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1. Introduction {#sec1-sensors-16-00992} =============== Over the last few years, studies have exposed the limitations of traditional wireless sensor networks (WSNs) \[[@B1-sensors-16-00992],[@B2-sensors-16-00992],[@B3-sensors-16-00992]\], including sensor system management and the sensing data usage model. Recently, the sensor-cloud architecture has been proposed and has been receiving great interest \[[@B1-sensors-16-00992],[@B4-sensors-16-00992],[@B5-sensors-16-00992],[@B6-sensors-16-00992]\] among researchers in the field of WSNs, as well as cloud computing. The integration between WSNs and the cloud is motivated by taking advantage of the powerful processing and storage abilities of cloud computing for sensing data. By enabling such an integration, sensor-cloud sensing-as-a-service (SSaaS) can provide sensing data to multiple applications at the same time, instead of the current sensing data usage model for a dedicated application. The sensor-cloud can help improve the utilization of sensor resources, as well as sensor management and functions as an interface between physical sensor networks and the cyber world \[[@B1-sensors-16-00992],[@B7-sensors-16-00992]\]. For those reasons, the sensor-cloud is being viewed as a potential substitute of traditional WSNs \[[@B1-sensors-16-00992],[@B7-sensors-16-00992]\]. In the sensor-cloud architecture \[[@B1-sensors-16-00992],[@B4-sensors-16-00992],[@B5-sensors-16-00992]\], the application model is changed as follows: Physical sensors perform sensing and forward sensing data to the sensor-cloud.The sensor-cloud virtualizes sensor nodes as virtual sensors and provides sensing-as-a-service to users and applications.Applications/users buy sensing services on demand from the sensor-cloud. One of main targets of the sensor-cloud is to enable a single physical sensor network to provide sensing services to multiple applications at the same time and enable users/applications to request sensing services on demand based on their needs (i.e., by allowing applications to specify their region of interest and sensing frequency \[[@B1-sensors-16-00992],[@B4-sensors-16-00992],[@B5-sensors-16-00992]\]). Although a number of studies have been initially investigated, there is still a lack of a specific and efficient scheme on processing the down-stream traffic of application requests from cloud-to-sensors (C2S), in other words on how the sensor-cloud should process application requests and interact with physical sensors to efficiently support multiple applications on demand, toward the above targets. Without optimizing application requests as in the current sensor-cloud, only applications having the same requirements (i.e., sensing interval, latency) can reuse the sensing data of each other. Therefore, any dedicated application request with a new requirement will be sent to physical nodes. To support multiple applications with different requirements, a physical sensor may have to run multiple schedules (i.e., multiple dedicated sensing intervals) for different applications. In several existing studies, dedicated sensing requests \[[@B8-sensors-16-00992],[@B9-sensors-16-00992]\] are used. This approach is obviously inefficient for constrained resource devices like sensors, as they have to process a great number of requests from different applications with possibly high data redundancy. Some other sensor-cloud designs provide only limited sensing services within a fixed sensing rate \[[@B7-sensors-16-00992],[@B10-sensors-16-00992]\], which may not satisfy all applications. Allowing on-demand sensing services on the sensor-cloud poses enormous technical challenges. For example, on-demand sensing services may lead to dynamic operations at physical sensor nodes. In particular, when a new sensing request with a new sensing requirement is sent to physical WSNs, physical nodes have to add or update their sensing tasks to meet the requirements of all applications. On-demand