关键词： Mobile edge computing   task offloading   resource allocation   genetic algorithm   deep learning  

摘要： With the rapid development of 5G and the Internet of Things (IoT), the traditional cloud computing architecture struggle to support the booming computation-intensive and latency-sensitive applications. Mobile edge computing (MEC) has emerged as a solution which enables abundant IoT tasks to be offloaded to edge services. However, task offloading and resource allocation remain challenges in MEC framework. In this paper, we add the total number of offloaded tasks to the optimization objective and apply algorithm called Deep Learning Trained by Genetic Algorithm (DL-GA) to maximize the value function, which is defined as a weighted sum of energy consumption, latency, and the number of offloaded tasks. First, we use GA to optimize the task offloading scheme and store the states and labels of scenario. Each state consists of five parameters: the IDs of all tasks generated in this scenario, the cost of each task, whether the task is offloaded, bandwidth occupied by offloaded task and remaining bandwidth of edge server. The labels are the tasks that are currently selected for offloading. Then, these states and labels will be used to train neural network. Finally, the trained neural network can quickly give optimization solutions. Simulation results show that DL-GA can execute 75 to 450 times faster than GA without losing much optimization power. At the same time, DL-GA has stronger optimization capability compared to Deep Q-Learning Network (DQN).

关键词： & nbsp   Mobile edge computing   the divisible task offloading   double deep Q-network   self-adaptive e-greedy exploration strategy   prioritized experience replay technique  

摘要： Mobile edge computing (MEC) is a promising computing paradigm that enables offloading tasks to edge servers to decrease the load on user equipment (UE) and the latency of services. However, blind offloading of data-intensive tasks may cause edge node congestion and increase the task's time delay. Therefore, developing a new incentive deep reinforcement learning-based scheme is imperative to solve such divisible task offloading in this end-edge-cloud orchestrated environment. First, we build a novel MEC-based state space framework containing multiple servers and UE, and introduce a new dynamic task division into multiple subtasks mode to execute in parallel on multiple nodes with multiple service providers. Next, we model the process of the divisible task offloading by the Markov Decision Process (MDP) and derive a sufficient condition in the subtask offloading to maximize the quality of physical experience (QoPE) of the divisible task. Finally, we propose a new self-adaptive Q-network with prioritized experience replay (SQ-PER) algorithm based on double deep Q-network (DDQN), in which the experience replay technique and the traditional e-greedy exploration strategy are optimized to improve learning efficiency and stability. Simulation results show that the SQ-PER algorithm makes better utilization of environmental resources and significantly reduces time delay of the task than other methods in complex scenarios.

关键词： Internet of Things   unmanned aerial vehicles   energy harvesting   wireless power transfer   non-orthogonal multiple access   mobile edge computing   physical layer security  

摘要： This paper investigates a system for unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) and non-orthogonal multiple access (NOMA) mobile edge computing (MEC) in the Internet of Things (IoT) -UWNMI, wherein a UAV equipped with an energy transmitter (ET) acts as a relay. In particular, we consider a situation involving two clusters of IoT devices (IDs) that have limited resources and therefore are unable to compute their own tasks and must instead offload them to a base station (BS) via the UAV relay (UR) in the presence of a passive eavesdropper. In the UWNMI system, the effects of imperfect channel state information (ICSI) and imperfect successive interference cancellation (ISIC) are considered, along with the usage of artificial noise (AN) to improve the physical layer security (PLS) of the system. We derive closed-form expressions for the successful computation probability (SCP) and secrecy outage probability (SOP) under the Nakagami-m fading channel model in order to evaluate the performance of the system. Moreover, we present optimization problems with the objectives of optimizing the position and height of the UR, the time switching ratio (TSR), and the power allocation for the AN in order to maximize the SCP and minimize the SOP. These problems are solved using a particle swarm optimization (PSO)-based algorithm. In addition, Monte Carlo simulation results are presented to validate the accuracy of our analysis based on simulations of the system performance under different values of the system parameters, including the number of antennas at the BS, the number of IDs in each cluster, the TSR, and the position and height of the UR.

关键词： & nbsp   Generative adversarial network   5G   mobile edge computing   synthetic data generation   resource efficiency   performance evaluation  

摘要： Mobile Edge Computing (MEC) in 5G networks has emerged as a promising technology to enable efficient and low-latency services for mobile users. In this paper, we present a novel synthetic data generation approach tailored for evaluating MEC in 5G networks. Our methodology incorporates resource-efficient techniques to generate realistic synthetic datasets that capture the spatio-temporal patterns of mobile traffic and user behavior. By leveraging advanced modeling techniques, including multi-head attention and bidirectional LSTM, we accurately model the complex dependencies in the data while optimizing computational resources. The proposed synthetic data generator enables the creation of diverse datasets that closely resemble real-world scenarios, facilitating the evaluation of MEC performance and optimizing resource utilization. Through extensive experiments and evaluations, we demonstrate the effectiveness of our approach in enabling accurate assessments of MEC in 5G networks. Our work contributes to the field by providing a robust methodology for synthetic data generation specifically tailored for MEC evaluation, addressing the need for resource-efficient evaluation frameworks in the context of emerging technologies. The results of our study provide valuable insights for the design and optimization of MEC systems in real-world deployments.

关键词： unmanned aerial vehicle   UAV   satisfaction   cooperative trajectory optimisation   mobile edge computing   MEC  

摘要： This paper investigates an unmanned aerial vehicle (UAV)-assisted mobile edge computing scenario, where multiple UAVs provide computational services for user devices (UDs) with different service priorities. Taking into account UDs' different service priorities as well as diverse computation-offloading demands, a satisfaction-model is proposed, which gives consideration to UDs' satisfaction degree toward the offloading services as well as the fairness of their offloading amount. We aim to maximise UDs' sum satisfaction by jointly optimising the offloading time allocation, computing time allocation, users' transmit power, as well as UAV trajectory. A non-convex optimisation problem is formulated and an effective solution framework is proposed, where the problem is decomposed into three sub-problems that are solved alternately and iteratively by applying successive convex approximation (SCA) technique. Numerical results show that the proposed scheme achieves higher satisfaction with lower standard deviation as compared to benchmark schemes.

关键词： Mobile edge computing   unmanned aerial vehicles   resource allocation   blockchain   multi-task offloading   matching mechanism  

摘要： Mobile edge computing (MEC) is a technology deployed at the edge of mobile networks to enhance computation capabilities and reduce transmission distances. It has been extensively researched in the context of both the internet of things (IoT) and 5G communication. Recently, unmanned aerial vehicles (UAVs) have been integrated into MEC networks to create a novel architecture that utilizes line-of-sight (LOS) transmission links. In this architecture, UAVs act as relay nodes to facilitate the offloading of computing tasks from UAVs to edge computing stations (ECSs). However, the problem of creating an incentive system that guarantees the confidentiality and integrity of communication while simultaneously making it easier for UAVs and ECSs to coordinate a variety of activities remains unsolved. Consequently, this paper proposes a blockchain-based architecture for UAV-assisted MEC networks that addresses the aforementioned issues of security and privacy and investigates the problem of multi-task matching based on this architecture. A joint optimization problem is explicitly proposed to maximize both task completion rates and social welfare. Consequently, the formulated problem is decomposed into two subproblems: 1) The double auction problem, which aims to maximize societal utility and determines the winning pairs and trading price;and 2) The auction losers matching problem, which aims to increase task completion rates. Then, to identify the appropriate matching pairings and establish the trading price, a satisfaction breakeven-based double auction (SBDA) method is suggested. Sequentially, two auction losers' second selection schemes, the shortest distance (SD) and the largest difference (LD) scheme are proposed to realize second matching to enhance the task completion rate. Finally, numerical simulations are given to show the effectiveness of the proposed mechanism. Particularly, the SBDA+LD mechanism has the best system utility and overall income compared with other sch

关键词： Mobile edge computing   offloading strategy   joint optimization  

摘要： Offloading strategies in mobile edge computing are hot research, whereas, existing offloading strategies at the edge hard handle the issues of multi-user intensive task scheduling, resulting in the poor utilization of network resource. Therefore, this makes the quality of experience for end users far from satisfactory. To address this, this paper proposes a novel joint offloading strategy consisting of the back propagation neural network and the genetic algorithm. Firstly, using the genetic algorithm optimizes the learning error of the back propagation neural network, and then energy consumption in the system and response delay are jointly optimized by the back propagation neural network. Under long-term total overhead-cost constraints, the joint strategy can achieve the search of the optimal solutions to generate superior calculated offloading results. Unlike those approaches devoting into reducing response delay only for end users, this work takes account into the total overhead-cost in the system thereby affording more efficient for application service providers. Multiple simulation results indicate that the proposed strategy can not only reduce the average response delay of the mobile edge computing system, but also remain a low average energy consumption.

关键词： blockchain   BC   federated learning   FL   resource scheduling   mobile edge computing   MEC   multi-task execution   task running time   utility   energy loss   resource utilisation cost   saturation throughput   trust score  

摘要： Federated learning (FL) can improve the privacy-preserving issue of users' IoT devices, in which users complete the local training and transfer the updated model data to the central server for a global update. Due to high latency, the central server-based FL may suffer from huge energy loss at local user devices. MEC-based FL can improve the model accuracy and energy consumption at user devices via edge server-based task execution. Along with FL, blockchain can improve data security via permission-based access. Existing works explored only single type of IoT task without any appropriate resource scheduling for multiple tasks with different preferences, FL, and blockchain operations. This paper provides a merit-based resource scheduling scheme for different tasks with preferences, blockchain, and FL operations by checking resources, deadlines, delays, and resource costs. The simulation results verify that 45% running time and 53% cost gain is achieved in proposed scheme over the baseline schemes.

关键词： Edge computing   Servers   Protocols   Antennas   Performance evaluation   Computational modeling   Signal to noise ratio   Mobile communications   MIMO communication   Mobile edge computing   computation offloading   offloading decision   access reservation protocol   multiple-input multiple-output  

摘要： For several years, mobile edge computing (MEC) has been highlighted as a promising technique to support emerging computation-intensive applications in cellular networks, e.g., 5G and 6G. Most previous studies have mainly focused on jointly optimizing communication (i.e., radio) and computation resources to improve offloading performance without considering specific communication protocols required for operating the MEC systems in practice. In this article, we newly design a contention-based access reservation protocol (ARP) for efficiently supporting simultaneous task offloading requests from a number of edge devices (EDs), and further define necessary signalings required to determine an optimal offloading factor. Thereafter, we formulate an optimization problem to find the optimal offloading factor that minimizes the task-completion latency. Through simulations, we mainly evaluate the task-completion latency performance of the MEC system incorporated with our proposed access reservation protocol under a multiple-input multiple-output (MIMO) environment. Particularly, we thoroughly investigate several practical aspects such as the effect of the number of edge devices attempting to join the task offloading, the number of antennas equipped at the edge device, and the amount of reserved radio resources required for configuring access reservation protocol on the optimal offloading decision. From the results, we verify the validity of our approach, and provide meaningful insights regarding how the network should be configured to fruitfully exploit the offloading with the MEC systems.

关键词： Artificial neural network   deep learning   Internet of Vehicles   Internet of Things   information-centric networking   mobile edge computing  

摘要： With the rapid advancement of Internet of Things (IoT) communication technologies, the Internet of Vehicles (IoV) has gained significant attention for providing the real-time exchange of emergency traffic information among vehicles and Road Side Units (RSU) to improve ultimate driving experiences and road safety. Information-Centric Networking (ICN) has emerged as a novel networking architecture that shifts the communication model from Internet protocol (IP) based host-centric to content-centric architecture. ICN provides support to push and pull-based messages for efficient content dissemination and retrieval by aiming at content names rather than IP addresses. The Mobile Edge Computing (MEC) paradigm facilitates proximity-based real-time traffic applications and services, reducing the content retrieval latency from the core network without the excessive broadcast overhead. Deep Learning (DL) techniques have been tremendously successful in detecting the severity of real-time traffic data. The integration of DL based ANN model for edge-based ICN-IoV brings real-time traffic prediction, content caching, and forwarding of push-based messages closer to the target area. Furthermore, the deployment of mobile edge servers at critical network positions enhances the availability and responsiveness of the name-based content in the ICN paradigm. In this paper, we propose Mobile Edge-based Emergency Messages Dissemination Scheme (MEMDS) to deliver push-based messages delivery at the event-reported geographical location. We also propose a hybrid DL-based Artificial Neural Network (ANN) and MEMDS model to detect and predict the severity of the safety application under real traces from different cities based on specific parameters. The simulation results demonstrate that the proposed scheme significantly improves the data delivery ratio, average delay, hop count, content retrieval delay, and network overhead than DCN and flooding techniques. Secondly, the proposed hybrid model su