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task offloading and Computation in Hybrid uRLLC and eMBB Cognitive Radio Enabled 5G Based Multiuser Edge Computing

IEEE Access 2025年 13卷 79871-79893页

作者： Olawole, Akinbode A. Takawira, Fambirai Chabalala, Chabalala S. Obafemi Awolowo University Department of Electronic and Electrical Engineering Ile-Ife 220005 Nigeria University of the Witwatersrand School of Electrical and Information Engineering Johannesburg 2050 South Africa

Multi-user edge computing (MEC) is a network architecture that enables cloud computing capabilities at the edge of a network, reducing latency and user equipment's energy consumption. An MEC system that can efficiently supports both the ultra-Reliable Low Latency Communication (uRLLC) and enhanced Mobile Broadband (eMBB) services is crucial in providing a diverse and efficient communication for various Internet of Things (IoT) applications. The current MEC models in literature are either deterministic or based on average-based metric hence, not suitable in a practical scenario where task offloading and computation activities are stochastic processes and, the wireless channel is often not interference-free. In this paper, we study the joint task offloading and computation in a mixed traffic of two 5G-based MEC. We consider user equipment (UE) that are cognitive radio enabled and, whose performance are energy constrained. In view of this, energy efficient MEC is formulated as a stochastic optimization with long-term objective while, taking into consideration the tail distribution of the eMBB queue length. The target is to minimize energy consumption and, maximize the achievable data rate subject to probabilistic and statistical constraint on the eMBB task length based on Extreme Value Theorem (EVT), uRLLC reliability and, system capacity. The performance of the proposed MEC model is studied in terms of the latency, energy consumption, user density, and reliability. Finally, we demonstrate numerical results to prove the superior effectiveness in the performance of our proposed model over the existing model. © 2013 IEEE.

关键词： computational task offloading enhanced mobile broadband (eMBB) fifth generation(5G) multi-user edge computing (MEC) stochastic processes ultra-reliable low latency communications (uRLLC)

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Energy-Efficient task offloading in Massive MIMO-Aided Multi-Pair Fog-Computing Networks

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IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第4期69卷 2123-2137页

作者： Wang, Kunlun Zhou, Yong Li, Jun Shi, Long Chen, Wen Hanzo, Lajos ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China East China Normal Univ Sch Commun & Elect Engn Shanghai 200241 Peoples R China Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Univ Southampton Dept Elect & Comp Sci Southampton SO17 1BJ Hants England

The energy-efficient task offloading problem of a massive multiple-input multiple-output (MIMO)-aided fog computing system is solved, where multiple task nodes offload their computational tasks to be solved via a massive MIMO-aided fog access node to multiple processing nodes in the fog for execution. By considering realistic imperfect channel state information (CSI), we formulate a joint task offloading and power allocation problem for minimizing the total energy consumption, including both computation and communication power consumptions. We solve the resultant non-convex optimization problem in two steps. First, we solve the computational task allocation and computational resource allocation for a given power allocation. Then, we conceive a sequential optimization framework for determining the specific power allocation decision that minimizes the total energy consumption of the fog access node. Given the computational tasks, the computational resources, and the power allocation, we propose an iterative algorithm for the system optimization. The simulation results show that the proposed scheme significantly reduces the total energy consumption compared to the benchmark schemes.

关键词： Fog computing massive MIMO computational task offloading energy efficiency fog access node

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VESBELT: An energy-efficient and low-latency aware task offloading in Maritime Internet-of-Things networks using ensemble neural networks

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 161卷 572-585页

作者： Ghoshal, Sudip Chandra Hossain, Md Maruf Das, Bishozit Chandra Roy, Palash Razzaque, Md. Abdur Azad, Saiful Hassan, Mohammad Mehedi Savaglio, Claudio Fortino, Giancarlo Green Univ Bangladesh Dept Comp Sci & Engn Dhaka Bangladesh Univ Dhaka Dept Comp Sci & Engn Green Networking Res Grp Dhaka 1000 Bangladesh King Saud Univ Coll Comp & Informat Sci Informat Syst Dept Riyadh 11543 Saudi Arabia Univ Calabria Dept Informat Modeling Elect & Syst Engn I-87036 Arcavacata Di Rende cs Italy

Due to increasing maritime activities, the number of Maritime Internet-of-things (MIoT) devices requiring real-time marine data processing is growing exponentially. To offload maritime tasks and address the limited computational capabilities of heterogeneous MIoT devices, edge and cloud computing networks are employed. However, these networks introduce several challenges, including increased energy consumption and service latency within the complex marine network environment. Current state-of-the-art solutions address these issues by focusing exclusively on real-time offloading data, neglecting the relationship with past offloading tasks. In this work, we develop an optimization framework, named VESBELT, for offloading tasks from V essel users to nearby E dge S ervers or the cloud server, aiming to reduce E nergy consumption and service L atency T rade-off through a multi-objective linear programming problem. However, finding optimal solutions from this formulation is considered an NP-hard problem. To address this, we introduce VESBELT-ECNN, VESBELT-EANN, and VESBELT-ELSTM systems that leverage ensemble convolutional, artificial neural networks and Long- short-term memory, respectively, to achieve solutions in polynomial time. The developed ensemble models integrate multiple combinations of deep learning models and exploit the pre-trained models to provide realtime solutions with better prediction accuracy. The experimental findings, obtained using Python programming version 3.10.2, indicate that the proposed VESBELT-ECNN, VESBELT-EANN, and VESBELT-ELSTM systems outperform existing approaches in terms of user Quality of Experience (QoE) in the timeliness domain and energy savings.

关键词： computational task offloading Energy-efficient computing Quality of experience (QoE) Ensemble neural networks

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Green Computation offloading With DRL in Multi-Access Edge Computing

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2024年第11期35卷

作者： Yin, Changkui Mao, Yingchi Chen, Meng Rong, Yi Liu, Yinqiu He, Xiaoming Hohai Univ Coll Comp Sci & Software Engn Nanjing Peoples R China SHENZHEN URBAN TRANSPORT PLANNING CTR CO LTD Shenzhen Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Nanjing Univ Posts & Telecommun Coll Internet Things Nanjing Peoples R China

In multi-access edge computing (MEC), computational task offloading of mobile terminals (MT) is expected to provide the green applications with the restriction of energy consumption and service latency. Nevertheless, the diverse statuses of a range of edge servers and mobile terminals, along with the fluctuating offloading routes, present a challenge in the realm of computational task offloading. In order to bolster green applications, we present an innovative computational task offloading model as our initial approach. In particular, the nascent model is constrained by energy consumption and service latency considerations: (1) Smart mobile terminals with computational capabilities could serve as carriers;(2) The diverse computational and communication capacities of edge servers have the potential to enhance the offloading process;(3) The unpredictable routing paths of mobile terminals and edge servers could result in varied information transmissions. We then propose an improved deep reinforcement learning (DRL) algorithm named PS-DDPG with the prioritized experience replay (PER) and the stochastic weight averaging (SWA) mechanisms based on deep deterministic policy gradients (DDPG) to seek an optimal offloading mode, saving energy consumption. Next, we introduce an enhanced deep reinforcement learning (DRL) algorithm named PS-DDPG, incorporating the prioritized experience replay (PER) and stochastic weight averaging (SWA) techniques rooted in deep deterministic policy gradients (DDPG). This approach aims to identify an efficient offloading strategy, thereby reducing energy consumption. Fortunately, D4PG$$ {\mathrm{D}}<^>4\mathrm{PG} $$ algorithm is proposed for each MT, which is responsible for making decisions regarding task partition, channel allocation, and power transmission control. Our developed approach achieves the ultimate estimation of observed values and enhances memory via write operations. The replay buffer holds data from previous D$$ D $$ time slots

关键词： computational task offloading deep deterministic policy gradients (DDPG) multi-access edge computing

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offloading Deep Learning Powered Vision tasks From UAV to 5G Edge Server With Denoising

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第6期72卷 8035-8048页

作者： Ozer, Sedat Ilhan, Huseyin Enes Ozkanoglu, Mehmet Akif Cirpan, Hakan Ali Ozyegin Univ Dept Comp Sci Ozer Lab TR-34794 Istanbul Turkiye Istanbul Tech Univ Dept Elect & Telecommun Engn TR-06800 Istanbul Turkiye Ozyegin Univ Dept Comp Sci Ozer Lab TR-34467 Istanbul Turkiye Bilkent Univ Dept Comp Engn OzerLab Ankara Turkiye

offloading computationally heavy tasks from an unmanned aerial vehicle (UAV) to a remote server helps improve battery life and can help reduce resource requirements. Deep learning based state-of-the-art computer vision tasks, such as object segmentation and detection, are computationally heavy algorithms, requiring large memory and computing power. Many UAVs are using (pretrained) off-the-shelf versions of such algorithms. offloading such power-hungry algorithms to a remote server could help UAVs save power significantly. However, deep learning based algorithms are susceptible to noise, and a wireless communication system, by its nature, introduces noise to the original signal. When the signal represents an image, noise affects the image. There has not been much work studying the effect of the noise introduced by the communication system on pretrained deep networks. In this work, we first analyze how reliable it is to offload deep learning based computer vision tasks (including both object segmentation and detection) by focusing on the effect of various parameters of a 5G wireless communication system on the transmitted image and demonstrate how the introduced noise of the used 5G system reduces the performance of the offloaded deep learning task. Then solutions are introduced to eliminate (or reduce) the negative effect of the noise. Proposed framework starts with introducing many classical techniques as alternative solutions, and then introduces a novel deep learning based solution to denoise the given noisy input image. The performance of various denoising algorithms on offloading both object segmentation and object detection tasks are compared. Our proposed deep transformer-based denoiser algorithm (NR-Net) yields state-of-the-art results in our experiments.

关键词： Deep learning 5G computational task offloading object segmentation object detection image denoising intelligent communication edge computing noise-removing Net

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HNIO: A Hybrid Nature-Inspired Optimization Algorithm for Energy Minimization in UAV-Assisted Mobile Edge Computing

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第3期19卷 3264-3275页

作者： Chen, Yang Pi, Dechang Yang, Shengxiang Xu, Yue Chen, Junfu Mohamed, Ali Wagdy Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211100 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza 12613 Egypt Amer Univ Cairo Dept Math & Actuarial Sci Sch Sci Engn Cairo 11835 Egypt

Mobile edge computing (MEC) is an emerging computing paradigm that decreases the computing time and extends the lifespan of user equipments (UEs). In MEC, the computational tasks are offloaded from UEs to the base station (BS) at the edge of the network for processing. However, MEC cannot cope with environments where there are no BS or where communication facilities have been destroyed. In this paper, we study the problem of minimizing the energy consumption of UAV equipped with MEC servers as a mobile base station to serve users. The problem involves user offloading decision, UAV location and allocation with computational resources, and is a hybrid optimization problem with continuous and discrete variables. To address this problem, we propose a hybrid nature-inspired optimization algorithm (HNIO) and its version for discrete optimization, where HNIO incorporates mutation and population diversity detection mechanisms to boost its global optimization capability, and we design a probabilistic selection-based coding strategy for the discrete optimization version. The experimental study is conducted based on ten cases with different numbers of UEs. Comparing HNIO with several other state-of-the-art optimization algorithms, it is concluded from the Friedman and Wilcoxon's test of the experimental results that HNIO shows better precision and stability in nine out of the ten cases with higher number of UEs.

关键词： Optimization Autonomous aerial vehicles task analysis Statistics Sociology computational modeling Servers Mobile edge computing UAV nature-inspired algorithms computational task offloading discrete optimization convergence analysis

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Joint Optimization of computational tasks offloading for Efficient and Secure Manufacturing Through Cloud-Fog-Edge-Terminal Architecture

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computational INTELLIGENCE 2024年

作者： Liu, Zhifeng Lin, Zhiwen Zhang, Yueze Chen, Chuanhai Guo, Jinyan Qi, Baobao Tao, Fei Jilin Univ Key Lab CNC Equipment Reliabil Minist Educ Changchun 130025 Peoples R China Jilin Univ Jilin Prov Key Lab Adv Mfg & Intelligent Technol H Changchun 130025 Peoples R China Beijing Univ Technol Machinery Ind Key Lab Heavy Machine Tool Digital D Beijing 100124 Peoples R China Jilin Univ Sch Mech & Aerosp Engn Changchun 130025 Peoples R China Beijing Univ Technol Beijing Key Lab Adv Mfg Technol Beijing 100124 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

Within the realm of smart manufacturing, the growing complexity and scale of computational tasks expose the limitations of conventional computing architectures with rigid task allocation and fragmented collaboration, which impede the efficiency and security of smart manufacturing systems. To address these challenges, this paper presents an innovative cloud-fog-edge-terminal collaborative strategy that provides an adaptive and comprehensive offloading solution for tasks with complex dependencies and large scale, thereby enhancing the seamless collaborative potential of hierarchical computing structures. Additionally, a joint optimization mathematical model for collaborative computational offloading is developed, aiming to minimize task offloading time and assess manufacturing risk. To refine the solution, an advanced multi-objective optimization algorithm is formulated to identify the optimal solutions. The effectiveness and practical applicability of the proposed method are substantiated through simulation experiments and empirical case studies, demonstrating a performance enhancement of 12-29% over other benchmarks. The joint optimization method effectively synchronizes cloud-fog-edge-terminal computing resources, realizing efficient and secure task offloading and execution in smart manufacturing scenarios.

关键词： Manufacturing Optimization Collaboration computational modeling Computer architecture Cloud computing Smart manufacturing Security Edge computing Delays computational task offloading cloud-fog-edge-terminal architecture optimization of latency optimization of manufacturing risk joint optimization algorithm

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Queuing-based energy-efficient processing algorithm for smart transportation through V2V communication

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第23期36卷

作者： Mohammadi, Laya Khajehvand, Vahid Islamic Azad Univ Dept Comp & Informat Technol Engn Qazvin Branch Qazvin Iran

Applications of intelligent systems installed in vehicles require substantial computational processing for various tasks. These intensive computations result in high energy consumption and power demands within vehicles. computational offloading based on Vehicle-to-Vehicle (V2V) communication in vehicular fog computing (VFC) has been proposed as a promising solution to enhance energy efficiency in transportation applications. In this paper, the primary objective is addressing this concern by identifying the optimal nearby vehicle that minimizes energy consumption for the offloading and execution of computational tasks. Therefore, a decision-making and intelligent task offloading mechanism based on queueing theory is proposed. By modeling the problem environment based on queueing theory and modeling the behavior of distributed tasks with discrete-time Markov chain, the proposed solution can predict the future behavior of vehicles in selecting the most energy-efficient processing node. Therefore, this paper investigates three energy decision parameters based on queueing theory extracted from the Markov model to enhance the performance of the proposed algorithm. Experimental results demonstrate that the computational energy parameter achieves the most significant improvement. The proposed algorithm outperforms previous methods, improving energy-efficient system performance by 6.25% and 2.67%, and reducing delivery failure rate by 6.52% and 2.72%. It also decreases overall transportation system processing energy consumption by 0.05% for 100-500 vehicle arrival rates, resulting in an average total processing energy consumption of 0.48%.

关键词： computational energy efficiency computational task offloading intelligent decision-making and prediction intelligent transportation system queuing theory V2V communication

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