# An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments
## 2 Related Works
However, the proposed scheme tries only to minimize the number of sensor nodes capable of fulfilling the requirements of the applications, not considering the similarity and correlations between the nodes measurements. In this way, there is no guarantee that redundant nodes will not be chosen. In addition, inside a specific region, all sensor nodes must be homogenous. Hence, the virtual sensors comprise of homogeneous sensor nodes (although different virtual sensors, from different regions, may form a Virtual Sensor Group). In our solution, we do not impose these restrictions, since every WSN can be composed of heterogeneous sensor nodes and the measurements from the selected nodes can represent multiple geographical areas, based on the correlations between their measurements. In this way, the subset of nodes selected by the middleware will not contain redundant nodes, improving the network lifetime. The authors in [40; 41; 42] introduce an interactive model for the sensor cloud environments to efficiently provide on-demand sensing services for multiple applications with different requirements at the same time. It is a novel concept since the middleware can now perform the aggregation of application requests to minimize the resource consumptions of the constrained physical sensor nodes while attending all the requirements of the users' applications. Hence, with few queries sent to the sensor nodes, the middleware could ensure the responses needed by many applications. However, all nodes still receive the consolidated request and adapt their sensing interval in order to attend all applications. In our work, the middleware selects only a subset of physical sensor nodes to performing the transmissions of their responses. Nevertheless, we will investigate how to integrate AC\(\times\)SIMv2 with the interactive model in future works. [PERSON] et al. [19] present a framework (Virtual Sensing Framework (VSF)) to predicts multiple consecutive sensor data while some the sensors are inactive, thus, reducing sensing and data transmissions activities. Similar to our work, VSF exploits the temporal and spatial correlations amongst sensed data to select the sensor nodes to respond the users' queries. However, the work focuses only on WSN environments, not considering how the correlations could be done in sensor cloud environments with multiples WSN. Figure 3: (a) Eleven sensor nodes deployed over a laboratory. (b) The 11 sensors build a routing tree. Notice correlated sensors nodes that are not close to each other. [PERSON] and [PERSON] [43] consider the provisioning of virtual sensors by clustering sensor nodes not physically close to each other. Nevertheless, each node is responsible for just one type of sensor (for example, temperature or humidity, but not both), and the node selection is based on the nodes with the highest energy value. In ACxSIMv2, however, each sensor node can measure multiples variables and the selection process does not take into account only the energy parameter since, in a heterogeneous environment, nodes with higher energy can not necessarily attend all applications requirements. The work in [22] explores the concept of multidimensional behavioral clustering [44] for reducing message transmission in traditional WSN. In that sense, the sink node creates virtual clusters by clustering the sensors nodes according to the correlations of the measurements from all monitored (dimensions). However, the work is not suitable for sensor cloud environments since it takes into account all dimensions at once to perform the clustering of nodes. In addition, the work uses the energy as a parameter to choose the nodes from the clusters. However, in a sensor cloud comprised of heterogeneous sensor nodes, there is no guarantee that sensor nodes with high energy could attend the user's queries. From the above-mentioned works, it is clear the lack of an efficient approach to support virtual sensors in sensor clouds.