关键词： Cloud computing   ensemble learning and resource scheduling   mobile edge computing  

摘要： An edge cloud architecture plays a key role in improving the user task computing service system by combining the powerful data processing capability of cloud centres with the low latency of edge computing. Existing methods for maximizing the efficiency of an edge cloud architecture take into account time and task parameters but ignore other factors such as load balancing, cost, and user satisfaction when scheduling resources. In this work, we propose a many-objective resource scheduling model for optimizing the performance of an edge cloud architecture, which takes into account the time spent on task, cost, load balance, user satisfaction, and trust measurement. The resource scheduling model converges to the optimal solution using a novel many-objective ensemble optimization algorithm based on a dynamic selection mechanism. The study also explores the support set convergence of eight evolutionary operators using the ensemble algorithm. The model solutions are dynamically updated with the help of the dynamic integration probability, and then a selection criteria is used to pick the best solutions from the pool of generated solutions. Two simulations on a benchmark dataset are used to verify the usefulness and performance of the designed algorithm. Our approach was able to locate more than half of the best solutions on the benchmark functions, and it also showed to be a better model solution than the some of the popular many-objective algorithms for dealing with the edge cloud resource scheduling problem, according to the results obtained from the simulations.

关键词： mobile edge computing   wireless sensor networks   semantic communications   attention mechanism   joint source channel coding   deep neural network  

摘要： Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost-effectiveness, and ease of deployment. The rapid advancement of 5G technology and mobile edge computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices. However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. In response to the aforementioned challenges, this paper proposes a semantic communication solution. Specifically, considering the limited computational and storage resources of WSN devices, we propose a flexible Attention-based Adaptive Coding (AAC) module. This module integrates window and channel attention mechanisms, dynamically adjusts semantic information in response to the current channel state, and facilitates adaptation of a single model across various Signal-to-Noise Ratio (SNR) environments. Furthermore, to validate the effectiveness of this approach, the paper introduces an end-to-end Joint Source Channel Coding (JSCC) scheme for image semantic communication, employing the AAC module. Experimental results demonstrate that the proposed scheme surpasses existing deep JSCC schemes across datasets of varying resolutions;furthermore, they validate the efficacy of the proposed AAC module, which is capable of dynamically adjusting critical information according to the current channel state. This enables the model to be trained over a range of SNRs and obtain better results.

关键词： Asymptotic performance bound   Cloud-assisted edge server   Effective speed   Energy constraint   Heuristic algorithm   Mobile edge computing   Task scheduling  

摘要： In this paper, we study task scheduling with or without energy constraint in mobile edge computing with multiple cloud-assisted edge servers as combinatorial optimization problems within the framework of classical scheduling theory. The first problem is to schedule a list of independent tasks on a mobile device and several heterogeneous edge servers and cloud servers, such that the makespan is minimized. The second problem is to schedule a list of independent tasks and to determine the computation and communication speeds of a mobile device, such that the makespan is minimized and the energy consumption of the mobile device does not exceed certain energy budget. The paper makes the following tangible contributions. We design heuristic task scheduling algorithms for both problems by considering the heterogeneity of computation and communication speeds. We derive a lower bound for the optimal schedule and prove an asymptotic performance bound for our heuristic algorithms. We experimentally evaluate the performance of our heuristic algorithms and show that their performance is very close to that of an optimal algorithm. Our analysis employs three key techniques, namely, the method of communication unification (i.e., all tasks have the same communication to computation ratio), the concept of effective speed of an edge server or a cloud server (i.e., the perceived speed of a server by ignoring the details and differences of communication speed and computation speed, wireless communication time and wired communication time, a regular edge server and a cloud-assisted edge server, execution time and waiting time), and the construction of virtual tasks (i.e., imaginary tasks which do not exist). Such unique techniques make it possible to derive a lower bound for the optimal solution, to derive an upper bound for the heuristic solution, to prove an asymptotic performance bound, and to find the best edge server order. To the best of the author's knowledge, this is the first pa

关键词： Resource allocation   offloading decision   mobile edge computing   unmanned aerial vehicle   D2D  

摘要： In this paper, we consider the resource allocation and offloading decisions of device-to-device (D2D) cooperative UAV-assisted mobile edge computing (MEC) system, where the device with task request is served by unmanned aerial vehicle (UAV) equipped with MEC server and D2D device with idle resources. On the one hand, to ensure the fairness of time-delay sensitive devices, when UAV computing resources are relatively sufficient, an optimization model is established to minimize the maximum delay of device computing tasks. The original non-convex objective problem is decomposed into two subproblems, and the suboptimal solution of the optimization problem is obtained by alternate iteration of two subproblems. On the other hand, when the device only needs to complete the task within a tolerable delay, we consider the offloading priorities of task to minimize UAV computing resources. Then we build the model of joint offloading decision and power allocation optimization. Through theoretical analysis based on KKT conditions, we elicit the relationship between the amount of computing task data and the optimal resource allocation. The simulation results show that the D2D cooperation scheme proposed in this paper is effective in reducing the completion delay of computing tasks and saving UAV computing resources.

关键词： Quantum healthcare model   Smart IoT   Mobile edge computing   5G networks   Edge-IoT ecosystem  

摘要： Edge computing (EC) aided Internet of Things (IoT) based applications require real-time processing as well as high-volume data-intensive services as 5G networks evolve. It is difficult to fit IoT services into available edge nodes (ENs) while maintaining performance measures on quality of service (QoS) because of the heterogeneity, restricted resources, and changing resource demand of IoT applications. This study aims to examine a quantum healthcare model that is built on mobile edge computing networks linked with smart IoT. Serverless computing with edge computing may handle quick or small-scale tasks effectively at edge devices, lowering latency. Moreover, serverless edge computing now faces significant hurdles from security and processing performance. In order to provide trustworthy and secure edge serverless services, it is possible to use blockchain technology to boost processing speed and quantum computing to enhance security. In terms of decreasing dependency on IoT cloud analytics or storage facilities, the test results were promising. Lastly, we cover the many aspects that impact the viability of integrating an edge-IoT ecosystem, such as design, capability requirements, functional challenges, and selection criteria.

关键词： Mobile edge computing   Non-orthogonal multiple access   Reconfigurable intelligent surface   Deep reinforcement learning   Twin delayed deep deterministic policy   gradient  

摘要： Reconfigurable intelligent surface (RIS) is expected to enhance task offloading performance in non-line-of -sight mobile edge computing (MEC) scenarios. This paper aims at reducing the long-term energy consumption for task offloading in a MEC system assisted by double RISs while considering non-orthogonal multiple access (NOMA). Due to the non-convexity of the formulated problem, we propose an action-constrained deep reinforcement learning (DRL) framework based on twin delayed deep deterministic policy gradient (TD3) algorithm that includes inner and outer optimization processes, which greatly reduces the action space size of the DRL agent, making it easy to implement and achieve fast convergence. During the inner optimization phase, by utilizing theoretical derivations, we propose a low-complexity method to optimally derive the transmit power, the local computing frequency, and the computation resource allocation in the base station. In the outer optimization phase, building on the solution derived from the inner optimization, we further use the TD3 algorithm to jointly determine the phase shifting of RIS elements, the offloading ratio, and the transmission time for each time slot. Experiment results demonstrate that the proposed algorithm achieves rapid convergence performance. In comparison to single RIS assisted offloading, the double RISs assisted offloading scheme proposed in this paper can reduce energy consumption by 42.8% on average.

关键词： Mobile edge computing  

摘要： As digital technology advances, the proliferation of connected devices poses significant challenges and opportunities in mobile crowdsourcing and edge computing. This narrative review focuses on the need for privacy protection in these fields, emphasizing the increasing importance of data security in a data-driven world. Through an analysis of contemporary academic literature, this review provides an understanding of the current trends and privacy concerns in mobile crowdsourcing and edge computing. We present insights and highlight advancements in privacy-preserving techniques, addressing identity, data, and location privacy. This review also discusses the potential directions that can be useful resources for researchers, industry professionals, and policymakers. © 2024, CC BY.

关键词： Mobile edge computing  

摘要： Multi-Access Edge Computing (MEC) emerged as a viable computing allocation method that facilitates offloading tasks to edge servers for efficient processing. The integration of MEC with 5G, referred to as 5G-MEC, provides real-time processing and data-driven decision-making in close proximity to the user. The 5G-MEC has gained significant recognition in task offloading as an essential tool for applications that require low delay. Nevertheless, few studies consider the dropped task ratio metric. Disregarding this metric might possibly undermine system efficiency. In this paper, the dropped task ratio and delay has been minimized in a realistic 5G-MEC task offloading scenario implemented in NS3. We utilize Mixed Integer Linear Programming (MILP) and Genetic Algorithm (GA) to optimize delay and dropped task ratio. We examined the effect of the number of tasks and users on the dropped task ratio and delay. Compared to two traditional offloading schemes, First Come First Serve (FCFS) and Shortest Task First (STF), our proposed method effectively works in 5G-MEC task offloading scenario. For MILP, the dropped task ratio and delay has been minimized by 20% and 2ms compared to GA. © 2024, CC BY-NC-ND.

关键词： Mobile edge computing  

摘要： Federated Learning is a promising approach for learning from user data while preserving data privacy. However, the high requirements of the model training process make it difficult for clients with limited memory or bandwidth to participate. To tackle this problem, Split Federated Learning is utilized, where clients upload their intermediate model training outcomes to a cloud server for collaborative server-client model training. This methodology facilitates resource-constrained clients' participation in model training but also increases the training time and communication overhead. To overcome these limitations, we propose a novel algorithm, called Hierarchical Split Federated Learning (HierSFL), that amalgamates models at the edge and cloud phases, presenting qualitative directives for determining the best aggregation time-frames to reduce computation and communication expenses. By implementing local differential privacy at the client and edge server levels, we enhance privacy during local model parameter updates. Our experiments using CIFAR-10 and MNIST datasets show that HierSFL outperforms standard FL approaches with better training accuracy, training time, and communication-computing trade-offs. HierSFL offers a promising solution to mobile edge computing's challenges, ultimately leading to faster content delivery and improved mobile service quality. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Mobile edge computing offers a myriad of opportunities to innovate and introduce novel applications, thereby enhancing user experiences considerably. A critical issue extensively investigated in this domain is efficient deployment of Service Function Chains (SFCs) across the physical network, spanning from the edge to the cloud. This problem is known to be NPhard. As a result of its practical importance, there is significant interest in the development of high-quality sub-optimal solutions. In this paper, we consider this problem and propose a novel near-optimal heuristic that is extremely efficient and scalable. We compare our solution to the state-of-the-art heuristics and to the theoretical optimum. In our large scale evaluations, we use realistic topologies which are previously reported in the literature. We demonstrate that the execution time offered by our solution grows slowly as the number of Virtual Network Function (VNF) forwarding graph embedding requests grows, and it handles one million requests in slightly more than 20 seconds for 100 nodes and 150 edges physical topology. © 2024, CC BY-NC-ND.