关键词： Virtual reality   Adaptive transmission   Edge cache   Buffer management   5G   Mobile edge computing  

摘要： Virtual Reality(VR) is a key industry for the development of the digital economy in the *** VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream implementation of *** this paper,a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge Computing(MEC)-equipped 5G networks is proposed,aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive *** support VR content proactive caching and intelligent buffer management,users' behavioral similarity and head movement trajectory are jointly used for viewpoint *** tile-based content is proactively cached in the MEC nodes based on the popularity of the VR ***,a hierarchical buffer-based adaptive update algorithm is presented,which jointly considers bandwidth,buffer,and predicted viewpoint status to update the tile chunk in client ***,according to the decomposition of the problem,the buffer update problem is modeled as an optimization problem,and the corresponding solution algorithms are ***,the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations,and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.

关键词： Internet of things   Mobile edge computing   Quality of service   Quality of service evaluation   Quality of service optimization  

摘要： With the evolvement of the Internet of things(IoT), mobile edge computing(MEC) has emerged as a promising computing paradigm to support IoT data analysis and processing. In MEC for IoT, the differentiated requirements on quality of service(QoS) have been growing rapidly, making QoS a multi-dimensional concept including several attributes, such as performance, dependability, energy efficiency, and economic factors. To guarantee the QoS of IoT applications, theories and techniques of multi-dimensional QoS evaluation and optimization have become important theoretical foundations and supporting technologies for the research and application of MEC for IoT,which have attracted significant attention from both academia and industry. This paper aims to survey the existing studies on multi-dimensional QoS evaluation and optimization of MEC for IoT, and provide insights and guidance for future research in this field. This paper summarizes the multi-dimensional and multi-attribute QoS metrics in Io T scenarios, and then several QoS evaluation methods are presented. For QoS optimization, the main research problems in this field are summarized, and optimization models as well as their corresponding solutions are elaborated. We take notice of the booming of edge intelligence in artificial intelligence-empowered Io T scenarios, and illustrate the new research topics and the state-of-the-art approaches related to QoS evaluation and optimization. We discuss the challenges and future research directions.

关键词： Internet of Vehicles   mobile edge computing   resource allocation   reinforcement learning   task offloading  

摘要： Edge computing plays a crucial role in the field of the Internet of Vehicles (IoV), meeting the resource and latency requirements of time-sensitive vehicle applications. However, the emergence of numerous compute-intensive and latency-sensitive applications, such as augmented reality and autonomous driving, has led to a situation where traditional edge computing architectures cannot meet the increasing application demands of the IoV. This paper extends the paradigm of vehicular edge computing to a collaborative cloud-edge cluster resource provisioning framework. Integrating compute resources from multiple Edge Service Providers (ESP) and the cloud enables horizontal and vertical collaborative computation offloading among service nodes. To facilitate resource sharing among different ESPs, we introduce a dynamic pricing model and utilize software-defined networking (SDN) to tackle this scenario's complex resource management challenges. Furthermore, with the optimization objectives of minimizing task computation latency and maximizing the profits of ESPs, we establish a mathematical model. Before resource allocation, we employ a clustering algorithm to determine initial offloading decisions, reducing the dimensionality of the action space. Subsequently, we employ the Double Deep Q-Network (DDQN) algorithm to achieve a rational allocation of compute resources. Simulation results demonstrate that compared to the Deep Q-Network (DQN) algorithm and greedy strategy, the proposed approach reduces latency by 18.18% and 34.85%, respectively, while increasing the profits of edge service providers by 16.25% and 33.33%, respectively.

关键词： Blockchain   Mobile Edge Computing   Multi-Armed Bandits   Task-offloading  

摘要： As mobile devices proliferate and mobile applications diversify, Mobile Edge Computing (MEC) has become widely adopted to efficiently allocate computing resources at the network edge and alleviate network congestion. In the MEC initial phase, the absence of vital information presents challenges in devising task-offloading policies, and identifying malicious devices responsible for providing inaccurate feedback is complex. To fill in such gaps, we introduce a consortium blockchain-enabled Committee Voting based Task Offloading Model (CVTOM) to collaboratively formulate resource allocation policies and establish deterrence against malicious servers producing erroneous results intentionally. Different voting principle mechanisms of each committee member are first designed in a Blockchain-enabled system which helps to represent the system's resource status. Additionally, we propose a Multi-Armed Bandits related Thompson Sampling based Adaptive Preference Optimization (TSAPO) algorithm for task-offloading policy, enhancing the timely identification of potent edge servers to improve computing resource utilization which first considers dynamic edge server space and parallel computing scenarios. The solid proof process greatly contributes to the theoretical analysis of the TSAPO. The simulation experiments demonstrate the delay and budget can be reduced by around 25% and 10% respectively, showcasing the superior performance of our approach. © 2002-2012 IEEE.

关键词： Mobile edge computing  

摘要： Edge Intelligence (EI) integrates Edge Computing (EC) and Artificial Intelligence (AI) to push the capabilities of AI to the network edge for real-time, efficient and secure intelligent decision-making and computation. However, EI faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EI in the eyes of stakeholders. This survey comprehensively summarizes the characteristics, architecture, technologies, and solutions of trustworthy EI. Specifically, we first emphasize the need for trustworthy EI in the context of the trend toward large models. We then provide an initial definition of trustworthy EI, explore its key characteristics and give a multi-layered architecture for trustworthy EI. Then, we present enabling technologies for trustworthy EI systems. Subsequently, we summarize several important issues that hinder the achievement of trustworthy EI and provide an in-depth literature review of the state-of-the-art solutions for realizing the trustworthiness of EI. Finally, we discuss the corresponding research challenges and open issues. IEEE

关键词： Mobile edge computing  

摘要： Motivated by the drawbacks of cloud-based federated learning (FL), cooperative federated edge learning (CFEL) has been proposed to improve efficiency for FL over mobile edge networks, where multiple edge servers collaboratively coordinate the distributed model training across a large number of edge devices. However, CFEL faces critical challenges arising from dynamic and heterogeneous device properties, which slow down the convergence and increase resource consumption. This paper proposes a heterogeneity-aware CFEL scheme called Heterogeneity-Aware Cooperative Edge-based Federated Averaging (HCEF) that aims to maximize the model accuracy while minimizing the training time and energy consumption via adaptive computation and communication compression in CFEL. By theoretically analyzing how local update frequency and gradient compression affect the convergence error bound in CFEL, we develop an efficient online control algorithm for HCEF to dynamically determine local update frequencies and compression ratios for heterogeneous devices. Experimental results show that compared with prior schemes, the proposed HCEF scheme can maintain higher model accuracy while reducing training latency and improving energy efficiency simultaneously. © 2002-2012 IEEE.

关键词： Mobile edge computing  

摘要： With the rapid development and application of driverless technology, the number and location of vehicles, the channel and bandwidth of wireless network are time-varying, which leads to the increase of offloading delay and energy consumption of existing algorithms. To solve this problem, the vehicle terminal task offloading decision problem is modelled as a Markov decision process, and a task offloading algorithm based on DDQN is proposed. In order to guide agents to quickly select optimal strategies, this paper proposes an offloading mechanism based on task feature. In order to solve the problem that the processing delay of some edge server tasks exceeds the upper limit of their delay, a task scheduling mechanism based on buffer delay is proposed. Simulation results show that the proposed method has greater performance advantages in reducing delay and energy consumption compared with existing algorithms. © 2024 Inderscience Enterprises Ltd.

关键词： Mobile edge computing  

摘要： In recent decades, the rising aging population has increased the importance of fall detection (FD) systems as assistive technology. To enhance accuracy, deep learning (DL) techniques are widely applied to these systems. A typical FD system involves placing edge devices (EDs) on individuals to collect real-time data, which is transmitted to a cloud center (CC) or processed by the ED itself. However, this architecture faces challenges such as limited ED model size and data transmission latency to the CC. Mobile edge computing (MEC) has been explored to overcome these challenges, which allows computations at MEC servers deployed between EDs and the CC. A multilayer mobile edge computing (MLMEC) framework is proposed to manage the tradeoff between accuracy and latency. MLMEC divides the architecture into multiple stations, each equipped with a neural network model. If the front-end equipment lacks deterministic detection capability, data are transmitted to a station with robust back-end computing for detection. The knowledge distillation (KD) approach is employed to improve front-end detection accuracy. The KD approach allows high-power back-end stations to provide additional learning experiences, enhancing precision and reducing latency and processing load. Simulation results demonstrate improved accuracy with the KD approach (11.65% average improvement for the SisFall dataset and 2.78% for the FallAllD dataset). The proposed MLMEC with the KD approach outperforms without the KD approach regarding data latency rate by 54.15% for the FallAllD dataset and 46.67% for the SisFall dataset. In summary, the MLMEC FD system exhibits improved accuracy and reduced latency. © 2024 IEEE.

关键词： Deep reinforcement learning   Function parallelism   Mobile edge computing   Network function virtualization  

摘要： This paper introduces a Deep Reinforcement Learning (DRL)-based framework to enhance Network Function Parallelism (NFP) in Mobile Edge Computing (MEC). Leveraging Network Function Virtualization (NFV), the proposed framework optimizes service delay by solving a fairness-aware throughput maximization problem for service function chain placement. It aims to maximize the long-term cumulative reward while satisfying Quality of Service (QoS) requirements. The framework also preserves resources for future requests by efficiently managing the initialized network functions distribution. Simulation results demonstrate the superior performance of the proposed framework across various metrics. Specifically, our framework improves the average delay and deployment rate by 1.2% and 2.4% compared to the existing best method. © 2024 The Author(s)

关键词： Mobile edge computing  

摘要： The integration of energy harvesting capabilities into mobile edge computing (MEC) edge servers enables their deployment beyond the reach of electrical grids, expanding MEC services to isolated regions and geographically challenging terrains. However, the fluctuating nature of renewable energy sources, such as solar and wind, necessitates dynamic management of server computing power in response to variable energy harvesting rates. Unlike conventional models that assume predetermined amounts of harvested energy per time period, this study illustrates the complex interdependencies between server power consumption and variable energy harvesting rates due to battery charging characteristics. To address this, we introduce a novel energy harvesting model that comprehensively accounts for the interaction between computing power management and energy harvesting rates. We develop both offline and online offline optimal computing power management strategies aimed at maximizing the average computational capacity of edge servers. An analytical solution to the resulting nonlinear optimization problem is provided to determine the optimal computing power configurations. Simulation results indicate that the proposed strategy effectively balances energy harvesting rates and energy utilization, thereby enhancing computational performance in dynamic energy environments. © 2002-2012 IEEE.