关键词： Mobile edge computing  

摘要： The combination of non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) has recently received great attention for maximizing energy efficiency (EE) in wireless networks. Previous studies mainly focus on single-ratio EE maximization using pure orthogonal multiple access (OMA) or NOMA scheme in MEC networks. This paper proposes a dynamic hybrid NOMA scheme that dynamically transforms between pure OMA and pure NOMA under the general network status in the multi-user MEC network. An offloading multi-ratio and non-convex EE problem is formulated. Two iterative algorithms, i.e., the successive Dinkelbach method-based algorithm and the closed-form nested fractional programming (FP) algorithm, are proposed to efficiently solve the offloading EE over multiple users' time and power within polynomial time based on different FP techniques, which provides feasible ideas to solve such multi-ratio optimization problems. The Dinkelbach method-based algorithm decomposes and optimizes the offloading EE user-wise. The nested FP algorithm jointly maximizes multiple users' EE by transforming the multi-ratio problem into equivalent linear forms, where the closed-form power solution is obtained. Simulation results show the superiority of the dynamic hybrid NOMA strategy and the feasibility of the two proposed algorithms. Critical insights are obtained over the dynamic hybrid NOMA strategies in the general MEC network. © 2002-2012 IEEE.

关键词： Mobile edge computing  

摘要： In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%. © 2025 IEEE. All rights reserved.

关键词： Feature extraction   Internet of Things   Data mining   Servers   Vectors   Eigenvalues and eigenfunctions   Multi-access edge computing   Cloud computing   Bandwidth   Performance evaluation   Mobile edge computing   feature extraction  

摘要： Extracting discriminative features of data on Internet of Things (IoT) devices can reduce the amount of data uploaded by IoT devices to edge/cloud servers, thereby reducing the response time, which has attracted widespread attention from industry and academia. However, many existing related approaches have the following two limitations: (i) extracting a large number of redundant features, resulting in a waste of bandwidth resources, and (ii) extracting harmful features, resulting in low performance. To this end, this paper proposes a feature extractor generation algorithm for extracting non-redundant features, namely the Nor algorithm. The Nor algorithm analyzes existing discriminative feature extractors and finds that they contain a large number of redundant vectors. It further analyzes the locations where the redundant vectors appear and proposes how to remove them. The goal of this paper is to reduce the amount of data uploaded by IoT devices to the cloud/edge server by removing redundant vectors of the feature extractor, thereby reducing the data transmission time and the time it takes for the cloud/edge server to process features, and improving image recognition accuracy. Experimental results show that the Nor algorithm can effectively remove redundant vectors in the feature extractor and reduce the amount of feature data uploaded by IoT devices to the cloud/edge server. For example, in the Yale dataset, we can reduce the amount of feature data that needs to be uploaded to the edge server by 68.8% compared to the WAPL algorithm.

关键词： Mobile edge computing  

摘要： In recent years, unmanned aerial vehicles (UAVs) equipped with edge servers have emerged to expand service coverage. However, their inherent limitations in bandwidth, computational capacity, and energy resources restrict their ability to meet the demands of computation-intensive applications. To overcome these challenges, an air-ground collaborative mobile edge computing (MEC) network with enhanced resources is employed to improve Quality of Service (QoS). This network can provide support for a wide range of real-time applications where the computed results are critical. We adopt Age of Processing (AoP) as a metric to measure the freshness of processed results. This study investigates AoP-aware task offloading and resource allocation in a multi-UAV-assisted MEC system, optimizing the trade-off between user-experienced AoP and UAV energy consumption through joint design of user-UAV association, resource allocation, offloading ratios, and transmit power. To solve the formulated non-convex problem, we employ a cooperative Heterogeneous-Agent Proximal Policy Optimization (HAPPO) approach, utilizing a sequential policy update strategy within a centralized training, decentralized execution framework to mitigate inefficiencies from overlapping UAV coverage. Simulation results demonstrate that the proposed approach can achieve better performance compared with the other approaches. © 1967-2012 IEEE.

关键词： Mobile edge computing  

摘要： With the proliferation of mobile devices, the demand for high-quality mobile services characterized by low latency and high continuity continues to rise. Ensuring the quality of these services is essential to providing intelligent interconnections among individuals, machines, and devices. In the current mobile network landscape, the adoption of network function virtualization (NFV)-based edge-cloud paradigm places network resources in proximity to end-clients, resulting in a substantial reduction in service delays and enhanced efficiency of mobile service management. Nevertheless, the widely decentralized nature of the edge-cloud environment, combined with the stringent quality of service (QoS) demands of delay-sensitive services in dynamic mobile situations, presents a formidable challenge to the deployment of service function chains (SFC). This paper explores a joint deployment and migration of SFCs for mobility-aware services within an NFV-based edge-cloud environment. We formulate a multistage decision-making problem to tackle dynamic mobility patterns of mobile services, aiming to minimize long-term deployment and migration costs, average service latency, while satisfying diverse QoS constraints and adhering to the physical resource constraints of the edge-cloud environment. To tackle the multistage dynamic SFC deployment challenge outlined above, we introduce a deep reinforcement learning (DRL)-based online algorithm. This algorithm autonomously detects variations in the extensively distributed and intricate edge-cloud environment. It generates online deployment solutions without human intervention, facilitating adaptive and rapid service provisioning. Additionally, the algorithm supports the migration of mobile services, ensuring the QoS for high-mobility service requests with low-latency characteristics. Comprehensive experimental results showcase the superiority of our proposed scheme over competing approaches, exhibiting notable advantages in end-to-end la

关键词： Mobile edge computing  

摘要： Under a dramatic increase in mobile data traffic, a promising solution for edge computing systems to maintain their local service is the task migration that may be implemented by means of Autonomous mobile agents (AMA). In designing an optimal scheme for task offloading to AMA, we define a system cost as a minimization objective function that comprises two parts. First, an offloading cost which can be interpreted as the cost of using computational resources from the AMA. Second, a penalty cost due to potential task expiration. To minimize the expected (time-average) cost over a given time horizon, we formulate a Dynamic programming (DP). However, the DP Equation suffers from the well-known curse of dimensionality, which makes computations intractable, especially for infinite system state space. To reduce the computational burden, we identify three important properties of the optimal policy and show that it suffices to evaluate the DP Equation on a finite subset of the state space only. We then prove that the optimal task offloading decision at a state can be inferred from that at its adjacent states, further reducing the computational load. We present simulations to verify the theoretical results and to provide insights into the considered system. © 2025 IEEE.

关键词： Mobile edge computing  

摘要： The unprecedented prosperity of the Industrial Internet of Things (IIoT) has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multitask processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an improved two-stage multiobjective evolutionary algorithm (IT-MOEA). IT-MOEA employs a novel multiobjective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multiobjective algorithms by 41.0% in terms of delay and energy consumption. © 2014 IEEE.

关键词： Mobile edge computing  

摘要： Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can expand the coverage of mobile edge computing (MEC) services by reflecting and transmitting signals simultaneously, enabling full-space coverage. The orientation of the STAR-RIS plays a crucial role in optimizing the gain of received and transmitted signals, and a rotatable STAR-RIS offers potential enhancement for MEC systems. This article investigates a rotatable STAR-RIS-assisted MEC system, operated under three protocols, namely energy splitting (ES), mode switching, and time switching. The goal is to minimize energy consumption for multiple moving user devices (UDs) through the joint optimization of STAR-RIS configurations, orientation, computation resource allocation, transmission power, and task offloading strategies. Considering the mobility of UDs, we model the original optimization problem as a sequential decision-making process across multiple time slots. The high-dimensional, highly coupled, and nonlinear nature makes it a challenging nonconvex decision-making problem for traditional optimization algorithms. Therefore, a deep reinforcement learning (DRL) approach is employed, specifically utilizing soft actor-critic algorithm to train the DRL model. Simulation results demonstrate that the proposed algorithm outperforms the benchmarks in both convergence speed and energy efficiency, while reducing energy consumption by up to 52.7% compared to the fixed STAR-RIS scheme. Among three operating protocols, the ES yields the best performance. © 2014 IEEE.

关键词： Mobile edge computing  

摘要： Effective load balancing is critical in contemporary mobile computing environments to ensure optimal resource utilization and adherence to strict deadlines. This study presents a novel framework for deadline-constrained load balancing, termed DCLD-net, which leverages the integration of Graph Convolutional Networks (GCN) and an attention-based Bidirectional Gated Recurrent Unit (BiGRU). The proposed framework utilizes the spatial modeling capabilities of GCNs to capture intricate network topologies, while the temporal dynamics and attention mechanisms of BiGRU enable the identification of critical patterns in traffic flow data. By incorporating key factors such as sticky sessions, load distribution, content-based factors such as videos or file downloads, instance health checks, and other relevant parameters, the DCLD-net model achieves high accuracy in predicting task schedules. These schedules allocate tasks to virtual machines (VMs), enabling dynamic traffic routing through a load balancer that efficiently assigns tasks. Extensive experimental evaluations demonstrate that the proposed attention-based BiGRU-GCN model significantly outperforms traditional methods in both deadline compliance and load balancing efficiency, offering a robust solution to the challenges of modern cloud computing. Experimental results demonstrate that DCLD-net outperforms traditional methods by 27% in deadline compliance and 29.3% in load balancing efficiency. © 1975-2011 IEEE.

关键词： Mobile edge computing  

摘要： Task offloading, as an effective solution, provides low latency and sufficient computing resources for mobile users in the network. However, how to reasonably offload to reduce system overhead is a challenging issue today. This article takes user terminals, edge servers, and idle vehicles with resources as the network structure, and is inspired by the highly social nature of the gray wolf pack. It proposes a new offloading method of edge computing task based on hunting optimizing mechanism of gray wolf for the Internet of Vehicle (IOV). Firstly, an adaptive weight factor is proposed to balance the weight ratio of delay and energy consumption in the system cost under the constraints of delay and energy consumption. With delay and computing resources of vehicles and servers as constraints, a multi constraint minimization system cost problem is proposed. Secondly, the hunting process of the gray Wolf optimization algorithm is used to find the optimal solution of the unloading scheme, The Levy flight strategy was added to enhance the global search ability of the algorithm, and a dynamic weight strategy was introduced to improve the convergence performance of the algorithm. Finally, the improved gray Wolf optimization algorithm was used to solve the optimal unloading plan and minimum cost. The simulation results show that compared with traditional gray Wolf optimization algorithm offloading schemes, the proposed scheme in this paper requires lower system costs. © 2004-2012 IEEE.