Synopses & Reviews
Synopsis
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.
Synopsis
1 Introduction
1.1 Motivation
1.2 Problem Statement
1.3 Research Objectives
1.4 Contributions
1.5 Organization
2 Background
2.1 Sensor-Cloud Architecture
2.2 Service Composition
2.3 Spatio-Temporal Crowdsourced Services
2.4 Incentive Models
2.5 Chapter Summary
3 Spatio-Temporal Linear Composition of Sensor-Cloud Services
3.1 Introduction
3.2 Background
3.3 Spatio-Temporal Model for Sensor-Cloud Services
3.4 Spatio-Temporal Selection of Sensor-Cloud Services
3.5 Spatio-Temporal Quality Model for Line Segment Services
3.6 Spatio-Temporal Linear Composition of Sensor-Cloud Services
3.7 Failure-Proof Spatio-Temporal Composition of Sensor Cloud Services
3.8 Performance Study
3.9 Chapter Summary
4 Crowdsourced Coverage as a Service: Two-Level Composition of SensorCloud Services
4.1 Introduction
4.2 Coverage as a Service (CaaS)
4.3 Double-Layered Crowdsourced Sensor-Cloud Service Composition
4.4 Experimental Results
4.5 Chapter Summary
5 Incentive-Based Crowdsourcing of Hotspot Services 84
5.1 Introduction
5.2 Background
5.3 System Model and Problem Formulation
5.4 Spatio-Temporal Incentive-Based Approach
5.5 Experiment Results
5.6 Chapter Summary
6 Conclusion
6.1 Research Objectives Revisited
6.2 Future Research
Synopsis
To the best of our knowledge, this work is among pioneer effort in developing a crowdsourced sensor-cloud service composition framework taking into account
spatio-temporal aspects. This research 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.
- Provides a novel service framework to manage crowdsourced sensor data. This service framework aims to provide 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.
- Delivers novel frameworks to compose crowdsourced sensor-cloud services. These frameworks will focus 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.
- Presents an incentive model to drive the coverage of crowdsourced service providers. A new spati-temporal incentive model targets changing coverage of the crowdsourced providers to achieve demanded coverage of crowdsourced sensor-cloud services within a region.