In recent years, parked vehicle-assisted multi-access edge computing (PVMEC) has emerged to expand the computational power of MEC networks by utilizing the opportunistic resources of parked vehicles (PVs) for computat...
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In recent years, parked vehicle-assisted multi-access edge computing (PVMEC) has emerged to expand the computational power of MEC networks by utilizing the opportunistic resources of parked vehicles (PVs) for computationoffloading. In this article, we study a joint optimization problem of partialoffloading and resource allocation in a PVMEC paradigm that enables each mobile device (MD) to offload its task partially to either the MEC server or nearby PVs. The problem is first formulated as a mixed-integer nonlinear programming problem with the aim of maximizing the total offloading utility of all MDs in terms of the benefit of reducing latency through offloading and the overall cost of using computing and networking resources. We then propose a partialoffloading scheme, which employs a differentiation method to derive the optimal offloading ratio and resource allocation while optimizing the task assignment using a metaheuristic solution based on the whale optimization algorithm. Finally, evaluation results justify the superior system utility of our proposal compared with existing baselines.
The development of Industrial Internet of Things (IIoT) and Industry 4.0 has completely changed the traditional manufacturing industry. Intelligent IIoT technology usually involves a large number of intensive computin...
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The development of Industrial Internet of Things (IIoT) and Industry 4.0 has completely changed the traditional manufacturing industry. Intelligent IIoT technology usually involves a large number of intensive computing tasks. Resource-constrained IIoT devices often cannot meet the realtime requirements of these tasks. As a promising paradigm, the mobile-edge computing (MEC) system migrates the computation intensive tasks from resource-constrained IIoT devices to nearby MEC servers, thereby obtaining lower delay and energy consumption. However, considering the varying channel conditions as well as the distinct delay requirements for various computing tasks, it is challenging to coordinate the computing task offloading among multiple users. In this article, we propose an autonomous partialoffloading system for delay-sensitive computation tasks in multiuser IIoT MEC systems. Our goal is to provide offloading services with minimum delay for better Quality of Service (QoS). Enlighten by the recent advancement of reinforcement learning (RL), we propose two RL-based offloading strategies to automatically optimize the delay performance. Specifically, we first implement the Q-learning algorithm to provide a discrete partialoffloading decision. Then, to further optimize the system performance with more flexible task offloading, the offloading decisions are given as continuous based on deep deterministic policy gradient (DDPG). The simulation results show that the Q-learning scheme reduces the delay by 23%, and the DDPG scheme reduces the delay by 30%.
Mobile-edge computing (MEC) and wireless power transfer are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Inter...
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Mobile-edge computing (MEC) and wireless power transfer are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things. However, the computation performance and the harvested energy are significantly impacted by the severe propagation loss. In order to address this issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless-powered system is studied in this paper. The computation rate maximization problems in a UAV-enabled MEC wireless powered system are investigated under both partial and binary computationoffloading modes, subject to the energy-harvesting causal constraint and the UAV's speed constraint. These problems are non-convex and challenging to solve. A two-stage algorithm and a three-stage alternative algorithm are, respectively, proposed for solving the formulated problems. The closed-form expressions for the optimal central processing unit frequencies, user offloading time, and user transmit power are derived. The optimal selection scheme on whether users choose to locally compute or offload computation tasks is proposed for the binary computationoffloading mode. Simulation results show that our proposed resource allocation schemes outperform other benchmark schemes. The results also demonstrate that the proposed schemes converge fast and have low computational complexity.
Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. Resource allocation strategies for maximizing the computation efficiency are critically important. In this paper, computati...
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Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. Resource allocation strategies for maximizing the computation efficiency are critically important. In this paper, computation efficiency maximization problems are formulated in wireless-powered MEC networks under both partial and binary computationoffloading modes. A practical non-linear energy harvesting model is considered. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) are considered and evaluated for offloading. The energy harvesting time, the local computing frequency, and the offloading time and power are jointly optimized to maximize the computation efficiency under the max-min fairness criterion. Two iterative algorithms and two alternative optimization algorithms are respectively proposed to address the non-convex problems formulated in this paper. Simulation results show that the proposed resource allocation schemes outperform the benchmark schemes in terms of user fairness. Moreover, a tradeoff is elucidated between the achievable computation efficiency and the total number of computed bits. Furthermore, simulation results demonstrate that the partial computation offloading mode outperforms the binary computationoffloading mode and NOMA outperforms TDMA in terms of computation efficiency.
In order to improve the performance of the mobile edge computing (MEC) system, the intelligent reflecting surface (IRS) has recently been included. This paper investigates the computation performance of an IRS-aided M...
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In order to improve the performance of the mobile edge computing (MEC) system, the intelligent reflecting surface (IRS) has recently been included. This paper investigates the computation performance of an IRS-aided MEC system, where an access point services multi-MEC devices by utilizing two distributed IRSs to operate a partialoffloading strategy. Through collaboratively constructing the cooperative passive beamforming at the two IRSs, the central processing unit (CPU) frequency, the offloading time allocation, and the transmit power of users, an optimization problem is constructed to maximize the sum computation rate of the proposed system. To tackle this non-convex issue, the authors first illustrate that the design of passive beamforming is independent to optimize other variables and propose to solve it alternatively. Then the joint optimization problem of other parameters is proved to be a convex problem and is resolvable by adopting the Lagrange dual approach. Compared with the benchmark schemes, the simulation outcomes demonstrated that the performance of the proposed system is vastly improved by deploying two distributed IRSs.
Edge computing is an emerging distributed computing paradigm that reduces computation latency and energy consumption by offloading application tasks from user devices to near-edge servers for execution. In order to ut...
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ISBN:
(纸本)9798350310900
Edge computing is an emerging distributed computing paradigm that reduces computation latency and energy consumption by offloading application tasks from user devices to near-edge servers for execution. In order to utilize the computing resources of edge servers and increase efficiency, collaborative edge computing is proposed as a new type of edge computing method where multiple edge servers can work together to solve a task. By dividing a task into interrelated subtasks, each subtask can be processed locally at the device or offloaded to an edge server to minimize processing latency. In the paper, we propose a GNN-DRL-based offloading strategy that considers the edge servers' task attributes and network topology to make an optimal offloading decision for each application subtask. Experiments show that our proposed method performs better than state-of-the-art and baseline strategies in reducing the average latency for each task.
The limited battery capacity and low computing capability of wireless Internet of Things (IoT) devices can hardly support computation-intensive and delay-sensitive applications. While recent development of wireless po...
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The limited battery capacity and low computing capability of wireless Internet of Things (IoT) devices can hardly support computation-intensive and delay-sensitive applications. While recent development of wireless power transfer (WPT) and mobile edge computing (MEC) technologies help IoT devices harvest energy and offload computation tasks to edge servers. While it is still challenging to design an efficient offloading policy to improve the performance of the IoT network. In this article, we consider a MEC network that has WPT capability and adopts the non-orthogonal multiple access (NOMA) technology to offload tasks partially. Our goal is to propose an online algorithm to optimize resource allocation under a wireless dynamic channel scenario. In order to obtain the optimal offloading decision and resource allocation efficiently, we propose a Deep Reinforcement learning-based Online Sample-improving (DROS) framework which implements a deep neural network to input the discretized channel gains to obtain the optimal WPT duration. Based on the WPT duration derived by DNN, we design an optimization algorithm to derive the optimal energy proportion for offloading data. Numerical results verify that compared with traditional optimization algorithms, our proposed DROS has significantly sped up convergence for better solutions.
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