关键词： Rendering (computer graphics)   Delays   Quality of experience   Wireless communication   Optimization   Streaming media   Real-time systems   FoV prediction   mobile edge computing   proximal policy optimization   rendered tile reuse   virtual reality  

摘要： Interactive real-time wireless Virtual Reality (VR) has become increasingly popular because it provides an immersive experience for VR users anytime, anywhere. Introducing multi-access edge computing (MEC) to wireless VR service can solve the problem that VR devices having insufficient rendering power. However, the problem that some tiles in the overlapping field of views (FoVs) may be rendered repeatedly usually be ignored. Repeated rendering would waste the computing resources of edge nodes and damage users' quality of experience (QoE). This paper proposes a rendered tile reuse scheme based on FoV prediction and 3C (caching, computing, and communication) optimization for the MEC-assisted VR service. Firstly, we model the above scheme as an optimization problem that aims to maximize the total users' QoE value under the motion to photons (MTP) delay constraints. Secondly, we use the recurrent neural network model with gated recurrent unit (GRU) architecture to dynamically predict the users' FoV in the next time slot. Thirdly, we use the proximal policy optimization (PPO)to learn the question's solution iteratively based on the results of FoV prediction. The simulation results show that our proposed algorithm is superior to other algorithms in improving the value of total users' QoE and reducing the MTP delay.

关键词： Training   Computational modeling   6G mobile communication   Mobile handsets   Collaborative work   Resource management   Optimization   6G   federated learning   heterogeneous network   mobile edge computing  

摘要： 6G is envisioned to achieve ubiquitous Artificial Intelligence (AI) in heterogeneous and massive-scale networks, where FEderated Edge Learning (FEEL) is an effective way to assist AI technology to be implemented at the network edge. FEEL enables training a machine learning model through communication among multiple edge devices based on data from a large number of users. However, the heterogeneity among distributed edge devices and the limitation of resources may degrade the training efficiency of FEEL over 6G-enabled mobile edge computing (MEC) networks. Taking this challenge into account, a novel federated learning scheme is proposed in this paper to accelerate the training process. In particular, we formulate a training efficiency maximization problem, where a novel analytical relation among the training loss, resource consumption and heterogeneity is inferred by convergence analysis. Then, we propose a searching algorithm named IFBA to obtain the optimal inexactness of local models and frequency band allocation for edge devices in order to mitigate the straggler effect caused by the heterogeneity and resource limitation of devices. As a result, the training efficiency can be improved by adapting the inexactness of local models and frequency band allocation for edge devices on-demand. Simulation results validate that AceFL has advantages in training efficiency improvement and heterogeneity adaption, e.g., accelerating the training by 5% - 15% under different heterogeneity.

关键词： Federated learning   Mobile edge computing   Fairness   Reliable  

摘要： Federated learning-enabled mobile edge computing implements privacy-preserving collaborative machine learning of complex models. However, mobile end devices have high mobility and are vulnerable to be corrupted due to exposuring to an open network environment, which not only affect the performance of federated learning models but also bring serious security issues. To solve the above problem, we propose a reliable and fair federated learning mechanism for mobile edge computing. To begin with, in order to select end nodes as reliable as possible, a reputation-based end nodes selection scheme is proposed, which includes the construction of the reputation model and the concealment of selected end nodes. Then, corresponding counter strategies are proposed for the possible attack behaviors launched by corrupted end nodes during the training process. To avoid the impact of poisoning attacks on the model performance, a new global model aggregation strategy is proposed to maintain the model performance by meritocratic campaigning. To solve the fairness problem caused by end nodes' escape, a reward-penalty scheme using blockchain is proposed. Finally, the numerical results clearly show that the proposed mechanism is effective for reliable and fair federated learning for mobile edge computing, maintaining high accuracy under attacks from malicious end nodes while penalizing them to ensure fairness.

关键词： Mobile edge computing   Offloading   Scheduling   Hybrid optimization   Q -learning  

摘要： Mobile Edge Computing (MEC) has arisen as a promising computing paradigm consisting of three tiers: Smart Mobile Devices (SMDs), fog nodes, and the cloud. The MEC enables computational offloading and execution schedules to cope with the problems of insufficient resources for the SMDs and the computational tasks' dead-lines. The offloading problem determines in what order and source of the network the tasks should be performed to minimize execution time and power consumption. The main aim of the current paper is to reduce execution time and energy consumption by optimizing tasks' offloading and scheduling in MEC networks. As a result, the task scheduling and offloading are modeled as an optimization problem. Then, an enhanced hybridization of Artificial Ecosystem-based Optimization (AEO) and Arithmetic Optimization Algorithm (AOA), named E-AEO-AOA, is presented to optimize it. In the E-AEO-AOA, the AOA and AEO algorithms are initially discretized. Next, the Q-learning strategy is modified and recruited to hybridize the algorithms in a complementary manner. Subsequently, chaos theory is utilized in a local search procedure to enhance the exploitation capability of the E-AEO-AOA. Eventually, the performance of E-AEO-AOA is examined on fifteen MEC networks. In the experiments, the E-AEO-AOA is compared with AEO, AO, AOA, JS, MRFO, STOA, SCA, and TSA algorithms statistically. Be-sides, the algorithms' convergence rate and solutions dispersity are visually compared. Moreover, the algorithms are compared by the Wilcoxon signed-rank test. The experimental results indicate that the E-AEO-AOA surpassed competitor algorithms in 90% of cases. Likewise, in 6% of the cases, the E-AEO-AOA produced the same results as AEO, AOA and MRFO.

关键词： Offloading optimization   Mobile Edge Computing   Swarm intelligence   Binary cuckoo search   Task offloading  

摘要： Computational task offloading facilitates real-time applications on constrained mobile devices that require a large amount of processing resources, high storage capacity and battery power. Mobile Edge Computing (MEC) is a computing paradigm that shifts computing resources closer to the user at the network's edges. Heavy tasks are then offloaded to edge nodes, thereby reducing the computations required on the mobile side. However, offloading computational tasks may result in additional energy consumption and delays, due to network congestion and time spent in server queues. Thus, to minimize completion time and energy consumption, it is essential to optimize offloading decisions, while also considering the financial costs. In this paper, we propose an Offloading Decision-Making Framework based on the Binary Cuckoo Search Algorithm for Mobile Edge Computing (ODM-BCSA). We formulated an offloading problem as a mixed-integer optimization problem to minimize time, energy, and payment costs. We resolved the problem of resource allocation using the Binary Cuckoo Search Algorithm (BCSA). The simulation results revealed that offloading decisions depend on multiple parameters, including the number of mobile devices being handled by the edge server, the bandwidth, and the number of tasks. Decisions made also depended on the priority assigned to each objective. Finally, we compared the ODM-BCSA against a brute force search, proving that the ODM-BCSA is more efficient and greatly minimizes execution time when a huge number of mobile devices are involved (by 99.9 %).

关键词： Task analysis   Wireless communication   Communication system security   Jamming   Security   Internet of Things   Computational modeling   Mobile edge computing   wireless power transfer   physical layer security   secrecy energy efficiency  

摘要： This paper investigates secure offloading for a wireless powered mobile edge computing (MEC) system. Specifically, an energy-constrained user is first charged by a power beacon (PB) via wireless power transfer (WPT) and then leverages the harvested energy to offload its computation tasks to a MEC server in the presence of multiple eavesdroppers. To enhance the security of computation offloading, we propose a novel physical layer security-assisted scheme, in which the PB can also play the role of a cooperative jammer by transmitting jamming signals to interfere with the eavesdroppers. The objective is to maximize secrecy energy efficiency (SEE) of the MEC system by jointly optimizing the transmit power for WPT, offloading, and jamming, the time allocated for WPT and offloading, and the computation task partition while satisfying the secrecy offloading rate constraint and the energy causality constraint. To deal with the highly non-convex problem, we develop an efficient two-layer algorithm by resorting to variable substitutions, successive convex approximation, and the Dinkelbach method. Simulation results demonstrate that our designed scheme yields a SEE performance enhancement as compared to benchmarks.

关键词： Mobile edge computing   Computing offloading   Multi-objective algorithm   Immune algorithm  

摘要： Mobile edge computing (MEC) technology enables mobile devices in communication network systems offloading tasks to edge servers, effectively reducing response time and energy consumption. However, task offloading decisions become a significant difficulty when the system's mobile and service device count rises. In this paper, the problem of response time and energy consumption of the system is modeled as a multi-objective optimization problem, and we design an improved evolutionary algorithm based on immune algorithm, which can effectively obtain a set of solutions between response time and energy consumption. The simulation results show that our scheme can meet the response time requirements and obtain a lower energy consumption strategy when compared to the offloading scheme in the existing literature.

关键词： Internet of Vehicles   Blockchain   Mobile edge computing   Authentication mechanism   Privacy protection  

摘要： Blockchain technology can provide excellent support for identity authentication and access control mechanisms. In particular, blockchain technology can ensure that large amounts of confidential data generated by the Internet of Vehicles devices are stored and transmitted in a safe and reliable environment, which is the key to making system services optimal. In addition, mobile edge computing is the best solution for IoV applications to deal with low latency and limited computing and storage capacity of vehicle-mounted devices. Mobile edge computing can help IoV systems achieve a variety of functions and features, the most important of which is the ability to process terminal data in real-time. Even though the amount of data generated by IoV devices is growing rapidly, the system is still characterized by low latency and high efficiency. Because the communication between IoV devices is carried out in an untrusted environment, it is particularly important to design a secure and effective identity authentication scheme. Therefore, this paper proposes an efficient, safe, and time-sensitive authentication mechanism for devices on the Internet of Vehicles, which applies to a large number of scenarios. The mechanism is based on the blockchain concept and mobile edge computing technology. Security analysis shows that the proposed scheme meets the security requirements of the Internet of Vehicles and is resistant to many known attacks. By comparing with existing advanced IoT authentication schemes, the performance evaluation of the mechanism shows that the scheme enhances security features while reducing computation and communication overhead.

关键词： Interference   Decoding   Resource management   Delays   Autonomous aerial vehicles   Tin   Receivers   Rate-splitting   central cloud   mobile edge computing   hybrid networks   unmanned aerial vehicles  

摘要： This paper proposes, and evaluates the benefits of, a hybrid central cloud (CC) and mobile edge computing (MEC) platform, especially introduced to balance the network resources for joint communication and computation. The transmission is further empowered by splitting the users' messages into private and common parts, to mitigate the interference within the CC and MEC platforms. While several power-hungry, computationally-limited unmanned aerial vehicles (UAVs) are deployed at the cell-edge to boost the CC connectivity and relieve part of its computation burden, the CC connects to the base-stations via capacity-limited fronthauls. The paper then considers the problem of maximizing the weighted sum-rate subject to fronthaul and computation capacity, achievable rates, power, delay, and data-split constraints. Thereby determining the beamforming vectors associated with the private and common messages, the computation allocations, and the data-split factors. Such intricate non-convex optimization problem is tackled using an iterative algorithm that relies on well-chosen discrete relaxation, successive convex approximation, and fractional programming, and can be compellingly implemented in a distributed fashion. The simulations illustrate the proposed algorithm's capabilities for empowering joint communication and computation, and highlight the pronounced role of rate-splitting and common message decoding in alleviating large-scale interference in hybrid CC/MEC networks.

关键词： Servers   Memory   Cloud computing   Redundancy   Indexes   Multi-access edge computing   Low latency communication   Data deduplication   edge storage system   mobile edge computing   optimization problem   storage resource balance  

摘要： In the mobile edge computing (MEC) environment, edge servers with storage and computing resources are deployed at base stations within users' geographic proximity to extend the capabilities of cloud computing to the network edge. Edge storage system (ESS), is comprised by connected edge servers in a specific area, which ensures low-latency services for users. However, high data storage overheads incurred by edge servers' limited storage capacities is a key challenge in ensuring the performance of applications deployed on an ESS. Data deduplication, as a classic data reduction technology, has been widely applied in cloud storage systems. It also offers a promising solution to reducing data redundancy in ESSs. However, the unique characteristics of MEC, such as edge servers' geographic distribution and coverage, render cloud data deduplication mechanisms obsolete. In addition, data distribution must be balanced over edge storage systems to accommodate future data demands, which cannot be undermined by data deduplication. Thus, balanced edge data deduplication (BEDD) must consider deduplication ratio, data storage benefits, and resource balance systematically under the latency constraint. In this article, we model the novel BEDD problem formally and prove its NP-hardness. Then, we propose an optimal approach for solving the BEDD problem exactly in small-scale scenarios and a sub-optimal approach to solve large-scale BEDD problems with a theoretical performance guarantee. Extensive and comprehensive experiments conducted on a real-world dataset demonstrate the significant performance improvements of our approaches against four representative approaches.