关键词： Mobile Edge Computing   Buffer-and-server-aided relay   Task offloading   Convex optimization  

摘要： Mobile Edge Computing (MEC) has attracted significant research efforts in the recent years. However, these works consider mostly the computation resources located at the cloud centers and wireless access nodes, ignoring the possibility of utilizing server-empowered relays to improve the performance. In this paper, we study stochastic relay-assisted MEC in systems with discrete transmission time-line and block fading wireless channels. In order to clearly identify and inspect the fundamental affecting factors, we investigate the building block of this architecture, namely a hierarchical network consisting of a source, a buffer-and-server-aided relay and another higher-level computing node. We provide a framework to take into account the effects of the fading channels, the task arrival dynamics as well as the queuing delays in both the transmission and computation buffers, which facilitates the derivation of the expression for the Average Response Time (ART). Based on that and the system average power consumption in each slot, we introduce the concept of Average Response Energy (ARE) as a novel metric to capture the energy efficiency in MEC while considering the stochastic nature of the system parameters. Accordingly, we propose two offloading schemes with their respective problem formulations, namely the Minimum ART (MART) and the Minimum ARE (MARE) schemes, to optimize the transmission power and task assignment probability while keeping the system queues stable. We demonstrate the difference of the formulated problems with the relevant problem in a recent work, analyze the properties of the problems and noting them, we propose effective solution methods. Using extensive simulations, we validate the presented analysis and show the effectiveness of the proposed schemes in comparison with various baseline methods adapting existing approaches. Moreover, we provide essential insights on the performance of the proposed schemes in terms of the average delay and power consu

关键词： Data offloading   mobile edge computing   security   machine learning   artificial intelligence   XGBoost   salp swarm algorithm  

摘要： Mobile edge computing(MEC)provides effective cloud services and functionality at the edge device,to improve the quality of service(QoS)of end users by offloading the high computation ***,the introduction of deep learning(DL)and hardware technologies paves amethod in detecting the current traffic status,data offloading,and cyberattacks in *** study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC(AIMDO-SMEC)*** proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks(SNN)to determine the traffic status in the MEC ***,an adaptive sampling cross entropy(ASCE)technique is utilized for data offloading in MEC ***,the modified salp swarm algorithm(MSSA)with extreme gradient boosting(XGBoost)technique was implemented to identification and classification of cyberattack that exist in the MEC *** examining the enhanced outcomes of the AIMDO-SMEC technique,a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDOSMEC technique with the minimal completion time of tasks(CTT)of 0.680.

关键词： Task analysis   Delays   Geometry   Stochastic processes   Servers   Edge computing   Computational modeling   Edge computing   human-type communication   multiple-association   multi-access   mobile edge computing   stochastic geometry   ultra-dense network  

摘要： The recent advances and unprecedented ubiquity of computation-intensive applications such as virtual reality and mobile augmented reality force new approaches to handle the accompanying challenges. Ultra-Dense Network (UDN) as a leading direction in 5G and beyond offers an opportunistic degree of freedom to be exploited where a massive number of low-power and low-cost Small Cells (SCs) are deployed. In particular, integrating Edge Computing Servers (ECSs) within the SCs at the edge of the cellular network paves the way to tackle several challenges including the processing of computation-intensive tasks with low latency. To exploit the full potentials of UDNs and ECSs, we deploy multiple associations of SCs to the same user while partitioning and offloading its computation-intensive task to the integrated ECSs therein. In this regard, we formulate a mathematical framework using tools from stochastic geometry to evaluate the average processing delay per user. Extensive Monte-Carlo simulations are conducted to verify the accuracy of our analytical results under different system parameters. Results show the existence of an optimal order of multiple-association. In addition, we propose a novel offline task division approach which significantly reduces the overall delay.

关键词： Blockchains   Vehicular ad hoc networks   Task analysis   Reliability   Computational modeling   Security   Markov processes   Vehicular ad hoc networks   mobile edge computing   blockchain   Markov decision process  

摘要： Vehicular ad hoc networks (VANETs) have become an important branch of future 6G smart wireless communications. As an emerging key technology, multi-access edge computing (MEC) provides low-latency, high-speed, and high-capacity network services for the VANETs. In this paper, we propose a novel framework for blockchain-based, hierarchical multi-access edge computing for the future VANET ecosystem (BMEC-FV). In the underlying VANET environment, we propose a trust model to ensure the security of the communication link between vehicles. Multiple MEC servers calculate the trust between vehicles through computing offloading. Meanwhile, the blockchain system plays an important role to manage the entire BMEC-FV architecture. We aim to optimize the throughput and the quality of services (QoS) for MEC users in the lower layer of the system architecture. In this framework, the main challenge is how to effectively reach consensus among blockchain nodes while ensuring the performance of MEC systems and blockchains. The blocksize of blockchain nodes, the number of consensus nodes, reliable features of each vehicle, and the number of producing blocks for each block producer are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space, and reward function. Since it is difficult for this to be solved by traditional methods, we propose a novel deep compressed neural network scheme. Simulation results illustrate the superiority of the BMEC-FV ecosystem.

关键词： Optimization   Radio frequency   Millimeter wave communication   Resource management   Wireless communication   Simulation   Array signal processing   Millimeter-wave communication   mobile edge computing   reconfigurable intelligent surface   computation efficiency   hybrid beamforming  

摘要： In this paper, we present the computation-efficient resource allocation (RA) schemes for millimeter-wave mobile edge computing (mmWave-MEC) system with the aid of reconfigurable intelligent surface (RIS), which is used to assist the uplink communication from the users to the base station (BS). By means of the theoretical analysis, the achievable rate and computation efficiency (CE) are derived. Then, the optimization problem for the CE maximization under the constraints of the minimum rate, maximum power consumption and local CPU frequency is formulated, where the joint design of the hybrid beamforming at the BS and the passive beamforming at the RIS as well as the local resource allocation of each user is carried out. An effective iterative algorithm based on the penalized inexact block coordinate descent (BCD) method is proposed to obtain the computation-efficient RA scheme. Next, a low-complexity suboptimal RA scheme based on the BCD method is proposed, and corresponding algorithm is presented. Simulation results show that the proposed schemes are effective, and high CE can be attained. Moreover, the second scheme can achieve the CE performance close to the first scheme but with lower complexity. Besides, it is effective to deploy the RIS scheme in mmWave-MEC system, which can strike a balance between the CE and energy consumption when compared to the conventional relay schemes.

关键词： Software-defined networks   Fault tolerance   Distributed control plane   Stochastic network calculus   Mobile edge computing  

摘要： Mobile edge computing (MEC) is a key feature of next-generation mobile networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the users. With the advent of the next-generation mobile networks, researchers have turned their attention to various aspects of edge computing in an effort to leverage the new capabilities offered by 5G. So, the integration of software defined networking (SDN) and MEC techniques was seriously considered to facilitate the orchestration and management of Mobile Edge Hosts (MEH). Edge clouds can be installed as an interface between the local servers and the core to provide the required services based on the known concept of the SDN networks. Nonetheless, the problem of reliability and fault tolerance will be of great importance in such networks. The paper introduced a dynamic architecture that focuses on the end-to-end mobility support required to maintain service continuity and quality of service. This paper also presents an SDN control plane with stochastic network calculus (SNC) framework to control MEC data flows. In accordance with the entrance processes of different QoS-class data flows, closed-form problems were formulated to determine the correlation between resource utilization and the violation probability of each data flow. Compared to other solutions investigated in the literature, the proposed approach exhibits a significant increase in the throughput distributed over the active links of mobile edge hosts. It also proved that the outage index and the system's aggregate data rate can be effectively improved by up to 32%. (C) 2022 Published by Elsevier B.V.

关键词： Wireless body area networks   anti-eavesdropping   mobile edge computing   power control  

摘要： Wireless body area network (WBAN) suffers secure challenges, especially the eavesdropping attack, due to constraint resources. In this article, deep reinforcement learning (DRL) and mobile edge computing (MEC) technology are adopted to formulate a DRL-MEC-based jamming-aided anti-eavesdropping (DMEC-JAE) scheme to resist the eavesdropping attack without considering the channel state information. In this scheme, a MEC sensor is chosen to send artificial jamming signals to improve the secrecy rate of the system. Power control technique is utilized to optimize the transmission power of both the source sensor and the MEC sensor to save energy. The remaining energy of the MEC sensor is concerned to ensure routine data transmission and jamming signal transmission. Additionally, the DMEC-JAE scheme integrates with transfer learning for a higher learning rate. The performance bounds of the scheme concerning the secrecy rate, energy consumption, and the utility are evaluated. Simulation results show that the DMEC-JAE scheme can approach the performance bounds with high learning speed, which outperforms the benchmark schemes.

关键词： Task analysis   Wireless sensor networks   Wireless communication   Sensors   Optimization   Fading channels   Servers   Mobile edge computing   wireless power transfer   computation offloading   resource allocation   real-time online optimization  

摘要： Wireless powered multi-access edge computing (MEC) has emerged as a promising paradigm to enable high-performance computation of energy-constrained wireless devices (WDs) in internet of things (IoT) systems. However, to overcome the severe path loss of both energy transfer and data communications, wireless powered MEC suffers from high operating power consumption. To achieve sustainable and economic system operation, this paper focuses on developing energy-efficient online data processing strategy for wireless powered MEC systems under stochastic fading channels. In particular, we consider a hybrid access point (HAP) transmitting RF energy to and processing the sensing data offloaded from multiple WDs. Under an average power constraint of the HAP, we target at maximizing the long-term average data sensing rate of the WDs while maintaining task data queue stability. To this end, we formulate a multi-stage stochastic optimization problem to control the energy transfer and task data processing in sequential time slots. Without the knowledge of future channel fading, it is very challenging to determine the sequential control actions that are tightly coupled by the battery and data buffer dynamics. To solve the problem, we propose a Lyapunov optimization-based online algorithm named LEESE, which decomposes the multi-stage stochastic problem into per-slot deterministic optimization problems. We show that each per-slot problem can be equivalently transformed into a convex optimization problem. To facilitate online implementation in large-scale MEC systems, instead of solving the per-slot problem with off-the-shelf convex algorithms, we propose a block coordinate descent (BCD)-based method that produces a close-to-optimal solution in less than 0.04% of the computation delay. Simulation results demonstrate that the proposed LEESE algorithm can provide 18% higher data sensing rate than the representative benchmark methods considered, while incurring sub-millisecond computatio

关键词： Task analysis   Optimization   Ultra reliable low latency communication   Real-time systems   Industrial Internet of Things   Digital twins   Computational modeling   Digital twin   metaverse   mobile edge computing   ultra-reliable and low latency communications  

摘要： In this letter, we propose a novel digital twin scheme supported metaverse by jointly considering the integrated model of communications, computing, and storage through the employment of mobile edge computing (MEC) and ultra-reliable and low latency communications (URLLC). The MEC-based URLLC digital twin architecture is proposed to provide powerful computing infrastructure by exploring task offloading, and task caching techniques in nearby edge servers to reduce the latency. In addition, the proposed digital twin scheme can guarantee stringent requirements of reliability and low latency, which are highly applicable for the future networked systems of metaverse. For this first time in the literature, our paper addresses the optimal problem of the latency/reliablity in digital twins-enabled metaverse by optimizing various communication and computation variables, namely, offloading portions, edge caching policies, bandwidth allocation, transmit power, computation resources of user devices and edge servers. The proposed scheme can improve the quality-of-experience of the digital twin in terms of latency and reliability with respect to metaverse applications.

关键词： Mobile edge computing   MES deployment   K-Means   Task offloading  

摘要： Recent years have witnessed the effect of Mobile Edge Computing (MEC) during resource-intensive and time-critical applications toward various mobile devices. Therefore, Mobile Edge Severs (MES) are widely deployed adjacent to the 5G Base Station (BS) to upgrade the performance of the specific application system. Unfortunately, there have rare researches for the location planning of edge servers in the MEC scenario. The deployment of MES may cover a wide range of theoretical concerns, such as computation offloading cost, system performance. In this paper, we consider the problem of optimization of MES deployment in multiple BSs scenarios. To achieve this, we proposed an approach based on the improved K-Means clustering to determine the theoretical location and amount of edge servers. Besides, mobile computation tasks are strategically assigned to the distance-first edge server. To this end, we then develop a reasonable deployment scheme based on K-means for edge servers, which can effectively reduce the network delay, energy consumption, and cost of edge servers. We have compared the density-based clustering algorithm proposed in the recent research. Extensive simulation results indicate that our strategy reduces average completion time by 15.7%, power consumption by 22%, and overhead by 19% in edge server deployment issues.