When Mobile Crowd Sensing Meets Traditional Industry

Shu, Lei; Chen, Yuanfang; Huo, Zhiqiang; Bergmann, Neil W.; Wang, Lei

doi:10.1109/access.2017.2657820

Cited by 55 publications

(38 citation statements)

References 33 publications

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“…Even larger volumes of data are used in [351], where the authors employ video analytics in order to implement motion detection framework through motion blobs and successfully provide a features-based person tracking system. Other human-in-the-loop concepts are mobile apps developed to support the customer integration in the product design phase and subsequently the design of the manufacturing network [348], cross-disciplinary mobile crowdsensing of pervasive sensor data applied in industrial processes [350], as well as automated methodologies for worker path generation and safety assessment [352]. 13) Security: Security aspects in factory automation and industrial operations have become a hot topic in the last years since monitoring and control tasks are more and more complex.…”

Section: Computation and Data Analytics Articlesmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

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Section: Computation and Data Analytics Articlesmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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“…In particular, people will be among the most valuable data sources in Industry 4.0, for example: feedback from customers will be used to improve mass customization [22] and data gathered from worker devices will be used for network collaboration [7]. In this context, crowdsensing and crowdsourcing, often referred to as mobile crowdsensing and mobile crowdsourcing, provide a new way to collect data collaboratively, by exploiting the pervasive presence of Internet-connected geolocated smart user devices, such as smartphones, smartwatches and tablets [12]. In crowdsensing [23], these devices serve as moving sensors to collect data from different places.…”

Section: Advanced Analyticsmentioning

confidence: 99%

“…In the following, when no further differentiation is needed, MCS will be used to refer to both mobile crowdsensing and mobile crowdsourcing. Although these two novel paradigms are very powerful, as they enable the collection of massive amounts of useful local data and information in a cost-effective and scalable way, they have not been widely used in industries yet, due to the strict requirements that characterize this environment [12].…”

Section: Advanced Analyticsmentioning

confidence: 99%

“…In fact, the main objective of Industry 4.0 is to improve the perceived quality of products according to customers' feedback, while optimizing the related industrial processes by increasing their flexibility, speed, productivity and quality [25]. Accordingly, the following application domains are the levers that will drive the development of Industry 4.0 [9,12,26]. Figure 1 shows the enabling technologies of Industry 4.0 and their related application domains.…”

Section: Application Domainsmentioning

confidence: 99%

“…In Industry 4.0, workers and customers are an integral part and the core of most of the decision processes, both in terms of objective measurements of their activities and subjective feedback gathering. In this context, the use of crowdsensing and crowdsourcing introduces many advantages [12]. In crowdsensing and crowdsourcing, data are collected collaboratively by exploiting the pervasive presence of Internet-connected geolocated devices.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0

Pilloni

2018

Future Internet

112

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We are living in the era of the fourth industrial revolution, namely Industry 4.0. This paper presents the main aspects related to Industry 4.0, the technologies that will enable this revolution, and the main application domains that will be affected by it. The effects that the introduction of Internet of Things (IoT), Cyber-Physical Systems (CPS), crowdsensing, crowdsourcing, cloud computing and big data will have on industrial processes will be discussed. The main objectives will be represented by improvements in: production efficiency, quality and cost-effectiveness; workplace health and safety, as well as quality of working conditions; products' quality and availability, according to mass customisation requirements. The paper will further discuss the common denominator of these enhancements, i.e., data collection and analysis. As data and information will be crucial for Industry 4.0, crowdsensing and crowdsourcing will introduce new advantages and challenges, which will make most of the industrial processes easier with respect to traditional technologies.

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An osmotic computing infrastructure for urban pollution monitoring

2019

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Summary Urban pollution control systems suffer from the presence of fixed stations in a greater number than mobile monitoring devices. Data gathered from such stations provide detailed and reliable information, thanks to equipment quality and effective measuring protocols, but these sampled data are gathered from very limited areas and through discontinuous monitoring campaigns. Fortunately, the spread of technologies for mobility has fostered the development of new approaches like mobile crowdsensing (MCS), increasing the chances of using mobile devices, even personal ones, as suitable sensors for the urban monitoring scenario. Nevertheless, one of the open challenges is the management of integrated heterogeneous data flows, differing in terms of typology, technical specifications (eg, transmission protocols), and semantics. The osmotic computing paradigm aims at creating an abstract level between mobile devices/Internet‐of‐Things devices and a cloud platform, which enables opportunistic filtering and the addition of metadata for improving the data processing flow. This work focuses on the design and development of a middleware that integrates data coming from mobile and Internet‐of‐Things devices specifically deployed in urban contexts using the osmotic computing paradigm. Moreover, a component of the osmotic membrane has been developed for security management.

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When Mobile Crowd Sensing Meets Traditional Industry

Cited by 55 publications

References 33 publications

Data Management in Industry 4.0: State of the Art and Open Challenges

Data Management in Industry 4.0: State of the Art and Open Challenges

How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0

An osmotic computing infrastructure for urban pollution monitoring

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