雾计算中支持外包与撤销的属性基加密方案

雾计算将云计算的计算能力、数据分析应用等扩展到网络边缘,可满足物联网设备的低时延、移动性等要求,但同时也存在数据安全和隐私保护问题。传统云计算中的属性基加密技术不适用于雾环境中计算资源有限的物联网设备,并且难以管理属性变更。为此,提出一种支持加解密外包和撤销的属性基加密方案,构建“云-雾-终端”的三层系统模型,通过引入属性组密钥的技术,实现动态密钥更新,满足雾计算中属性即时撤销的要求。在此基础上,将终端设备中部分复杂的加解密运算外包给雾节点,以提高计算效率。实验结果表明,与KeyGen、Enc等方案相比,该方案具有更优的计算高效性和可靠性。     

Journey from cloud of things to fog of things: Survey,new trends,and research directions

With the advent of the Internet of Things (IoT) paradigm, the cloud model is unable to offer satisfactory services for latency-sensitive and real-time applications due to high latency and scalability issues. Hence, an emerging computing paradigm named as fog/edge computing was evolved, to offer services close to the data source and optimize the quality of services (QoS) parameters such as latency, scalability, reliability, energy, privacy, and security of data. This article presents the evolution in the computing paradigm from the client-server model to edge computing along with their objectives and limitations. A state-of-the-art review of Cloud Computing and Cloud of Things (CoT) is presented that addressed the techniques, constraints, limitations, and research challenges. Further, we have discussed the role and mechanism of fog/edge computing and Fog of Things (FoT), along with necessitating amalgamation with CoT. We reviewed the several architecture, features, applications, and existing research challenges of fog/edge computing. The comprehensive survey of these computing paradigms offers the depth knowledge about the various aspects, trends, motivation, vision, and integrated architectures. In the end, experimental tools and future research directions are discussed with the hope that this study will work as a stepping-stone in the field of emerging computing paradigms.     

System modelling and performance evaluation of a three-tier Cloud of Things

The emergent paradigm of fog computing advocates that the computational resources can be extended to the edge of the network, so that the transmission latency and bandwidth burden caused by cloud computing can be effectively reduced. Moreover, fog computing can support and facilitate some kinds of applications that do not cope well with some features of cloud computing, for instance, applications that require low and predictable latency, and geographically distributed applications. However, fog computing is not a substitute but instead a powerful complement to the cloud computing. This paper focuses on studying the interplay and cooperation between the edge (fog) and the core (cloud) in the context of the Internet of Things (IoT). We first propose a three-tier system architecture and mathematically characterize each tier in terms of energy consumption and latency. After that, simulations are performed to evaluate the system performance with and without the fog involvement. The simulation results show that the three-tier system outperforms the two-tier system in terms of the assessed metrics.     

Fog computing for next-generation Internet of Things: Fundamental,state-of-the-art and research challenges

In recent times, the Internet of Things (IoT) applications, including smart transportation, smart healthcare, smart grid, smart city, etc. generate a large volume of real-time data for decision making. In the past decades, real-time sensory data have been offloaded to centralized cloud servers for data analysis through a reliable communication channel. However, due to the long communication distance between end-users and centralized cloud servers, the chances of increasing network congestion, data loss, latency, and energy consumption are getting significantly higher. To address the challenges mentioned above, fog computing emerges in a distributed environment that extends the computation and storage facilities at the edge of the network. Compared to centralized cloud infrastructure, a distributed fog framework can support delay-sensitive IoT applications with minimum latency and energy consumption while analyzing the data using a set of resource-constraint fog/edge devices. Thus our survey covers the layered IoT architecture, evaluation metrics, and applications aspects of fog computing and its progress in the last four years. Furthermore, the layered architecture of the standard fog framework and different state-of-the-art techniques for utilizing computing resources of fog networks have been covered in this study. Moreover, we included an IoT use case scenario to demonstrate the fog data offloading and resource provisioning example in heterogeneous vehicular fog networks. Finally, we examine various challenges and potential solutions to establish interoperable communication and computation for next-generation IoT applications in fog networks.     

雾计算平台的任务调度算法研究

雾计算平台中的任务调度问题是无法在多项式时间复杂度内求取精确解的NP-问题。本文在根据雾计算任务调度流程,构建雾计算平台任务调度数学模型基础上,采用改进人工蜂群算法,将任务调度映射为蜂群寻找蜜源的过程,在种群初始化阶段过引入混沌思想,改善了人工蜂群算法缺陷,扩大了蜂群搜索范围,避免陷入局部最优解。实验结果表明,改进后的人工蜂群算法具有更快的算法收敛速度,算法解析所对应的任务调度策略,也具有更高的任务处理总性能,表明本文所研究的改进人工蜂群算法,达到了提高雾计算资源利用率,提高雾计算任务处理效率的目的。     

Efficient and dynamic scaling of fog nodes for IoT devices

It is predicted by the year 2020, more than 50 billion devices will be connected to the Internet. Traditionally, cloud computing has been used as the preferred platform for aggregating, processing, and analyzing IoT traffic. However, the cloud may not be the preferred platform for IoT devices in terms of responsiveness and immediate processing and analysis of IoT data and requests. For this reason, fog or edge computing has emerged to overcome such problems, whereby fog nodes are placed in close proximity to IoT devices. Fog nodes are primarily responsible of the local aggregation, processing, and analysis of IoT workload, thereby resulting in significant notable performance and responsiveness. One of the open issues and challenges in the area of fog computing is efficient scalability in which a minimal number of fog nodes are allocated based on the IoT workload and such that the SLA and QoS parameters are satisfied. To address this problem, we present a queuing mathematical and analytical model to study and analyze the performance of fog computing system. Our mathematical model determines under any offered IoT workload the number of fog nodes needed so that the QoS parameters are satisfied. From the model, we derived formulas for key performance metrics which include system response time, system loss rate, system throughput, CPU utilization, and the mean number of messages request. Our analytical model is cross-validated using discrete event simulator simulations.     

Energy-aware task scheduling in mobile cloud computing

The limited energy supply, computing, storage and transmission capabilities of mobile devices pose a number of challenges for improving the quality of service (QoS) of various mobile applications, which has stimulated the emergence of many enhanced mobile computing paradigms, such as mobile cloud computing (MCC), fog computing, mobile edge computing (MEC), etc. The mobile devices need to partition mobile applications into related tasks and decide which tasks should be offloaded to remote computing facilities provided by cloud computing, fog nodes etc. It is very important yet tough to decide which tasks to be uploaded and where they are scheduled since this could greatly impact the applications’ timeliness and mobile devices’ lifetime. In this paper, we model the task scheduling problem at the end-user mobile device as an energy consumption optimization problem, while taking into account task dependency, data transmission and other constraint conditions such as task deadline and cost. We further present several heuristic algorithms to solve it. A series of simulation experiments are conducted to evaluate the performance of the algorithms and the results show that our proposed algorithms outperform the state-of-the-art algorithms in energy efficiency as well as response time.     

The trade-offs between Fog Processing and Communications in latency-sensitive Vehicular Fog Computing

In a vehicular fog computing paradigm, connected autonomous vehicles are envisioned as processing nodes (i.e. fog nodes) so that end-devices may offload processing tasks to them. As such, both local and distributed processing on fog nodes will depend heavily on wireless network conditions and the current traffic demand. In this work, we investigate the trade-offs on the operation of fog nodes under different vehicle densities and network conditions and formalize a Time Constrained One-Shot Open First Price Auction for resource allocation in vehicular fog computing. Through a large-scale simulation study, we assess important aspects of the performance of fog nodes in Vehicular Fog Computing. We show that current wireless network standards may dictate the limits of processing despite the availability of processing power of fog nodes. Our results indicate the existence of trade-offs on the operation of fog nodes regarding message overhead and processing redundancy to achieve high task completion ratio. Finally, we evaluate the social welfare distribution of the task allocation achieved using the auction where higher message rates lead to higher costs.     

SmartHerd management: A microservices-based fog computing–assisted IoT platform towards data-driven smart dairy farming

Internet of Things (IoT), fog computing, cloud computing, and data-driven techniques together offer a great opportunity for verticals such as dairy industry to increase productivity by getting actionable insights to improve farming practices, thereby increasing efficiency and yield. In this paper, we present SmartHerd, a fog computing–assisted end-to-end IoT platform for animal behavior analysis and health monitoring in a dairy farming scenario. The platform follows a microservices-oriented design to assist the distributed computing paradigm and addresses the major issue of constrained Internet connectivity in remote farm locations. We present the implementation of the designed software system in a 6-month mature real-world deployment, wherein the data from wearables on cows is sent to a fog-based platform for data classification and analysis, which includes decision-making capabilities and provides actionable insights to farmer towards the welfare of animals. With fog-based computational assistance in the SmartHerd setup, we see an 84% reduction in amount of data transferred to the cloud as compared with the conventional cloud-based approach.     

Migration of active objects in proactive

ProActive, which supports active objects as the basic units of activity and distribution used for building applications using ProActive, is a Java library for parallel, distributed and concurrent computing. In this paper, we discuss an important distribution function—migration of objects—that is not readily available in many systems designed for implementing distributed applications. The migration is done in a scalable and secure way based-on ProActive. It has the ability to easily migrate computation, especially in heterogeneous environments. Moreover, it can be used for dynamic load balancing and dynamic deployment purposes to gain improved performance and flexibility of distributed applications.     

A novel fog-computing-assisted architecture of E-healthcare system for pregnant women

Recently, there is a tremendous rise and adoption of smart wearable devices in smart healthcare applications. Moreover, the advancement in sensors and communication technology empowers to detect and analyse physiological data of an individual from the wearable device. At present, the smart wearable device based on internet of things is assisting the pregnancy woman to continuously monitor their health status for avoiding the severity. The physiological data analysis of wearable device is processed with the assistance of fog computing due to limited computational and energy capability in the wearable device. Additionally, fog computing overcomes the excess latency that is created by cloud computing during physiological data analysis. In this article, a smart health monitoring IoT and fog-assisted framework are proposed for obtaining and processing the temperature, blood pressure, ECG, and pulse oximeter parameters of the pregnant woman. Based on real time series data, the rule-based algorithm logged in the wearable device with fog computing to analyse the critical health conditions of pregnant women. The proposed wearable device is validated and tested on 80 pregnant women in real time, and wearable device is delivering the 98.75% accuracy in providing health recommendations.

     

离散事件系统规范DEVS研究

离散事件系统是一类常见的系统，如何对这类系统进行描述与建模是离散事件系统仿真研究的核心内容。离散事件系统规范DEVS是一种离散事件系统形式化描述方法，它具有层次化和模块化的特点，利用该方法可对复杂的离散事件系统进行建模、设计、分析和仿真。该文详细介绍了DEVS基本模型和耦合模型，给出了DEVS在耦合运算下的封闭性构造证明，并提出了一种具有嵌套层次结构的DEVS耦合模型实现算法，该算法对基于DEVS描述的离散事件系统的仿真实现具有一定参考价值。     

A verifiable data integrity scheme for distributed data sharing in fog computing architecture

Fog computing is a promising computing paradigm that brings computing resources close to end users at the edge of the network. Hence, it handles large-scale, geographically distributed, and latency-sensitive services. However, there are several security challenges that must be addressed due to the unreliable nature of this architecture. One can cite the verification of data integrity among the most critical issues in the context of fog computing. In fact, since data is often stored dynamically in a fully distributed manner, traditional solutions based on a centralized third-party auditor for integrity verification become unsuitable for such highly dynamic and distributed contexts. Indeed, the constant transfer of data to and from the central auditor results in high network latency and potential bottlenecks.Therefore, in this paper, we propose a new efficient public verification protocol that ensures the integrity of the data in fog computing architecture. Our solution protects data integrity and authenticity using the short integer solution problem (SIS) and identity-based signatures. Moreover, in order to legitimately modify the data, our protocol allows to distributively identify the data owners and to delegate their signatures to other entities in the architecture. Furthermore, it enables effective data integrity verification even when data is separately shared across several servers. This verification can be performed by any legitimate end user connected to the architecture, and without relying on any trusted third party. Finally, we prove that our protocol is highly efficient and outperforms existing solutions, as demonstrated by our extensive simulations and thorough security analysis that confirmed its security.     

基于粒计算的哈夫曼树SVM多分类模型研究

针对多分类问题,将粒计算与最优二叉树相结合来构建SVM多分类模型。应用粒计算思想粒化多分类问题,计算出每个类别的粒度;以粒度为权值集合,构建哈夫曼树,以解决类内样本分布不均和分类效率低下的问题;对粗粒结点分别设计多个SVM分类器;最后,以低温存储罐材料多分类问题为研究背景,对模型进行了仿真验证。与其他方法的对比分析表明,该模型提高了分类效率,为多分类问题的处理提供了一个新的研究思路。     

A family of simulation criteria to guide DEVS models validation rigorously,systematically and semi-automatically

The most common method to validate a DEVS model against the requirements is to simulate it several times under different conditions, with some simulation tool. The behavior of the model is compared with what the system is supposed to do. The number of different scenarios to simulate is usually infinite, therefore, selecting them becomes a crucial task. This selection, actually, is made following the experience or intuition of an engineer. Here we present a family of criteria to conduct DEVS model simulations in a disciplined way and covering the most significant simulations to increase the confidence on the model. This is achieved by analyzing the mathematical representation of the DEVS model and, thus, part of the validation process can be automatized.     

Improving quality-of-service in fog computing through efficient resource allocation

In today's world, large group migration of applications to the fog computing is registered in the information technology world. The main issue in fog computing is providing enhanced quality of service (QoS). QoS management consists of various method used for allocating fog-user applications in the virtual environment and selecting suitable method for allocating virtual resources to physical resource. The resources allocation in effective manner in the fog environment is also a major problem in fog computing; it occurs when the infrastructure is build using light-weight computing devices. In this article, the allocation of task and placement of virtual machine problems is explained in the single fog computing environment. The experiment is done and the result shows that the proposed framework improves QoS in fog environment.     

基于光学映射虚物体的并行绘制

尽管当前基于全局光照模型的图形绘制方法可以渲染出高质量的图象 ,但因为其计算量巨大 ,难以适用于诸如建筑物漫游、虚拟现实等对绘制速度有严格要求的场合 .为此引入光学映射虚物体的概念 ,利用构建在联网PC机上的集群系统 ,并行创建反射和折射虚物体 ,然后利用集群中各节点的图形加速硬件 ,像处理实际三维物体一样绘制这些虚物体 ,可以快速地绘制出反射 /折射效果的图象 .实验结果证明 ,该方法利用 CU P的计算能力和图形硬件的加速特性实现了真实感图形的高性能绘制 ,特别适用于诸如建筑物漫游、计算机动画和虚拟现实等要求交互式绘制的场合     

Fog computing for the integration of agents and web services in an autonomic reflexive middleware

Service-oriented architecture (SOA) has emerged as a dominant architecture for interoperability between applications, by using a weakly coupled model based on the flexibility provided by web services, which has led to a wide range of applications, which is known as cloud computing. On the other hand, multi-agent system (MAS) is widely used in the industry, because it provides an appropriate solution to complex problems, in a proactive and intelligent way. Specifically, intelligent environments (smart city, smart classroom, cyber-physical system, and smart factory) obtain great benefits by using both architectures, because MAS endows intelligence to the environment, while SOA enables users to interact with cloud services, which improve the capabilities of the devices deployed in the environment. Additionally, the fog computing paradigm extends the cloud computing paradigm to be closer to the things that produce and act on the intelligent environment, allowing to deal with issues like mobility, real time, low latency, geo-localization, among other aspects. In this sense, in this article we present a middleware, which not only is capable of allowing MAS and SOA to communicate in a bidirectional and transparent way, but also it uses the fog computing paradigm autonomously, according to the context and to the system load factor. Additionally, we analyze the performance of the incorporation of the fog computing paradigm in our middleware and compare it with other works.     

Discrete-event modelling of fire spreading

We deal here with the application of discrete-event System Specification (DEVS) formalism to implement a semi-physical fire spread model. Currently, models from physics finely representing forest fires are not efficient and still under development. If current softwares are devoted to the simulation of simple models of fire spread, nowadays there is no environment allowing us to model and simulate complex physical models of fire spread. Simulation models of such a type of models require being easily designed, modified and efficient in terms of execution time. DEVS formalism can be used to deal with these problems. This formalism enables the association of object-oriented hierarchical modelling with discrete-event techniques. Object-oriented hierarchical programming facilitates construction, maintenance and reusability of the simulation model. Discrete-events reduce the calculation domain to the active cells of the propagation domain (the heated ones).