检索结果-内蒙古大学图书馆

computational offloading and Resource Allocation for IoT applications using Decision Tree based Reinforcement Learning

引用

AD HOC NETWORKS 2025年 170卷

作者： Walia, Guneet Kaur Kumar, Mohit Dr BR Ambedkar NIT Dept Informat Technol Jalandhar 144008 Punjab India

The pervasive penetration of IoT devices in various domains such as autonomous vehicles, supply chain management, video surveillance, healthcare, industrial automation etc. necessitates for advanced computing paradigms to achieve real time response delivery. Edge computing offers prompt service response via its competent decentralized platform for catering disseminate workload, hence serving as front-runner for competently handling a wide spectrum of IoT applications. However, optimal distribution of workload in the form of incoming tasks to appropriate destinations remains a challenging issue due to multiple factors such as dynamic offloading decision, optimal resource allocation, heterogeneity of devices, unbalanced workload etc in collaborative Cloud-Edge layered architecture. Employing advanced Artificial Intelligence (AI)-based techniques, provides promising solutions to address the complex task assignment problem. However, existing solutions encounter significant challenges, including prolonged convergence time, extended learning periods for agents and inability to adapt to a stochastic environment. Hence, our work aims to design a unified framework for performing computational offloading and resource allocation in diverse IoT applications using Decision Tree Empowered Reinforcement Learning (DTRL) technique. The proposed work formulates the optimization problem for offloading decisions at runtime and allocates the optimal resources for incoming tasks to improve the Quality-of-Service parameters (QoS). The computational results conducted over a simulation environment proved that the proposed approach has the high convergence ability, exploration and exploitation capability and outperforms the existing state-of-the-art approaches in terms of delay, energy consumption, waiting time, task acceptance ratio and service cost.

关键词： Edge computing IoT computational offloading Resource allocation Delay

来源：评论

学校读者我要写书评

暂无评论

computational offloading and Delay Minimization for UAV-aided Edge Federated Learning 59

Computational Offloading and Delay Minimization for UAV-aide...

引用

59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Liaq, Mudassar Ejaz, Waleed Lakehead Univ Dept Elect & Comp Engn Thunder Bay ON Canada

ISBN: (纸本)9781728190549

The Internet of things (IoT) applications are generating large volumes of data, and processing this data securely, reliably, and timely is required for effective decision-making. However, the limited processing capability of IoT devices is a significant bottleneck in processing these datasets. A potential solution to overcome this challenge is federated learning using unmanned aerial vehicle (UAV) as mobile edge computing (MEC) servers. In this paper, we propose a UAV-aided edge federated learning (UAFL) framework where we utilize UAV-MEC's computation capacity to process some portion of the datasets from the straggling devices (devices which are unable to process their dataset in reasonable time and are lagging, increasing delay in the whole system). We formulate an optimization problem to minimize system delay considering UAV-MEC's computation power, computation and communication power of IoT devices, and quality of service constraints. We transform the proposed problem by introducing auxiliary variables and epigraph form and then solve the problem using concurrent deterministic simplex with root relaxation algorithm. Simulation results show that UAFL outperforms the traditional federated learning and edge-based learning system by approximately 5%.

关键词： computational offloading constraint IoT devices and UAVs resource optimization system delay minimization UAV-aided edge federated learning

来源：评论

学校读者我要写书评

暂无评论

Multitask-Oriented Efficient computational offloading Orchestrator for IoT Applications in Mobile-Edge Computing

引用

IEEE INTERNET OF THINGS JOURNAL 2025年第10期12卷 14096-14108页

作者： Wang, Leilei Deng, Xiaoheng Zhang, Honggang Wan, Shaohua Min, Geyong Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Xian Univ Technol Shanxi Key Lab Network Comp & Secur Technol Xian 710048 Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410075 Peoples R China Cent South Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China Univ Massachusetts Boston Engn Dept Boston MA 02125 USA Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4QF England

Mobile-edge computing (MEC) can accelerate computation-intensive applications and emerge as a promising technology for enabling Internet of Things (IoT). MEC improves the processing performance of tasks by assigning them to the edge nodes. However, with massive terminals contending for computation and communication resources simultaneously, how to develop a flexible computational offloading mechanism becomes the fundamental issue of MEC-enabled IoT systems. This article aims to develop an effective computational offloading decision scheme by jointly considering the computational resource and diverse user demands with two goals, i.e., minimizing both the latency and the energy consumption. Specifically, we develop a two-stage computational offloading mechanism, where the computational resources and offloading decisions can be allocated and coordinated with the variation of computation requirements. To achieve the two goals, this work introduces an edge node recommendation model within the cloud-edge-end architecture to reduce the offloading optimization search space. Furthermore, we propose a new computational offloading (CROCA) algorithm based on chemical reaction optimization (CRO) for optimizing offloading utility, which thoroughly considers the competition between mobile device requests and computational resources. Extensive evaluation results demonstrate that the proposed CROCA scheme can effectively improve the computational offloading performance.

关键词： computational modeling Optimization Mobile handsets Resource management Internet of Things Cloud computing Energy consumption Delays Servers Performance evaluation computational offloading edge node recommendation model (ENRM) Internet of Things (IoT) mobile-edge computing (MEC)-enabled IoT systems

来源：评论

学校读者我要写书评

暂无评论

Outage Analysis of Optimal UAV Cooperation with IRS via Energy Harvesting Enhancement Assisted computational offloading

引用

Computer Modeling in Engineering & Sciences 2024年第2期138卷 1885-1905页

作者： Baofeng Ji Ying Wang Weixing Wang Shahid Mumtaz Charalampos Tsimenidis School of Information Engineering Henan University of Science and TechnologyLuoyang471000China Research and Innovation Center of Intelligent System Longmen LaboratoryLuoyang471000China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System(CEMEE) Luoyang471000China Sustainable Digital Communications and Energy Systems(DIGCOM)Group Nottingham Trent UniversityNottinghamUK

The utilization of mobile edge computing(MEC)for unmanned aerial vehicle(UAV)communication presents a viable solution for achieving high reliability and low latency *** study explores the potential of employing intelligent reflective surfaces(IRS)andUAVs as relay nodes to efficiently offload user computing tasks to theMEC server system ***,the user node accesses the primary user spectrum,while adhering to the constraint of satisfying the primary user peak interference ***,the UAV acquires energy without interrupting the primary user’s regular communication by employing two energy harvesting schemes,namely time switching(TS)and power splitting(PS).The selection of the optimal UAV is based on the maximization of the instantaneous signal-to-noise ***,the analytical expression for the outage probability of the system in Rayleigh channels is derived and *** study investigates the impact of various system parameters,including the number of UAVs,peak interference power,TS,and PS factors,on the system’s outage performance through *** proposed system is also compared to two conventional benchmark schemes:the optimal UAV link transmission and the IRS link *** simulation results validate the theoretical derivation and demonstrate the superiority of the proposed scheme over the benchmark schemes.

关键词： Unmanned aerial vehicle(UAV) intelligent reflective surface(IRS) energy harvesting computational offloading outage probability

来源：评论

学校读者我要写书评

暂无评论

Intelligent computational offloading for mobile-edge server computing and hybrid optimal resource allocation

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2024年第27期83卷 69947-69972页

作者： Muralidhar, K. Shankar, S. Siva Unhelkar, Bhuvan Chakrabarti, Tulika Chakrabarti, Prasun Univ S Florida Muma Coll Business Tampa FL 33620 USA KG Reddy Coll Engn & Technol Dept Comp Sci & Engn Hyderabad Telangana India Sir Padampat Singhania Univ Dept Bas Sci Udaipur Rajasthan India Sir Padampat Singhania Univ Dept Comp Sci & Engn Udaipur Rajasthan India

To get beyond the limitations of mobile devices, the mobile cloud is an emerging technology. offloading resource-intensive applications to distant data centres enabled by the cloud is helping us achieve that. Real-time mobile user applications suffer while using a remote computing solution since MDs encounter increased network response times and delays. In this paper, we suggest an intelligent computational offloading model for MEC. The suggested model aims to apply the DL technique, which automatically chooses the computing source depending on performance, energy consumption, and workload. These factors are used to select the best edge server. For this, a Modified LSTM is proposed in this work. Additionally, TS on the edge cloud infrastructure are directly impacted by VM availability;as a consequence, VM availability is calculated while managing TS. When allocating resources, VM calculations like Make span, task completion times, resource consumption, and migration costs are considered. The capacity to deliver functioning services in the required amount of time after the task is offloaded to the VM is considered while allocating VM resources. Given that this is an optimization problem, the NBUJ hybrid optimization algorithm is developed to solve this issue. At last, the performance of the developed model is validated with the existing models in terms of fitness, migration cost, makespan, resource utilization and so on. And, it is noted that the developed model attains 97.4% of accuracy and 0.0274% of FNR which validates the performance of the proposed model.

关键词： MCC computational offloading Resource allocation NBUJ LSTM

来源：评论

学校读者我要写书评

暂无评论

An Improved and Efficient computational offloading Method Based on ADMM Strategy in Cloud-Edge Collaborative Computing Environment for Resilient Industry 5.0

引用

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 1392-1402页

作者： Shu, Wanneng Nie, Shaoliang Jian, Wencheng Ge, Xin South Cent Minzu Univ Coll Comp Sci Wuhan 430074 Peoples R China

With the rapid growth of data demand and the rise of the Industry 5.0, cloud-edge collaborative computing achieves intelligent connectivity and data sharing between devices for intelligent management and optimization of the production process. computational offloading, as a key technology in the cloud-edge collaborative computing environment, provides higher-quality user services and realizes higher data requirements for intelligent manufacturing and production. In this paper, we proposes an Improved Efficient Alternating Direction Method of Multipliers (IEADMM) for computational offloading in cloud-edge collaborative computing environment, aiming to reduce the total system cost with the optimization goal of minimizing the total time delay. The proposed IEADMM fully utilizes the Alternating Direction Method of Multipliers ADMM) to minimize the total delay by increasing the convergence speed. The experimental result shows that our proposed IEADMM has good performance and practicality for computational offloading in cloud-edge collaborative computing environment, and can be widely applied in practical application scenarios such as intelligent monitoring, Internet of Things (IoT), mobile games, etc.

关键词： Task analysis Servers Costs computational modeling Symbols Optical fiber networks Improved efficient alternating direction method of multipliers cloud-edge collaborative computing MEC server computational offloading resilient industry 50

来源：评论

学校读者我要写书评

暂无评论

Attention-Augmented MADDPG in NOMA-Based Vehicular Mobile Edge computational offloading

引用

IEEE INTERNET OF THINGS JOURNAL 2024年第16期11卷 27000-27014页

作者： Wu, Liangshun Qu, Junsuo Li, Shilin Zhang, Cong Du, Jianbo Sun, Xiang Zhou, Jiehan Xian Univ Posts & Telecommun Sch Automat Xian Key Lab Adv Control & Intelligent Proc Xian 710061 Peoples R China Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710061 Peoples R China Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA Univ Oulu Fac Informat Technol & Elect Engn Oulu 90570 Finland

Vehicular mobile edge computing (vMEC) and nonorthogonal multiple access (NOMA) have emerged as promising technologies for enabling low-latency and high-throughput applications in vehicular networks. In this article, we propose a novel multiagent deep deterministic policy gradient (MADDPG) approach for resource allocation in NOMA-based vMEC systems. Our approach leverages deep reinforcement learning (DRL) to enable vehicles to offload computation-intensive tasks to nearby edge servers, optimizing resource allocation decisions while ensuring low-latency communication. We introduce an attention mechanism within the MADDPG model to dynamically focus on relevant information from the input state and joint actions, enhancing the model's predictive accuracy. Additionally, we propose an attention-based experience replay method to expedite network convergence. The simulation results highlight the effectiveness of multiagent reinforcement learning (MARL) algorithms, such as MADDPG with attention, in achieving better convergence and performance in various scenarios. The influence of different model parameters, such as input data volumes, task load levels, and resource configurations, on optimization results is also evident. The decision making processes of agents are dynamic and depend on factors specific to the task and environment.

关键词： Attention mechanism computational offloading deep reinforce learning multiagent deep deterministic policy gradient (MADDPG) nonorthogonal multiple access (NOMA) vehicular Mobile Edge Computing (vMEC) Attention mechanism computational offloading deep reinforce learning multiagent deep deterministic policy gradient (MADDPG) nonorthogonal multiple access (NOMA) vehicular Mobile Edge Computing (vMEC)

来源：评论

学校读者我要写书评

暂无评论

Soft computing approaches for dynamic multi-objective evaluation of computational offloading: a literature review

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第9期27卷 12459-12481页

作者： Khan, Sheharyar Jiangbin, Zheng Ali, Hassan Northwestern Polytech Univ Sch Software Xian 710072 Peoples R China Northwestern Polytech Univ Sch Management Xian 710072 Peoples R China

Optimizing computational offloading in Mobile Edge Computing (MEC) environments presents a multifaceted challenge requiring innovative solutions. Soft computing, recognized for its ability to manage uncertainty and complexity, emerges as a promising approach for addressing the dynamic multi-objective evaluation inherent in computational offloading scenarios. This paper conducts a comprehensive review and analysis of soft computing approaches for Dynamic Multi-Objective Evaluation of computational offloading (DMOECO), aiming to identify trends, analyze existing literature, and offer insights for future research directions. Employing a systematic literature review (SLR) methodology, we meticulously scrutinize 50 research articles and scholarly publications spanning from 2016 to November 2023. Our review synthesizes advancements in soft computing techniques, including fuzzy logic, neural networks, evolutionary algorithms, and probabilistic reasoning, as applied to computational offloading optimization within MEC environments. Within this comprehensive review, existing approaches are categorized and analyzed into distinct research lines based on methodologies, objectives, evaluation metrics, and application domains. The evolution of soft computing-based DMOECO strategies is emphasized, showcasing their effectiveness in dynamically balancing various computational objectives, including energy consumption, latency, throughput, user experience, and other pertinent factors in computational offloading scenarios. Key challenges, including scalability issues, lack of real-world deployment validation, and the need for standardized evaluation benchmarks, are identified. Insights and recommendations are provided to enhance computational offloading optimization. Furthermore, collaborative efforts between academia and industry are advocated to bridge the theoretical developments with practical implementations. This study pioneers the use of SLR methodology, offering valuable perspec

关键词： Soft computing Mobile cloud computing computational offloading Hybrid computing Systematic literature review

来源：评论

学校读者我要写书评

暂无评论

Cooperative computational offloading in Mobile Edge Computing for Vehicles: A Model-Based DNN Approach

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第3期72卷 3376-3391页

作者： Munawar, Suleman Ali, Zaiwar Waqas, Muhammad Tu, Shanshan Hassan, Syed Ali Abbas, Ghulam Ghulam Ishaq Khan Inst Engn Sci & Technol Fac Comp Sci & Engn Topi 23460 Pakistan Univ Bahrain Coll Informat Technol Dept Comp Engn Sakhir 32038 Bahrain Edith Cowan Univ Sch Engn Perth WA 6027 Australia Beijing Univ Technol Fac Informat Technol Engn Res Ctr Intelligent Percept & Autonomous Cont Beijing 100021 Peoples R China Natl Univ Sci & Technol Sch Elect Engn & Comp Sci Islamabad 44000 Pakistan

Many advancements are being made in vehicular networks, such as self-driving, dynamic route scheduling, real-time traffic condition monitoring, and on-board infotainment services. However, these services require high computation power and precision and can be met using mobile edge computing (MEC) mechanisms for vehicular networks. MEC operates through the edge servers available at the roadside, also known as roadside units (RSU). MEC is very useful for vehicular networks because it has extremely low latency and supports operations that require near-real-time access to rapidly changing data. This paper proposes an efficient computational offloading, smart division of tasks, and cooperation among RSUs to increase service performance and decrease the delay in a vehicular network via MEC. The computational delay is further reduced by parallel processing. In the division of tasks, each task is divided into two sub-components which are fed to a deep neural network (DNN) for training. Consequently, this reduces the overall time delay and overhead. We also adopt an efficient routing policy to deliver the results through the shortest path to improve service reliability. The offloading, computing, division, and routing policies are formulated, and a model-based DNN approach is used to obtain an optimal solution. Simulation results prove that our proposed approach is suitable in a dynamic environment. We also compare our results with the existing state-of-the-art, showing that our proposed approach outperforms the existing schemes.

关键词： Task analysis Servers Delay effects computational modeling Routing Delays Cloud computing Deep neural network (DNN) computational offloading vehicular networks smart task division routing policy mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

On Applicability of Imagery-Based CNN to computational offloading Location Selection

引用

IEEE ACCESS 2023年 11卷 2433-2444页

作者： Ometov, Aleksandr Mezina, Anzhelika Nurmi, Jari Tampere Univ Fac Informat Technol & Commun Sci Elect Engn Unit Tampere Finland Brno Univ Technol Fac Elect Engn & Commun Dept Telecommun Brno Czech Republic

The progress in computational offloading is heavily pushing the development of the modern Information and Communications Technology domain. The growth in resource-constrained Internet of Things devices demands the development of new computational offloading strategies to be sustainably integrated in beyond 5G networks. One of the solutions to said demand is enabling Mobile Edge Computing (MEC) powered by advanced methods of Machine Learning (ML). This paper proposes the application of ML-powered computational offloading strategy in a wireless cellular network by applying the traditional fundamental Travelling Salesman Problem (TSP) on computational offloading location selection. The main specificity of the proposed approach is the use of imagery data. Thus, the paper executes a literature review to identify existing strategies. It further proposes a novel method utilizing the location-like imagery data to identify the most suitable computational location by executing the search for an identified route between locations using the proposed Deep Learning (DL) model. The model was evaluated and achieved MAE - 1,575, MSA - 10,119,205, R-2 - 0.98 on the testing dataset, which outperforms or is comparable with other well-known architectures. Moreover, the training time is proven to be 2-10 times faster. Interestingly, the MAE values are relatively low compared to the target values that should be predicted (despite rather high MSE results), which is confirmed by the almost perfect R-2 value. It is concluded that the proposed neural network can predict the target values, and this solution can be applied to real-world tasks.

关键词： computational offloading machine learning mobile edge computing optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：