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.
