# An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments
## 7 Conclusions  
Provisioning of virtual sensors is one of the foremost tasks in sensor cloud environments. It is defined by the selection and allocation of physical sensors nodes to respond queries from different users. Traditionally, most middlewares for sensor cloud proposed in the literature consider the selection of all physical sensor in the region of interest, leading to wasting of energy consumption. Recent works tried to explore correlation and prediction in homogenous environments which is not appropriate for heterogeneity environment of sensor clouds. In this paper, we presented ACxSIMv2, an extension of our previous work, named ACxSIMv1, to enhance provisioning of virtual sensors performed by middlewares in sensor cloud environments. ACxSIMv2 selects a optimal subset of heterogeneous sensor nodes, based on users' request, to create the required virtual sensors. ACxSIMv2 comprises two algorithms: ACASIMv2 and ACOSIMv2. The first, ACASIMv2, clusters heterogeneous sensor nodes based on the similarity of their measurements. It differs from traditional approaches in literature since most works cluster homogeneous sensor nodes usually based on the physical distance between them. As ACASIMv2 use data (measurements) similarity, the clusters may be compounded by nodes from different areas. However, ACASIMv2 guarantees that all nodes have measurements below some threshold. In that sense, a small subset from each cluster could \begin{table}
\begin{tabular}{c c c c} \hline \hline
**P** & **Energy (m)** & **Energy Savings(\%)** & **MSE** \\ \hline
0.2 & 26, 227.10 & 79.91 & 28.72 \\
0.3 & 39, 334.82 & 69.88 & 22.83 \\
0.4 & 52, 422.89 & 59.86 & 18.06 \\
0.5 & 65, 567.48 & 49.80 & 13.98 \\
0.6 & 78, 544.14 & 39.86 & 10.39 \\
0.7 & 91, 599.68 & 29.86 & 7.26 \\
0.8 & 104, 681.71 & 19.85 & 4.50 \\
0.9 & 117, 804.67 & 9.80 & 2.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: LEACH protocol results. represent multiples areas. The second algorithm, ACOSIMv2, takes place after the clusters formation. Based on an Ant Colony Optimization algorithm, ACOSIMv2 selects a subset of sensor nodes from each created cluster. The selection procedure is performed under three main guidelines: (i) all hetegeneous nodes together are able to process user's requests, (ii) minimize the number of selected nodes, and (iii) minimize the overall energy consumption the sensor cloud. We have performed simulations to evaluate the performance of ACxSIMv2. First, we have compared it with single-hop communication. Although a simplistic solution, single-hop is the standard approach used by classical works in sensor clouds literature. Then, we have compared ACxSIMv2 with LEACH protocol, a classical solution for cluster formation in traditional WSN. Finally, we also have compared it with our previous work (ACxSIMv1). Simulations have shown that ACxSIMv2 has better performance than the LEACH protocol regarding energy consumption (about 7.0\(\times\)) and MSE (2.0\(\times\)), indicating the feasibility of the proposed algorithm. These results are explained by the fact that ACxSIMv2 can significantly reduce the amount of traffic in the network (by selecting sensor nodes whose measurements represents multiple geographical areas) while maintaining the measurements error below some desired threshold. In addition, the simulations have shown that ACxSIMv2 has been able to attend the applications requirements in two different environments. The first one (Intel Lab Dataset) considered a small WSN deployed in a indoor office (a controllable environment). The second scenario (Green Orbs Dataset) represents a large WSN deploy in a forest (harsh environment). Our approach will be useful when a large number of heterogeneous sensors nodes are deployed in near future with the popularization of sensor cloud paradigm. By reducing the associated energy consumption of those nodes, the WSN will operate much longer without interruptions.