Spatio-temporal composition of sensor-cloud services

This research focuses on the design and development of a crowdsourced sensor-cloud framework with special emphasis on spatio-temporal service selection and composition.

We propose a new, two-level composition model for crowdsourced sensor-cloud services based on dynamic features including spatio-temporal aspects. The proposed approach is based on a formal sensor-cloud service model that abstracts the functional and non-functional aspects of sensor data in the cloud in terms of spatio-temporal features. A spatio-temporal indexing technique is proposed that is based on the 3D R-tree, enabling fast identification of appropriate sensor-cloud services. Our novel quality model considers dynamic features of sensors to select and compose sensor-cloud services. This model introduces a new QoS as a service which is formulated as a composition of crowdsourced sensor-cloud services. We present new QoS-aware spatio-temporal composition algorithms to select the optimal composition plan. We present a novel, heuristic failure-proof service composition algorithm for real-time reaction to sensor-cloud services which become unavailable because they are no longer spatially or temporally available. We also provide a greedy redistribution algorithm that offers incentives to crowdsourced service providers to achieve optimal balanced crowdsourced coverage within an area. Experimental results validate the performance and effectiveness of these composition approaches. The results show that our algorithms have a satisfying scalability as the number of services becomes larger.