关键词： Mobile edge computing  

摘要： Over the past few years, many efforts have been dedicated to studying cyberbullying in social edge computing devices, and most of them focus on three roles: victims, perpetrators, and bystanders. If we want to obtain a deep insight into the formation, evolution, and intervention of cyberbullying in devices at the edge of the Internet, it is necessary to explore more fine-grained roles. This paper presents a multi-level method for role feature modeling and proposes a differential evolution-assisted K-means (DEK) method to identify diverse roles. Our work aims to provide a role identification scheme for cyberbullying scenarios for social edge computing environments to alleviate the general safety issues that cyberbullying brings. The experiments on ten real-world datasets obtained from Weibo and five public datasets show that the proposed DEK outperforms the existing approaches on the method level. After clustering, we obtained nine roles and analyzed the characteristics of each role and their evolution trends under different cyberbullying scenarios. Our work in this paper can be placed in devices at the edge of the Internet, leading to better real-time identification performance and adapting to the broad geographic location and high mobility of mobile devices. © 2024, CC BY.

关键词： Mobile edge computing  

摘要： Recently, the integration of mobile edge computing (MEC) and generative artificial intelligence (GAI) technology has given rise to a new area called mobile edge generation and computing (MEGC), which offers mobile users heterogeneous services such as task computing and content generation. In this letter, we investigate the joint communication, computation, and the AIGC resource allocation problem in an MEGC system. A latency minimization problem is first formulated to enhance the quality of service for mobile users. Due to the strong coupling of the optimization variables, we propose a new deep reinforcement learning-based algorithm to solve it efficiently, Numerical results demonstrate that the proposed algorithm can achieve lower latency than several baseline algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning   Cybertwin   Mobile edge computing   IoT   CNN   LSTM   Transfer learning   Workload offloading  

摘要： Cybertwin (CT) is an innovative network structure that digitally simulates humans and items in a virtual environment, significantly influencing Cybertwin instances more than regular VMs. Cybertwin-driven networks, combined with Mobile Edge Computing (MEC), provide practical options for transmitting IoT-enabled data. This research introduces a hybrid methodology integrating deep learning with Cybertwin-driven resource allocation to enhance energy-efficient workload offloading and resource management in MEC networks. Offloading work is essential in MEC networks since several applications require significant resources. The Cybertwin-driven approach considers user mobility, virtualization, processing power, load migrations, and resource demand as crucial elements in the decision-making process for offloading. The model optimizes job allocation between on-premises and distant execution using a task-offloading strategy to reduce the operating burden on the MEC network. The model uses a hybrid partitioning approach and a cost function to optimize resource allocation efficiently. This cost function accounts for energy consumption and service delays associated with job assignment, execution, and fulfilment. The model calculates the cost of several segmentation and offloading procedures and chooses the lowest cost to enhance energy efficiency and performance. The approach employs a deep learning architecture called "CNN-LSTM-TL" to accomplish energy-efficient task offloading, utilizing pre-trained transfer learning models. Batch normalization is used to speed up model training and improve its robustness. The model is trained and assessed using an extensive mobile edge computing public dataset. The experimental findings confirm the efficacy of the proposed methodology, indicating a 20% decrease in energy usage compared to conventional methods while achieving comparable or superior performance levels. Simulation studies emphasize the advantages of incorporating Cybertwin-driven

关键词： Mobile edge computing   Secure offloading   Dynamic pricing   Resource allocation   Homomorphic encryption  

摘要： With the development of sixth-generation (6G) wireless communication technology, billions of vehicles will access the network in the future, and the number of vehicle applications and user data will also increase dramatically. Traditional cloud computing faces serious problems of latency and energy consumption in handling massive data. Mobile edge computing (MEC) has emerged to dramatically improve computing efficiency and reduce energy consumption by placing computing servers at network edge locations close to vehicles. However, the service range of MEC servers is limited and cannot fully satisfy user requirements. In addition, the task offloading process has security risks. The current research focuses on how to reduce the energy consumption and latency overhead of task offloading and neglects the economic cost or data transmission security of vehicles. To solve the above problems, we propose a cooperative security offloading (CSO) scheme for auxiliary vehicles and MEC servers. Firstly, we propose a dynamic pricing mechanism for computing resources by considering the credibility of MEC servers and auxiliary vehicles, the urgency of the task, and the number of users competing for auxiliary vehicles. Secondly, to prevent malicious MEC servers and eavesdroppers from attacking, we employ homomorphic encryption to protect user privacy. Meanwhile, efficient and secure computing services are achieved by optimizing user selection decisions, offloading decisions, and resource allocation decisions. Finally, the optimal decisions are obtained by the dueling DQN-based resource allocation and pricing strategy (DDRP) and the cost-minimizing security offloading (CMSO) algorithm, which minimizes the economic cost of users while maximizing security. Simulation results show that, compared with some existing schemes, the CSO scheme effectively reduces the economic cost of users while ensuring the security of data transmission.

关键词： Mobile edge computing   content caching   system average cost   deep reinforcement learning   collaborative mechanism  

摘要： Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as *** an MEC environment,servers are deployed closer to mobile terminals to exploit storage infrastructure,improve content delivery efficiency,and enhance user ***,due to the limited capacity of edge servers,it remains a significant challenge to meet the changing,time-varying,and customized needs for highly diversified content of ***,techniques for caching content at the edge are becoming popular for addressing the above *** is capable of filling the communication gap between the users and content providers while relieving pressure on remote cloud ***,existing static caching strategies are still inefficient in handling the dynamics of the time-varying popularity of content and meeting users’demands for highly diversified entity *** address this challenge,we introduce a novel method for content caching over MEC,i.e.,*** synthesizes a content popularity prediction model,which takes users’stay time and their request traces as inputs,and a deep reinforcement learning model for yielding dynamic caching *** results demonstrate that PRIME,when tested upon the MovieLens 1M dataset for user request patterns and the Shanghai Telecom dataset for user mobility,outperforms its peers in terms of cache hit rates,transmission latency,and system cost.

关键词： Integrated sensing and communication   Mobile edge computing   Resource allocation   Reinforcement learning  

摘要： The combination of integrated sensing and communication(ISAC)with mobile edge computing(MEC)enhances the overall safety and efficiency for vehicle to everything(V2X)***,existing works have not considered the potential impacts on base station(BS)sensing performance when users offload their computational tasks via *** could leave insufficient resources allocated to the sensing tasks,resulting in low sensing *** address this issue,we propose a cooperative power,bandwidth and computation resource allocation(RA)scheme in this paper,maximizing the overall utility of Cram
er-Rao bound(CRB)for sensing accuracy,computation latency for processing sensing information,and communication and computation latency for computational *** solve the RA problem,a twin delayed deep deterministic policy gradient(TD3)algorithm is adopted to explore and obtain the effective solution of the RA ***,we investigate the performance tradeoff between sensing accuracy and summation of communication latency and computation latency for computational tasks,as well as the relationship between computation latency for processing sensing information and that of computational tasks by numerical *** demonstrates that compared to other benchmark methods,TD3 achieves an average utility improvement of 97.11%and 27.90%in terms of the maximum summation of communication latency and computation latency for computational tasks and improves 3.60 and 1.04 times regarding the maximum computation latency for processing sensing information.

关键词： caching   cost   Internet of Things   mobile edge computing   non-orthogonal multiple access  

摘要： Mobile edge computing(MEC)is a promising paradigm by deploying edge servers(nodes)with computation and storage capacity close to IoT *** Providers can cache data in edge servers and provide services for IoT devices,which effectively reduces the delay for acquiring *** the increasing number of IoT devices requesting for services,the spectrum resources are generally *** order to effectively meet the challenge of limited spectrum resources,the Non-Orthogonal Multiple Access(NOMA)is proposed to improve the transmission *** this paper,we consider the caching scenario in a NOMA-enabled MEC *** the devices compete for the limited resources and tend to minimize their own *** formulate the caching problem,and the goal is to minimize the delay cost for each individual device subject to resource *** reformulate the optimization as a non-cooperative game *** prove the existence of Nash equilibrium(NE)solution in the game ***,we design the Game-based Cost-Efficient Edge Caching Algorithm(GCECA)to solve the *** effectiveness of our GCECA algorithm is validated by both parameter analysis and comparison experiments.

关键词： mobile edge computing   mobility   service migration   deep reinforcement learning   deep deterministic policy gradient  

摘要： Mobile Edge Computing(MEC)is a promising *** service migration is a key technology in *** order to maintain the continuity of services in a dynamic environment,mobile users need to migrate tasks between multiple servers in real *** to the uncertainty of movement,frequent migration will increase delays and costs and non-migration will lead to service ***,it is very challenging to design an optimal migration *** this paper,we investigate the multi-user task migration problem in a dynamic environment and minimizes the average service delay while meeting the migration *** order to optimize the service delay and migration cost,we propose an adaptive weight deep deterministic policy gradient(AWDDPG)*** distributed execution and centralized training are adopted to solve the high-dimensional *** show that the proposed algorithm can greatly reduce the migration cost and service delay compared with the other related algorithms.

关键词： Cloud Computing   Energy-aware   KubeEdge   Mobile Edge Computing   Monitoring  

摘要： In recent years, edge computing has acquired a significant interest in developing and deploying applications by bringing computational resources close to the data source, improving the quality of application experience, and optimizing system resource usage. Edge node deployment strategies in existing studies suppose that once edge nodes are registered in an edge computing system, they will permanently operate with high availability. However, deploying the orchestration solutions in edge computing environments is challenging because of the mobility of edge nodes (mobile nodes) and low availability due to energy limitations and network unstable. To solve this problem, we propose a standalone management structure for deploying mobile nodes in an edge-cloud computing environment that enables mobility support in a Kubernetes-based Edge-Cloud infrastructure. We develop self-managed mobile edge nodes constructed as standalone nodes, ensuring workloads continue operating even when the connection between the mobile node and the cloud is interrupted. Additionally, we consider an energy-aware deployment strategy through energy monitoring and workload scaling schemes at the self-managed mobile node. In this paper, we also implement the proposed architecture on the infrastructure built and established by OpenStack and KubeEdge open-source projects. Measurements show the feasibility of the proposed architecture in deploying mobile nodes that operate independently. © 2024, Korean Institute of Communications and Information Sciences. All rights reserved.

关键词： Cloud Computing   Mobile Edge Computing   Optimization   Scheduling Algorithms  

摘要： Multi-access edge computing has the ability to provide high bandwidth, and low latency, ensuring high efficiency in performing network operations and thus, it seems to be promising in the technical field. MEC allows processing and analysis of data at the network edges but it has finite number of resources which can be used. To overcome this restriction, a scheduling algorithm can be used by an orchestrator to deliver high quality services by choosing when and where each process should be executed. The scheduling algorithm must meet the expected outcome by utilizing lesser number of resources. This paper provides a scheduling algorithm containing two cooperative levels with an orchestrator layer acting at the center. The first level schedules local processes on the MEC servers and the next layer represents the orchestrator and allocates processes to nearby stations or cloud. Depending on latency and throughput, the processes are executed according to their priority. A resource optimization algorithm has also been proposed for extra performance. This offers a cost-efficient solution which provides good service availability. The proposed algorithm has a balanced wait time (Avg) and blocking percentage (Avg) of 2.37ms and 0.4 respectively. The blocking percentage is 1.65 times better than Shortest Job First Scheduling (SJFS) and 1.3 times better than Earliest Deadline First Scheduling (EDFS). The optimization algorithm can work on many kinds of network traffic models such as uniformly distributed and base stations with unbalanced loads. © 2024, Modern Education and Computer Science Press. All rights reserved.