## 4 Performance Evaluation  
We performed a simulation to evaluate the performance of the proposed DCS. We assume that 400 nodes are randomly deployed in a square area, \(200\times 200\) m\({}^{2}\). Also, we assume that each sensor  
Figure 5: Example of insertion.  
node has initial energy of 1,000 units and a storage capacity of 100 units. A message transmission from a sensor node to its neighbor node consumes 0.1 energy units for sending the message and 0.05 energy units for receiving the message. The radio range each node has is 15 m. Every node generates 50 events that are normal distribution of values, and a percentage of 80% of the events falls into a percentage of 20% of the reading range. We measure the energy level of each sensor node, and compare the results with that of KDDCS which is one of the most well-known data centric storage. Table 1 shows the simulation parameters.  
Table 2 shows the average energy level of each sensor node in the hot-spot area and entire area. As shown in the table, entire sensor nodes of the KDDCS consume more energy than those of the proposed DCS by about 73%. In the hot-spot area, sensor nodes of KDDCS consume much more energy than those of the proposed DCS by about 97%. The reasons why the proposed GDCS outperforms KDDCS are as follows. The first reason is that the KDDCS needs to visit the node that contains the split information to forward the packet to the next node. Also, in a KDDCS' hot spot handling strategy, a node in the hot spot moves data to neighbor nodes whenever adapt the area covered by a sensor node.  
In this paper, we implement a simple application that uses our GDCS based on TinyOS [10]. Then, we run the implemented application based on TOSSIM [11] to show how our GDCS distributes the workload of a hot spot area. Table 3 shows the TOSSIM parameters. The number of nodes is 256 and the width and height of the sensor network are was 100 m. The radio range is 10 m and the number of bits per grid is 8.  
\begin{table}
\begin{tabular}{|l|r|} \hline Number of Sensor Nodes & 400 \\ Sensor Network Area & \(200\times 200\) m\({}^{2}\) \\ Initial Energy of a Sensor Node & 1,000 units \\ Energy Consumption per Transmit & 0.1 units \\ Energy Consumption per Receive & 0.05 units \\ Storage Capacity & 100 units \\ Radio Range & 15 m \\ \hline \end{tabular}
\end{table}
Table 1: Parameters for performance evaluation.  
\begin{table}
\begin{tabular}{|l|r|r|} \hline Energy Level & KDDCS & GDCS \\ \hline Average Energy Level of a Sensor Node & 752.38 units & 857.69 units \\ Average Energy Level of a Sensor Node in Hot-spot & 331.13 units & 611.01 units \\ \hline \end{tabular}
\end{table}
Table 2: Average energy level of each sensor node.  
\begin{table}
\begin{tabular}{|l|r|} \hline Number of Nodes & 256 \\ Sensor Network Area & \(100\times 100\) m\({}^{2}\) \\ Radio Range & 10 m \\ b & 8 (Number of Cells is 256) \\ \hline \end{tabular}
\end{table}
Table 3: Parameters for running TOSSIM.  
When we set the number of levels as 1, _i.e._, multilevel grid techniques are not used, as shown in Table 4, the sensor nodes of the hot spot area receive 40% of the total messages. However, when the number of levels was 2, the ratio of the messages of sensor nodes in hot spot area to the total message is decreased to about 21%. The results mean that the proposed GDCS distributes the workload of hot spot areas effectively.