关键词： Cloud computing   Computer architecture   Edge computing   Industries   Servers   Quality of service   Security   Mobile edge computing   mobile cloud computing   fog computing   connected vehicles   V2X communication   architectural design  

摘要： As vehicles playing an increasingly important role in people's daily life, requirements on safer and more comfortable driving experience have arisen. Connected vehicles (CVs) can provide enabling technologies to realize these requirements and have attracted widespread attentions from both academia and industry. These requirements ask for a well-designed computing architecture to support the Quality-of-Service (QoS) of CV applications. Computation offloading techniques, such as cloud, edge, and fog computing, can help CVs process computation-intensive and large-scale computing tasks. Additionally, different cloud/edge/fog computing architectures are suitable for supporting different types of CV applications with highly different QoS requirements, which demonstrates the importance of the computing architecture design. However, most of the existing surveys on cloud/edge/fog computing for CVs overlook the computing architecture design, where they (i) only focus on one specific computing architecture and (ii) lack discussions on benefits, research challenges, and system requirements of different architectural alternatives. In this article, we provide a comprehensive survey on different architectural design alternatives based on cloud/edge/fog computing for CVs. The contributions of this article are: (i) providing a comprehensive literature survey on existing proposed architectural design alternatives based on cloud/edge/fog computing for CVs, (ii) proposing a new classification of computing architectures based on cloud/edge/fog computing for CVs: computation-aided and computation-enabled architectures, (iii) presenting a holistic comparison among different cloud/edge/fog computing architectures for CVs based on functional requirements of CV systems, including advantages, disadvantages, and research challenges, (iv) presenting a holistic overview on the design of CV systems from both academia and industry perspectives, including activities in industry, functional requirem

关键词： Vehicle safety monitoring   Mobile edge computing   Image uploading  

摘要： This paper proposes an image uploading system for vehicle safety monitoring applications based on mobile edge computing technologies. In this system, numerous camera nodes are placed within vehicles to collect vehicle journey data, and when emergency events such as accidents are detected, a camera with a view in the direction of the accident can upload more data than those in other directions. In addition, other vehicles within the area of the accident will also upload image data about the event, meaning that a greater amount of useful data on the accident will be collected for analysis and assistance to drivers. We propose to use mobile edge computing because of its many advantages, such as fast service response, low end-to-end delay and suitability for large data storage systems. We also propose solutions to minimize the uploading of redundant, repetitive and similar image data, a problem that has been encountered in previous systems. The results of both experiments and simulations showed that our system achieves better performance than those proposed in related studies.

关键词： Internet of Vehicles   mobile edge computing   task offloading   reinforcement learning   privacy security  

摘要： With the rapid increase of vehicles, the explosive growth of data flow and the increasing shortage of spectrum resources, the performance of existing task offloading scheme is poor, and the on-board terminal can't achieve efficient computing. Therefore, this article proposes a task offload strategy based on reinforcement learning computing in edge computing architecture of Internet of vehicles. Firstly, the system architecture of Internet of vehicles is designed. The Road Side Unit receives the vehicle data in community and transmits it to Mobile Edge Computing server for data analysis, while the control center collects all vehicle information. Then, the calculation model, communication model, interference model and privacy issues are constructed to ensure the rationality of task offloading in Internet of vehicles. Finally, the user cost function is minimized as objective function, and double-layer deep Q-network in deep reinforcement learning algorithm is used to solve the problem for real-time change of network state caused by user movement. The results show that the proposed offloading strategy can achieve fast convergence. Besides, the impact of user number, vehicle speed and MEC computing power on user cost is the least compared with other offloading schemes. The task offloading rate of our proposed strategy is the highest with better performance, which is more suitable for the scenario of Internet of vehicles.

关键词： Task analysis   Power system reliability   Probability   Edge computing   System performance   Wireless communication   Internet of Things   Mobile edge computing   latency   opportunistic selection   performance evaluation  

摘要： In this paper, we investigate intelligent mobile edge computing networks (MEC) with multiple computational access points (CAPs) for Internet of Things (IoT), where the user with limited computational capability has some tasks to be computed with the help of the CAPs. We use the communication and computational latency to measure the system outage probability. In order to enhance the system performance, two CAP relay selection criteria are proposed, where one is based on the computational capability at the CAPs while the other criterion is based on the instantaneous channel parameters from the user to the CAPs. To further improve the system performance, the offloading ratio is optimized through minimizing the system outage probability, which determines how many parts of tasks to be computed at the CAPs. Numerical and simulation results are finally presented to verify the effectiveness of the proposed CAP selection criteria and offloading ratio strategy.

关键词： Edge computing   Internet of things (IoT)   Mobile edge computing   Modules placement   Resources management  

摘要： Using mobile and Internet of Things (IoT) applications is becoming very popular and obtained researchers’ interest and commercial investment, in order to fulfill future vision and the requirements for smart cities. These applications have common demands such as fast response, distributed nature, and awareness of service location. However, these requirements’ nature cannot be satisfied by central systems services that reside in the clouds. Therefore, edge computing paradigm has emerged to satisfy such demands, by providing an extension for cloud resources at the network edge, and consequently, they become closer to end-user devices. In this paper, exploiting edge resources is studied;therefore, a cooperative-hierarchical approach for executing the pre-partitioned applications’ modules between edges resources is proposed, in order to reduce traffic between the network core and the cloud, where this proposed approach has a polynomial-time complexity. Furthermore, edge computing increases the efficiency of providing services, and improves end-user experience. To validate our proposed cooperative-hierarchical approach for modules placement between edge nodes’ resources, iFogSim toolkit is used. The obtained simulation results show that the proposed approach reduces network’s load and the total delay compared to a baseline approach for modules’ placement, moreover, it increases the network’s overall throughput. Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved.

关键词： Intelligent information services   mobile edge computing   intelligent algorithms  

摘要： In this paper, we investigate mobile edge computing (MEC) networks for intelligent information services, where there are N users equipped with K antennas and one access point (AP). The users have some computational tasks, and some of them can be decoupled by the AP, at the cost of a fee charged by the AP. For the considered system, we firstly consider two important metrics of interest: latency and fee. Then, we formulate a stochastic game to model the interaction between users and the AP. In this game, the AP sets prices to maximize its profit, while users devise the offloading strategy to reduce both the latency and charge. We further optimize the system by applying the array signal processing schemes on the users, in order to reduce the transmission latency. Simulation results are finally presented to verify the effectiveness of the stochastic game, and it is shown that the array signal processing scheme can help reduce the transmission latency significantly.

关键词： Resource management   Servers   Energy consumption   Machine learning   Mobile handsets   Edge computing   Cloud computing   Mobile edge computing   resource allocation   computational offloading   deep learning   energy efficient  

摘要： Mobile Edge Computing (MEC) has emerged as an alternative to cloud computing to meet the latency and Quality-of-Service (QoS) requirements of mobile devices. In this paper, we address the problem of server resource allocation in MEC. Due to the dynamic load conditions on MEC servers, their resources need to be used intelligently to meet the QoS requirements of the users and to minimize server energy consumption. We present a novel resource allocation algorithm, called Power Migration Expand (PowMigExpand). Our algorithm assigns user requests to the optimal server and allocates optimal amount of resources to User Equipment (UE) based on our comprehensive utility function. PowMigExpand also migrates UE requests to new servers, when needed due to the mobility of users. We also present a low cost Energy Efficient Smart Allocator (EESA) algorithm that uses deep learning for energy efficient allocation of requests to optimal servers. The proposed algorithms consider varying load of incoming requests and their heterogeneous nature, energy efficient activation of servers, and Virtual Machine (VM) migration for smart resource allocation and, thus, is the first comprehensive approach to address the complex and multidimensional resource allocation problem using deep learning. We compare our proposed algorithms with other resource allocation approaches and show that our approach can handle the dynamic load conditions better. The proposed algorithms improve the service rate and the overall utility with minimum energy consumption. On average, it reduces 26% energy consumption of MESs and improves the service rate by 23%, compared with other algorithms. We also get more than 70% accuracy for EESA in allocating the resources of multiple servers to multiple users.

关键词： Servers   Task analysis   Delays   Energy consumption   Optimization   Mobile handsets   Quality of service   Mobile edge computing   Internet of Things   delay   service cache   task offloading  

摘要： As more and more compute-intensive and delay-sensitive applications are deployed on smart mobile devices, mobile edge computing is considered an effective way to solve the limited computing ability of smart mobile devices (SMDs). At present, latency has become the most critical indicator of the quality of service (QoS), and more and more studies focus on this aspect. Unlike previous work, our work fully takes into account the limited storage and computing ability of edge servers. To effectively reduce the delay of SMDs and improve QoS, we propose a Delay Control Strategy Joint Service Caching and Task Offloading (DCS-OCTO) in a three-tier mobile edge computing (MEC) system consist of multi-user, multi-edge server and remote cloud servers. Some of the key challenges include service heterogeneity, unknown system dynamics, spatial demand coupling, and decentralized coordination. In particular, a very compelling but rarely studied issue is the dynamic service caching in the three-tier MEC system. The DCS-OCTO strategy is proposed based on Lyapunov optimization and Gibbs sampling. It works online without requiring prior information and achieves provable near-optimal performance. Finally, simulation results show that the strategy effectively reduces the overall system delay while ensuring low energy consumption.

关键词： Servers   Resource management   Blockchain   Task analysis   Edge computing   Wireless communication   Simulation   Mobile edge computing   auction   cross-server resource allocation   blockchain   incentive mechanism  

摘要： Mobile edge computing (MEC), as a promising technology, provides proximate and prompt computing service for mobile users on various applications. With appropriate incentives, profit-driven users can offload multi-task requests across heterogeneous edge servers. However, such incentive trade lacks a trustworthy platform. Due to the decentralized nature of MEC, trading information from players is easily tampered with by edge servers, which poses a threat to cross-server resource allocation. In this paper, we jointly consider incentives and cross-server resource allocation in blockchain-driven MEC, where the blockchain prevents malicious edge servers from tampering with player information by maintaining a continuous tamper-proof ledger database. Particularly, we propose two double auction mechanisms, namely a double auction mechanism based on breakeven (DAMB) and a more efficient breakeven-free double auction mechanism (BFDA), in which users request multi-task service with claimed bids and edge servers cooperate with each other to serve users. A delegated proof of stake (DPoS) based blockchain technology is leveraged to realize decentralized, untampered, safe and fair resource allocation consensus mechanism. The simulation results show that the proposed DAMB and BFDA can significantly improve the system efficiency of MEC.

关键词： Wireless communication   Edge computing   Computer architecture   Wireless sensor networks   Internet of Things   Resource management   Wireless power transfer   Wireless power transfer   mobile edge computing   Industrial Internet of Things   network architecture   resource management  

摘要： The convergence of wireless power transfer (WPT) and mobile edge computing (MEC) has fostered the rise of wireless powered MEC, which is taken as a crucial technology for the sustainability of the Industrial Internet of Things (IIoT) systems. This article provides an overview of wireless powered MEC enabled IIoT systems, including use cases, network requirements, system architecture, and resource management. First, we highlight four key requirements to drive operational efficiency from the perspective of being transformed IIoT projects. Then an integration architecture of wireless powered MEC for IIoT is proposed. Designed with joint consideration of energy, communication, and computing resources, the architecture is enabled by an efficient system schedule mechanism to achieve age-aware data update, green and sustainable energy supply, as well as hierarchical and resilient computation. Finally, a case study is provided to verify the feasibility of the proposed architecture and demonstrate the efficiency of the proposed resource management approach.