关键词： Mobile edge computing   Offloading   Deep learning   Distributed learning  

摘要： This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) choose to offload their computation tasks to an edge server. To conserve energy and maintain quality of service for WDs, the optimization of joint offloading decision and bandwidth allocation is formulated as a mixed integer programming problem. However, the problem is computationally limited by the curse of dimensionality, which cannot be solved by general optimization tools in an effective and efficient way, especially for large-scale WDs. In this paper, we propose a distributed deep learning-based offloading (DDLO) algorithm for MEC networks, where multiple parallel DNNs are used to generate offloading decisions. We adopt a shared replay memory to store newly generated offloading decisions which are further to train and improve all DNNs. Extensive numerical results show that the proposed DDLO algorithm can generate near-optimal offloading decisions in less than one second.

关键词： mobile edge computing   computational offloading   optimal stopping theory   structured tasks   time optimization   sequential decision making  

摘要： Vehicular edge computing is a new computing paradigm. By introducing edge computing into the Internet of Vehicles (IoV), service providers are able to serve users with low-latency services, as edge computing deploys resources (e.g., computation, storage, and bandwidth) at the side close to the IoV users. When mobile nodes are moving and generating structured tasks, they can connect with the roadside units (RSUs) and then choose a proper time and several suitable Mobile Edge Computing (MEC) servers to offload the tasks. However, how to offload tasks in sequence efficiently is challenging. In response to this problem, in this paper, we propose a time-optimized, multi-task-offloading model adopting the principles of Optimal Stopping Theory (OST) with the objective of maximizing the probability of offloading to the optimal servers. When the server utilization is close to uniformly distributed, we propose another OST-based model with the objective of minimizing the total offloading delay. The proposed models are experimentally compared and evaluated with related OST models using simulated data sets and real data sets, and sensitivity analysis is performed. The results show that the proposed offloading models can be efficiently implemented in the mobile nodes and significantly reduce the total expected processing time of the tasks.

关键词： DDQN Model   Internet of Vehicles   Markov Decision Model   Mobile Edge Computing   System Model  

摘要： An efficient and reasonable resource allocation strategy can greatly improve the service quality of Internet of Vehicles (IoV). However, most of the current allocation methods have overestimation problem, and it is difficult to provide high-performance IoV network services. To solve this problem, this paper proposes a network resource allocation strategy based on deep learning network model DDQN. Firstly, the method implements the refined modeling of IoV model, including communication model, user layer computing model, edge layer offloading model, mobile model, etc., similar to the actual complex IoV application scenario. Then, the DDQN network model is used to calculate and solve the mathematical model of resource allocation. By decoupling the selection of target Q value action and the calculation of target Q value, the phenomenon of overestimation is avoided. It can provide higher-quality network services and ensure superior computing and processing performance in actual complex scenarios. Finally, simulation results show that the proposed method can maintain the network delay within 65 ms and show excellent network performance in high concurrency and complex scenes with task data volume of 500 kbits.

关键词： mobile edge computing   ultra-dense network   computation offloading   deep reinforcement learning  

摘要： Mobile edge computing (MEC) has the potential to realize intensive applications in 5G networks. Through migrating intensive tasks to edge servers, MEC can expand the computing power of wireless networks. Fifth generation networks need to meet service requirements, such as wide coverage, high capacity, low latency and low power consumption. Therefore, the network architecture of MEC combined with ultra-dense networks (UDNs) will become a typical model in the future. This paper designs a MEC architecture in a UDN, which is our research background. First, the system model is established in the UDN, and the optimization problems is proposed. Second, the action classification (AC) algorithm is utilized to filter the effective action in Q-learning. Then, the optimal computation offloading strategy and resource allocation scheme are obtained using a deep reinforcement learning-based AC algorithm, which is known as the DQN-AC algorithm. Finally, the simulation experiments show that the proposed DQN-AC algorithm can effectively reduce the system weighted cost compared with the full local computation algorithm, full offloading computation algorithm and Q-learning algorithm.

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： Mobile edge computing   Early-warning of financial risks   Internet of things   Service preloading   Back propagation neural network   Cash flow  

摘要： The present work aims to apply the Internet of Things (IoT) to help enterprises improve financial management, timely discover hidden financial risks, and enhance the ability to resist risks. Firstly, the traditional financial risk early-warning model based on accounting information is analyzed. Accordingly, a corporate risk early-warning model is put forward from the perspective of cash flow, and the financial risk early-warning indicator system is established. Secondly, the Backpropagation neural network (BPNN) is employed to mine the financial data of the enterprise. After pre-processing, the data are input into the model according to the type of financial risk early-warning indicators to obtain the financial risk prediction result of the company. Thirdly, the mobile edge computing (MEC) service is introduced into the corporate financial management to improve the calculation performance of the corporate financial information processing and strengthen the timeliness of cash flow information and risk warning cooperating with IoT. Finally, an edge service preloading optimization model is established based on the Geographic Point of Interest Information and BPNN. According to the feature vector of the user location, the next service probability of the user is predicted, and the service preloading is carried out on the accessed edge server. Finally, the effect of this model is verified by experiments. The experimental results indicate that the prediction accuracy of the risk early-warning model designed here for financial health and crisis is 91.6% and 75%, respectively, and the response rate of the service preloading optimization model is between 2.1% and 5%. Therefore, service preloading can improve the response speed of corporate financial risk early-warning to a certain extent. This exploration provides a reference for the study of the corporate financial risk early-warning model based on MEC and IoT.

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： 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.