关键词： Mobile edge computing  

摘要： As the 5G era develops quickly, there has been a significant growth in the amount of network data. Concurrently, traditional routing algorithms are encountering growing challenges. Traditional routing algorithms typically rely on maintaining stable end-to-end paths to facilitate smooth data transmission. However, in wireless multi-hop networks, the mobility of nodes and the dynamic nature of network topology make maintaining stable end-to-end paths exceedingly difficult, if not impossible. Consequently, data transmission efficiency decreases while network overhead increases. To address these challenges, opportunistic routing has emerged. Opportunistic routing is self-organizing networks that rely on chance encounters between nodes to transmit messages, and the store-and-carry forwarding technique is used for data transmission between nodes. Typically, most traditional opportunistic routing algorithms divide nodes into communities. However, such approaches often overlook node characteristics, potentially leading to disparate nodes being assigned to separate communities despite positive correlations. Thus, accurately dividing communities remains a significant challenge. To address this, we propose the Efficient Community Detection Data Transmitting (ECDDT) algorithm. During community division, ECDDT combines social attributes and network topology using mobile edge computing. Subsequently, to expedite task execution, we schedule these tasks in a reasonable manner. Through simulation experiments across four diverse real datasets, we evaluate the algorithm's efficacy. Our findings demonstrate favorable performance in delivery rate and routing overhead. © 2024, The Authors. All rights reserved.

关键词： ant colony system   energy consumption   mobile edge computing   resource allocation   task offloading  

摘要： As a burgeoning paradigm, collaborative mobile edge computing (C-MEC) can cater to growing computation demand of mobile devices (MDs). However, there are great challenges for joint task offloading and resource allocation. In addition, failures on both MDs and edge servers greatly affect reliable task execution. This paper investigates the joint optimization problem of offloading decision, power allocation, and computation resource allocation in the multi-user C-MEC system, where nondivisible tasks may be executed locally, offloaded to nearby collaborative devices, or processed by the edge server. We aim at minimizing the total energy consumption of MDs while satisfying the reliability and delay constraints, and the replication technique is adopted to enhance task execution reliability. To tackle this problem, we first transform it into a bi-level optimization problem, and then propose an iterative algorithm named JOC. Specifically, the offloading decision is constructed in the upper level on the basis of ant colony system (ACS), and the allocation of working power, transmission power, and computation resources is optimized in the lower level by using the monotonic optimization approach. Simulation experimental results reveal that the proposed algorithm saves more energy and achieves a higher task success rate in comparison with baseline schemes.

关键词： Computational offloading problem   energy   mobile edge computing   multimedia computation offloading algorithm for 5G network   volcano eruption Algorithm  

摘要： The communication services of multimedia, such as live online and short videos, gained significant attention and have been prominent in daily social interactions due to the fast development of 5G technology. However, the increase in multimedia communication requirements presents further difficulties for both the communication capacity of the wireless network and its processing capability. Mobile Edge Computing (MEC) is considered an effective technology to overcome the mentioned challenges. The subject of long-term task allocation including resource coordination is the primary focus of this study, with a special emphasis on multimedia services that must be processed, uploaded, and shared in network. The optimization issue is formulated as a stochastic optimization issue to reduce the system's time-average energy consumption. An online energy-efficient task allocation and computing offloading method is proposed to determine task allocation, coordinate and optimize wireless, computing resource allocation, considering dynamic wireless state and service delay constraints. The proposed method is implemented in MATLAB and the efficacy of the MCO-VEA framework is estimated and performance metrics, such as minimum user rate, sum rate, energy, and spectral performance are analyzed. Then, the performance of MEC-MCO-VEA provides 22.35%, 25.84% and 36.58% higher throughput and 25.45%, 41.28%, and 28.56% higher energy efficiency compared to the existing models, such as an energy-effective multimedia task assignment with computing offloading for mobile edge computing (BA-LO-EE), energy efficiency using a communication deep neural network optimized with power allocation optimization (EE-CDNN-PAO), and energy efficiency using deep transfer deterministic policy gradient with deep deterministic policy gradient algorithm (DT-DPG-EE), respectively.

关键词： Multi-agent deep reinforcement learning   LEO satellite   Computation offloading   Resource allocation   Mobile edge computing  

摘要： Due to the limitations caused by geographical conditions and economic requirements, it is difficult to provide computing services by terrestrial networks for mobile terminals in remote areas. To address this issue, mobile edge computing (MEC) servers can be deployed in the low earth orbit (LEO) satellites to act as a complement and accommodate the unserved terminals. However, offloading computing tasks to servers in satellites may increase the energy consumption of ground terminals. Considering the limited battery capacity of ground terminals, how to perform the computation offloading and resource allocation are key challenges in the LEO satellite edge computing networks. Therefore, in this paper, we investigate the energy minimization problem for LEO satellite edge computing networks, where a multi-agent deep reinforcement learning algorithm with global rewards is proposed to optimize the transmit power, CPU frequency, bit allocation, offloading decision and bandwidth allocation via a decentralized method. Simulation results show that our proposed algorithm can converge faster. Most importantly, compared with the random algorithm, the proximal policy optimization (PPO) algorithm, and the deep deterministic policy gradient (DDPG) algorithm, the ground terminals' energy consumption can be effectively reduced by our proposed algorithm.

关键词： Dynamic decision-making   Dynamic optimization algorithm   Energy efficient offloading   Genetic algorithm   Hill climbing   Mobile cloud computing   Mobile edge computing   Mobile energy consumption   PSO   Scalability analysis   Statistical analysis   Task offloading   Workload analysis  

摘要： The rapid evolution of mobile devices has greatly advanced secure medical image transmission, yet challenges persist due to resource limitations and security concerns inherent to these devices. In response, this paper introduces a Dynamic Energy-Efficient Offloading Algorithm (DEEO), seamlessly integrated into the Mobile Edge-Cloud Computing (MECC) environment. DEEO empowers mobile devices to efficiently offload computationally intensive secure image transmission tasks to the nearest edge server or fog access point. This integration optimizes resource utilization, minimizes energy consumption, and ensures the confidentiality and integrity of sensitive medical image data. Through rigorous evaluations and comparative analysis, our approach demonstrates clear superiority over existing solutions. This integrated framework is poised to significantly enhance healthcare applications, offering heightened efficiency, elevated security, and an overall improved user experience. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： deep learning   mobile edge computing   user admission control   resource scheduling  

摘要： It is widely recognized that the future wireless networks are able to efficiently slice heterogeneous resources to provide customized services for various use cases. However, it is challenging to meet the diverse requirements of ever-growing applications, especially the stringent requirements of numerous delay-sensitive and/or computation-intensive applications. To tackle this challenge, we should not only consider user admission control to cope with resource limitations, but also make resource management more intelligent and flexible to meet diverse service needs. Taking advantages of mobile edge computing(MEC)and network slicing, in this paper, we propose deep edge slicing(DES),to jointly optimize user admission control and resource scheduling with the aim of minimizing the system cost while guaranteeing multitudinous quality-of-service (QoS) requirements. Specifically, we first apply a deep reinforcement learning approach to select the optimal set of access users with different service requests for maximizing resource *** a deep learning algorithm is employed to predict traffic data for allocating the communication and computing resources to different slices in advance. Finally, we realize the dynamic scheduling of heterogeneous resources by solving the optimization problem of minimizing the system cost. Simulation results demonstrate that DES can greatly reduce the system cost compared to other benchmarks.

关键词： Mobile edge computing   multi-user-multi-edge   joint optimization   Gini coefficient   adaptive genetic algorithm  

摘要： To support the explosive growth of Information and Communications Technology(ICT),Mobile Edge Comput-ing(MEC)provides users with low latency and high bandwidth service by offloading computational tasks to the network’s ***,resource-constrained mobile devices still suffer from a capacity mismatch when faced with latency-sensitive and compute-intensive emerging *** address the difficulty of running computationally intensive applications on resource-constrained clients,a model of the computation offloading problem in a network consisting of multiple mobile users and edge cloud servers is studied in this *** a user benefit function EoU(Experience of Users)is proposed jointly considering energy consumption and time *** EoU maximization problem is decomposed into two steps,i.e.,resource allocation and offloading *** offloading decision is usually given by heuristic algorithms which are often faced with the challenge of slow convergence and poor ***,a combined offloading algorithm,i.e.,a Gini coefficient-based adaptive genetic algorithm(GCAGA),is proposed to alleviate the *** proposed algorithm optimizes the offloading decision by maximizing EoU and accelerates the convergence with the Gini *** simulation compares the proposed algorithm with the genetic algorithm(GA)and adaptive genetic algorithm(AGA).Experiment results show that the Gini coefficient and the adaptive heuristic operators can accelerate the convergence speed,and the proposed algorithm performs better in terms of convergence while obtaining higher *** simulation code of the proposed algorithm is available:https://***/Grox888/Mobile_Edge_Computing/tree/GCAGA.

关键词： Delays   Task analysis   Artificial neural networks   Resource management   Training   Inference algorithms   Heuristic algorithms   Mobile edge computing   inference delay   dynamic neural network   training cost  

摘要： Mobile edge computing (MEC) can be used to reduce the task delay for users with limited computing resources. However, in 6G networks, the diversity of tasks is greatly increased. For those extremely delay-sensitive small-size computing tasks, the inference delay of neural network (NN)-based algorithms such as resource allocation and task offloading cannot be ignored. As a hyperparameter, the inference cost of NN is usually difficult to adjust. Dynamic neural network (DyNN) is an emerging technique that improves the model efficiency by adjusting the network architecture on-demand according to the sample characteristics during inference. In this paper, we propose a DyNN-based resource management method for MEC that dynamically adjusts the depth and width of the NN according to the features of the task, improving computational efficiency and achieving a balance between inference delay and the management performance of computational and communication resources. Furthermore, to reduce the training cost of DyNN, a new training method is proposed in this paper, where all the blocks in DyNN are gradually trained in the order of size. Simulation results demonstrate that the proposed DyNN-based resource management method outperforms the traditional optimization algorithm and the static-NN-based method.

关键词： Task analysis   Resource management   Field programmable gate arrays   Approximation algorithms   Wireless communication   Servers   Reinforcement learning   Heterogeneous edge environments   latency minimization   mobile edge computing  

摘要： Mobile edge computing has emerged as a prevalent computing paradigm to support applications that demand low latency and high computational capacity. Hardware reconfigurable accelerators exhibit high energy efficiency and low latency compared to general-purpose servers, making them ideal for integration into mobile edge computing systems. This article investigates the problem of joint task offloading, access point selection, and resource allocation in heterogeneous edge environments for latency minimization. Given the heterogeneity of edge computing devices and the interdependence of the decisions required for offloading, access point selection, and resource allocation, it is challenging to optimize over them simultaneously. We decomposed the proposed problem into two disjoint subproblems and developed algorithms for each of them. The first subproblem is to jointly determine access point selection and communication resource allocation decisions, for which we have proposed an algorithm with a provable approximation ratio of 2.62/(1-8 lambda), where lambda is a tunable parameter balancing the approximation ratio and time complexity. Additionally, we offer a faster variant of the algorithm with an approximation ratio of (root 3+1)(2). The second subproblem is to determine offloading and computing resource allocation decisions jointly and is NP-hard, where we developed algorithms based on relaxation and rounding. We conducted comprehensive numerical simulations to evaluate the proposed algorithms, and the results demonstrated that our algorithms outperformed existing baselines and achieved near-optimal performance across various settings.

关键词： Mobile edge computing   unmanned aerial vehicle   reinforcement learning   Markov decision process (MDP)   Mobile edge computing   unmanned aerial vehicle   reinforcement learning   Markov decision process (MDP)  

摘要： Mobile edge computing (MEC), located at the network's edge, enhances distributed computing. However, its fixed position presents limitations during emergencies. Integrating unmanned aerial vehicles (UAVs) into MEC systems offers a solution but introduces challenges in managing UAV collaboration. This paper proposes a Reinforcement Deep Q-Learning based multi-UAV MEC framework to optimize quality of service (QoS) and route planning. The proposed framework addresses these challenges by modeling user demand and using multi-factor optimization considering user demand, risk, and distance. A Markov Decision Process (MDP) models user demand for higher QoS. The reinforcement learning reward matrix incorporates terminal user demand, risk, and distance for efficient energy use and resource allocation. Simulations demonstrate the effectiveness of our proposed method, offering valuable insights for future research in this domain.