关键词： Mobile edge computing   blockchain   image sharing   user equipment assignment   service entity deployment  

摘要： Integrating dense small cell (DSC) networks with mobile edge computing is employed by SG to tackle the contradiction between the computation limitations of user equipment (UE) and the stringent latency requirement of services. This paper investigates the service-oriented edge resource allocation problem in DSC networks, determining where to deploy the service entity, how many service entities should he deployed at each edge cloud, and how to assign the UEs to service entities. The problem is challenging for the following three aspects: 1) Service entity deployment and UE assignment are highly coupled. 2) Due to the overlap of coverage regions of densely deployed small cells, the allocation mechanism of different base stations has mutual effects on the overall service performance. 3) Considering the limited resources of edge clouds, it is a thorny problem to encourage edge clouds to cache and share service startup images. We devote the following efforts to tackle the problem under these challenges. First, we explore blockchain's decentralized, traceable, and secure characteristics, and propose a scheme to encourage image sharing in mobile edge computing. Second, we formulate the service-oriented edge resource allocation as mixed integer non-linear programming. Third, towards the target of reducing the computational complexity, we decouple UE assignment from service entity deployment and solve it through Gibbs sampling. Moreover, the power of Lyapunov optimization and convex optimization is incorporated to reduce the long-term power consumption and budget. Experiment results demonstrate the superiority of our approach over current notable solutions.

关键词： Mobile edge computing   Internet of Things   Unmanned aerial vehicle   Multiobjective trajectory optimization  

摘要： Mobile edge computing (MEC) is used to provide IT services environment and cloud computing capabilities at the edge of the network. As the technology of unmanned aerial vehicles (UAVs) matures, the growing attempts have been made to use UAVs to replace fixed ground stations for MEC due to their flexibility. In this work, we study the multiobjective trajectory optimization for mobile edge computing system assisted by a single UAV, where the UAV is used to provide computing services for Internet of Things (IoT) devices located on the ground. A multiobjective trajectory optimization problem is formulated, which not only needs to minimize the energy consumption of the MEC system to provide computing services to all IoT devices, but also minimize the task urgency indicator by optimizing the UAV's flight trajectory. In this problem, the number and the locations of hover points (HPs) of UAVs have been taken into consideration. To solve this problem, a multiobjective trajectory optimization algorithm with a cutting and padding encoding strategy is proposed, where the cutting and padding encoding strategy is used to help optimize the population whose individuals may have different lengths. The verification experiments are carried out on a set of instances with up to 400 IoT devices and the experimental results demonstrate the promising performance of the proposed algorithm for trajectory optimization problems in a single-UAV-assisted MEC system.

关键词： Mobile edge computing   Service placement   Profit maximization   Backhaul flow  

摘要： With the development over the past decades, mobile edge computing has been widely used in many fields. Benefit from the low latency brought by the proximity of mobile edge computing to the user side and the relief of bandwidth pressure, mobile edge computing can play a significant role in services with large backhaul traffic such as live video streaming. However, the edge server does not have the same powerful hardware as the cloud server, and the limited resources on the edge server make the deployment of services need to be carefully considered. Small-sized service providers in edge networks may not have their own edge servers and need to rent the edge servers of edge server providers to deploy their own services. In this paper, we present a service provider profit maximization problem under the service scenario of large backhaul traffic. Considering the needs of server providers and service providers, we design a novel method, Distributed Profit-based Matching Algorithm (DPMA), to solve this problem according to the idea of Deferred Acceptance algorithm. The experimental results show that the proposed method is superior to the existing profit maximization methods in mobile edge computing.

关键词： Task analysis   Sensors   Resource management   Delays   Reinforcement learning   Safety   Servers   Connected automated vehicles (CAVs)   deep reinforcement learning (DRL)   federated reinforcement learning (FRL)   intelligent transportation systems   mobile edge computing   vehicular networks  

摘要： For future connected automated vehicles (CAVs) networks, the joint optimization of communication, sensing, and computing resources is crucial to guarantee the performance of cooperative automated driving's safety, which is attracting more and more attention. However, the existing works have not considered the low-latency requirement for the raw perception data sharing with both wireless communication link capability and computing efficiency constraints, causing a serious threat to the cooperative automated driving's safety in CAVs networks. In this article, a vehicle-road-base station cooperation architecture is designed, and a federated reinforcement learning (FRL)-based task offloading and resource allocation algorithm in the CAVs network is proposed to reduce the task execution delay with different communication and computing constraints. The problem of execution delay minimization is theoretically formulated and analyzed under three task practical offloading modes. To adapt to the dynamic topology of the CAVs network, we design a deep reinforcement learning algorithm to achieve the optimal task offloading and resource allocation. To further reduce the data transmission overhead of the centralized reinforcement learning algorithm, the FRL-enabled algorithm is proposed to minimize the execution delay of the optimal task offloading and resource allocation among multiple CAVs. Both the simulation and hardware testbed results verify that the proposed algorithms can not only reduce the execution delay and the communication overhead but also improve the system throughput.

关键词： Mobile edge computing  

摘要： Federated Learning (FL) with quantization and deliberately added noise over wireless networks is a promising approach to preserve user differential privacy while reducing wireless resources. Specifically, an FL learning process can be fused with quantized Binomial mechanism-based updates contributed by multiple users to reduce the communication overhead/cost and to protect the privacy of participating users. However, optimizing quantization parameters, communication resources (e.g., transmit power, bandwidth, and quantization bits), and the added noise to guarantee the differential privacy requirement and performance of the learned FL model remains an open and challenging problem. This article aims to jointly optimize the level of quantization, parameters of the Binomial mechanism, and communication resources so as to maximize the convergence rate under the constraints of the wireless network and differential privacy (DP) requirement. To that end, we first derive a novel DP budget estimation of the FL with quantization and Binomial noise that is tighter than the state-of-the-art bound. We then analyze the relationship between the convergence rate and the transmit power, the bandwidth, the transmission time, and the quantization/noise parameters and provide a theoretical bound on the convergence rate. This theoretical bound is decomposed into two components, including the variance of the global gradient and an upper bound on the quadratic bias that can be minimized by optimizing the communication resources, quantization, and added noise parameters. The resulting optimization turns out to be a Mixed-Integer Non-linear Programming (MINLP) problem. To tackle it, we first transform this MINLP problem into a new problem whose solutions are proved to be the optimal solutions of the original one. We then propose an approximate algorithm to solve the transformed problem with an arbitrary relative error guarantee. Extensive simulations show that under the same wireless resour

关键词： Mobile Edge Computing   Task Offloading Optimization   Reinforcement Learning   Q-learning  

摘要： The edge-cloud computing architecture has been introduced to industrial circles to ensure the time constraints for industrial computing tasks. Besides the central cloud, various numbers of edge servers (ESes) are deployed in a distributed manner. In the meantime, most large factories currently use auto guided vehicles (AGVs). They usually travel along a given route and can help offload tasks to ESes. An ES maybe accessed by multiple AGVs, thus incurring offloading and processing delays due to resource competition. In this paper, we investigate the offloading scheduling issue for cyclical tasks and put forth the Multi-AGV Cyclical Offloading Optimization (MCOO) algorithm to reduce conflicts. The solution divides the offloading optimization problem into two parts. Firstly, the load balancing algorithm and greedy algorithm are utilized to find the optimal allocation of tasks for a single AGV under limited conditions. Then, multiple AGVs are asynchronously trained by applying the Reinforcement Learning-based A3C algorithm to optimize the offloading scheme. The simulation results show that the MCOO algorithm improves the global offloading performance both in task volume and adaptability compared with the baseline algorithms.

关键词： Mobile edge computing  

摘要： The Metaverse has emerged as the successor of the conventional mobile internet to change people's lifestyles. It has strict visual and physical requirements to ensure an immersive experience (i.e., high visual quality, low motion-to-photon latency, and real-time tactile and control experience). However, the current technologies fall short to satisfy these requirements. Mobile edge computing (MEC) has been indispensable to enable low latency and powerful computing. Moreover, the sixth generation (6G) networks promise to provide end users with seamless communications. In this paper, we explore and demonstrate the synergistic relationship between 6G and mobile-edge technologies in empowering the Metaverse with ubiquitous communications and computation. This includes the usage of heterogeneous radios, intelligent reflecting surfaces (IRS), non-orthogonal multiple access (NOMA), and digital twins (DTs) - assisted MEC. We also discuss emerging communication paradigms (i.e., semantic communication, holographic-type communication, and haptic communication) to further satisfy the demand for human-type communications and fulfill user preferences and immersive experiences in the Metaverse. Copyright © 2022, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Integrated sensing, computation, and communication (ISCC) has been recently considered as a promising technique for beyond 5G systems. In ISCC systems, the competition for communication and computation resources between sensing tasks for ambient intelligence and computation tasks from mobile devices becomes an increasingly challenging issue. To address it, we first propose an efficient sensing framework with a novel action detection module. In this module, a threshold is used for detecting whether the sensing target is static and thus the overhead can be reduced. Subsequently, we mathematically analyze the sensing performance of the proposed framework and theoretically prove its effectiveness with the help of the sampling theorem. Based on sensing performance models, we formulate a sensing performance maximization problem while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve it, we propose an optimal resource allocation strategy, in which the minimum resource is allocated to computation tasks, and the rest is devoted to the sensing task. Besides, a threshold selection policy is derived and the results further demonstrate the necessity of the proposed sensing framework. Finally, a real-world test of action recognition tasks based on USRP B210 is conducted to verify the sensing performance analysis. Extensive experiments demonstrate the performance improvement of our proposal by comparing it with some benchmark schemes. Copyright © 2022, The Authors. All rights reserved.

关键词： Mobile edge computing  

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. Copyright © 2022, The Authors. All rights reserved.

关键词： Mobile handsets   Servers   Resource management   Games   Base stations   Interference   Mobile computing   Mobile device allocation   interference   data rate   game theory   nash equilibrium   potential game   mobile edge computing  

摘要： Mobile edge computing (MEC), as an emerging and prospective mobile computing paradigm, allows a content provider to serve its users by allocating their mobile devices to nearby edge servers to lower the latency in the delivery of its content to those mobile services. From the content provider's perspective, a cost-effective mobile device allocation (MDA) aims to allocate maximum mobile devices to minimum edge servers. However, the allocation of excessive mobile devices to an edge server may result in severe communication interference and consequently, impact mobile devices' data rates. Sometimes, not all the mobile devices can be allocated to edge servers and thus have to retrieve content from the remote cloud through base stations with high latency. The connection between these mobile devices and base stations also incur communication interference. In this paper, we formally model this Interference-aware mobile edge device allocation (I-MEDA) problem, and propose a game-theoretic based approach named I-MEDAGame to formulate the I-MEDA problem as an I-MEDA game. In the I-MEDA game, allocation decisions are made for individual mobile devices in parallel to alleviate the need for centralized optimization. Our theoretical analysis of I-MEDAGame shows that it admits at least one Nash equilibrium. To solve the I-MEDA problem, I-MEDAGame employs a novel decentralized algorithm to find the Nash equilibrium of the IMEDA game. The performance of I-MEDAGame is theoretically analyzed and experimentally evaluated. The results show that I-MEDAGame can solve the I-MEDA problem effectively and efficiently, outperforming four representative approaches significantly.