关键词： Internet of Vehicles   mobile edge computing   blockchain   resource allocation   asynchronous advantage actor-critic  

摘要： Recently, the rise of the Internet of Vehicles (IoV) has driven the broad development of intelligent transportation and smart cities. In order to promote the computing power of mobile vehicles and decrease the content delivery latency of suppliers, mobile edge computing (MEC) is recognized as a promising computational paradigm and used in vehicular networks. However, there are some essential issues to be considered: 1) privacy and authenticity of data transmission in IoV, and 2) reasonable resource allocation for collaborative computing and caching. In this paper, to solve above issues, blockchain technology is introduced and adopted to ensure the accuracy and reliability of data transmission and interaction. Meanwhile, we develop an intelligent framework of resource allocation about computing and caching for blockchain-enabled MEC systems in IoV. Through jointly considering and optimizing offloading decision of computation task carried by vehicle, caching decision, the number of offloaded consensus nodes, block interval and block size, the weighted consumption costs of energy consumption and computation overheads can be decreased, and the transactional throughput of the blockchain can be increased. Moreover, due to the continuity and dynamic of the available resources of mobile vehicles and computing servers, the optimization problem is modeled as a Markov decision process (MDP). Facing the large-scale and dynamic characteristics of the system, the asynchronous advantage actor-critic (A3C) approach is introduced to deal with the optimization problem. Simulation results show that our proposed scheme achieves significant advantages over other comparison schemes, such as the total reward of the proposed scheme is about 14% higher than that of the deep Q-network based scheme.

关键词： mobile edge computing   software defined network   deep reinforcement learning   hybrid computation offloading   load balancing  

摘要： With the growth of the Internet of Things, smart devices are subsequently generating a large number of computation-intensive and latency-sensitive tasks. Mobile edge computing can provide resources in close proximity, greatly reducing service latency and alleviating congestion in mobile core networks. Due to the instability of the mobile edge computing environment, it was difficult to guarantee the quality of service for users. To address this problem, a hybrid computation offloading framework based on Deep Deterministic Policy Gradient (DDPG) in IoT is proposed. The framework is a system consisting of edge servers and user devices. It is used to acquire the environment state through Software Defined Network technologies and generate the offloading strategy by Deep Deterministic Policy Gradient. The optimization objectives in this paper include the total system overhead of the mobile edge computing system, and considering both network load and computational load, an optimal offloading strategy can be obtained to enable users to obtain a better quality of service. Finally, the experimental results show that the algorithm outperforms the comparison algorithm and can reduce the system latency by 20%, while the network load and computational load are also more stable.

关键词： Mobile edge computing   multimodal optimization   particle swarm algorithm   server placement problem  

摘要： Mobile edge computing (MEC) deploys computing and storage resources close to mobile devices, enabling resource demanding applications to run on mobile devices with short network latency. In the past few years, large numbers of research works focused on the research hotspots in MEC, such as computation offloading and energy efficiency. However, few researchers have investigated the deployment of edge servers. On the one hand, blindly deploying numerous edge servers will result in a large amount of capital expenditure. On the other hand, the deployment of edge servers is a multimodal problem that should provide decision makers with multiple deployment options to deal with the impact of unmeasured real-world factors. Considering these factors, we study the multimodal optimization problem of edge server placement (MESP) with the goal of minimizing the average system response time in this work. Regarding the difficulty of the MESP problem, we propose a heuristic algorithm that combines particle swarm optimization and niching technology to obtain a set of competitive placement solutions. Extensive experiments over a real-world dataset show that the proposed algorithm can significantly reduce the system response time.

关键词： Augmented reality   Offloading   Mobile edge computing   Energy efficiency   Latency   5G  

摘要： Augmented reality (AR) is a recent communication paradigm that is considered one of the primary functions of the fifth-generation cellular system (5G). AR is one of the ultra-reliable low latency communications since it requires a total delay of about 5 ms for AR applications. Since mobile devices are battery-operated and have many limitations in computing resources, i.e., processing, storage, and energy, computing tasks associated with mobile AR applications have to be offloaded to nearby devices with sufficient computing resources. To this end, this paper provides a system structure for mobile AR and web-based AR applications;based on the modified multi-level mobile edge computing system (MM-MEC). Furthermore, the required offloading algorithms for managing data offloading from mobile devices to edge servers are introduced. The algorithms are latency and energy-aware, as the selection of the executing device for the AR computing tasks achieves the highest energy and latency efficiency. The proposed structure and offloading techniques have been validated through simulation processes. The developed framework and the proposed offloading schemes have been simulated over a reliable environment for various simulation The suggested system and its offloading methods were simulated in a dependable environment for a number of simulation situations.

关键词： Wireless body area networks   Deep reinforcement learning   Offloading policy   Mobile edge computing  

摘要： Wireless body area network (WBAN) is widely adopted in healthcare services, providing remote real-time and continuous healthcare monitoring. With the massive increase of detective sensor data, WBAN is largely restricted by limited storage and computation capacity, resulting in severely decreased efficiency and reliability. Mobile edge computing (MEC) technique can be combined with WBAN to resolve this issue. This paper studies the joint optimization problem of computational offloading and resource allocation (JCORA) in MEC for healthcare service scenarios. We formulate JCORA as a Markov decision process and propose a deep deterministic policy gradient-based WBAN offloading strategy (DDPG-WOS) to optimize time delay and energy consumption in interfered transmission channels. This scheme employs MEC to mitigate the computation pressure on a single WBAN and increase the transmission ability. Further, DDPG-WOS optimizes the offloading strategy-making process by considering the channel condition, transmission quality, computation ability and energy consumption. Simulation results verify the effectiveness of the proposed optimization schema in reducing energy consumption and computation latency and increasing the utility of WBAN compared to two competitive solutions.

关键词： Mobile edge computing  

摘要： This paper designs a helper-assisted resource allocation strategy in non-orthogonal multiple access (NOMA)enabled mobile edge computing (MEC) systems, in order to guarantee the quality of service (QoS) of the energy/delay-sensitive user equipments (UEs). To achieve a tradeoff between the energy consumption and the delay, we introduce a novel performance metric, called energy-delay tradeoff, which is defined as the weighted sum of energy consumption and delay. The joint optimization of user association, resource block (RB) assignment, power allocation, task assignment, and computation resource allocation is formulated as a mixed-integer nonlinear programming problem with the aim of minimizing the maximal energy-delay tradeoff. Due to the non-convexity of the formulated problem with coupled and 0-1 variables, this problem cannot be directly solved with polynomial complexity. To tackle this challenge, we first decouple the formulated problem into a power allocation, task assignment and computation resource allocation (PATACRA) subproblem. Then, with the solution obtained from the PATACRA subproblem, we equivalently reformulate the original problem as a discrete user association and RB assignment (DUARA) problem. For the PATACRA subproblem, an iterative parametric convex approximation (IPCA) algorithm is proposed. Then, based on the solution obtained from the PATACRA subproblem, we first model the DUARA problem as a four-sided matching problem, and then propose a low-complexity four-sided UE-RB-helper-server matching (FS-URHSM) algorithm. Theoretical analysis demonstrates that the proposed algorithms are guaranteed to converge to stable solutions with polynomial complexity. Finally, simulation results are provided to show the superior performance of our proposed algorithm in terms of the energy consumption and the delay. © 2023, CC BY.

关键词： Alternating direction method of multipliers   D2D-assisted computation offloading   Dynamic pricing   Mobile edge computing   Social relationships  

摘要： The development of collaborative computing architecture in mobile edge computing provides an effective solution for the task processing of mobile devices with low computing capability, which can make up for computation resource and physical size restrictions of mobile devices and improve the performance of mobile communication networks in the future. In this paper, we focus on network congestion caused by increased load in hotspots. In particular, we consider the integrated network architecture of mobile edge computing (MEC) and device-to-device (D2D) communication to reduce the sum overhead of all task request users (TRUs). Accordingly, the computing offloading decision, wireless resource allocation strategy, computing resource allocation strategy, and assistant selection decision are optimized to minimize the sum overhead of the energy consumption and the cost of purchasing computing resource of all TRUs. The formulated overhead minimization problem is a mixed integer nonlinear programming problem. In order to tackle this problem, an algorithm based on the distance and the transaction price is first proposed to determine the target assistant user (TAU) of each TRU. Second, for a given TAU selection strategy, the original problem is further transformed into a convex problem and decomposed to make it more tractable. Finally, a distributed algorithm based on the alternating direction method of multipliers (ADMM) is adopted to solve the optimization problem. The simulation results prove the effectiveness of the proposed scheme in reducing user overhead.

关键词： mobile edge computing   dynamic service migration   delay   network connection   deep reinforcement learning  

摘要： In mobile edge computing, service migration can not only reduce the access latency but also reduce the network costs for users. However, due to bandwidth bottleneck, migration costs should also be considered during service migration. In this way, the trade-off between benefits of service migration and total service costs is very important for the cloud service providers. In this paper, we propose an efficient dynamic service migration algorithm named SMDQN, which is based on reinforcement learning. We consider each mobile application service can be hosted on one or more edge nodes and each edge node has limited resources. SMDQN takes total delay and migration costs into consideration. And to reduce the size of Markov decision process space, we devise the deep reinforcement learning algorithm to make a fast decision. We implement the algorithm and test the performance and stability of it. The simulation result shows that it can minimize the service costs and adapt well to different mobile access patterns.

关键词： Mobile edge computing   Literature similarity checking   Hash index   Lightweight  

摘要： With the advent of information age, mobile devices have become one of the major convenient equipment that aids people's daily office activities such as academic research, one of whose major tasks is to check the repetition rate or similarity among different English literatures. Traditional literature similarity checking solutions in cloud paradigm often call for intensive computational cost and long waiting time. To tackle this issue, in this paper, we modify the tra-ditional literature similarity checking solution in cloud paradigm to make it suitable for the light-weight mobile edge environment. Furthermore, we put forward a lightweight similarity checking approach SCMEC for English literatures in mobile edge computing environment. To validate the advantages of SCMEC, we have designed massive experiments on a dataset. The reported experimental results show that SCMEC can deliver a satisfactory similarity checking result of literatures compared to other existing approaches.

关键词： Mobile edge computing   cooperative security   online scheduling   DDoS   prototype  

摘要： Mobile edge computing (MEC), extending computing services from cloud to edge, is recognized as one of key pillars to facilitate real-time services and tackle backhaul bottleneck. However, it is not economically efficient to attach intensive security appliances to every MEC node to defend application-level DDoS attacks and ensure the availability of services. Thus, we explore the elasticity of security defense among MEC nodes by proposing a COoperative DEfense (CODE) framework for MEC, referred to as CODE4MEC. CODE4MEC aims to adapt to traffic changes by coordinating container-carried defensive resources among cooperative MEC nodes in an automatic way. Towards this aim, we propose four control plane functions to enable a life-cycle management for CODE4MEC, namely, CODE triggering, scheduling, coordination and releasing. However, an effective CODE4MEC requires non-trivial algorithmic schemes, in particular for CODE scheduling and coordination functions. We thus design an online combinatorial auction mechanism for real-time CODE scheduling, and prove a tighter performance bound relative to prior arts. As for CODE coordination, a flow-based traffic and context information coordination scheme is proposed to enable classical defense schemes to work properly and efficiently. Finally, using a combination of real testbed and simulation evaluations, we validate the effectiveness of CODE4MEC.