关键词： Mobile edge computing  

摘要： Reconfigurable intelligent surface (RIS)-empowered communication is being considered as an enabling technology for sixth generation (6G) wireless networks. The key idea of RIS-assisted communication is to enhance the capacity, coverage, energy efficiency, physical layer security, and many other aspects of modern wireless networks. At the same time, mobile edge computing (MEC) has already shown its huge potential by extending the computation, communication, and caching capabilities of a standalone cloud server to the network edge. In this article, we first provide an overview of how MEC and RIS can benefit each other. We envision that the integration of MEC and RIS will bring an unprecedented transformation to the future evolution of wireless networks. We provide a system-level perspective on the MEC-aided RIS (and RIS-assisted MEC) that will evolve wireless network towards 6G. We also outline some of the fundamental challenges that pertain to the implementation of MEC-aided RIS (and RIS-assisted MEC) networks. Finally, the key research trends in the RIS-assisted MEC are discussed. © 2021, CC BY.

关键词： Mobile edge computing   Resource allocation   Real-time applications   Operating system   p-fairness  

摘要： In this paper, we study the mobile edge computing (MEC) resource allocation problem from the view of operating systems (OSs). To be more specific, we consider a multi-OS MEC resource allocation problem, where end users (EUs)/fog nodes (FNs) have different OSs. The main obstacle of the problem is the heterogeneity. First, for EUs, according to their OSs? properties, EUs can be mainly categorized into soft real-time EUs and hard real-time EUs. Second, there exists different OSs for FNs. Thus, a unified FNs? qualification constraint may be in need to develop. This constraint should be applicable for diverse FNs. Third, although the conventional proportional fairness is a universal fairness criterion for the service provider (SP) to allocate resources for different EUs, the specified proportions which guarantee the service quality are seemed hard to be developed. Therefore, a user satisfaction mechanism (USM) is proposed to first denote the different requirements between soft real-time and hard real-time applications of EUs, and second test whether FNs are qualified for the MEC service. In addition, a novel p-fairness is designed for the differentiated and quality-guaranteed services provided by SPs. Then, the USM and p-fairness driven MEC resource allocation problem is formulated as a mixed integer non-linear programming (MINLP) problem, where we propose a variable-substitution convexity transformation (VSCT) scheme to tackle it. In VSCT, we first utilize the variable substitution to transform the integer variables? quadric constraints to linear constraints. Then, we relax several constraints and the problem is transformed as a convex optimization problem which is solved iteratively. Finally, we analyze the upper and lower bounds of the proposed VSCT. In the simulation, we discuss the performances of USM and p-fairness (i.e., user satisfaction). Moreover, we compare VSCT with the existing optimization method (i.e., branch & bound). Finally, we demonstrate some interest

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) facilitates computation offloading to edge server, as well as task processing via device-to-device (D2D) collaboration. Existing works mainly focus on centralized network-assisted offloading solutions, which are unscalable to scenarios involving collaboration among massive users. In this paper, we propose a joint framework of decentralized D2D collaboration and efficient task offloading for a large-population MEC system. Specifically, we utilize the power of two choices for D2D collaboration, which enables users to beneficially assist each other in a decentralized manner. Due to short-range D2D communication and user movements, we formulate a mean field model on a finite-degree and dynamic graph to analyze the state evolution of D2D collaboration. We derive the existence, uniqueness and convergence of the state stationary point so as to provide a tractable collaboration performance. Complementing this D2D collaboration, we further build a Stackelberg game to model users' task offloading, where edge server is the leader to determine a service price, while users are followers to make offloading decisions. By embedding the Stackelberg game into Lyapunov optimization, we develop an online offloading and pricing scheme, which could optimize server's service utility and users' system cost simultaneously. Extensive evaluations show that our D2D collaboration can mitigate users' workloads by 73.8% and task offloading can achieve high energy efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Training   Task analysis   Computational modeling   Optimization   Resource management   Performance evaluation   Games   Mobile edge computing   computation offloading   deep reinforcement learning   distributed and collective training   n-step return   deep neuroevolution   policy gradient  

摘要： Mobile edge computing (MEC) is a promising solution to support resource-constrained devices by offloading tasks to the edge servers. However, traditional approaches (e.g., linear programming and game-theory methods) for computation offloading mainly focus on the immediate performance, potentially leading to performance degradation in the long run. Recent breakthroughs regarding deep reinforcement learning (DRL) provide alternative methods, which focus on maximizing the cumulative reward. Nonetheless, there exists a large gap to deploy real DRL applications in MEC. This is because: 1) training a well-performed DRL agent typically requires data with large quantities and high diversity, and 2) DRL training is usually accompanied by huge costs caused by trial-and-error. To address this mismatch, we study the applications of DRL on the multi-user computation offloading problem from a more practical perspective. In particular, we propose a distributed and collective DRL algorithm called DC-DRL with several improvements: 1) a distributed and collective training scheme that assimilates knowledge from multiple MEC environments, which not only greatly increases data amount and diversity but also spreads the exploration costs, 2) an updating method called adaptive n-step learning, which can improve training efficiency without suffering from the high variance caused by distributed training, and 3) combining the advantages of deep neuroevolution and policy gradient to maximize the utilization of multiple environments and prevent the premature convergence. Lastly, evaluation results demonstrate the effectiveness of our proposed algorithm. Compared with the baselines, the exploration costs and final system costs are reduced by at least 43 and 9.4 percent, respectively.

关键词： Servers   Urban areas   Mobile computing   Task analysis   Mobile handsets   Time factors   Cloud computing   Mobile service   distributed system   mobile edge computing   service deployment  

摘要： The dramatically increasing of mobile applications make it convenient for users to complete complex tasks on their mobile devices. However, the latency brought by unstable wireless networks and the computation failures caused by constrained resources limit the development of mobile computing. A popular approach to solve this problem is to establish a mobile service provisioning system based on a mobile edge computing (MEC) paradigm. In the MEC paradigm, plenty of machines are placed at the edge of the network so that the performance of applications can be optimized by using the involved microservice instances deployed on them. In this paper, we explore the deployment problem of microserivce-based applications in the MEC environment and propose an approach to help to optimize the cost of application deployment with the constraints of resources and the requirement of performance. We conduct a series of experiments to evaluate the performance of our approach. The result shows that our approach can improve the average response time of mobile services.

关键词： Mobile edge computing   Service chain caching   Task offloading   Lyapunov Optimization  

摘要： Caching and offloading in Mobile Edge Computing (MEC) are hot topics recently. Existing caching strategies at the edge ignore the programming ability of edge network and design strategies independently thus network resource is under utilization and the quality of experience (QOE) for end users is far from satisfactory. In this paper, we design intelligently joint caching and offloading strategies under the assumption that applications can be in the form of divisible service chain. Different from common approaches that target on reducing response latency only for users, our system take the leasing cost into consideration thus is more efficient for Application Service Providers (ASP). To fulfill our design, we novelly utilize open Jackson queuing network to formulate this joint optimization problem under long term cost restrictions and design a pipeline of algorithm to search for the optimal solution. More specifically, we design a cost adaptive algorithm derived from Lyapunov drift-plus-penalty function so that the long-term problem can be optimized in the slot-by-slot basis. Moreover, we propose to exploit resource-based utility function and device-number-based relative distance to jointly find optimal caching and offloading scheme. Extensive simulation results demonstrate that our approach can effectively reduce the average service latency of the MEC system and keep a low average leasing cost. (C) 2021 Elsevier B.V. All rights reserved.

关键词： blockchain   IoT   cloudlet   mobile edge computing   Identity Based Encryption  

摘要： IoT (Internet of Things) sensors usually generate a large amount of data that is shared with a centralized cloud to provide various services. Traditional IoT architecture is heavily centralized, where data stored in a cloud environment is prone to several kinds of threats. As such, both data generated by sensors as well as IoT network shall be protected. Blockchain is an emerging technology that can be used in a wide spectrum of applications for better protection. However, the traditional blockchain protocol for Bitcoin cannot be used for IoT data protection because Bitcoin is a payment system, and the IoT ecosystem has a different architecture and requirements. Hence, more suitable blockchain architecture and encryption schemes for IoT are needed. In this paper, we propose a privacy-preserving blockchain architecture for IoT using Hierarchical Identity Based Encryption (HIBE). The proposed architecture fits well with event-driven IoT devices and uses the edge and the cloudlet computing paradigm as well as HIBE to preserve the privacy. Furthermore, the efficiency of the proposed architecture is demonstrated using a real-life case, namely, traffic speed radars. The security of the proposed architecture is analyzed and is verified through theoretical analysis that considers the existence of a malicious adversary. The performance of the proposed architecture is evaluated by conducting extensive experiments using the Contiki OS. The experimental results provide the system and network performance of the proposed architecture.

关键词： Mobile edge computing   Unmanned aerial vehicle   Deep deterministic policy gradient   Computation offloading  

摘要： Unmanned Aerial Vehicle (UAV) can play an important role in wireless systems as it can be deployed flexibly to help improve coverage and quality of communication. In this paper, we consider a UAV-assisted Mobile Edge Computing (MEC) system, in which a UAV equipped with computing resources can provide offloading services to nearby user equipments (UEs). The UE offloads a portion of the computing tasks to the UAV, while the remaining tasks are locally executed at this UE. Subject to constraints on discrete variables and energy consumption, we aim to minimize the maximum processing delay by jointly optimizing user scheduling, task offloading ratio, UAV flight angle and flight speed. Considering the non-convexity of this problem, the high-dimensional state space and the continuous action space, we propose a computation offloading algorithm based on Deep Deterministic Policy Gradient (DDPG) in Reinforcement Learning (RL). With this algorithm, we can obtain the optimal computation offloading policy in an uncontrollable dynamic environment. Extensive experiments have been conducted, and the results show that the proposed DDPG-based algorithm can quickly converge to the optimum. Meanwhile, our algorithm can achieve a significant improvement in processing delay as compared with baseline algorithms, e.g., Deep Q Network (DQN).

关键词： Blockchain   Mobile edge computing   Differential evolution   Encoding   Mining decision   Resource allocation  

摘要： This paper studies a mobile edge computing-enabled wireless blockchain network, in which a set of Internet of Things (IoT) devices can act as miners to participate in mining. In this blockchain network, we jointly optimize the mining decision and resource allocation to maximize the total profit of all miners. When using evolutionary algorithms to solve this problem, each individual usually represents the mining decisions and resource allocations of all miners, which results in the redundant search space due to the fact that not all miners participate in mining. In this paper, we propose a new differential evolution (DE) algorithm, called DEMiDRA. In DEMiDRA, each individual represents the resource allocation of a participating miner and the resource allocations of all participating miners constitute the whole population. Then, DE is adopted to optimize the resource allocation. As for the optimization of the mining decision, we need to select miners to participate in mining and update the number of participating miners. Since the population size is equal to the number of participating miners, we transform the update of the number of participating miners into the adjustment of the population size and design an adaptive strategy. Besides, a tabu strategy is developed to prevent unpromising miners from participating in mining. The effectiveness of DEMiDRA is verified by comparing it with three other algorithms on a set of instances.

关键词： Mobile edge computing   Bi-objective optimization   Resource allocation   Energy efficiency   Hybrid local search  

摘要： The Mobile Edge Computing (MEC) environment provides leading-edge services to smart mobile devices (SMDs). Besides, computation offloading is a promising service in 5G: it reduces battery drain and applications' execution time. In this context, we consider a general system consisting of a multi-cell communication network where each base station (BS) is equipped with a MEC server to provide computation offloading services to nearby mobile users. In addition, each SMD handles multiple independent offloadable heavy tasks that are latency-sensitive. The purpose of this article is to jointly optimize tasks' offloading decisions as well as the allocation of critical radio resources while minimizing the overall energy consumption. Therefore, we have formulated a bi-objective optimization problem that is NP-hard. Because of the short decision time constraint, the optimal solution implementation is infeasible. Accordingly, with the use of the weighted aggregation approach, we propose Intelligent Truncation based Hybrid Local Search (ITHLS) solution. In critical radio resources situations, our solution jointly minimizes the number of penalized SMDs and the overall consumed energy. Finally, simulation experiments were realized to study the ITHLS solution performance compared to some effective state of the art solutions, and the simulation results in terms of decision-making time, energy and number of truncated SMDs are very promising.