关键词： deep learning   internet of things   mobile edge computing   resource allocation   task offloading  

摘要： Mobile edge computing (MEC) provides users with low latency and low energy consumption services by unloading computing tasks to MEC servers, overcoming the shortcomings of traditional cloud computing. In mobile edge computing, how to make unloading decision is one of the key technologies to provide low latency and low energy consumption services. In addition, due to the limited bandwidth resources of wireless channel, unreasonable bandwidth allocation will increase the energy consumption and delay of user devices. Therefore, how to reasonably allocate resource is also the key to the implementation of edge computing. To jointly optimize delay, energy consumption, and computational resources, this paper proposes a multi-channel search algorithm based on Monte Carlo tree search (MCS-MCTS). Firstly, with the cost of delay and energy consumption as the optimization objective, the decision on computing resource allocation and transmission power modeling is modeled as a convex optimization problem. The optimal transmission power allocation problem is solved by using gradient descent method, and the optimal computational resource allocation problem is solved by means of Lagrange multiplier method and Karush-Kuhn-Tucker (KKT) condition. Subsequently, the MCS-MCTS algorithm is used to deal with the binary unloading decision problem. Besides, the simulated annealing algorithm is introduced to avoid the search results falling into local optimum. The numerical results show that MCS-MCTS algorithm can obtain near optimal unloading decision and resource allocation decision in linear coherent time. Compared with existing heuristic search algorithms, this algorithm can achieve near optimal performance while reducing time complexity and improving system energy efficiency. © 2024 East China University of Science and Technology. All rights reserved.

关键词： Mobile edge computing  

摘要： With the rapid advancements in the Internet of Things (IoT), edge computing has a significant role in many eHealth applications. However, security and data privacy are major challenges due to the widespread popularity of the digital health domain. The network and data storage should withstand adversarial entities and allow access to only legitimate users. Medical users and servers must be registered with a trusted third-party authority to obtain permissions to authenticate remaining users. Millions of smart medical devices connect online to collect critical patient information, analyze reports, and perform meaningful decisions without human interaction. In this scenario, standard security is essential to safeguard eHealth applications. This research provides a secured, lightweight mobile edge computing framework to address these issues. The empirical results show that our framework mitigates computational overheads. The informal and formal analysis shows that our framework withstands potential attacks. © 2018 IEEE.

关键词： air-ground   architecture   collaborative   mobile edge computing  

摘要： By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this *** proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for ***,we introduce the AGC-MEC architecture and elaborate three typical use ***,we discuss four main challenges in the AGC-MEC as well as their potential ***,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement ***,we highlight several potential research directions of the AGC-MEC.

关键词： Mobile edge computing  

摘要： In order to increase energy efficiency in Mobile-Edge Computing (MEC) systems, this research suggests a novel approach that combines Non-Orthogonal Multiple Access (NOMA) with energy collecting in a full-duplex (FD) context. The main optimization objective is to increase edge computing capabilities while lowering the system's overall energy usage. A combined resource allocation approach that takes into account CPU frequency, uplink and downlink power control, time scheduling, and data offloading for local computing users is introduced in order to do this. According to the suggested technique, mobile users can delegate their computationally demanding jobs to MEC servers connected by base stations via NOMA, which promotes effective information exchange, to MEC servers. Base stations also use full-duplex technology, which allows them to accept offloading jobs while also transmitting energy to other users, maximizing the efficiency of the available resources. The Block Coordinate Descent (BCD) approach is used to create a joint communication and computing resource allocation algorithm, which addresses the non-convex aspect of the optimization model. The original non-convex problem is effectively solved by this approach, which also produces an ideal answer in closed form. The NOMA-assisted FD-MEC method suggested in this research outperforms conventional Orthogonal Multiple Access (OMA) and Half Duplex (HD) approaches, according to simulation results. Energy efficiency is increased as a result of a large decrease in the system's overall energy usage. This result demonstrates the potential of combining NOMA, energy harvesting, and full-duplex technologies to create MEC systems that are more environmentally friendly and energy-efficient. © 2024, The Authors. All rights reserved.

关键词： Metaverse   Data privacy   Privacy computing   Mobile edge computing   Blockchain   Extended reality  

摘要： Metaverse, the core of the next -generation Internet, is a computer -generated holographic digital environment that simultaneously combines spatio-temporal, immersive, real-time, sustainable, interoperable, and datasensitive characteristics. It cleverly blends the virtual and real worlds, allowing users to create, communicate, and transact in virtual form. With the rapid development of emerging technologies including augmented reality, virtual reality and blockchain, the metaverse system is becoming more and more sophisticated and widely used in various fields such as social, tourism, industry and economy. However, the high level of interaction with the real world also means a huge risk of privacy leakage both for individuals and enterprises, which has hindered the wide deployment of metaverse. Then, it is inevitable to apply privacy computing techniques in the framework of metaverse, which is a current research hotspot. In this paper, we conduct comprehensive research on the necessity, taxonomy and challenges when privacy computing meets metaverse. Specifically, we first introduce the underlying technologies and various applications of metaverse, on which we analyze the challenges of data usage in metaverse, especially data privacy. Next, we review and summarize state-of-theart solutions based on federated learning, differential privacy, homomorphic encryption, and zero -knowledge proofs for different privacy problems in metaverse. Finally, we show the current security and privacy challenges in the development of metaverse and provide open directions for building a well -established privacy -preserving metaverse system. For easy access and reference, we integrate the related publications and their codes into a GitHub repository: https://***/6lyc/***.

关键词： Energy efficiency   Mobile edge computing   Mobility-awareness   Resource allocation  

摘要： Mobile edge computing (MEC) is a promising paradigm that helps mobile terminals save energy and increase battery life by offloading tasks to MEC servers. To obtain full benefit from MEC, the joint optimization of offloading decision -making and resource allocation, while considering user mobility, is crucial and challenging. The objective of this paper was to propose mobility -aware and energy -efficient offloading schemes for MEC in cellular networks. The proposed schemes helped to make offloading decisions, allocate transit power, and schedule transmissions among multiple moving users. An optimal scheme was proposed, in terms of minimizing total energy consumed by all users, which was based on Mixed -Integer Programming (MIP). Considering the high computational complexity of solving MIP problems, a practical heuristic scheme was also proposed. Simulation results showed the effectiveness in reducing energy consumption of both the proposed optimal and heuristic schemes and demonstrated the importance of mobility awareness as well.

关键词： Mobile edge computing   Ultra-dense network   Offloading   Non-orthogonal multiple access   Deep reinforcement learning  

摘要： To address the current challenge of smart devices in healthcare Internet of things (IoT) struggling to efficiently process intensive applications in real-time, a collaborative cloud-edge offloading model tailored for ultra-dense edge computing (UDEC) networks is developed. While numerous studies have delved into the optimization of offloading in mobile edge computing (MEC), it is imperative to consider non-orthogonal multiple access (NOMA) as a physical technology when addressing the offloading optimization process in MEC. The multiuser sharing of spectrum resources in NOMA can enhance the network spectrum utilization and reduce the computational delay when users transmit computing tasks. Consequently, a model for NOMA-assisted UDEC systems is proposed. The model takes into account joint offloading decisions, computational resources, and sub-channel resources and is modeled as a complex nonlinear mixed-integer programming problem. The aim is to decrease the task execution delay and energy consumption of smart devices while ensuring that users' maximum acceptable delay for processing medical computational tasks is met efficiently and in a timely manner. Deep deterministic policy gradient (DDPG), a deep reinforcement learning method, is employed to solve the joint optimization problem. The final simulation results show that the algorithm converges well. The proposed offloading scheme can reduce the system cost by 54.5 and 69.9% in comparison with scenarios where users solely perform local computations and offload their tasks to the base station (BS). The application of NOMA communication in our offloading scheme boosts network spectrum utilization and trims down the system cost by 87.09% when contrasted with orthogonal multiple access (OMA).

关键词： Mobile edge computing   unmanned aerial vehicles   service caching and task offloading   online algorithm   machine learning  

摘要： Unmanned Aerial Vehicles (UAVs) have gained increasing attention by both academic and industrial communities, due to their flexible deployment and efficient line-of-sight communication. Recently, UAVs equipped with base stations have been envisioned as a key technology to provide 5G network services for mobile users. In this article, we provide timely services on the data streams of mobile users in a UAV-aided Mobile Edge Computing (MEC) network, in which each UAV is equipped with a 5G small-cell base station for communication and data processing. Specifically, we first formulate a flow-time minimization problem by jointly caching services and offloading tasks of mobile users to the UAV-aided MEC with the aim to minimize the flow time, where the flow time of a user request is referred to the time duration from the request issuing time point to its completion point, subject to resource and energy capacity on each UAV. We then propose a spatial-temporal learning optimization framework. We also devise an online algorithm with a competitive ratio for the problem based upon the framework, by leveraging the round-robin scheduling and dual fitting techniques. Finally, we evaluate the performance of the proposed algorithms through experimental simulation. The simulation results demonstrate that the proposed algorithms outperform their comparison counterparts, by reducing the flow time no less than 19% on average.

关键词： Autonomous aerial vehicles   Trajectory   Task analysis   Resource management   Jamming   Security   Internet of Things   UAV   trajectory design   power control   security   mobile edge computing   federated learning   fairness  

摘要： Unmanned aerial vehicles (UAVs)-aided mobile-edge computing (MEC) systems face several challenges that hinder their practical implementation. First, the broadcast nature of wireless communications can cause security issues. Second, UAVs have constrained onboard power. Finally, the UAV should be able to serve a maximum number of ground users (GUs). It is also crucial to maintain fairness such that all GUs get equal opportunities to securely offload tasks to UAVs. We seek to address the aforementioned challenges by designing an intelligent mechanism, FairLearn, which maximizes the fairness in secure MEC services by controlling the UAV 3D trajectory, transmission power, and scheduling time for task offloading by mobile GUs. To this end, we formulate a maximization problem and solve it using a deep neural network (DNN)-based model, where the UAVs collaboratively learn the model by utilizing a federated learning (FL) approach. Each UAV uses a reinforcement learning (RL)-based approach to individually generate the training dataset, making the training data span different network scenarios. Our model is based on UAV pairs, where one UAV executes the GUs' offloaded tasks, while the other is a jammer that suppresses eavesdroppers. The simulation evaluation of FairLearn shows that it significantly improves the performance of UAV-enabled MEC systems.

关键词： Mobile edge computing   DDoS attacks   container   queue networks   scheduling  

摘要： Application-layer distributed denial-of-service (DDoS) attacks incapacitate systems by using up their resources, causing service interruptions, financial losses, and more. Consequently, advanced deep-learning techniques are used to detect and mitigate these attacks in cloud infrastructures. However, in mobile edge computing (MEC), it becomes economically impractical to equip each node with defensive resources, as these resources may largely remain unused in edge devices. Furthermore, current methods are mainly concentrated on improving the accuracy of DDoS attack detection and saving CPU resources, neglecting the effective allocation of computational power for benign tasks under DDoS attacks. To address these issues, this paper introduces SecEG, a secure and efficient strategy against DDoS attacks for MEC that integrates container-based task isolation with lightweight online anomaly detection on edge nodes. More specifically, a new model is proposed to analyze resource contention dynamics between DDoS attacks and benign tasks. Subsequently, by employing periodic packet sampling and real-time attack intensity predicting, an autoencoder-based method is proposed to detect DDoS attacks. We leverage an efficient scheduling method to optimize the edge resource allocation and the service quality for benign users during DDoS attacks. When executed in the real-world edge environment, our experimental findings validate the efficacy of the proposed SecEG strategy. Compared to conventional methods, the service rate of benign requests increases by 23% under intense DDoS attacks, and the CPU resource is saved up to 35%.