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| 100 |
1 |
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|a Neiat, Azadeh Ghari,
|e author.
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| 245 |
1 |
0 |
|a Crowdsourcing of sensor cloud services /
|c Azadeh Ghari Neiat, Athman Bouguettaya.
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| 264 |
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1 |
|a Cham, Switzerland :
|b Springer,
|c [2018]
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| 300 |
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|a 1 online resource
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 338 |
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|a online resource
|b cr
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|a Print version record.
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| 505 |
0 |
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|a 1 Introduction -- 2 Background -- 3 Spatio-Temporal Linear Composition of Sensor-Cloud Services -- 4 Crowdsourced Coverage as a Service: Two-Level Composition of SensorCloud Services -- 5 Incentive-Based Crowdsourcing of Hotspot Services 84 -- 6 Conclusion.
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| 520 |
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|a This book develops a crowdsourced sensor-cloud service composition framework taking into account spatio-temporal aspects. This book also unfolds new horizons to service-oriented computing towards the direction of crowdsourced sensor data based applications, in the broader context of Internet of Things (IoT). It is a massive challenge for the IoT research field how to effectively and efficiently capture, manage and deliver sensed data as user-desired services. The outcome of this research will contribute to solving this very important question, by designing a novel service framework and a set of unique service selection and composition frameworks. Delivering a novel service framework to manage crowdsourced sensor data provides high-level abstraction (i.e., sensor-cloud service) to model crowdsourced sensor data from functional and non-functional perspectives, seamlessly turning the raw data into "ready to go" services. A creative indexing model is developed to capture and manage the spatio-temporal dynamism of crowdsourced service providers. Delivering novel frameworks to compose crowdsourced sensor-cloud services is vital. These frameworks focuses on spatio-temporal composition of crowdsourced sensor-cloud services, which is a new territory for existing service oriented computing research. A creative failure-proof model is also designed to prevent composition failure caused by fluctuating QoS. Delivering an incentive model to drive the coverage of crowdsourced service providers is also vital. A new spatio-temporal incentive model targets changing coverage of the crowdsourced providers to achieve demanded coverage of crowdsourced sensor-cloud services within a region. The outcome of this research is expected to potentially create a sensor services crowdsourcing market and new commercial opportunities focusing on crowdsourced data based applications. The crowdsourced community based approach adds significant value to journey planning and map services thus creating a competitive edge for a technologically-minded companies incentivizing new start-ups, thus enabling higher market innovation. This book primarily targets researchers and practitioners, who conduct research work in service oriented computing, Internet of Things (IoT), smart city and spatio-temporal travel planning, as well as advanced-level students studying this field. Small and Medium Entrepreneurs, who invest in crowdsourced IoT services and journey planning infrastructures, will also want to purchase this book.
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| 650 |
|
0 |
|a Internet of things.
|0 http://id.loc.gov/authorities/subjects/sh2013000266
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| 650 |
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|a Computer networks.
|0 http://id.loc.gov/authorities/subjects/sh85029513
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| 650 |
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|a Cloud computing.
|0 http://id.loc.gov/authorities/subjects/sh2008004883
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|2 bisacsh
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| 650 |
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7 |
|a COMPUTERS
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|2 bisacsh
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| 650 |
|
7 |
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|2 bisacsh
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| 650 |
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|x Hardware
|x General.
|2 bisacsh
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| 650 |
|
7 |
|a COMPUTERS
|x Information Technology.
|2 bisacsh
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| 650 |
|
7 |
|a COMPUTERS
|x Machine Theory.
|2 bisacsh
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| 650 |
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|a COMPUTERS
|x Reference.
|2 bisacsh
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|
7 |
|a Information architecture.
|2 bicssc
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| 650 |
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|a Network hardware.
|2 bicssc
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| 650 |
|
7 |
|a Information retrieval.
|2 bicssc
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|
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|a Computer networks.
|2 fast
|0 (OCoLC)fst00872297
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| 650 |
|
7 |
|a Internet of things.
|2 fast
|0 (OCoLC)fst01894151
|
| 655 |
|
4 |
|a Electronic books.
|
| 700 |
1 |
|
|a Bouguettaya, Athman.
|0 http://id.loc.gov/authorities/names/n98052710
|
| 776 |
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|z 3319915355
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|t Library of Congress classification
|a TK5105.8857 .N45 2018
|l Online
|c UC-FullText
|u https://link.springer.com/10.1007/978-3-319-91536-4
|z Springer Nature
|g ebooks
|i 12555613
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