关键词： computation offloading   hierarchical demand-weighted index   mobile edge computing   multiobjective optimization   simultaneous wireless information and power transfer  

摘要： Mobile devices are affected by limited energy and channel resources in the dynamic scenarios with high mobility and complexity, which will trigger the risks such as task failure or low offloading efficiency. This article proposes a multiuser and multiserver MEC network framework based on simultaneous wireless information and power transfer (a.k.a., SWIPT). We first consider multiple mobile devices with fixed task information in which the tasks can be either processed in-local or offloaded to the MEC server for processing via uplink transmission. Our method also embraces a hierarchical demand-weighted index (HDWI) and priority channel transmission scheduling rule, which can evaluate the status of device services and effectively conduct the hierarchical offloading of tasks. In this case, our model not only ensures the continuity of the service provided by mobile devices but also evaluates the relationship between energy consumption and device delay during the offloading process. Finally, we propose an efficient mobile device cost hierarchical offloading algorithm (MCHOA) to deal with the issues produced by the constructed multiobjective optimization mathematical model. MCHOA complies with the principle of HDWI and is combined with a multiobjective evolutionary algorithm based on decomposition to solve various mathematical tasks including obtaining the Pareto optimal curve regarding the average time consumption and the average energy consumption of devices. The experimental results show that MCHOA can simultaneously reduce time consumption and energy consumption costs by at least 13.3% and 37.5%, respectively. Our experiments also validate the superiority of the proposed algorithm and the application prospect of the model.

关键词： mobile edge computing   For-Hire Vehicle   task offloading   multi-objective optimization   vehicle path planning   DQN  

摘要： As autonomous driving and in-vehicle applications develop rapidly, vehicles face significant computing challenges. In order to address this issue, vehicle edge computing (VEC) has emerged. In this paper, we study the issue of time delay and energy consumption for task offloading in VEC systems. Considering varying computing resources and changes in vehicle position due to vehicle mobility, we integrate offloading decisions with resource allocation. The problem is summarized as minimizing the delay and energy consumption of completing vehicle tasks. To solve the multi-objective optimization solution, we propose an improved multi-objective particle swarm optimization algorithm (IMOPSO). At first, we initialize particles by entropy weight method and Roulette Wheel Selection. Then, we introduce the transition probability to control the particles' exploration in the search space for optimal solutions. Extensive simulation results demonstrate that the IMOPSO algorithm is superior to other alternative algorithms.

关键词： mobile edge computing   location prediction   service dynamic allocation   service deployment cost  

摘要： With the rapid deployment of edge base stations and the widespread application of 5G technology, Mobile Edge Computing (MEC)has gradually transitioned from a theoretical concept to practical implementation, playing a key role in emerging human-machine interactions and innovative mobile applications. In the MEC environment, efficiently allocating services, effectively utilizing edge device resources, and ensuring timely service responses have become critical research topics. Existing studies often treat MEC service allocation as an offline strategy, where the real-time location of users is used as input, and static optimization is applied. However, this approach overlooks dynamic factors such as user mobility. To address this limitation, this paper constructs a model based on constraints, optimization objectives, and server connection methods, determines experimental parameters and evaluation metrics, and sets up an experimental framework. We propose an Edge Location Prediction Model (ELPM) suitable for the MEC scenario, which integrates Spatial-Temporal Graph Neural Networks and attention mechanisms. By leveraging attention parameters, ELPM acquires spatio-temporal adaptive weights, enabling accurate location predictions. We also design an improved service allocation strategy, MESDA, based on the Gray Wolf Optimization (GWO) algorithm. MESDA dynamically adjusts its exploration and exploitation components, and introduces a random factor to enhance the algorithm's ability to determine the direction during later stages. To validate the effectiveness of the proposed methods, we conduct multiple controlled experiments focusing on both location prediction models and service allocation algorithms. The results show that, compared to the baseline methods, our approach achieves improvements of 2.56%, 5.29%, and 2.16% in terms of the average user connection to edge servers, average service deployment cost, and average service allocation execution time, respectively, demonstrat

关键词： mobile edge computing   invalid action masking   deep reinforcement learning   hierarchical reinforcement learning   UAV  

摘要： With the rapid growth of IoT data and increasing demands for low-latency computation, UAV-assisted mobile edge computing (MEC) offers a flexible solution to overcome the limitations of fixed MEC servers. To better reflect concurrent service scenarios, this paper innovatively develops a multi-channel task modeling method, enabling UAVs to simultaneously select multiple users and offload tasks via separate channels, thereby breaking the constraints of traditional sequential assumptions. To address task conflicts and resource waste caused by sequential service assumptions, this paper proposes a hierarchical reinforcement learning-based trajectory and offloading decision-making framework (H-TAOD) for UAVs with multi-channel parallel processing. The framework decouples UAV trajectory planning and task offloading into two sub-tasks optimized via appropriate reinforcement learning methods. An invalid action masking (IAM) mechanism is introduced to avoid channel conflicts. Simulation results verify the superiority of H-TAOD in reward, delay, and convergence.

关键词： mobile edge computing   energy harvesting   Lyapunov optimization   deep reinforcement learning   Q-learning   A3C algorithm  

摘要： Mobile edge computing (MEC) systems empowered by energy harvesting (EH) significantly enhance sustainable computing capabilities for mobile devices (MDs). This paper investigates a multi-user multi-server MEC network, in which energy-constrained users dynamically harvest ambient energy to flexibly allocate resources among local computation, task offloading, or intentional task discarding. We formulate a stochastic optimization problem aiming to minimize the time-averaged weighted sum of execution delay, energy consumption, and task discard penalty. To address the energy causality constraints and temporal coupling effects, we develop a Lyapunov optimization-based drift-plus-penalty framework that decomposes the long-term optimization into sequential per-time-slot subproblems. Furthermore, to overcome the curse of dimensionality in high-dimensional action, we propose hierarchical deep reinforcement learning (DRL) solutions incorporating both Q-learning with experience replay and asynchronous advantage actor-critic (A3C) architectures. Extensive simulations demonstrate that our DRL-driven approach achieves lower costs compared with conventional model predictive control methods, while maintaining robust performance under stochastic energy arrivals and channel variations.

关键词： Mobile edge computing   Task offloading   Cloud computing   Salp swarm algorithm   Mobile application offloading  

摘要： Background The realization of computation-intensive applications such as real-time video processing, virtual/augmented reality, and face recognition becomes possible for mobile devices with the latest advances in communication technologies. This application requires complex computation for better user experience and real-time decision-making. However, the Internet of Things (IoT) and mobile devices have computational power and limited energy. Executing these computational-intensive tasks on edge devices may result in high energy consumption or high computation latency. In recent times, mobile edge computing (MEC) has been used and modernized for offloading this complex task. In MEC, IoT devices transmit their tasks to edge servers, which consecutively carry out faster computation. Methods However, several IoT devices and edge servers put an upper limit on executing concurrent tasks. Furthermore, implementing a smaller size task (1 KB) over an edge server leads to improved energy consumption. Thus, there is a need to have an optimum range for task offloading so that the energy consumption and response time will be minimal. The evolutionary algorithm is the best for resolving the multiobjective task. Energy, memory, and delay reduction together with the detection of the offloading task is the multiobjective to achieve. Therefore, this study presents an improved salp swarm algorithm-based Mobile Application Offloading Algorithm (ISSA-MAOA) technique for MEC. Results This technique harnesses the optimization capabilities of the improved salp swarm algorithm (ISSA) to intelligently allocate computing tasks between mobile devices and the cloud, aiming to concurrently minimize energy consumption, and memory usage, and reduce task completion delays. Through the proposed ISSA-MAOA, the study endeavors to contribute to the enhancement of mobile cloud computing (MCC) frameworks, providing a more efficient and sustainable solution for offloading tasks in mobile applications. Th

关键词： Mobile edge computing   Artificial intelligence   Machine learning   Quality of experience   Energy efficiency  

摘要： Mobile Edge Computing (MEC) systems face critical challenges in optimizing computation offloading decisions while maintaining quality of experience (QoE) and energy efficiency, particularly in dynamic multi-user environments. This paper introduces a novel Adaptive AI-enhanced offloading (AAEO) framework that uniquely integrates three complementary AI approaches: deep reinforcement learning for real-time decision-making, evolutionary algorithms for global optimization, and federated learning for distributed knowledge sharing. The key innovation lies in our hybrid architecture's ability to dynamically adjust offloading strategies based on real-time network conditions, user mobility patterns, and application requirements, addressing limitations of existing single-algorithm solutions. Through extensive MATLAB simulations with 50-200 mobile users and 4-10 edge servers, our framework demonstrates superior performance compared to state-of-the-art methods. The AAEO framework achieves up to a 35% improvement in QoE and a 40% reduction in energy consumption, while maintaining stable task completion times with only a 12% increase under maximum user load. The system's security analysis yields a 98% threat detection rate, with response times under 100 ms. Meanwhile, reliability metrics indicate a 99.8% task completion rate and a mean time to failure of 1,200 h. These results validate the proposed hybrid AI approach's effectiveness in addressing the complex challenges of next-generation MEC systems, particularly in heterogeneous environments requiring real-time adaptation.

关键词： 5G network task offloading   Computation offloading   genetic algorithm   mobile edge computing   multi-objective optimization   particle swarm optimizationalgorithm  

摘要： Mobile Edge Computing (MEC) is considered one of the enabling and promising technologies in 5G networks, especially with the massive data movement of various devices and the increased demand for computing. Here, computational offloading of tasks to edge clouds provides an effective, flexible, low-latency solution for mobile users in the network. However, the limited computing resources in edge clouds and the dynamic demands of mobile users make it difficult to schedule computing requests to appropriate edge clouds, and make the offloading process energetically expensive for devices. Therefore, it is very important to design an energy-efficient offloading strategy. To this end, we first formulate the transmission power allocation (PA) problem for mobile phone users to minimize power consumption. Using a quasi-convex technique, we address the (PA) problem using a (Hybrid GAPSO) algorithm resulting from combining the Genetic Algorithm (GA) with the Particle Swarm Optimization (PSO) algorithm. Next, we model the joint request offloading and resource scheduling (JRORS) problem as a mixed-nonlinear program to minimize the response delay of requests. The (JRORS) problem can be divided into two problems, namely the request offloading (RO) problem and the computing resource scheduling (RS) problem. Therefore, we analyze the JRORS problem as a dual decision problem and propose a multi-objective particle swarm optimization algorithm referred as (MO-PSO). The simulation results show that (HGAPSO) can save transmission power consumption and has good convergence property, and (MO-PSO) outperforms existing methods in terms of response rate and can maintain good performance in a dynamic network. © 2025, Semarak Ilmu Publishing. All rights reserved.

关键词： Mobile edge computing   Task offloading   Double deep networks   Predictive mobility   Resource optimization  

摘要： In Mobile Edge Computing (MEC), the exponential growth of connected devices and user mobility presents significant challenges in optimizing task offloading, reducing latency, and energy usage. Predictive and adaptive task offloading mechanisms are essential as devices become more mobile and generate demanding tasks. Current methods, such as local computing and random scheduling, struggle to efficiently manage resources and maintain Quality of Service (QoS) in dynamic environments. This paper proposes an optimized Geographic Distributed Edge Layering (GDEL) architecture integrated with Double Deep Q-Networks (DDQN) to enable predictive, mobility-aware offloading. Our model leverages reinforcement learning through a Markov Decision Process (MDP) framework to dynamically allocate resources across distributed edge nodes, making optimal decisions on whether to offload or process tasks locally based on real-time conditions. Simulations show that our model outperforms other methods in key performance metrics, reducing task completion time by up to 48 %, lowering offloading decision latency by 49.3 %, and decreasing energy consumption by 26.5 % compared to traditional models.

关键词： Federated learning   Low Earth orbit satellite   Mobile edge computing   Deep reinforcement learning   Computation offloading  

摘要： Recent studies have shown that system capacity is very important for cellular *** this paper,we consider maximizing the weighted sum-rate of the cellular network downlink and uplink,where each cell consists of a full-duplex(FD)base station(BS)and half-duplex(HD)*** learning(FL)can train models in the absence of centralized data,which can achieve privacy protection of user data.A low Earth orbit(LEO)satellite edge computing system(LSECS)can be formed by placing the mobile edge computing(MEC)servers on LEO satellites,which greatly increases the processing capacities of the ***,we consider a combination of FL and MEC and propose an FL-based computation offloading algorithm to maximize the weighted sum-rate while ensuring the security of user *** consider solving the sub-channel assignment and power allocation problems using deep reinforcement learning(DRL)algorithms with excellent global search *** simulation results show that our proposed algorithm achieves the maximum weighted sum-rate compared with the baseline algorithms and excellent convergence.