关键词： Federated learning   model allocation   mobile edge computing  

摘要： Federated Learning (FL) is believed as a promising manner of distributed machine learning for 5G and future 6G networks in the context of mobile edge computing (MEC). From the worker's viewpoint, a problem is that no incentive drives them to participate in FL. Thus, different from the conventional server-centric model allocation methods, we study the worker-centric strategy in this paper. Because communicating with the FL server and training an FL model locally are energy-hungry, a dilemma for each worker is how to make the tradeoff between participating in FL training and the volume restriction of its battery. To address this issue, we formulate a WorkerFirst problem. Its NP-hardness is also proved. Next, we devise a DDQN algorithm and a DQL algorithm to strive for near-optimal decisions for each worker. These two algorithms are proposed by leveraging the deep reinforcement learning framework, while considering the energy consumption, training timespan, and the communication overheads of workers, simultaneously. The benefit of adopting such a DRL-based decision-making framework is that workers can execute the algorithms locally and dynamically adapt to the varying MEC environment. The evaluation results show that the proposed DDQN and DQL algorithms can learn good policies without knowing any prior knowledge of network conditions.

关键词： industrial internet   mobile edge computing   federated learning   deep reinforcement learning   computing offloading   resource allocation  

摘要： Industrial Internet mobile edge computing (MEC) deploys edge servers near base stations to bring computing resources to the edge of industrial networks to meet the energy-saving requirements of Industrial Internet terminal devices. This paper considers a wireless MEC system in an intelligent factory that has multiple edge servers and mobile smart industrial terminal devices. In this paper, the terminal device has the choice of either offloading the task in whole or in part to the edge server, or performing it locally. Through combined optimization of the task offload ratio, number of subcarriers, transmission power, and computing frequency, the system can achieve minimum total energy consumption. A computing offloading and resource allocation approach that combines federated learning (FL) and deep reinforcement learning (DRL) is suggested to address the optimization problem. According to the simulation results, the proposed algorithm displays fast convergence. Compared with baseline algorithms, this algorithm has significant advantages in optimizing the performance of energy consumption.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) integrated with radio frequency-based wireless power transfer (WPT) has became a promising trend to shorten task delay and achieve sustainable operations of computing system, as it continuously provides energy to wireless devices (WDs) and allows computation tasks offloading to a powerful edge server nearby. Furthermore, introducing the relay technique to WPT-MEC system could improve offloading capability and energy efficiency, especially when the condition of the wireless channels between server and WDs is poor. In this paper, we seek to maximize the energy efficiency (EE) for a multiuser relay-aided MEC network powered by WPT, where binary offloading policy is followed in WDs and partial offloading policy is adopted in the relay. We aim to jointly optimize the relay’s computing configurations, wireless charge time fraction and WDs offloading strategy, that faces great challenge due to the strong coupling of multi-user computing mode selection and hybrid offloading model among WDs and relay. To tackle the problem, we formulated it as a mixed integer nonlinear programming (MINLP) problem and design an iterative algorithm based on Dinkelbach’s method and alternating direction method of multipliers (ADMM) decomposition technique to solve it. We first transform the original fractional programming problem into a more tractable problem in a subtraction form based on Dinkelbach’s method. Then according to ADMM technique, we decompose the multi-user offloading problem into multiple subproblems which can be solved in a distributed model. For an iterative calculating step, we propose a Bi-Search algorithm for CPU frequency and transmission power optimization and a DAI-Based algorithm to choose the time fraction of wireless power charging. The simulation results show that our proposed algorithm convergence fast and derive a 15% higher energy efficiency than other benchmark methods. © 2023, CC BY-NC-SA.

关键词： mobile edge computing   computation offloading   average delay   energy consumption  

摘要： With the proliferation of the Internet of Things (IoT) and the development of wireless communication technologies such as 5G, new types of services are emerging and mobile data traffic is growing exponentially. The mobile computing model has shifted from traditional cloud computing to mobile edge computing (MEC) to ensure QoS. The main feature of MEC is to "sink" network resources to the edge of the network to meet the needs of delay-sensitive and computation-intensive services, and to provide users with better services. Computation offloading is one of the major research issues in MEC. In this paper, we summarize the state of the art in task offloading in MEC. First, we introduce the basic concepts and typical application scenarios of MEC, and then we formulate the task offloading problem. In this paper, we analyze and summarize the state of research in the industry in terms of key technologies, schemes, scenarios, and objectives. Finally, we provide an outlook on the challenges and future research directions of computational offloading techniques and indicate the suggested direction of follow-up research work.

关键词： Mobile edge computing  

摘要： With the rapid development of artificial general intelligence (AGI), various multimedia services based on pretrained foundation models (PFMs) need to be effectively deployed. With edge servers that have cloud-level computing power, edge intelligence can extend the capabilities of AGI to mobile edge networks. However, compared with cloud data centers, resource-limited edge servers can only cache and execute a small number of PFMs, which typically consist of billions of parameters and require intensive computing power and GPU memory during inference. To address this challenge, in this paper, we propose a joint foundation model caching and inference framework that aims to balance the tradeoff among inference latency, accuracy, and resource consumption by managing cached PFMs and user requests efficiently during the provisioning of generative AI services. Specifically, considering the in-context learning ability of PFMs, a new metric named the Age of Context (AoC), is proposed to model the freshness and relevance between examples in past demonstrations and current service requests. Based on the AoC, we propose a least context caching algorithm to manage cached PFMs at edge servers with historical prompts and inference results. The numerical results demonstrate that the proposed algorithm can reduce system costs compared with existing baselines by effectively utilizing contextual information. © 2023, CC BY.

关键词： contract theory   resource allocation   computation offloading   service caching   heterogeneous networks   mobile edge computing  

摘要： An optimal method for resource allocation based on contract theory is proposed to improve energy utilization. In heterogeneous networks (HetNets), distributed heterogeneous network architectures are designed to balance different computing capacities, and MEC server gains are designed based on the amount of allocated computing tasks. An optimal function based on contract theory is developed to optimize the revenue gain of MEC servers while considering constraints such as service caching, computation offloading, and the number of resources allocated. As the objective function is a complex problem, it is solved utilizing equivalent transformations and variations of the reduced constraints. A greedy algorithm is applied to solve the optimal function. A comparative experiment on resource allocation is conducted, and energy utilization parameters are calculated to compare the effectiveness of the proposed algorithm and the main algorithm. The results show that the proposed incentive mechanism has a significant advantage in improving the utility of the MEC server.

关键词： Virtual reality   mobile edge computing   quality of experience   multimedia-assisted system  

摘要： In recent years, virtual reality (VR) has gradually entered the daily education and teaching activities from pure scientific research. In the area of assistance teaching, some typical computer softwares still play some important roles. This makes remote teaching activities can just learn voice, yet cannot possess the feeling of realistic existence. Especially in scenario of COVID-19, remote teaching activities with proper perceptibility are in urgent demand. To deal with the current challenge, this paper proposes a wireless VR-based multimedia-assisted teaching system framework under mobile edge computing networks. In this framework, cooperative edge caching and adaptive streaming based on viewport prediction are adopted to jointly improve the quality of experience (QoE) of VR users. First, we investigated the resource management problem of caching and adaptive streaming in this framework. Considering the complexity of the formulated problem, a distributed learning scheme is proposed to solve the problem. The experimental data are verified and the experimental results prove that the studied methods improve the performance of user QoE.

关键词： Mobile Edge Computing   LTE networks   Energy Efficient intercommunication   Base radio station   Power consumption capacity   Energy conservation  

摘要： The Mobile Edge Computing is a novel prototype that was developed recently due to the benefits and expanding nature of electronic data-processing techniques close to broadcasting networks. In this context nowadays the uses of wireless cellular facilities have been increased drastically in quantity of cellular users and estimation tasks of the subscribers may be offloaded to network interface for remote implementation. Therefore, required information, and hence power consumption capacity of the base radio station has enhanced substantially. Additionally, this increases the running cost of the total system and also causes global-warming. So, referring to the base radio station power consumption capacity in Long-Term Evolution (LTE) has been the main impediment for merchants to become eco-friendly and valuable in the competing mobile industry. It needs an innovative process to develop Energy Efficient intercommunication in LTE networks. Significance of this study has involved vast research and a global investigation process. The active energy source assignment, equal load sharing, carrier accumulation and bandgap enlargement is therefore categorized in groups and projected in this study for the methods of energy conservation. Every single procedure has unique advantages and drawbacks, which leads to compromise amongst conservation of energy and additional performance for measuring the problems of research design. This study focuses on the different energy conservation methods for the LTE networks and briefly examines their usefulness through a complete comparative analysis. With the gradually increasing number of wireless customers an optimization problem is employed here to assess the LTE system performance and Energy Consumption Rate.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) enables resource-limited IoT devices to complete computation-intensive or delay-sensitive task by offloading the task to adjacent edge server deployed at the base station (BS), thus becoming an important technology in 5G and beyond. Due to channel occlusion, some users may not be able to access the computation capability directly from the BS. Confronted with this issue, many other devices in the MEC system can serve as cooperative nodes to collect the tasks of these users and further forward them to the BS. In this paper, we study a MEC system in which multiple users continuously generate the tasks and offload the tasks to the BS through a cooperative node. As the tasks are continuously generated, users should simultaneously execute the task generation in the current time frame and the task offloading of the last time frame, i.e. the task is processed in a streaming model. To optimize the power consumption of the users and the cooperative node for finishing these streaming tasks, we investigate the duration of each step in finishing the tasks together with multiuser offloading ratio and bandwidth allocation within two cases: the BS has abundant computation capacity (Case I) and the BS has limited computation capacity (Case II). For both cases, the formulated optimization problems are nonconvex due to fractional structure of the objective function and complicated variable coupling. To overcome this challenge, in Case I, the objective function is transformed with Dinkelbach method. Then the multiuser offloading ratio and bandwidth allocation, the duration of each step are optimized separately, for which bisection search and interior-point method are utilized to reach the optimal solution. In case II, the problem is even more complex due to the constraint of the BS's computation capacity. Regarding this issue, convergent solution is searched levaraging difference of convex algorithm. Finally, simulation is carried out to verify the effective

关键词： Mobile edge computing  

摘要： The explosive development of the Internet of Things (IoT) has led to increased interest in mobile edge computing (MEC), which provides computational resources at network edges to accommodate computation-intensive and latency-sensitive applications. Intelligent reflecting surfaces (IRSs) have gained attention as a solution to overcome blockage problems during the offloading uplink transmission in MEC systems. This paper explores IRS-aided multi-cell networks that enable servers to serve neighboring cells and cooperate to handle resource exhaustion. We aim to minimize the joint energy and latency cost, by jointly optimizing computation tasks, edge computing resources, user beamforming, and IRS phase shifts. The problem is decomposed into two subproblems—the MEC subproblem and the IRS communication subproblem—using the block coordinate descent (BCD) technique. The MEC subproblem is reformulated as a nonconvex quadratic constrained problem (QCP), while the IRS communication subproblem is transformed into a weight-sum-rate problem with auxiliary variables. We propose an efficient algorithm to iteratively optimize MEC resources and IRS communication until convergence. Numerical results show that our algorithm outperforms benchmarks and that multi-cell MEC systems achieve additional performance gains when supported by IRS. Copyright © 2023, The Authors. All rights reserved.