Abstract
The Sensing as a Service is emerging as a new Internet of Things (IoT) business model for sensors and data sharing in the cloud. Under this paradigm, a resource allocation model for the assignment of both sensors and cloud resources to clients/applications is proposed. This model, contrarily to previous approaches, is adequate for emerging IoT Sensing as a Service business models supporting multi-sensing applications and mashups of Things in the cloud. A heuristic algorithm is also proposed having this model as a basis. Results show that the approach is able to incorporate strategies that lead to the allocation of fewer devices, while selecting the most adequate ones for application needs.
Highlights
For the Internet of Things (IoT) not to become just a collection of Things, unable to be discovered, a move towards the Web of Things (WoT) is required
The right set of virtual Things to be created in the cloud, their mapping to mashup elements and their materialization onto physical Things should be determined while using resources efficiently, which is discussed
It is possible to observe that the worst strategy is LMC that reaches the total number of available devices more quickly while fulfilling fewer mashup elements than the other strategies
Summary
For the Internet of Things (IoT) not to become just a collection of Things, unable to be discovered, a move towards the Web of Things (WoT) is required. The Se-aaS is a cloud-based service model for sensors/data to be shared, allowing for a multi-client access to sensor resources, and multi-supplier deployment of sensors [3]. This way, everyone can benefit from the IoT ecosystem, while benefiting from cloud’s storage and processing capabilities. Managing mashups in the cloud brings new challenges when assigning resources (devices and cloud) to consumer needs, as discussed . The proposed model is adequate for many emerging IoT Se-aaS business models, like the ones supporting multi-sensing applications, mashups of Things, and/or integration of data from multiple domains, allowing for a more efficient orchestration of both sensor and cloud resources to face client requests.
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