Fog Computing for Big Data Analytics in IoT Aided Smart Grid Networks

Wireless Personal Communications - The recent integration of Internet of Things and Cloud Computing (CC) technologies into a Smart Grid (SG) revolutionizes its operation. The scalable and unlimited...     

基于雾计算的智能工厂实时任务调度架构

针对传统的基于云的任务调度架构中没有充分利用智能工厂的资源,以及远距离传输导致高传输时延的问题,提出了一种基于雾计算的实时任务调度架构。设计了一种基于雾计算的智能工厂网络架构;考虑到工厂任务的时延敏感性和优先级特性,提出了一种基于动态优先级的任务调度模型,该模型被雾节点用来调度和执行等待队列中的任务;基于提出的网络架构和任务调度模型,提出了一种任务卸载策略,该策略可以被用于解决智能工厂中的资源利用问题。仿真结果证明了提出的实时任务调度架构在智能工厂中应用的可行性和有效性。     

云计算驱动了雾计算的发展

认为雾计算/边缘计算(MEC)是云计算的延伸,其发展源自物联网(IoT)实时反馈型应用需求的驱动,通过雾计算/MEC将数据采集、数据处理和应用分析程序集中在网络边缘设备中,使云端计算、网络、存储能力得以向边缘扩展。提出在IoT中采用中枢智能与边缘智能的两级架构,实现雾计算与云计算的协作,提高IoT处理效率。     

Smart Identification Frameworks for Ubiquitous Computing Applications

We present our results of the conceptual design and the implementation of ubiquitous computing applications using smart identification technologies. First, we describe such technologies and their potential application areas, then give an overview of some of the applications we have developed. Based on the experience we have gained from developing these systems, we point out design concepts that we have found useful for structuring and implementing such applications. Building upon these concepts, we have created two frameworks based on Jini (i.e., distributed Java objects) and Web Services to support the development of ubiquitous computing applications that make use of smart identification technology. We describe our prototype frameworks, discuss the underlying concepts and present some lessons learned.     

Secure and Efficient Computing Resource Management in Blockchain-Based Vehicular Fog Computing

In vehicular fog computing(VFC),the resource transactions in the Internet of Vehicles(IoV)have become a novel resource management scheme that can improve system resource utilization and the quality of vehicle services.In this paper,in order to improve the security and fairness of resource transactions,we design a blockchain-based resource management scheme for VFC.First,we propose the concept of resource coin(RC)and develop a blockchain-based secure computing reource trading mechanism in terms of RC.As a node of the blockchain network,the roadside unit(RSU)participates in verifying the legitimacy of transactions and the creation of new blocks.Next,we propose a resource management scheme based on contract theory,encouraging parked vehicles to contribute computing resource so that RSU could complete proof of work(PoW)quickly,improve the success probability of block creation and get RC rewards.We use the gradient descent method to solve the computing resource utilization that can maximize the RC revenue of RSUs and vehicles during the block creation.Finally,the performance of this model is validated in simulation result and analysis.     

A Latency-Aware Multiple Data Replicas Placement Strategy for Fog Computing

Journal of Signal Processing Systems - With the rapid increase of the number of IoT devices, transmitting big amount of data from these devices to data centers which are far away will cause...     

An Energy-Efficient Load Balancing Approach for Scientific Workflows in Fog Computing

Wireless Personal Communications - Fog computing provides cloud services at the user end. User requests are processed on the fog nodes deployed near the end-user layer in a fog computing...     

TD-SCDMA智能天线电磁辐射计算方法分析

由于TD-SCDMA智能天线的赋形特性不同于一般移动通信天线,智能天线的电磁辐射特性也与其他移动通信电线不同,本文由此提出在智能天线的系统中进行电磁辐射计算时需要考虑的一些因素,以及对智能天线电磁辐射的计算方法。     

EPRFL: An Efficient Privacy-Preserving and Robust Federated Learning Scheme for Fog Computing

Federated learning(FL) is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties.When combined with Fog Computing, FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT). However, implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks, and ensuring robust data aggregation. To address these challenges, we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL) scheme for fog computing scenarios. Specifically, we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm, which is not only resistant to model inference and collusion attacks,but also robust to fog node dropping. Then, we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication overhead. To minimize training delays, we develop a dynamic task scheduling strategy based on comprehensive score. Theoretical analysis demonstrates that EPRFL offers robust security and low latency. Extensive experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance, and resource efficiency.     

Proactive Load Balancing Mechanism for Fog Computing Supported by Parked Vehicles in IoV-SDN

Internet of Vehicles(IoV)is a new style of vehicular ad hoc network that is used to connect the sensors of each vehicle with each other and with other vehicles’sensors through the internet.These sensors generate different tasks that should be analyzed and processed in some given period of time.They send the tasks to the cloud servers but these sending operations increase bandwidth consumption and latency.Fog computing is a simple cloud at the network edge that is used to process the jobs in a short period of time instead of sending them to cloud computing facilities.In some situations,fog computing cannot execute some tasks due to lack of resources.Thus,in these situations it transfers them to cloud computing that leads to an increase in latency and bandwidth occupation again.Moreover,several fog servers may be fuelled while other servers are empty.This implies an unfair distribution of jobs.In this research study,we shall merge the software defined network(SDN)with IoV and fog computing and use the parked vehicle as assistant fog computing node.This can improve the capabilities of the fog computing layer and help in decreasing the number of migrated tasks to the cloud servers.This increases the ratio of time sensitive tasks that meet the deadline.In addition,a new load balancing strategy is proposed.It works proactively to balance the load locally and globally by the local fog managers and SDN controller,respectively.The simulation experiments show that the proposed system is more efficient than VANET-Fog-Cloud and IoV-Fog-Cloud frameworks in terms of average response time and percentage of bandwidth consumption,meeting the deadline,and resource utilization.     

基于雾计算的NB-IoT框架、关键技术及应用

针对窄带物联网(NB-IoT)技术特点和业务类型,提出了基于雾计算的NBIoT网络架构,通过为NB-IoT接入点(AP)配置雾计算设备,将接入点升级为具有存储和计算能力的雾接入点(F-AP),使得数据收集、传输、处理和计算更靠近终端设备,提高应用系统的响应速度,节约网络带宽。     

QoE Aware IoT Application Placement in Fog Computing Using Modified-TOPSIS

Mobile Networks and Applications - Over the years, fog computing has emerged as a paradigm to complement the cloud computing in handling the delay sensitive IoT applications in a better manner....     

云计算在智能电网中的应用及其安全问题研究

文章认为在智能电网中引入云计算,构建智能电网云可以为智能电网的发展提供有效支持。基于智能电网的特征,依托作为一种崭新的存储和计算模式的云计算技术,文章阐述了云计算技术如何为智能电网的数据存储和分析提供技术支持,设计了一种智能电网云计算的结构,分析了云计算技术给智能电网带来的诸多益处;并就智能电网云可能存在的安全威胁和相应的防范措施进行讨论。     

Editorial: Big Data Innovation for Sustainable Cognitive Computing

Mobile Networks and Applications -     

近距离移动智能终端的协同计算机制

提出使用智能移动设备之间的本地化协作弥补云—端协作的缺陷,可以以极低的成本实现设备性能的提升,降低设备网络通信和能量消耗。针对智能设备本地化协作中面临的设备异构性的问题,提出了一种解释型语言和相应中间件支持,用于开发智能终端间的协作应用,并对协作应用提供运行时的支持。另外,还提出了移动智能设备协作领域面临的开放性研究的一些问题。     

Towards a Sustainable Green Design for Next-Generation Networks

Recently distributed real-time database systems are intended to manage large volumes of dispersed data. To develop distributed real-time data processing, a reality and stay competitive well defined protocols and algorithms must be required to access and manipulate the data. An admission control policy is a major task to access real-time data which has become a challenging task due to random arrival of user requests and transaction timing constraints. This paper proposes an optimal admission control policy based on deep reinforcement algorithm and memetic algorithm which can efficiently handle the load balancing problem without affecting the Quality of Service (QoS) parameters. A Markov decision process (MDP) is formulated for admission control problem, which provides an optimized solution for dynamic resource sharing. The possible solutions for MDP problem are obtained by using reinforcement learning and linear programming with an average reward. The deep reinforcement learning algorithm reformulates the arrived requests from different users and admits only the needed request, which improves the number of sessions of the system. Then we frame the load balancing problem as a dynamic and stochastic assignment problem and obtain optimal control policies using memetic algorithm. Therefore proposed admission control problem is changed to memetic logic in such a way that session corresponds to individual elements of the initial chromosome. The performance of proposed optimal admission control policy is compared with other approaches through simulation and it depicts that the proposed system outperforms the other techniques in terms of throughput, execution time and miss ratio which leads to better QoS.

     

绿色清洗中国干洗业可持续发展的方向

正确评价干洗剂对人体的毒副作用，合理地设定干洗过程中挥发性有害物质的残留限量，是开发干洗业环境无害技术和绿色服务的先决条件。只有在充分保证环境安全的前提下，通过逐步推行化学清洁产品的总体选择战略，从环境管理和经济发展的结合上来规范企业的环境行为，才能最终实现干洗业的可持续发展。     

雾计算在广播电视监测中的应用研究

近年来,“云计算”以其独特的优势和巨大的商业价值,在各个领域得到了飞速的发展.作为云计算的延伸,在网络接入设备指数增长而带宽有限的情况下,雾计算将带来消除存储瓶颈、解决传输限制的新一代计算变革.本文介绍雾计算的特征,并以广播电视直播比对、广播质量评估为例,研究雾计算在广播电视监测领域的应用与前景.     

基于雾计算的权重OBDD访问结构属性密码体制研究

基于密文策略的属性密码体制在云存储中对于实现数据的安全分享和细粒度访问控制起到了关键性的作用,但基于密文策略的属性密码体制存在效率低、访问策略不灵活以及单一属性授权中心带来的单点失效威胁问题.对此,提出基于雾计算的权重有序二叉决策图(Ordered Binary Decision Diagram,OBDD)访问结构的属...     

A Broad Learning-Driven Network Traffic Analysis System Based on Fog Computing Paradigm

The development of communication technologies which support traffic-intensive applications presents new challenges in designing a real-time traffic analysis architecture and an accurate method that suitable for a wide variety of traffic types.Current traffic analysis methods are executed on the cloud,which needs to upload the traffic data.Fog computing is a more promising way to save bandwidth resources by offloading these tasks to the fog nodes.However,traffic analysis models based on traditional machine learning need to retrain all traffic data when updating the trained model,which are not suitable for fog computing due to the poor computing power.In this study,we design a novel fog computing based traffic analysis system using broad learning.For one thing,fog computing can provide a distributed architecture for saving the bandwidth resources.For another,we use the broad learning to incrementally train the traffic data,which is more suitable for fog computing because it can support incremental updates of models without retraining all data.We implement our system on the Raspberry Pi,and experimental results show that we have a 98%probability to accurately identify these traffic data.Moreover,our method has a faster training speed compared with Convolutional Neural Network(CNN).