## 4 Performance Evaluation  
We show the performance of the proposed skyline query processing method through simulation. Some skyline query processing methods in sensor networks have been proposed, but none of them are based on DCSs. Consequently, instead of comparing our method with existing skyline query processing methods, we measure how much energy is consumed additionally after a DCS is built on a sensor network.  
In this simulation, we use GDCS as a DCS, and the simulation parameters are shown in Table 1. We assume that the sensor network is deployed in a 200 \(\times\) 200 m\({}^{2}\) region. Within the region, there are 400 randomly located sensors. The initial energy level of each sensor node is 100, and the energy consumption level for a message transmission is 0.1, and receiving a message consumes 0.05 units of energy level. All sensor nodes have the same transmission range (15 m).  
All nodes collect 100 sensor readings of normal distribution, and store them inside sensor network by using GDCS. The 80% of the events falls into the 20% of the reading range, and in 1%-25% of  
\begin{table}
\begin{tabular}{|l|l|} \hline Parameters & Values \\ \hline Number of sensor nodes & 400 \\ Region of sensor network & 200 \(\times\) 200 m\({}^{2}\) \\ Initial energy of node & 1,000 units \\ Energy consumption required for a message transmission & 0.1 units \\ Energy consumption required for receiving a message & 0.05 units \\ Storage capacity & 100 units \\ Transmission range of a sensor node & 15 m \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
nodes (20 sensor nodes) do not store sensor reading. We generate 50 skyline queries and send the queries to SNs in the sensor network. We exclude the communication cost from the BS to each SN. Consumed energy level is calculated with measured communication cost for SQM transmissions between sensor nodes and candidate data transmissions from CNs to the BS.  
Simulation results are shown in Table 2 when the number of sensor nodes that do not store sensor readings is 20. The table shows the number of sensor nodes that participate in query processing, the average energy level of a sensor network consumed for skyline query processing, the average energy level of a sensor node consumed for processing a skyline query, the average energy level of a sensor node to maintain GDCS and the average energy level of a sensor node to maintain GDCS and process a skyline query. As shown in Table 2, the nodes which participated in query processing are only 55, or just 14% of all sensor nodes. Also, the average energy level consumed additionally to process a skyline query is only about 0.5 units.  
The proposed algorithm is affected by the number of sensor nodes without storing data. Figure 3 shows the energy level consumed to process skyline query when the number of sensor nodes not storing sensor readings varies. As the number of sensor nodes without data increases, the energy consumption increases. If a SN and the sensor nodes near the SN do not store data, the number of SQM transmissions increases.  
\begin{table}
\begin{tabular}{|l|c|} \hline Energy level & Measured value \\ \hline Number of sensor nodes participated in skyline query & 55 numbers of nodes (13.75\%) \\ processing & 220.55 units \\ Total energy level consumed for skyline query processing & 4.01 units \\ consumed for skyline query processing & 752.38 units \\ Average energy level of a sensor node (GDCS maintenance + skyline query processing) & 752.93 units \\ \hline \end{tabular}
\end{table}
Table 2: Energy consumption while query processing.  
Figure 3: Energy consumption with varying the number of node without storing data.