) in healthcare, issues such as a lack of computational resources and communication interference in high-density WBAN scenarios have become increasingly prominent. To address these issues, this paper introduces Mobile Edge Computing (MEC) to tackle the lack of computational resources and employs power control to mitigate communication interference among WBANs. We describe the joint optimization problem of power control and task offloading in high-density WBAN scenarios as a Markov Decision Process (MDP) and propose a Power Control and Task Offloading (PCTO) algorithm based on the Deep Deterministic Policy Gradient (DDPG) method to achieve coordinated optimization of transmission power and task offloading. Furthermore, we improve the algorithm’s learning efficiency and sample utilization by incorporating a prioritized experience replay mechanism. By constructing a multi-objective reward function that comprehensively considers delay, energy consumption, and Signal-to-Interference-plus-Noise Ratio (SINR), and incorporating Quality of Service (QoS) constraints, the algorithm is capable of making intelligent decisions in complex wireless communication environments. The simulation results demonstrate that, compared to existing methods, the proposed approach significantly reduces system delay while improving SINR and service satisfaction in various WBAN density scenarios.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"267 ","pages":"Article 111351"},"PeriodicalIF":4.4,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072271","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":"Advancing social network security with magteon-turing L3TM: A multi-layered defense system against cyber threats","authors":"Muhammad Nadeem,&nbsp;Chen Hongsong","doi":"10.1016/j.comnet.2025.111375","DOIUrl":"10.1016/j.comnet.2025.111375","url":null,"abstract":"<div><div>Magteon-Turing L3TM is a highly secure and scalable framework specifically designed for real-time social network protection. It integrates advanced large language models (LLMs), including Megatron-Turing NLG, Swarm OpenAI, Langchain, advance Bagging and Ensembling techniques to strengthen threat detection and mitigation capabilities. While previous studies in social network security have largely focused on detecting isolated attack types using dedicated models, such approaches fall short in dynamic environments where networks face multiple, evolving threats. In contrast, the proposed Magteon-Turing L3TM framework is built to detect and defend against a wide spectrum of attacks, eliminating the need for narrowly specialized solutions. This research introduces a novel methodology by integrating Megatron-Turing NLG with multiple learning models, each statistically, probabilistically, and experimentally validated using real-time data from Facebook and Twitter. During evaluation, the framework achieved an accuracy of 98.5 % on Facebook and 98.7 % on Twitter, confirming its reliability and adaptability in real-world conditions. Unlike traditional systems that require retraining for every new threat, Magteon-Turing L3TM can be fine-tuned in response to emerging attacks by dynamically adjusting to specific community and agent-based threat profiles. This makes it the first framework of its kind to unify high-performance LLMs and adaptive learning in a cohesive, real-time security system capable of countering diverse social network vulnerabilities.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"267 ","pages":"Article 111375"},"PeriodicalIF":4.4,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125084","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":"DP-SAFL: Semi-asynchronous federated learning with differential privacy in heterogeneous edge computing","authors":"Chunrong He ,&nbsp;Songtao Guo ,&nbsp;Guiyan Liu ,&nbsp;Wei Zhang","doi":"10.1016/j.comnet.2025.111346","DOIUrl":"10.1016/j.comnet.2025.111346","url":null,"abstract":"<div><div>Due to edge heterogeneity and data imbalance in edge computing, asynchronous federated learning (FL) is proposed to address the significant latency caused by synchronous FL. Asynchronous FL demands frequent communications of edge devices, which imposes a great burden on the resource-constrained devices, and leads to the design of semi-asynchronous FL. However, the privacy problem caused by the open environment of edge computing has not been solved in the semi-asynchronous FL. Thus, this paper takes the first step to propose a novel framework, DP-SAFL, for protecting sensitive data and model parameters through the incorporation of <span><math><mrow><mo>(</mo><mi>ɛ</mi><mo>,</mo><mi>δ</mi><mo>)</mo></mrow></math></span>-differential privacy (DP) into semi-asynchronous FL in the heterogeneous edge computing. To protect updated parameters from disclosure, we first add Gaussian noises to the local model of mobile devices (workers) and global model of edge server (parameter server), and then ensure the global DP in both the uplink and downlink channels. Moreover, we carry out a theoretical convergence analysis and develop an upper bound on the loss function of semi-asynchronous FL model after <span><math><mi>K</mi></math></span> global aggregations, indicating a better convergence performance than that of synchronous FL with DP. Extensive evaluations demonstrate that our DP-SAFL can achieve a tradeoff between privacy level and convergence performance with a reasonable privacy budget <span><math><mi>ɛ</mi></math></span>, which is superior to previous work.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"267 ","pages":"Article 111346"},"PeriodicalIF":4.4,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071988","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":"Quantum Approximate Optimization Algorithm applied to multi-objective routing for large scale 6G networks","authors":"Oumayma Bouchmal ,&nbsp;Bruno Cimoli ,&nbsp;Ripalta Stabile ,&nbsp;Juan Jose Vegas Olmos ,&nbsp;Idelfonso Tafur Monroy","doi":"10.1016/j.comnet.2025.111345","DOIUrl":"10.1016/j.comnet.2025.111345","url":null,"abstract":"<div><div>A multi-objective optimization problem involves optimizing two or more conflicting objectives simultaneously. This type of problem arises in many scientific and industrial areas and it is classified as NP-Hard. Network routing optimization with multiple objectives falls into this category. In the context of 6G networks, solving this problem will become even more challenging due to the exponential growth of Internet of Things devices and the high quality of service requirements. Finding good quality solutions for large-scale networks will be increasingly difficult. In this paper, we introduce a quantum-inspired routing optimization scheme in which noisy-intermediate scale quantum computers (NISQ) can be used to solve the Multi-Objective Routing Problem (MORP). We evaluate the application of the proposed scheme in detail by first developing the mathematical formulas for both single-objective and multi-objective routing and mapping the problem onto gate-based models by using the quadratic unconstrained binary optimization (QUBO) approach. To validate the proposed scheme, we use the quantum approximate optimization algorithm (QAOA), the go-to approach for solving combinatorial optimization problems that are classically intractable. For the simulation, we use the IBM-Qasm simulator and Qiskit framework. Additionally, we use the Chernoff Bound as a standard technique to estimate the sample complexity of QAOA. Finally, we provide a detailed numerical and theoretical analysis of the proposed scheme, including its time complexity, resource requirements, and the challenges associated with it. Our results demonstrate that the proposed approach operates with a time complexity of <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>E</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> per iteration in both single and multi-objective scenarios, with an overall runtime of (<em>n</em>_iteration + <em>n</em>_CB) <span><math><mi>⋅</mi></math></span> <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>E</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> influenced by the sampling overhead, significantly outperforming Dijkstra’s algorithm in the multi-objective case, where the complexity increases to <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mn>2</mn></mrow><mrow><mi>k</mi></mrow></msup><mrow><mo>(</mo><mi>N</mi><mrow><mo>(</mo><mi>k</mi><mo>+</mo><mo>log</mo><mi>N</mi><mo>)</mo></mrow><mo>+</mo><msup><mrow><mn>2</mn></mrow><mrow><mi>k</mi></mrow></msup><mi>E</mi><mo>)</mo></mrow><mo>)</mo></mrow></mrow></math></span>.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"267 ","pages":"Article 111345"},"PeriodicalIF":4.4,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088939","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":"Electromagnetic emission-aware Machine Learning enabled scheduling framework for Unmanned Aerial Vehicles","authors":"Muhammad Ali Jamshed ,&nbsp;Ali Nauman ,&nbsp;Ayman A. Althuwayb ,&nbsp;Haris Pervaiz ,&nbsp;Sung Won Kim","doi":"10.1016/j.comnet.2025.111311","DOIUrl":"10.1016/j.comnet.2025.111311","url":null,"abstract":"<div><div>Recently, there has been a notable increase in the number of User Proximity Wireless Devices (UPWD). This growth has significantly raised users’ exposure to Electromagnetic Field (EMF), potentially leading to various physiological effects. The use of Non-Terrestrial Networks (NTN) has emerged as an optimistic solution to improve wireless coverage in rural areas. NTN mainly consist of satellites, with High Altitude Platform Stations (HAPS) and Unmanned Aerial Vehicles (UAV) considered special use cases. It is well established that optimizing exposure over time (Dose), rather than dealing with a fixed value, plays a crucial role in reducing uplink EMF exposure levels. In this paper, for the first time, we showcase that the combined use of UAV and the Dose metric can help keep the regulated uplink EMF exposure level well below the required threshold. This paper employs a combination of Non-Orthogonal Multiple Access (NOMA), UAV technology, Machine Learning (ML), and the Dose metric to optimize EMF exposure in the uplink of wireless communication systems. The ML based technique consists of a combination of k-medoids-based clustering and Silhouette analysis. To further reduce uplink EMF exposure, a power allocation policy is developed by transforming a non-convex problem into a convex one for solution. The numerical results indicate that the proposed scheme, which integrates NOMA, NTN, and ML, achieves at least a 89% reduction in EMF contrast to existing methods.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"267 ","pages":"Article 111311"},"PeriodicalIF":4.4,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083970","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":"An explicit rate control based traffic transmission and schedule scheme in UAV-IOT network slicing system","authors":"Hanwu Wang ,&nbsp;Moshe Zukerman ,&nbsp;Yingchao Zhao ,&nbsp;Ying Fu","doi":"10.1016/j.comnet.2025.111347","DOIUrl":"10.1016/j.comnet.2025.111347","url":null,"abstract":"<div><div>The UAV-IOT network slicing system plays a critical role in internet of things practical applications. However traffic transmission in such a kind of system is a challenging issue due to the time-varying characteristics of the wireless link state and their transmission capacity. To realize the effective traffic transmission and high link bandwidth utilization, we construct appropriate traffic schedulers at each hop of RSNs to regulate both the forward and backward traffic transmission respectively. Specifically we incorporate the control theory method into the schedulers and design a proportional–integral–derivative controller to facilitate the explicit traffic rate control and adjustment at each scheduler. Each RSN only needs to deal with its local sending and receiving nodes in a distributed manner, and different transmission hops can be coordinated with each other so that the end to end traffic transmission will be regulated to and stabilized at the desired levels. We make the stability analysis on rate controller to derive the desired scope of the control parameters. Both the theoretical analysis and simulation results validate that our proposed transmission scheme can achieve an effective end to end traffic transmission with high resource efficiency and global stability as well over the whole UAV-IOT network slicing system.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"267 ","pages":"Article 111347"},"PeriodicalIF":4.4,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088938","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":"A comprehensive survey on large language models for multimedia data security: challenges and solutions","authors":"Ankit Kumar ,&nbsp;Mikail Mohammed Salim ,&nbsp;David Camacho ,&nbsp;Jong Hyuk Park","doi":"10.1016/j.comnet.2025.111379","DOIUrl":"10.1016/j.comnet.2025.111379","url":null,"abstract":"<div><div>The rapid expansion of IoT applications utilizes multimedia data integrated with Large Language Models (LLMs) for interpreting digital information by leveraging the capabilities of artificial intelligence (AI) driven neural network systems. These models are extensively used as generative AI tools for data augmentation but data security and privacy remain a fundamental concern associated with LLM model in the digital domain. Traditional security approach shows potential challenges in addressing emerging threats such as adversarial attacks, data poisoning, or privacy breaches, especially in dynamic and resource-constrained IoT environments. Such malicious attacks target the LLM model during the learning and evaluation phase to exploit the vulnerabilities for unauthorized access. The proposed study conducts a comprehensive survey of the transformative potential of LLM models for securing multimedia data offering analysis of their capabilities, challenges, and solutions. The proposed study explores potential security threats and remedies for each type of multimedia data and investigates the various traditional and emerging data protection schemes. The study systematically classifies emerging attacks on LLM models during training and testing phases which include membership attacks, adversarial perturbations, prompt injection, etc. The study also investigates the various robust defense mechanism such as adversarial training, regularization, encryption, etc. The study evaluates the efficiency of potential LLM models such as generative LLM, transformer-based, and other multimodal systems in securing image, text, and video multimedia data highlighting their adaptability and scalability. The proposed survey compares state-of-the-art solutions and underscores the efficiency of LLM-driven mechanisms over traditional approaches in mitigating emerging attacks such as zero-day threats on multimedia data. It ensures real-time compliance with standard regulations like GDPR (General Data Protection Regulation). The proposed work identifies some open challenges including privacy-preserving LLM deployment, black-box interpretability, personalized LLM privacy risk, and cross-model security integration. It also highlights some robust future solutions such as lightweight LLM design and hybrid security frameworks. The proposed work bridges critical research gaps by providing insights into LLM-based emerging techniques to safeguard sensitive data in IoT-based real-world applications.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"267 ","pages":"Article 111379"},"PeriodicalIF":4.4,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071569","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":"Cooperative offloading multi-access edge computing (COMEC) for cell-edge users in heterogeneous dense networks","authors":"Muhammad Saleem Khan ,&nbsp;Sobia Jangsher ,&nbsp;Junaid Qadir ,&nbsp;Hassaan Khaliq Qureshi","doi":"10.1016/j.comnet.2025.111332","DOIUrl":"10.1016/j.comnet.2025.111332","url":null,"abstract":"<div><div>Multi-access edge computing (MEC) addresses the rising computational demands of advanced applications by bringing processing closer to users. Yet, cell-edge users often face high latency and low throughput—challenges that can be mitigated by deploying multiple MEC servers for simultaneous task offloading in dense heterogeneous networks. This paper investigates the performance gains of collaborative computing and presents a novel <strong>C</strong>ooperative <strong>O</strong>ffloading <strong>M</strong>ulti-access <strong>E</strong>dge <strong>C</strong>omputing (COMEC) scheme. The COMEC aims to optimize resource allocation for cell-edge users by reducing latency and maximizing energy efficiency (EE). In this way, cell-edge users with limited battery and computational power can sustain low-latency applications for a longer time. A bi-objective optimization problem is formulated to maximize the EE of edge users while simultaneously minimizing the latency. We propose an iterative algorithm named ORA-ETO to solve the mixed integer non-linear fractional (MINLF) problem. The proposed scheme has been evaluated using both the Rayleigh and WINNER-II propagation models within an asymmetric cell configuration. The obtained results validate the efficacy of the proposed COMEC scheme for cell-edge users, achieving performance gains of over 55% compared to dense multi-server-assisted MEC and CoMP-assisted MEC architectures. The COMEC scheme is statistically more significant (<span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>01</mn></mrow></math></span>) and has more stable performance with standard deviation less than <span><math><mrow><mn>0</mn><mo>.</mo><mn>082</mn></mrow></math></span> kbps/J, making it a superior choice for cell edge users. The effect size (<span><math><mrow><msup><mrow><mi>η</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>71</mn></mrow></math></span>) confirms that the choice of scheme has a considerable impact on EE of cell-edge users.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"267 ","pages":"Article 111332"},"PeriodicalIF":4.4,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107449","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}