Overview
- Provides Crowdsourced WiFi Coverage as a Service
- Presents Spatio-Temporal Composition of Sensor-Cloud Services
- Introduces a novel, heuristic failure-proof service composition algorithm based on the incremental re-planning algorithm D* Lite for real-time reaction to sensor-cloud services which become unavailable because they are no longer spatially or temporally available
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About this book
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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Keywords
Table of contents (6 chapters)
Reviews
“The book is suitable for scientists and programmers, both for research and for developing practical applications. … the proposed approaches, models, and algorithms could be useful for managing similar space-intensive and/or time-intensive crowdsourced cloud services.” (Snezhana Gocheva-Ilieva, Computing Reviews, February, 2019)
Authors and Affiliations
Bibliographic Information
Book Title: Crowdsourcing of Sensor Cloud Services
Authors: Azadeh Ghari Neiat, Athman Bouguettaya
DOI: https://doi.org/10.1007/978-3-319-91536-4
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2018
Hardcover ISBN: 978-3-319-91535-7Published: 09 July 2018
Softcover ISBN: 978-3-030-08269-7Published: 24 January 2019
eBook ISBN: 978-3-319-91536-4Published: 25 June 2018
Edition Number: 1
Number of Pages: XIX, 116
Number of Illustrations: 7 b/w illustrations, 36 illustrations in colour
Topics: Information Systems Applications (incl. Internet), Management of Computing and Information Systems, Computer Communication Networks