关键词： Mobile edge computing  

摘要： The Metaverse has emerged as the next generation of the Internet. It aims to provide an immersive, persistent virtual space where people can live, learn, work and interact with each other. However, the existing technology is inadequate to guarantee high visual quality and ultra-low latency service for the Metaverse players. Mobile Edge Computing (MEC) is a paradigm where proximal edge servers are utilized to perform computation-intensive and latency-sensitive tasks like image processing and video analysis. In MEC, the large amount of data is processed by edge servers closest to where it is captured, thus significantly reducing the latency and providing almost real-time performance. In this paper, we integrate fundamental elements (5G and 6G wireless communications, Blockchain, digital twin and artificial intelligence) into the MEC framework to facilitate the Metaverse. We also elaborate on the research problems and applications in the MEC-enabled Metaverse. Finally, we provide a case study to establish a thorough knowledge of the user utility maximization problem in a real-world scenario and gain some insights about trends in potential research directions. © 2022, CC BY.

关键词： Mobile edge computing   Reconfigurable intelligent surfaces   Lyapunov stochastic optimization   Dynamic resource allocation  

摘要： In this paper, we propose a novel algorithm for energy-efficient low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with reconfigurable intelligent surfaces (RISs). We consider a scenario where new computing requests are continuously generated by a set of devices and are handled through a dynamic queueing system. Building on stochastic optimization tools, we devise a dynamic learning algorithm that jointly optimizes the allocation of radio resources (i.e., power, transmission rates, sleep mode and duty cycle), computation resources (i.e., CPU cycles), and RIS reflectivity parameters (i.e., phase shifts), while guaranteeing a target performance in terms of average end-to-end delay. The proposed strategy enables dynamic control of the system, performing a low-complexity optimization on a per-slot basis while dealing with time-varying radio channels and task arrivals, whose statistics are unknown. The presence and optimization of RISs helps boosting the performance of dynamic MEC, thanks to the capability to shape and adapt the wireless propagation environment. Numerical results assess the performance in terms of service delay, learning, and adaptation capabilities of the proposed strategy for RIS-empowered MEC.

关键词： Unmanned aerial vehicle   Mobile edge computing   NOMA   User clustering   Successive convex approximation  

摘要： This paper studies a multi-unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system, where UAVs are equipped with a powerful MEC server to provide computation services for ground users. Under NOMA protocol and partial offloading mode, we aim to jointly optimize the NOMA user clustering, sub-carrier assignment, transmit power of users, CPU frequency, and 3-D location of UAVs to maximize the sum compu-tation bits of all users. Since the formulated problem is a mixed-integer non-linear optimization, we propose a three-stage algorithm to address the four sub-problems iteratively. First, two efficient NOMA user clustering algorithms are proposed to divide the users into several NOMA clusters. Second, a heuristic sub-carrier assign-ment algorithm is presented based on the channel gain and interfering users. Third, by adopting the successive convex approximation (SCA) technique and Lagrange dual method, the closed-form expressions of the transmit power and CPU frequency of users are derived. Forth, a convex optimization problem is obtained to optimize the 3-D location of UAVs. The numerical evaluations illustrate that the proposed system outperforms the benchmark schemes in terms of sum computation bits performance.

关键词： Fifth-generation   Mobile edge computing   Differentially private  

摘要： Machine learning has become a core technology in areas such as big data, Internet of Things, and cloud computing. Training machine learning models requires a large amount of data, How to protect these data with low cost and high efficiency is an important issue. This paper proposes a trajectory data collection model based on the 5G-based mobile edge computing, and uses the service characteristics of the MEC server to propose a differentially private protection scheme of trajectory data based on 5G-based mobile edge computing (5GMEC-DP). If the encrypted location information does not belong to the service range of the current MEC server, it will be projected to the service edge of the MEC server, thereby limiting the amount of noise of the Geo-Indistinguishability algorithm, making it suitable for the protection of trajectory data, and it also proves that the 5GMEC-DP algorithm still satisfies the definition of epsilon-Geo-Indistinguishability. Finally, experiments on the real data set prove that 5GMEC-DP can maintain the high practicability of the data when the degree of privacy protection is high. At symbolscript and symbolscript Average Qloss of each coordinate in the trajectory decreases by 64% and 81%, respectively, compared with Geo-Indistinguishability algorithm. The overall database availability loss decreases by 64% and 82%, respectively.

关键词： mobile edge computing   DDoS attacks   resource-based pricing   game theory   Nash equilibrium  

摘要： Resource-constrainted and located closer to users,edge servers are more vulnerable to Distributed Denial of Service(DDoS)*** order to mitigate the impact of DDoS attacks on benign users,this paper designed a Resource-based Pricing Collaborative approach(RPC)in mobile edge *** introducing the influence of resource prices on requester in economics,a collaboration model based on resource pricing was established,and the allocation of user request was regarded as a game strategy to obtain the overall minimum offloading cost of the user in *** article theoretically proved the existence and rationality of the Nash ***,simulation results verified the effectiveness and feasibility of the proposed approach in two experimental *** results shows that RPC can effectively improve the network ability to mitigate DDoS attacks,and alleviate the adverse effects of server attacks under delay constraints.

关键词： mobile edge computing   computation offloading   caching   resource allocation  

摘要： Applications with sensitive delay and sizeable data volumes,such as interactive gaming and augmented reality,have become popular in recent *** applications pose a huge challenge for mobile users with limited *** offloading is a mainstream technique to reduce execution delay and save energy for mobile ***,computation offloading requires communication between mobile users and mobile edge computing(MEC) *** a mechanism would difficultly meet users’ demand in some data-hungry and computation-intensive applications because the energy consumption and delay caused by transmissions are considerable expenses for *** task data can effectively reduce the data transmissions when users offload their tasks to the MEC *** limited caching space at the MEC server calls for judiciously decide which tasks should be *** by this,we consider the joint optimization of computation offloading and task caching in a cellular *** particular,it allows users to proactively cache or offload their tasks at the MEC *** objective of this paper is to minimize the system cost,which is defined as the weighted sum of task execution delay and energy consumption for all *** at establishing optimal performance bound for the system design,we formulate an optimization problem by jointly optimizing the task caching,computation offloading,and resource *** problem is a challenging mixed-integer non-linear programming problem and is NP-hard in *** solve it efficiently,by using convex optimization,Karmarkar ’s algorithm and the proposed fast search algorithm,we obtain an optimal solution of the formulated problem with manageable computational *** simulation results show that in comparison to some representative benchmark methods,the proposed solution can effectively reduce the system cost.

关键词： workflow application   task offloading   energy saving   heuristic algorithm   mobile edge computing  

摘要： With the rapid growth of the Industrial Internet of Things(IIoT), the Mobile Edge Computing(MEC) has coming widely used in many emerging scenarios. In MEC, each workflow task can be executed locally or offloaded to edge to help improve Quality of Service(QoS) and reduce energy consumption. However, most of the existing offloading strategies focus on independent applications, which cannot be applied efficiently to workflow applications with a series of dependent tasks. To address the issue,this paper proposes an energy-efficient task offloading strategy for large-scale workflow applications in MEC. First, we formulate the task offloading problem into an optimization problem with the goal of minimizing the utility cost, which is the trade-off between energy consumption and the total execution time. Then, a novel heuristic algorithm named Green DVFS-GA is proposed, which includes a task offloading step based on the genetic algorithm and a further step to reduce the energy consumption using Dynamic Voltage and Frequency Scaling(DVFS) technique. Experimental results show that our proposed strategy can significantly reduce the energy consumption and achieve the best trade-off compared with other strategies.

关键词： Mobile edge computing   service reliability   user coverage   application deployment   approximation approach  

摘要： Mobile edge computing (MEC), as an emerging technology, allows application vendors to deploy application instances on edge servers to deliver low-latency services to nearby end-users. However, due to hardware faults, software exceptions, or cyberattacks, edge servers are prone to failures in the highly distributed and dynamic MEC environment. Hence service reliability must be ensured when failures occur. This raises a critical and open problem - improving service reliability when deploying application instances in the MEC environment. In this article, we jointly consider both user coverage and service reliability when deploying application instances on edge servers with a given application deployment budget K. We formally define this joint Coverage-Reliability for K-Budgeted Edge Application Deployment (CR-BEAD) problem and model it as a constrained optimization problem. Next, we propose an optimal approach (named BEAD-O) based on integer programming to find optimal solutions to small-scale CR-BEAD problems. We also propose a greedy approach named BEAD-G with a constant approximation ratio of 1 - 1/e to solve large-scale CRBEAD problems efficiently. Extensive experimental evaluation against three representative approaches illustrates the effectiveness and efficiency of our approaches.

关键词： Mobile edge computing   Quality of experience   Task ofloading   Communication networks   Particle swarm optimizati on  

摘要： Mobile Edge Computing (MEC) has been considered a promising solution that can address capacity and performance challenges in legacy systems such as Mobile Cloud Computing (MCC). In particular, such challenges include intolerable delay, congestion in the core network, insufficient Quality of Experience (QoE), high cost of resource utility, such as energy and bandwidth. The aforementioned challenges originate from limited resources in mobile devices, the multi-hop connection between end-users and the cloud, high pressure from computation-intensive and delay-critical applications. Considering the limited resource setting at the MEC, improving the efficiency of task offloading in terms of both energy and delay in MEC applications is an important and urgent problem to be solved. In this paper, the key objective is to propose a task offloading scheme that minimizes the overall energy consumption along with satisfying capacity and delay requirements. Thus, we propose a MEC-assisted energy-efficient task offloading scheme that leverages the cooperative MEC framework. To achieve energy efficiency, we propose a novel hybrid approach established based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) to solve the optimization problem. The proposed approach considers efficient resource allocation such as sub-carriers, power, and bandwidth for offloading to guarantee minimum energy consumption. The simulation results demonstrate that the proposed strategy is computational-efficient compared to benchmark methods. Moreover, it improves energy utilization, energy gain, response delay, and offloading utility.

关键词： Mobile edge computing   Computation floading   Delay sensitivity   Centralized ofloading  

摘要： As a promising computing paradigm,Mobile Edge Computing(MEC)provides communication and computing capability at the edge of the network to address the concerns of massive computation requirements,constrained battery capacity and limited bandwidth of the Internet of Things(IoT)*** existing works on mobile edge task ignores the delay sensitivities,which may lead to the degraded utility of computation offloading and dissatisfied *** this paper,we study the delay sensitivity-aware computation offloading by jointly considering both user's tolerance towards delay of task execution and the network status under computation and communication ***,we use a specific multi-user and multi-server MEC system to define the latency sensitivity of task offloading based on the analysis of delay distribution of task ***,we propose a scoring mechanism to evaluate the sensitivity-dependent utility of task execution and devise a Centralized Iterative Redirection Offloading(CIRO)algorithm to collect all information in the MEC *** starting with an initial offloading strategy,the CIRO algorithm enables IoT devices to cooperate and iteratively redirect task offloading decisions to optimize the offloading strategy until it *** simulation results show that our method can significantly improve the utility of computation offloading in MEC systems and has lower time complexity than existing algorithms.