Mobile Edge Networks Research Articles

Optimizing Resource Allocation and Request Routing for AI-Generated Content (AIGC) Services in Mobile Edge Networks With Cell Coupling

Optimizing Resource Allocation and Request Routing for AI-Generated Content (AIGC) Services in Mobile Edge Networks With Cell Coupling

A Unified Framework for Guiding Generative AI With Wireless Perception in Resource Constrained Mobile Edge Networks

A Unified Framework for Guiding Generative AI With Wireless Perception in Resource Constrained Mobile Edge Networks

An Intelligent Reinforcement Learning–Based Method for Threat Detection in Mobile Edge Networks

An Intelligent Reinforcement Learning–Based Method for Threat Detection in Mobile Edge Networks

Dynamic and efficient resource allocation for 5G end‐to‐end network slicing: A multi‐agent deep reinforcement learning approach

SummaryThe rapid evolution of user equipment (UE) and 5G networks drives significant transformations, bringing technology closer to end‐users. Managing resources in densely crowded areas such as airports, train stations, and bus terminals poses challenges due to diverse user demands. Integrating mobile edge computing (MEC) and network function virtualization (NFV) becomes vital when the service provider's (SP) primary goal is maximizing profitability while maintaining service level agreement (SLA). Considering these challenges, our study addresses an online resource allocation problem in an MEC network where computing resources are limited, and the SP aims to boost profit by securely admitting more UE requests at each time slot. Each UE request arrival rate is unknown, and the requirement is specific resources with minimum cost and delay. The optimization problem objective is achieved by allocating resources to requests at the MEC network in appropriate cloudlets, utilizing abandoned instances, reutilizing idle and soft slice instances to shorten delay and reduce costs, and immediately scaling inappropriate instances, thus minimizing the instantiation of new instances. This paper proposes a deep reinforcement learning (DRL) method for request prediction and resource allocation to mitigate unnecessary resource waste. Simulation results demonstrate that the proposed approach effectively accepts network slice requests to maximize profit by leveraging resource availability, reutilizing instantiated resources, and upholding goodwill and SLA. Through extensive simulations, we show that our proposed DRL‐based approach outperforms other state‐of‐the‐art techniques, namely, MaxSR, DQN, and DDPG, by 76%, 33%, and 23%, respectively.

Adaptive spatio-temporal graph convolutional network with attention mechanism for mobile edge network traffic prediction

Adaptive spatio-temporal graph convolutional network with attention mechanism for mobile edge network traffic prediction

DDPG-based optimal task placement strategy for computation offloading in green mobile edge networks

DDPG-based optimal task placement strategy for computation offloading in green mobile edge networks

6G Cyber Physical System Based Smart Healthcare Modelling by Mobile Edge Network and Artificial Intelligence

6G Cyber Physical System Based Smart Healthcare Modelling by Mobile Edge Network and Artificial Intelligence

An online approach for cooperative cache updating and forwarding in mobile edge network

An online approach for cooperative cache updating and forwarding in mobile edge network

Bidirectional Service Function Chain Embedding for Interactive Applications in Mobile Edge networks

Bidirectional service function chain (BSFC) consists of multiple virtual network functions (VNFs). Through VNF deployment and link mapping, BSFCs can be embedded into resource-constrained mobile edge networks to provide low-latency network function services to users participating in interactive applications such as multi-player online games. Data from these users are routed through BSFCs to the edge node where the application is located for interaction and then returned to the users through the BSFCs, thus enabling synchronization among multiple users. However, the edge nodes or links have limited computing or bandwidth resources to serve only a fraction of users simultaneously. Therefore, the embedding decisions among different users can affect each other. In this paper, we propose a novel BSFC embedding strategy for interactive applications with the goal of minimizing computing and bandwidth resources while satisfying users' latency requirements. We first model the BSFC embedding problem as an integer nonlinear programming problem. Then, by closely examining the complexity of the problem, we propose a distributed algorithm based on game theory. We theoretically analyze the properties of the proposed algorithm and show that it can obtain a solution with a worst-case performance bound. Finally, extensive experiments show that the proposed algorithm outperforms several existing algorithms.

Asynchronous Privacy-Preservation Federated Learning Method for Mobile Edge Network in Industrial Internet of Things Ecosystem

The typical industrial Internet of Things (IIoT) network system relies on a real-time data upload for timely processing. However, the incidence of device heterogeneity, high network latency, or a malicious central server during transmission has a propensity for privacy leakage or loss of model accuracy. Federated learning comes in handy, as the edge server requires less time and enables local data processing to reduce the delay to the data upload. It allows neighboring edge nodes to share data while maintaining data privacy and confidentiality. However, this can be challenged by a network disruption making edge nodes or sensors go offline or experience an alteration in the learning process, thereby exposing the already transmitted model to a malicious server that eavesdrops on the channel, intercepts the model in transit, and gleans the information, evading the privacy of the model within the network. To mitigate this effect, this paper proposes asynchronous privacy-preservation federated learning for mobile edge networks in the IIoT ecosystem (APPFL-MEN) that incorporates the iteration model design update strategy (IMDUS) scheme, enabling the edge server to share more real-time model updates with online nodes and less data sharing with offline nodes, without exposing the privacy of the data to a malicious node or a hack. In addition, it adopts a double-weight modification strategy during communication between the edge node and the edge server or gateway for an enhanced model training process. Furthermore, it allows a convergence boosting process, resulting in a less error-prone, secured global model. The performance evaluation with numerical results shows good accuracy, efficiency, and lower bandwidth usage by APPFL-MEN while preserving model privacy compared to state-of-the-art methods.

A Novel SDN-Based Architecture of Task Offloading in Mobile Ad-Hoc Cloud

As the core function of mobile Ad-hoc cloud, task offloading has always been a research hotspot of mobile cloud computing, and the construction, offloading decision, task division and scheduling strategies of mobile Ad-hoc cloud directly affect the performance of offloading. In this paper, we propose a new mobile Ad-hoc cloud architecture based on SDN solving the performance bottleneck of opportunistic task offloading. The proposed solution constructs terminal clusters in Ad-hoc cloud to provide users with offloading service, and makes offloading decision and task allocation through SDN controller to achieve the effectiveness of task allocation and the rationality of resource scheduling. Simulation results show that compared with traditional scheduling method, our method has the advantages in offloading ratio and execution efficiency, and can be more effectively used in dynamic mobile edge network environment, to improve network performance and user service satisfaction.

Online Joint Optimization of Virtual Network Function Deployment and Trajectory Planning for Virtualized Service Provision in Multiple-Unmanned-Aerial-Vehicle Mobile-Edge Networks

The multiple-unmanned-aerial-vehicle (multi-UAV) mobile edge network is a promising networking paradigm that uses multiple resource-limited and trajectory-planned unmanned aerial vehicles (UAVs) as edge servers, upon which on-demand virtual network functions (VNFs) are deployed to provide low-delay virtualized network services for the requests of ground users (GUs), who often move randomly and have difficulty accessing the Internet. However, VNF deployment and UAV trajectory planning are both typical NP-complete problems, and the two operations have a strong coupling effect: they affect each other. Achieving optimal virtualized service provision (i.e., maximizing the number of accepted GU requests under a given period T while minimizing the energy consumption and the cost of accepting the requests in all UAVs) is a challenging issue. In this paper, we propose an improved online deep reinforcement learning (DRL) scheme to tackle this issue. First, we formulate the joint optimization of the two operations as a nonconvex mixed-integer nonlinear programming problem, which can be viewed as a sequence of one-frame joint VNF deployment and UAV-trajectory-planning optimization subproblems. Second, we propose an online DRL based on jointly optimizing discrete (VNF deployment) and continuous (UAV trajectory planning) actions to solve each subproblem, whose key idea is establishing and achieving the coupled influence of discrete and continuous actions. Finally, we evaluate the proposed scheme through extensive simulations, and the results demonstrate its effectiveness.

A novel user preference-aware content caching algorithm in mobile edge networks

A novel user preference-aware content caching algorithm in mobile edge networks

A mechanism for network resource allocation and task offloading in mobile edge computing and network engineering

AbstractAt present, most of the resource allocation methods in mobile edge computing allocate computing resources according to the time order in which task requests are calculated and unloaded, without considering the priority of tasks in practical applications. According to the computing requirements in such cases, a priority task‐oriented resource allocation method is proposed. According to the average processing time of the task execution, the corresponding priority for task is given. The tasks with different priorities are weighted to allocate computing resources, which not only ensures that the high‐priority tasks obtain sufficient computing resources, but also reduces the total time and energy consumption to complete the calculation of all tasks, thus improving the quality of service. The experimental results show that the proposed method can achieve better performance.

Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks

Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks

Optimized Federated Multitask Learning in Mobile Edge Networks: A Hybrid Client Selection and Model Aggregation Approach

Optimized Federated Multitask Learning in Mobile Edge Networks: A Hybrid Client Selection and Model Aggregation Approach

Mitigating Distributed DoS Attacks on Bandwidth Allocation for Federated Learning in Mobile Edge Networks

Mitigating Distributed DoS Attacks on Bandwidth Allocation for Federated Learning in Mobile Edge Networks

Scalable and Low-Latency Federated Learning With Cooperative Mobile Edge Networking

Federated learning (FL) enables collaborative model training without centralizing data. However, the traditional FL framework is cloud-based and suffers from high communication latency. On the other hand, the edge-based FL framework that relies on an edge server co-located with mobile base station for model aggregation has low communication latency but suffers from degraded model accuracy due to the limited coverage of edge server. In light of high-accuracy but high-latency cloud-based FL and low-latency but low-accuracy edge-based FL, this paper proposes a new FL framework based on cooperative mobile edge networking called cooperative federated edge learning (CFEL) to enable both high-accuracy and low-latency distributed intelligence at mobile edge networks. Considering the unique two-tier network architecture of CFEL, a novel federated optimization method dubbed cooperative edge-based federated averaging (CE-FedAvg) is further developed, wherein each edge server both coordinates collaborative model training among the devices within its own coverage and cooperates with other edge servers to learn a shared global model through decentralized consensus. Experimental results based on benchmark datasets show that CFEL can largely reduce the training time to achieve a target model accuracy compared with prior FL frameworks.

Bridge the Present and Future: A Cross-Layer Matching Game in Dynamic Cloud-Aided Mobile Edge Networks

Bridge the Present and Future: A Cross-Layer Matching Game in Dynamic Cloud-Aided Mobile Edge Networks

A decision-making mechanism for task offloading using learning automata and deep learning in mobile edge networks

The development of mobile networks has led to the emergence of challenges such as high delays in storage, computing and traffic management. To deal with these challenges, fifth-generation networks emphasize the use of technologies such as mobile cloud computing and mobile edge computing. Mobile Edge Cloud Computing (MECC) is an emerging distributed computing model that provides access to cloud computing services at the edge of the network and near mobile users. With offloading tasks at the edge of the network instead of transferring them to a remote cloud, MECC can realize flexibility and real-time processing. During computation offloading, the requirements of Internet of Things (IoT) applications may change at different stages, which is ignored in existing works. With this motivation, we propose a task offloading method under dynamic resource requirements during the use of IoT applications, which focuses on the problem of workload fluctuations. The proposed method uses a learning automata-based offload decision-maker to offload requests to the edge layer. An auto-scaling strategy is then developed using a long short-term memory network which can estimate the expected number of future requests. Finally, an Asynchronous Advantage Actor-Critic algorithm as a deep reinforcement learning-based approach decides to scale down or scale up. The effectiveness of the proposed method has been confirmed through extensive experiments using the iFogSim simulator. The numerical results show that the proposed method has better scalability and performance in terms of delay and energy consumption than the existing state-of-the-art methods.