关键词： Age of information   low-latency communications   mobile edge computing   offloading ratio  

摘要： In this paper, we analyze the average age of information (AoI) and the average peak AoI (PAoI) of a multiuser mobile edge computing (MEC) system where a base station (BS) generates and transmits computation-intensive packets to user equipments (UEs). In this MEC system, we focus on three computing schemes: (i) The local computing scheme where all computational tasks are computed by the local server at the UE, (ii) The edge computing scheme where all computational tasks are computed by the edge server at the BS, and (iii) The partial computing scheme where computational tasks are partially allocated at the edge server and the rest are computed by the local server. Considering exponentially distributed transmission time and computation time and adopting the first come first serve (FCFS) queuing policy, we derive closed-form expressions for the average AoI and average PAoI. To address the complexity of the average AoI expression, we derive simple upper and lower bounds on the average AoI, which allow us to explicitly examine the dependence of the optimal offloading decision on the MEC system parameters. Aided by simulation results, we verify our analysis and illustrate the impact of system parameters on the AoI performance.

关键词： Delays   Task analysis   Servers   Internet of Things   Edge computing   Ultra reliable low latency communication   Queueing analysis   Grant-free random access   6G   mobile edge computing   digital twin  

摘要： Digital twin-aided (DT) edge computing is investigated, where users utilize grant-free random access with adaptive rate to offload their tasks to the edge server. A novel, with lower implementation complexity, probabilistic partial offloading scheme is introduced, while each device is assumed to have an infinite buffer to store its tasks. The aim of the proposed work is to minimize the average delay of the partial offloading. To that end, the average delay of waiting in the queue, the delay of offloading, and the local computation delay are extracted by using queuing theory tools. Then, the non-convex problem of minimizing the average delay of all clients is formulated, while taking into account DT imperfections. Successive convex approximation (SCA), alternating optimization (AO), and various algebraic manipulations are utilized to transform the problem into an equivalent convex problem with tractable solution. Finally, simulation results showcase the value of the proposed analysis and offer important insights for the proposed DT-aided edge network. Specifically, the proposed partial offloading scheme is shown to be more delay efficient compared to both local computing and full offloading, particularly, for greater task generation rates at the users. Also, the impact of the DT imperfections at the average delay is shown to be more notable as the number of users, or the tasks' size, increases.

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： Mobile edge computing   Hyper-heuristics   Meta-heuristics   Task offloading   Optimization  

摘要： To overcome with the computation limitation of resource-constrained wireless IoT edge devices, providing an efficient task computation offloading and resource allocation in distributed mobile edge computing environment is consider as a challenging and promising solution. Hyper-heuristic in recent times is gaining popularity due to its general applicability of same solution to solve different types of problems. Hyper-heuristic is generally a heuristic method or framework which iteratively evaluates and chooses the best low-level heuristic, to solve different types of problems. In this paper, we try to solve wireless device task offloading in mobile edge computing, which is a non-convex and NP-Hard problem by using a proposed novel Hyper-Heuristic Framework using Stochastic Heuristic Selection (HHFSHS) using Contextual Multi-Armed Bandit (CMAB) with Epsilon-Decreasing strategy, considering two key Quality of Service (QoS) objectives computation time and energy consumption. These multiobjective criteria are modeled as single-objective optimization problem with the goal to minimize latency and energy consumption of wireless devices without losing the pareto optimality. Finally, evaluate its performance by comparing with other individual meta-heuristic algorithms.

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： 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： 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： 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： 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.