关键词： Auction   edge computing   cloudlet   fog computing   mobile edge computing   resource allocation   computing offloading   incentive   IoT   blockchain  

摘要： Edge computing as a promising technology provides lower latency, more efficient transmission, and faster speed of data processing since the edge servers are closer to the user devices. Each edge server with limited resources can offload latency-sensitive and computation-intensive tasks from nearby user devices. However, edge computing faces challenges such as resource allocation, energy consumption, security and privacy issues, etc. Auction mechanisms can well characterize bidirectional interactions between edge servers and user devices under the above constraints in edge computing. As demonstrated by the existing works, auction and mechanism design approaches are outstanding on achieving optimal allocation strategy while guaranteeing mutual satisfaction among edge servers and user devices, especially for scenarios with scarce resources. In this paper, we introduce a comprehensive survey of recent researches that apply auction approaches in edge computing. Firstly, a brief overview of edge computing including three common edge computing paradigms, i.e., cloudlet, fog computing and mobile edge computing, is presented. Then, we introduce fundamentals and backgrounds of auction schemes commonly used in edge computing systems. After then, a comprehensive survey of applications of auction-based approaches applied for edge computing is provided, which is categorized by different auction approaches. Finally, several open challenges and promising research directions are discussed.

关键词： Wireless communication   Marine vehicles   Rivers   Task analysis   Autonomous aerial vehicles   Quality of service   Energy consumption   Unmanned surface vehicles   mobile edge computing   QoS   intelligent reflecting surface  

摘要： To realize fully functioning inland waterway communications, portable and affordable unmanned surface vehicles (USVs) have become increasingly significant in wireless inland ship networks. However, USVs are struggling in handling resource-intensive services due to the limited battery and CPU resources. In addition, the power of signal transmitted between USVs and the shore base stations suffers from considerable attenuation. In this paper, an intelligent reflecting surface (IRS)-aided wireless inland ship mobile edge computing (MEC) network architecture is proposed to support USVs' communication and computation. The minimization of network energy consumption is formulated under a number of QoS requirements. To achieve an efficient solution to the formulated challenging problem, a low-complexity enhanced block coordinate descent algorithm is proposed and thus the original optimization problem can be divided into a list of sub-problems. The extensive results verify the effectiveness of jointly considering USVs' offloading decisions, USVs' transmission power, unmanned aerial vehicle (UAV) trajectory and IRS phase shift-vector design for energy efficiency in a spare wireless inland ship MEC network and show that the proposed algorithm can decrease energy consumption by approximately 75% compared to a baseline algorithm without optimization design. Also, the network performance under different IRS elements and accuracy parameters are investigated.

关键词： wireless sensor networks   mobile edge computing   migration strategy   optimization coverage,networks lifetime  

摘要： The traditional network coverage mode with the cost of deploying a large number of sensor nodes has poor coverage effect. Aiming at this problem, this paper proposes a Novel Optimized Coverage Algorithm with Mobile Edge Computing of Migration Strategy (MEC-MS). First, the algorithm uses the network coverage model to give the expression method of the distance measurement and the judgment conditions of the best and worst paths. Secondly, it analyzes the necessary conditions for improving the coverage quality and the prerequisite for the existence of redundant coverage for adjacent the redundant coverage nodes by the theory of probability. Thirdly, using the precondition of redundant coverage, we give the calculation process of the sensor nodes own redundant coverage and the calculation method of the redundant node coverage expectation. Finally, the algorithm compares the number of working sensor nodes with the other two algorithms under different parameters. The experimental results show that the average number of working sensor nodes in the MEC-MS algorithm is 9.74% lower than that of the other two algorithms, and the average value of network coverage is 9.92% higher than that of the other two algorithms, which verify the effectiveness of the algorithm in this paper.

关键词： Edge computing   Path planning   Quality of service   Planning   Task analysis   Reinforcement learning   Prediction algorithms   Unmanned Aerial Vehicle   Mobile Edge Computing   Path Planning   Reinforcement Learning  

摘要： Unmanned Aerial vehicles (UAVs) are widely used as network processors in mobile networks, but more recently, UAVs have been used in Mobile Edge Computing as mobile servers. However, there are significant challenges to use UAVs in complex environments with obstacles and cooperation between UAVs. We introduce a new multi-UAV Mobile Edge Computing platform, which aims to provide better Quality-of-Service and path planning based on reinforcement learning to address these issues. The contributions of our work include: 1) optimizing the quality of service for mobile edge computing and path planning in the same reinforcement learning framework;2) using a sigmoid-like function to depict the terminal users' demand to ensure a higher quality of service;3) applying synthetic considerations of the terminal users' demand, risk and geometric distance in reinforcement learning reward matrix to ensure the quality of service, risk avoidance, and the cost-savings. Simulations have shown the effectiveness and feasibility of our platform, which can help advance related researches. The source code can be found at https://***/bczhangbczhang.

关键词： mobile edge computing   blockchain   proof of learning   resource allocation   asynchronous advantage Actor-Critic   temporal convolutional network  

摘要： Currently, the concept of Mobile Edge Computing (MEC) has been applied as a solution against the plethora of demands for high-quality computing services. It comprises several essential processes, such as resource allocation, data transmission, and task processing. Furthermore, researchers applied blockchain technology, aiming to enhance the robustness of the MEC system. At present, resource allocation in the MEC system is a very hot field, but there are still some problems in the resource allocation process under the traditional MEC architecture, such as privacy disclosure and so on. Moreover, the resource allocation problem in a blockchain-enabled MEC system will be more complicated, while the mining process may have an impact on resource allocation policy. To address this issue, this paper investigates the resource allocation problem with blockchain-based MEC system architecture. A brand new consensus mechanism: proof of learning (PoL), is applied to the system, which does not waste the computing resources of edge computing servers. Based on this, we modeled the system mathematically, focusing on server processing latency, mining latency, rewards under the new consensus, and total cost. The asynchronous advantage Actor-Critic (A3C) algorithm is used to optimize resource allocation policy. To better capture the long-time trend of the system, the temporal convolutional network (TCN) is implemented to represent the policy function and state-value function in the reinforcement learning model. The results show that the A3C algorithm based on TCN not only converges faster but also is more stable.

关键词： Blockchains   Internet of Things   Security   Servers   Data security   Quality of service   Protocols   Blockchain   mobile edge computing   IoT   security   forensics  

摘要： Security and forensics represent two key components for network management, especially to guarantee the trusted operation of massive access networks such as the Internet of Things (IoT). As a core technology to provide low latency and high communication for IoT, Mobile Edge Computing (MEC) pulls computing resources from remote cloud centers to devices. The process of MEC service involves three types of entities: devices, data generated by devices and digital evidence generated after the data interaction. These entities are fully distributed and difficult to protect through traditional, highly centralized security and authentication mechanisms. As a decentralized shared ledger and database, the emerging blockchain is considered to provide cooperative trust and collaborative action among multiple subjects while ensuring the integrity and confidentiality of data. Because of its anonymity, non-tampering and traceability, the blockchain arouses research on the combination of blockchain and edge computing for device security, data security and forensics in IoT. This survey analyzes the application of blockchain in MEC-IoT systems and mainly focuses on approaches and technologies to manage the security and forensics issues for IoT. Finally, we present open issues and prospects for future work and research directions.

关键词： Computation offloading   Mobile edge computing   Resource allocation   Networks optimization  

摘要： Computation offloading is one of the key technologies in Mobile Edge Computing (MEC), which makes up for the deficiencies of mobile devices in terms of storage resource, computing capacity, and energy efficiency. On one hand, computation offloading of task requests not only relieves the communication pressure on the core networks but also reduces the delay caused by long-distance data transmission. On the other hand, emerging applications in 5/6G also rely on the computation offloading technology for efficient service provisioning to users. At present, the industry and academia have conducted a lot of researches on the computation offloading methods in MEC networks with a diversity of meaningful techniques and approaches. In this paper, we present a comprehensive survey of the computation offloading in MEC networks including applications, offloading objectives, and offloading approaches. Particularly, we discuss key issues on various offloading objectives, including delay minimization, energy consumption minimization, revenue maximization, and system utility maximization. The approaches to achieve these objectives mainly include mathematical solver, heuristic algorithms, Lyapunov optimization, game theory, and Markov Decision Process (MDP) and Reinforcement Learning (RL). We compare the approaches by characterizing their pros and cons as well as targeting applications. Finally, from the four aspects of subtasks dependency and online task requests, server selection, real-time environment perception, and security, we analyze the current challenges and future directions of computation offloading in MEC networks.

关键词： Mobile edge computing  

摘要： Distributed computing (cloud) networks, e.g., mobile edge computing (MEC), are playing an increasingly important role in the efficient hosting, running, and delivery of real-time stream-processing applications such as industrial automation, immersive video, and augmented reality. While such applications require timely processing of real-time streams that are simultaneously useful for multiple users/devices, existing technologies lack efficient mechanisms to handle their increasingly multicast nature, leading to unnecessary traffic redundancy and associated network congestion. In this paper, we address the design of distributed packet processing, routing, and duplication policies for optimal control of multicast stream-processing services. We present a characterization of the enlarged capacity region that results from efficient packet duplication, and design the first fully distributed multicast traffic management policy that stabilizes any input rate in the interior of the capacity region while minimizing overall operational cost. Numerical results demonstrate the effectiveness of the proposed policy to achieve throughput- and cost-optimal delivery of stream-processing services over distributed computing networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of vehicle   Mobile edge computing   Budget   Offloading strategy   Latency  

摘要： In this paper, we investigate a multiuser mobile edge computing (MEC)-aided smart Internet of vehicle (IoV) network, where one edge server can help accomplish the intensive calculating tasks from the vehicular users. For the MEC networks, most existing works mainly focus on minimizing the system latency to guarantee the user's quality of service (QoS) through designing some offloading strategies, which, however, fail to consider the pricing from the server and hence fail to take into account the budget constraint from the users. To address this issue, we jointly incorporate the budget constraint into the system design of the MEC-based IoV networks and then propose a joint deep reinforcement learning (DRL) approach combined with the convex optimization algorithm. Specifically, a deep Q-network (DQN) is firstly used to make the offloading decision, and then, the Lagrange multiplier method is employed to allocate the calculating capability of the server to multiple users. Simulations are finally presented to demonstrate that the proposed schemes outperform the conventional ones. In particular, the proposed scheme can effectively reduce the system latency by up to 56% compared to the conventional schemes.

关键词： Internet of vehicle   Mobile edge computing   Offloading strategy   Latency   Energy consumption  

摘要： In this paper, we investigate how to analytically design an analytical offloading strategy for a multiuser mobile edge computing (MEC)-based smart internet of vehicle (IoV), where there are multiple computational access points (CAPs) which can help compute tasks from the vehicular users. As it is difficult to derive an analytical offloading ratio for a general MEC-based IoV network, we turn to provide an analytical offloading scheme for some special MEC networks including one-to-one, one-to-two and two-to-one cases. For each case, we study the system performance by using the linear combination of latency and energy consumption, and derive the analytical offloading ratio through minimizing the system cost. Simulation results are finally presented to verify the proposed studies. In particular, the proposed analytical offloading scheme can achieve the optimal performance of the brute force (BF) scheme. The analytical results in this paper can serve as an important reference for the analytical offloading design for a general MEC-based IoV.