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Utility Based data computing Scheme to Provide Sensing Service in Internet of Things

IEEE TRANSACTIONS ON EMERGING TOPICS IN computing 2019年第2期7卷 337-348页

作者： Hui, Yilong Su, Zhou Guo, Song Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Hong Kong Polytech Univ Dept Comp Kowloon Hong Kong Peoples R China

Recently, the internet-of-things (IoT) has emerged as a new paradigm with an ever-increasing number of things to be connected to the internet. Different from the conventional paradigms, in the IoT the data computing scheme is needed to efficiently collect and offer data to provide sensing service. However, the existing data computing schemes are unfriendly which lack the integrated and incentive consideration to reduce the cost of data collection and encourage more participants for cooperation. Therefore, in this paper we propose a utility-based data computing scheme which allows vehicles to collect mobile data in the urban area, in order to provide sensing service in the IoT. First, we present an integrated architecture by introducing roadside buffers where each buffer can have a sink node to collect sensor data from vehicles. Next, by considering both the time cost and power cost during the data collection, we make the analysis of utilities in data computing process. Then, with a bargaining game to model the interaction among participants, a utility based data computing scheme is proposed with incentives where the optimal price can be determined for sensing service. Finally, extensive simulation experiments prove that the proposed scheme can efficiently improve the sensing service in IoT with a low cost.

关键词： Internet-of-things sensor data data computing bargaining game

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Object tracking method based on data computing

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JOURNAL OF SUPERcomputing 2019年第6期75卷 3217-3228页

作者： Zhang, Weiqiang Cho, Seoungjae Chae, Jeongsook Sung, Yunsick Cho, Kyungeun Dongguk Univ Seoul Dept Multimedia Engn 30 Pildongro 1 Gil Seoul 04620 South Korea

The data computing process is utilized in various areas such as autonomous driving. Autonomous vehicles are intended to detect and track nearby moving objects avoiding collisions and to navigate in complex situations, such as heavy traffic and dense pedestrian areas. Therefore, object tracking is the core technology in the environment perception systems of autonomous vehicles and requires the monitoring of surrounding objects and the prediction of the moving states of objects in real time. In this paper, a multiple object tracking method based on light detection and ranging (LiDAR) data is proposed by using a Kalman filter and data computing process. We suppose that the movements of the tracking objects are captured consecutively as frames;thus, model-based detection and tracking of dynamic objects are possible. A Kalman filter is applied for predicting posterior state of tracking object based on anterior state of the tracking object. State denotes the positions, shapes, and sizes of objects. By computing the likelihood probability between predicted tracking objects and clusters which registered from tracking objects, the data association process of the tracking objects can be generated. Experimental results showed enhanced object tracking performance in a dynamic environment. The average matching probability of the tracking object was greater than 92.9%.

关键词： data computing Object tracking Model-based object Kalman filter

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Modeling and Designing Fault-Tolerance Mechanisms for MPI-based MapReduce data computing Framework 1

Modeling and Designing Fault-Tolerance Mechanisms for MPI-ba...

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1st IEEE International Conference on Big data computing Service and Applications (BigdataService)

作者： Lin, Jian Liang, Fan Lu, Xiaoyi Zha, Li Xu, Zhiwei Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China

ISBN: (纸本)9781479981281

Fault-tolerance is a significant property for distributed and parallel computing systems. An emerging trend of Big data computing is to combine MPI and MapReduce technologies in a single framework. The distinctive state model in this kind of frameworks brings challenges to designing an efficient and transparent fault-tolerance mechanism. In this paper, a state model analysis method is proposed for uniformly modeling independent MPI, MapReduce and MPI-based MapReduce data computing frameworks. Based on this analysis, a library-level fault-tolerance mechanism with global persistent state model is proposed;a data-staging and routine-sharing based checkpoint approach is designed within this mechanism. The proposed mechanism has been implemented in dataMPI, a communication library supporting MPI-based MapReduce data computing applications. The experiments show that it can transparently enable fault-tolerance for applications. Taking TeraSort as an example, it introduces only 6.8% time overhead and 11% space overhead. For a failure-resume execution, it has a 10%-32% performance advantage compared with the naive checkpoint solutions based on local or parallel storages. The proposed mechanism also provides superior performance and resource utilization compared with Hadoop for both fault-free and failure-resume executions.

关键词： MapReduce MPI data computing fault-tolerance checkpoint

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Ubiquitous data computing and information using in a smart factory with wireless manufacturing

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Engineering Sciences 2013年第1期11卷 2-9页

作者： Cao Wei Jiang Pingyu Fu Yingbin School of Mechanical Engineering Xi'an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University

This study proposesan over all framework for applying wireless manufacturing(WM)technologies in a smart factory and establishes a smart factory data computing and information using system (dc-IUS). Several plug-and-play (PnP) application modules of the dc-IUS are presented in the fields of machining process and quality control,material flow and inventory control,and factory resource tracking. Different schemes are discussed about how and where to apply these functions. Then some running examples are studied to demonstrate the feasibility and reliability of dc-IUS. At last,the challenges of applying WM are discussed and a conclusion is given.

关键词： wireless manufacturing radio frequency identification smart factory data computing information

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A high-performance active digital library

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PARALLEL computing 1998年第12-13期24卷 1791-1806页

作者： Williams, R Sears, B CALTECH Ctr Adv Comp Res Pasadena CA 91125 USA CALTECH Space Radiat Lab Pasadena CA 91125 USA

We describe Javaflow and Paraflow, the client and server parts of a digital library, providing high-performance data-retrieval and data-mining services, with emphasis on user interface as well as computing efficiency. Paraflow is a component model for high-performance computing, implemented as a thin layer on Message Passing Interface (MPI);it controls a heterogeneous metacomputer, allowing groups of processes (services) to work collectively and communicate with each other by parallel messaging links (channels). Javaflow is a straightforward, intuitive, loosely coupled component model for Java applets, allowing them to be combined into a GUI that can run on a thin client from a standing start. Javaflow controls Paraflow through a well-exposed text interface which is carried by authenticated telnet, while the output of the computation can be web-pages, files, or high-speed graphics. As an example, we discuss the SARA remote-sensing library, which provides public retrieval of multichannel images of the Earth, as well as private, authenticated access to a variety of image-processing services running on parallel supercomputers. (C) 1998 Elsevier Science B.V. All rights reserved.

关键词： digital library MPI parallel computing data computing user interface Java

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Big data challenges in ocean observation: a survey

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PERSONAL AND UBIQUITOUS computing 2017年第1期21卷 55-65页

作者： Liu, Yingjian Qiu, Meng Liu, Chao Guo, Zhongwen Ocean Univ China Dept Comp Sci & Technol 238 Songling Rd Qingdao 266100 Peoples R China

Ocean observation plays an essential role in ocean exploration. Ocean science is entering into big data era with the exponentially growth of information technology and advances in ocean observatories. Ocean observatories are collections of platforms capable of carrying sensors to sample the ocean over appropriate spatiotemporal scales. data collected by these platforms help answer a range of fundamental and applied research questions. Many countries are spending considerable amount of resources on ocean observing programs for various purposes. Given the huge volume, diverse types, sustained measurement, and potential uses of ocean observing data, it is a typical kind of big data, namely marine big data. The traditional data-centric infrastructure is insufficient to deal with new challenges arising in ocean science. New distributed, large-scale modern infrastructure backbone is urgently required. This paper discusses some possible strategies to solve marine big data challenges in the phases of data storage, data computing, and analysis. Some applications in physics, chemistry, geology, and biology illustrate the significant uses of marine big data. Finally, we highlight some challenges and key issues in marine big data.

关键词： Ocean observation Marine big data data storage data computing data analysis

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PWCT: Visual Language for IoT and Cloud computing Applications and Systems 17

PWCT: Visual Language for IoT and Cloud Computing Applicatio...

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2nd International Conference on Internet of things, data and Cloud computing (ICC)

作者： Fayed, Mahmoud S. Al-Qurishi, Muhammad Alamri, Atif Al-Daraiseh, Ahmad A. King Saud Univ Coll Comp & Informat Sci Res Chair Pervas & Mobile Comp Riyadh Saudi Arabia

ISBN: (纸本)9781450347747

Developing IoT, data computing and Cloud computing software requires different programming skills and different programming languages. This cause a problem for many companies and researchers that need to hires many programmers to develop a complete solution. The problem is related directly to the financial cost and the development time which are very important factors to many research projects. In this paper we present and propose the PWCT visual programming tool for developing IoT, data computing and Cloud computing Applications and Systems without writing textual code directly. Using PWCT increase productivity and provide researchers with one visual programming tool to develop different solutions.

关键词： Internet of Things data computing Cloud computing Visual Programming Knowledge Representation and Reasoning

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Aggrandizing Hadoop in terms of Node Heterogeneity & data Locality

Aggrandizing Hadoop in terms of Node Heterogeneity & Data Lo...

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IEEE International Conference on Smart Structures and Systems (ICSSS)

作者： Sujitha, S. Jaganathan, Suresh Sri Sivasubramania Nadar Coll Engn Dept Comp Sci & Engn Madras Tamil Nadu India

ISBN: (纸本)9781467362405

The growth of data has increased exponentially in recent years. In this context, data-center scale computer systems are built to meet the high storage and processing demands of these applications. Such systems are composed of hundreds, thousands, or even millions of commodity computers connected through a LAN housed in a data center. It has a much larger scale than a traditional computer cluster. Hadoop enables the distributed processing of large data sets across clusters of commodity servers. It is designed to scale up from a single server to thousands of machines, with a very high degree of fault tolerance. Strength of Hadoop is in its ability to detect and handle failures. The original Hadoop native task scheduler implicitly assumes that cluster nodes are homogeneous. This assumption is used to identify a slow task and re-execute it. However, this assumption does not hold where the cluster nodes are heterogeneous, since speculatively identifying a slow task will give rise to erroneous conclusions. In MapReduce, the sub-task is transferred to a node for execution. The input to the subtask, if not present in the node, must be transferred from another node in the network. Transferring data takes time and delays execution. In this paper, we have proposed a methodology for improving Hadoop in-terms of Heterogeneity and data Locality. The performance of improved version can be measured using these metrics, i) execution time, ii) response time, iii) tasks submitted, iv) time related to jobs i. e. arrival, start and completion, v) completed task, vi) fairness, vii) locality and viii) mean completion time.

关键词： Big data Cloud computing Schedulers data computing Heterogeneity

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BlockchainPedia: A Comprehensive Framework for Blockchain Network Comparison 27

BlockchainPedia: A Comprehensive Framework for Blockchain Ne...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Irimia, Cosmin Bejan, Luciana Iftene, Adrian Alexandru Ioan Cuza the University of Iasi Faculty of Computer Science General Henri Mathias Berthelot Street No. 16 Iasi700259 Romania

Since the release of blockchain technology back in 2008, a huge number of blockchain-based systems have been developed. As of Sep'22, there are at least 1, 000 blockchain systems ready to be used, with at least four types of networks: public, private, hybrid, and consortium. With the many options you can choose from, a valid question arises: How can we choose a system that fits best with our needs? The purpose of Blockchain Pedia is to help users make the best decision in choosing the right blockchain system, giving the possibility to see the differences between multiple options. The application is designed for anyone who wants to build a project based on blockchain technology, giving recommendations based on the needs of the developer. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Blockchain data analysis data computing Distributed Networks

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Security Challenges and Future Privacy Perspectives in Cloud computing for Smart City Environments

Security Challenges and Future Privacy Perspectives in Cloud...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Tayal, Ankit Bhatia, Gaurav Singh, Jay Bansal, Mohit Anitha, G. Gl Bajaj Institute of Technology & Management Electrical Engineering Department Gr. Noida India Gl Bajaj Institute of Technology & Management Computer Application Department Gr. Noida India Institute of Aeronautical 5Engineering Department of Computer Science and Information Technology Hyderabad India

ISBN: (纸本)9798350357769

The context of smart cities and cloud computing is essential to understanding the broader landscape in which security challenges and privacy perspectives play pivotal roles in enhancing the quality of life for the residents in smart cities. Smart cities use information and communication technologies (ICT) to optimize resource efficiency. They leverage data, sensors, and connectivity to improve various aspects of urban living, including transportation, energy usage, public services, and sustainability. Cloud computing is an integral part of the infrastructure of smart cities that enables the collection, storage, analysis, and sharing of data and services. This research paper indicates many factors and challenges in the development of cloud-enabled smart city areas and provides potential recommendations. A guideline for future studies on this research topic has also been provided to serve the comparative analysis of the various findings and results. © 2023 IEEE.

关键词： Cloud computing Cyber Security data computing Internet of Things (IoT) Smart City

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