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| 1 | Energy-Efficient Computation Offloading and Resource Allocation in Fog Computing for Internet of Everything显示文摘With the dawning of the Internet of Everything(IoE) era, more and more novel applications are being deployed. However, resource constrained devices cannot fulfill the resource-requirements of these applications. This paper investigates the computation offloading problem of the coexistence and synergy between fog computing and cloud computing in IoE by jointly optimizing the offloading decisions, the allocation of computation resource and transmit power. Specifically, we propose an energy-efficient computation offloading and resource allocation(ECORA) scheme to minimize the system cost. The simulation results verify the proposed scheme can effectively decrease the system cost by up to 50% compared with the existing schemes, especially for the scenario that the computation resource of fog computing is relatively small or the number of devices increases. | Qiuping Li Junhui Zhao Yi Gong Qingmiao Zhang | 2019 | China Communications2019，16，3： | 19 |
| 2 | A Deep Learning Based Energy-Efficient Computational Offloading Method in Internet of Vehicles显示文摘With the emergence of advanced vehicular applications, the challenge of satisfying computational and communication demands of vehicles has become increasingly prominent. Fog computing is a potential solution to improve advanced vehicular services by enabling computational offloading at the edge of network. In this paper, we propose a fog-cloud computational offloading algorithm in Internet of Vehicles(IoV) to both minimize the power consumption of vehicles and that of the computational facilities. First, we establish the system model, and then formulate the offloading problem as an optimization problem, which is NP-hard. After that, we propose a heuristic algorithm to solve the offloading problem gradually. Specifically, we design a predictive combination transmission mode for vehicles, and establish a deep learning model for computational facilities to obtain the optimal workload allocation. Simulation results demonstrate the superiority of our algorithm in energy efficiency and network latency. | Xiaojie Wang Xiang Wei Lei Wang | 2019 | China Communications2019，16，3： | 15 |
| 3 | Three Tier Fog Networks: Enabling IoT/5G for Latency Sensitive Applications显示文摘Following the progression in Internet of Things(IoT) and 5G communication networks, the traditional cloud computing model have shifted to fog computing. Fog computing provides mobile computing, network control and storage to the network edges to assist latency critical and computation-intensive applications. Moreover, security features are improved in fog paradigm by processing critical data on edge devices instead of data centres outside the control plane of users. However, fog network deployment imposes many challenges including resource allocation, privacy of users, non-availability of programming model and testing software and support for the heterogenous networks. This article highlights these challenges and their potential solutions in detail. This article also discusses threetier fog network architecture, its standardization and benefits in detail. The proposed resource allocation mechanism for three tier fog networks based on swap matching is described. Results show that by practicing the proposed resource allocation mechanism, maximum throughput with reduced latency is achieved. | Romana Shahzadi Ambreen Niaz Mudassar Ali Muhammad Naeem Joel J.P.C.Rodrigues Farhan Qamar Syed Muhammad Anwar | 2019 | China Communications2019，16，3： | 6 |
| 4 | Efficient Task Completion for Parallel Offloading in Vehicular Fog Computing显示文摘In this paper,we investigate vehicular fog computing system and develop an effective parallel offloading scheme.The service time,that addresses task offloading delay,task decomposition and handover cost,is adopted as the metric of offloading performance.We propose an available resource-aware based parallel offloading scheme,which decides target fog nodes by RSU for computation offloading jointly considering effect of vehicles mobility and time-varying computation capability.Based on Hidden Markov model and Markov chain theories,proposed scheme effectively handles the imperfect system state information for fog nodes selection by jointly achieving mobility awareness and computation perception.Simulation results are presented to corroborate the theoretical analysis and validate the effectiveness of the proposed algorithm. | Jindou Xie Yunjian Jia Zhengchuan Chen Zhaojun Nan Liang Liang | 2019 | China Communications2019，16，11： | 5 |
| 5 | Edge Computing Based Applications in Vehicular Environments:Comparative Study and Main Issues显示文摘Despite the expanded efforts,the vehicular ad-hoc networks(VANETs)are still facing many challenges such as network performances,network scalability and context-awareness.Many solutions have been proposed to overcome these obstacles,and the edge computing,an extension of the cloud computing,is one of them.With edge computing,communication,storage and computational capabilities are brought closer to end users.This could offer many benefits to the global vehicular network including,for example,lower latency,network off-loading and context-awareness(location,environment factors,etc.).Different approaches of edge computing have been developed:mobile edge computing(MEC),fog computing(FC)and cloudlet are the main ones.After introducing the vehicular environment background,this paper aims to study and compare these different technologies.For that purpose their main features are compared and the state-of-the-art applications in VANETs are analyzed.In addition,MEC,FC,and cloudlet are classified and their suitability level is debated for different types of vehicular applications.Finally,some challenges and future research directions in the fields of edge computing and VANETs are discussed. | Leo Mendiboure Mohamed-Aymen Chalouf Francine Krief | 2019 | Journal of Computer Science & Technology2019，34，4： | 3 |
| 6 | Fog-IBDIS:Industrial Big Data Integration and Sharing with Fog Computing for Manufacturing Systems显示文摘Industrial big data integration and sharing(IBDIS)is of great significance in managing and providing data for big data analysis in manufacturing systems.A novel fog-computing-based IBDIS approach called Fog-IBDIS is proposed in order to integrate and share industrial big data with high raw data security and low network traffic loads by moving the integration task from the cloud to the edge of networks.First,a task flow graph(TFG)is designed to model the data analysis process.The TFG is composed of several tasks,which are executed by the data owners through the Fog-IBDIS platform in order to protect raw data privacy.Second,the function of Fog-IBDIS to enable data integration and sharing is presented in five modules:TFG management,compilation and running control,the data integration model,the basic algorithm library,and the management component.Finally,a case study is presented to illustrate the implementation of Fog-IBDIS,which ensures raw data security by deploying the analysis tasks executed by the data generators,and eases the network traffic load by greatly reducing the volume of transmitted data. | Junliang Wang Peng Zheng Youlong Lv Jingsong Bao Jie Zhang | 2019 | Engineering2019，5，4： | 2 |
| 7 | Virtualization Technology in Cloud Computing Based Radio Access Networks:A Primer显示文摘Since virtualization technology enables the abstraction and sharing of resources in a flexible management way, the overall expenses of network deployment can be significantly reduced. Therefore, the technology has been widely applied in the core network. With the tremendous growth in mobile traffic and services, it is natural to extend virtualization technology to the cloud computing based radio access networks(CCRANs) for achieving high spectral efficiency with low cost.In this paper, the virtualization technologies in CC-RANs are surveyed, including the system architecture, key enabling techniques, challenges, and open issues. The enabling key technologies for virtualization in CC-RANs mainly including virtual resource allocation, radio access network(RAN) slicing, mobility management, and social-awareness have been comprehensively surveyed to satisfy the isolation, customization and high-efficiency utilization of radio resources. The challenges and open issues mainly focus on virtualization levels for CC-RANs, signaling design for CC-RAN virtualization, performance analysis for CC-RAN virtualization, and network security for virtualized CC-RANs. | ZHANG Xian PENG Mugen | 2017 | ZTE Communications2017，15，4： | 2 |
| 8 | Game-Theoretic Online Resource Allocation Scheme on Fog Computing for Mobile Multimedia Users显示文摘Fog computing is introduced to relieve the problems triggered by the long distance between the cloud and terminal devices. In this paper, considering the mobility of terminal devices represented as mobile multimedia users(MMUs) and the continuity of requests delivered by them, we propose an online resource allocation scheme with respect to deciding the state of servers in fog nodes distributed at different zones on the premise of satisfying the quality of experience(QoE) based on a Stackelberg game. Specifically, a multi-round of a predictably\unpredictably dynamic scheme is derived from a single-round of a static scheme. The optimal allocation schemes are discussed in detail, and related experiments are designed. For simulations, comparing with non-strategy schemes, the performance of the dynamic scheme is better at minimizing the cost used to maintain fog nodes for providing services. | Yingmo Jie Mingchu Li Cheng Guo Ling Chen | 2019 | China Communications2019，16，3： | 2 |
| 9 | Enabling intelligence in fog computing to achieve energy and latency reduction显示文摘Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation, communication, storage, and analytics closer to the End Users (EUs). In order to address the issues of energy efficiency and latency requirements for the time-critical Internet-of-Things (IoT) applications, fog computing systems could apply intelligence features in their operations to take advantage of the readily available data and computing resources. In this paper, we propose an approach that involves device-driven and human-driven intelligence as key enablers to reduce energy consumption and latency in fog computing via two case studies. The first one makes use of the machine learning to detect user behaviors and perform adaptive low-latency Medium Access Control (MAC)-layer scheduling among sensor devices. In the second case study on task offloading, we design an algorithm for an intelligent EU device to select its offloading decision in the presence of multiple fog nodes nearby, at the same time, minimize its own energy and latency objectives. Our results show a huge but untapped potential of intelligence in tackling the challenges of fog computing。 | Quang Duy La Mao V. Ngo Thinh Quang Dinh Tony Q.S. Quek Hyundong Shin | 2019 | Digital Communications and Networks2019，5，1： | 2 |
| 10 | Churn-Resilient Task Scheduling in a Tiered IoT Infrastructure显示文摘Cloud-as-the-center computing paradigms face multiple challenges in the 5G and Internet of Things scenarios, where the service requests are usually initiated by the end-user devices located at network edge and have rigid time constraints. Therefore, Fog computing, or mobile edge computing, is introduced as a promising solution to the service provision in the tiered IoT infrastructure to compensate the shortage of traditional cloud-only architecture. In this cloud-to-things continuum, several cloudlet or mobile edge server entities are placed at the access network to handle the task offloading and processing problems at the network edge. This raises the resource scheduling problem in this tiered system, which is vital for the promotion of the system efficiency. Therefore, in this paper, a scheduling mechanism for the cloudlets or fog nodes are presented, which takes the mobile tasks’ deadline and resources requirements at the same time while promoting the overall profit of the system. First, the problem at the cloudlet, to which IoT devices offload their tasks, is formulated as a multi-dimensional 0-1 knapsack problem. Second, based on ant colony optimization, a scheduling algorithm is presented which treat this problem as a subset selection problem. Third, to promote the performance of the system in the dynamic environments,a churn-refined algorithm is further put forward. A series of simulation experiments have shown that out proposal outperforms many state-of-the-art algorithms in both profit and guarantee ratio. | Jianhua Fan Xianglin Wei Tongxiang Wang Tian Lan Suresh Subramaniam | 2019 | China Communications2019，16，8： | 1 |
| 11 | A Real Plug-and-Play Fog: Implementation of Service Placement in Wireless Multimedia Networks显示文摘Initially as an extension of cloud computing, fog computing has been inspiring new ideas about moving computing tasks to the edge of networks. In fog, we often repeat the procedure of placing services because of the geographical distribution of mobile users. We may not expect a fixed demand and supply relationship between users and service providers since users always prefer nearby service with less time delay and transmission consumption. That is, a plug-and-play service mode is what we need in fog. In this paper, we put forward a dynamic placement strategy for fog service to guarantee the normal service provision and optimize the Quality of Service (QoS). The simulation results show that our strategy can achieve better performance under metrics including energy consumption and end-to-end latency. Moreover, we design a real Plug-and-Play Fog (PnPF) based on Raspberry Pi and OpenWrt to provide fog services for wireless multimedia networks. | Jianwen Xu Kaoru Ota Mianxiong Dong | 2019 | China Communications2019，16，10： | 1 |
| 12 | SIoTFog:具备拜占庭容错机制的物联网雾计算网络(英文)显示文摘物联网技术爆炸式发展,从可穿戴设备到车辆互联,再到智慧城市等方面,正在改变人们的日常生活。过去,人们习惯将雾计算看作云计算的延伸;事实上,雾计算逐渐成为传输、处理分布式大数据的理想解决办法。提出一种考虑拜占庭容错的组网方法,以及两种针对物联网雾计算的资源分配算法。目的是设计一个称作'SIoTFog'的安全雾计算网络,能够抵御拜占庭错误影响并提高传输、处理物联网大数据的效率。考虑两种情况,即面对单一拜占庭错误和多拜占庭错误的不同隐患时,比较算法性能。选择时延、传输时总转发跳数和设备利用率作为性能指标。仿真结果表明,该方法可助力实现高效、可靠的雾计算网络。 | Jian-wen XU Kaoru OTA Mian-xiong DONG An-feng LIU Qiang LI | 2018 | Frontiers of Information Technology & Electronic Engineering2018，19，12： | 0 |
| 13 | 高效可验证的雾辅助私有集合交集计算(英文)显示文摘私有集合交集计算允许两方实体在不泄露除交集结果以外其他信息的前提下计算出两方实体的集合交集。随着雾计算的发展,将集合交集外包至雾的需求应运而生。然而,目前私有集合交集计算都是基于全同态加密和配对操作,所需代价较高且不支持移动,难以在雾计算中应用。提出一种高效可验证的雾辅助私有集合交集计算方案。在该方案中,实体将私有集合交集计算外包至雾,雾在没有解密能力的前提下计算集合交集。该方案不依赖全同态加密和配对操作,极大提高了计算效率。此外,构建并证明了该方案的安全性。最后,对比分析本方案与其他方案的通信复杂度和计算复杂度。分析结果表明,该方案更高效,更具现实意义。 | Qiang WANG Fu-cai ZHOU Tie-min MA Zi-feng XU | 2018 | Frontiers of Information Technology & Electronic Engineering2018，19，12： | 0 |
| 14 | Computation Offloading and Scheduling in Edge-Fog Cloud Computing显示文摘Resource allocation and task scheduling in the Cloud environment faces many challenges,such as time delay,energy consumption,and security.Also,executing computation tasks of mobile applications on mobile devices(MDs)requires a lot of resources,so they can offload to the Cloud.But Cloud is far from MDs and has challenges as high delay and power consumption.Edge computing with processing near the Internet of Things(IoT)devices have been able to reduce the delay to some extent,but the problem is distancing itself from the Cloud.The fog computing(FC),with the placement of sensors and Cloud,increase the speed and reduce the energy consumption.Thus,FC is suitable for IoT applications.In this article,we review the resource allocation and task scheduling methods in Cloud,Edge and Fog environments,such as traditional,heuristic,and meta-heuristics.We also categorize the researches related to task offloading in Mobile Cloud Computing(MCC),Mobile Edge Computing(MEC),and Mobile Fog Computing(MFC).Our categorization criteria include the issue,proposed strategy,objectives,framework,and test environment. | Dadmehr Rahbari Mohsen Nickray | 2019 | Journal of Electronic & Information Systems2019，1，1： | 0 |
| 15 | A Novel Transparent and Auditable Fog-Assisted Cloud Storage with Compensation Mechanism显示文摘This paper introduces a new fog-assisted cloud storage which can achieve much higher throughput compared to the traditional cloud-only storage architecture by reducing the traffics toward the cloud storage. The fog-storage service providers are transparency to end-users and therefore, no modification on the end-user devices is necessary. This new system is featured with(1) a stronger audit scheme which is naturally coupled with the proposed architecture and does not suffer from the replay attack and(2) a transparent and efficient compensation mechanism for the fog-storage service providers. We provide rigorous theoretical analysis on the correctness and soundness of the proposed system. To the best of our knowledge, this is the first paper to discuss about a storage data audit scheme for fog-assisted cloud storage as well as the compensation mechanism for the service providers of the fog-storage service providers. | Donghyun Kim Junggab Son Daehee Seo Yeojin Kim Hyobin Kim Jung Taek Seo | 2020 | Tsinghua Science and Technology2020，25，1： | 0 |
| 16 | Asymmetric Traffic Provisioning in Integrated Cloud-Fog Based on Flexible Multi-Flow Optical Transponder显示文摘To accommodate the asymmetric characteristic of Internet traffic, we propose a flexible node architecture based on multiflow optical transponders(MF-OTP) under the integrated Cloud-Fog framework. The proposed MF-OTP architecture is flexible in the following two degrees. Firstly, it can be flexibly adjusted to transmit either downstream or upstream traffic according to the timely traffic demand distribution. Secondly, it allows multiple sub-channels using flexible(i.e., same/different) modulation formats to serve the same traffic demand. To evaluate the efficiency of the flexible MT-OTP in serving asymmetric traffic, we propose two integer linear programming(ILP) models to address the routing, modulation and spectrum assignment(RMSA), with one aiming at minimizing the required number of sub-channels and another aiming at maximizing the volume of traffic transmission. Numerical simulations are conducted and the results show that the proposed models based on the flexible MF-OTP architecture requires less number of sub-channels and can serve more traffic demands. | Geun-soo Kim Jae-Hoon Kim Limei Peng | 2019 | China Communications2019，16，3： | 0 |
| 17 | FOG COMPUTING ENABLED INTERNET OF EVERYTHING显示文摘With the rapid development of ubiquitous networks and smart cities,the connection and communication of Internet of Everything(IoE)have drawn great attention from both academia and industry.The main challenge for constructing IoE is to enable real-time communication and high-efficiency computing among mobile devices.Mobile fog computing is promising to lower communication delay and offload network traffic.However,how to realize fog-enabled communication and computing in IoE with high-dynamic and heterogeneous network characters has not been fully investigated.Furthermore,deployment and reliable communications among fog nodes are also challenging. | Zhaolong Ning Lei Guo Joel Rodrigues Mohammad S.Obaidat | 2019 | China Communications2019，16，3： | 0 |
| 18 | Distributed Optimal Control for Traffic Networks with Fog Computing显示文摘This paper presents a distributed optimization strategy for large-scale traffic network based on fog computing. Different from the traditional cloud-based centralized optimization strategy, the fog-based distributed optimization strategy distributes its computing tasks to individual sub-processors, thus significantly reducing computation time. A traffic model is built and a series of communication rules between subsystems are set to ensure that the entire transportation network can be globally optimized while the subsystem is achieving its local optimization. Finally, this paper numerically simulates the operation of the traffic network by mixed-Integer programming, also, compares the advantages and disadvantages of the two optimization strategies. | Yijie Wang Lei Wang Saeed Amir Qing-Guo Wang | 2019 | China Communications2019，16，10： | 0 |
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