# An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments
## 1 Introduction
Initial works [15; 17; 18] considered the provisioning of all nodes within the region of interest to send their measurements periodically to the middleware. However, such approach can lead to an overhead of messages, increasing the sensor node energy consumption (which is generally battery-powered). Nevertheless, recent works [19; 20; 21; 22] proposed a different approach, clustering sensor nodes based on the similarity/correlation of the sensor nodes' measurements, instead of physical distance, as many traditional works on WSN field [5; 23]. Thus, the middleware can select only a small subset of nodes, whose measurements represent the most variance of the data, to start transmitting. However, such works have considered only homogeneous nodes, i.e., all nodes using the same set of sensors, making them not entirely suitable for sensor clouds environments. Because a sensor cloud connects several heterogeneous sensor nodes from different providers, the provisioning can be a complex task. As an example, consider Figure 2, which shows a sensor cloud environment composed of two WSN from different providers. The yellow nodes represent the \(A\) provider, while the green ones represent the \(B\) provider. The nodes from the \(A\) provider only have temperature sensors, while the nodes from the \(B\) provider have temperature and humidity sensors. First, at time \(t^{\prime}\), a user requires temperature measurements from a specific region of interest (black lines in the figure). Then, at time \(t^{\prime\prime}\), another user requires temperature and humidity measurements from the same region of interest. Consequently, the middleware must analyze and select from among all nodes the ones that are most suitable in order to create two virtual sensors, resulting in a combinatorial problem. Besides that, an algorithm for provisioning virtual sensors should be protocol independent since different WSN could use different technologies. For instance, a WSN could be based on IEEE 802.15.4 and Zigbee ([[http://www.zigbee.org](http://www.zigbee.org)]([http://www.zigbee.org](http://www.zigbee.org))) protocols layers while another WSN could be based on RF 433 hz and some open routing protocol. The objective of this paper is to present ACxSIMv2, an enhancement of our previous works [20; 21] to fully supports the provisioning of virtual sensors in heterogeneous environments. ACxSIMv2 comprises two algorithms: (i) ACASIMv2 which clusters heterogenous sensor nodes by the correlations of their measurements; and (ii) ACOSIMv2, an algorithm based on Ant Colony Optimization [24] to select the optimal set of sensor nodes which will compose the virtual sensors required by users. Figure 2: An example of provisioning  
ACxSIMv2 is independent of any routing protocol or technologies used by the WSN, and the optimal set of selected nodes has the following properties: (i) meet the queries requirements; and (ii) minimize the energy consumption of nodes that comprise the sensor cloud. The main contributions of this paper are:  
1. The formulation of the provisioning task as an optimization problem. The defined model supports heterogeneous WSN and multiple queries/applications requested by users;
2. An algorithm to cluster heterogenous sensor nodes based on data similarity;
3. The assignment of the minimum set of sensor nodes that are compatible with the requirements of the user's queries, and, at the same time, minimize the energy consumption; and
4. Through a comprehensive experimental study, it is shown that the proposed system achieves a significant improvement regarding the energy consumption of sensor nodes compared to the state-of-the-art approaches. The article is structured as follows: Section 2 reviews some related works. Section 3 defines the system model and the problem definition. Section 4 presents the two algorithms that compound ACxSIMv2. Section 5 presents the methodology used to evaluate the performance of ACxSIMv2 and discusses the results. Finally, in Section 7, we present conclusions with some future research directions.