Data Collection Middleware for Crowdsourcing-based Industrial Sensing Intelligence

Huo, Zhiqiang; Shu, Lei; Zhou, Zhangbing; Chen, Yuanfang; Li, Kailiang; Zeng, Junlin

doi:10.1145/2757757.2757758

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Therefore, we choose GSN [23] as gateway middleware to interface and enable sensor data streaming uploaded to communication modules in CDF. GSN is an Internet-scale infrastructure for rapid deployment and integration of heterogeneous IWSNs, which targets at flexible configuration, general abstraction and distributed query support, on this basis, CISI middleware [24] is selected to support crowdsensing-based applications based on GSN. To facilitate the interaction between GSN and CDF, we add Agent module for relaying sensor data streaming to Collector Broker module.…”

Section: An Example Of Cdf Systems: Equipment Fault Diagnosis In mentioning

confidence: 99%

Cloud-based Data-intensive Framework towards fault diagnosis in large-scale petrochemical plants

Huo

Mukherjee

Shu

et al. 2016

2016 International Wireless Communications and Mobile Computing Conference (IWCMC)

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Abstract-Industrial Wireless Sensor Networks (IWSNs) are expected to offer promising monitoring solutions to meet the demands of monitoring applications for fault diagnosis in largescale petrochemical plants, however, involves heterogeneity and Big Data problems due to large amounts of sensor data with high volume and velocity. Cloud Computing is an outstanding approach which provides a flexible platform to support the addressing of such heterogeneous and data-intensive problems with massive computing, storage, and data-based services. In this paper, we propose a Cloud-based Data-intensive Framework (CDF) for on-line equipment fault diagnosis system that facilitates the integration and processing of mass sensor data generated from Industrial Sensing Ecosystem (ISE). ISE enables data collection of interest with topic-specific industrial monitoring systems. Moreover, this approach contributes the establishment of on-line fault diagnosis monitoring system with sensor streaming computing and storage paradigms based on Hadoop as a key to the complex problems. Finally, we present a practical illustration referred to this framework serving equipment fault diagnosis systems with the ISE.

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Section: An Example Of Cdf Systems: Equipment Fault Diagnosis In mentioning

confidence: 99%

Cloud-based Data-intensive Framework towards fault diagnosis in large-scale petrochemical plants

Huo

Mukherjee

Shu

et al. 2016

2016 International Wireless Communications and Mobile Computing Conference (IWCMC)

Self Cite

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“…The IIoT 11–17 is an extended form of Internet of things concept used in various industrial sectors. A data crowdsourcing in IIoT attracts cooperation and cost‐effectiveness by storing data in a cloud 18–20 .…”

Section: Introductionmentioning

confidence: 99%

PSSCC: Provably secure communication framework for crowdsourced industrial Internet of Things environments

et al. 2020

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Summary Internet of things environment is adopted widely in different industries and business organizations with varying capacity. It provides a favorable environment to outsource the crowdsourced data in the cloud to minimize the cost of computation, which is called crowdsourcing. Crowdsourcing is a technique where individuals or organizations obtain goods and services. A professional or industry outsource the crowdsourced data in the cloud, where confidentiality and authenticity of data become essential. Signcryption is the cryptographic technique that serves both the authenticity and the privacy of transmitted messages. This technique ensures secure authentic data transmission and storage. Therefore, this paper proposes an identity‐based signcryption scheme. In the proposed PSSCC framework, the user does pairing free computation during signcryption, which makes efficient calculation on user‐side. Moreover, PSSCC framework is proved secure under modified bilinear Diffie‐Hellman inversion and modified bilinear strong Diffie‐Hellman problems. The performance analysis of PSSCC with related schemes indicates that the proposed system supports efficient communication along with less computation cost.

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Compressive sensing based data quality improvement for crowd-sensing applications

Cheng

Niu

Kong

et al. 2017

Journal of Network and Computer Applications

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Data Collection Middleware for Crowdsourcing-based Industrial Sensing Intelligence

Cited by 5 publications

References 18 publications

Cloud-based Data-intensive Framework towards fault diagnosis in large-scale petrochemical plants

Cloud-based Data-intensive Framework towards fault diagnosis in large-scale petrochemical plants

PSSCC: Provably secure communication framework for crowdsourced industrial Internet of Things environments

Compressive sensing based data quality improvement for crowd-sensing applications

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