## 4 Experiment and Result Analysis  
### Experiment Environment  
We use _NS_-2 network simulation software to simulate the sensor network [48]. _NS_-2 is the most popular event simulator for network research. For simplicity, we assume that there are no two simultaneous events in the sensor field. If an event does occur, all sensor nodes can detect the event and collect original data [46]. The system we use is as follows: the task of the network is vehicle classification, we use a 100 \(\times\) 100 m\({}^{2}\) sensor area, the sensor network has 16 clusters, and the adjacent nodes' spacing of the same cluster do not exceed the length of \(d\) (500 m). Nodes connect to other nodes in a random model, select a random destination, move at a speed of 10 m/s, send data at 2 Mb/s, and process data at 100 Mb/s. Communication uses the computational model for performance metrics in the third section. Energy consumption of the communication process is proportional to the size of the packet and the square of the distance between the two points. Data fusion reduces the size of data packets by reducing the data correlation. To simulate network mobility for analysis, we make the following assumptions:  
1. Nodes use two-way communication links within R meters, and the link will disconnect when one node moves to R meters or beyond. 2. Sensor nodes move at a non-constant speed in a range of [a,b] m/s. 3. The velocity direction of sensor nodes has an even distribution between 0 and 2\(\pi\). 4. The speed, the direction of motion and position of the sensor nodes are independent of each other. There is no relation between them. 5. A two-dimensional Poisson process with an intensity of \(\sigma\) simulates the location of the nodes in the network. The probability distribution of network area D with \(k\) nodes around \(A\) is: \[Prob(k\text{ nodes in }D)= \frac{\left(\sigma A\right)^{k}e^{-\sigma A}}{k!}\] (46)  
Assumption 1 shows that the signal to interference ratio (SIR) is still high within a certain distance from the target R. The SIR is the ratio between signal strength of the node signal, and the strength of co-channel interference from other nodes within the same area. Close by, sensors receive exact signals. However, as the distance from R increases, SIR will decline, and the bit error rate will rapidly increase to unacceptable levels. Signals may also meet blind areas and multipath attenuation, so the actual transmission area is not fully symmetric. If all nodes in the network use the same transmission power, the network is still approximately homogenous. Assumption 2 simulates a moving environment where nodes move at different speeds. In our simulation, we focused on a range of high node speeds because the shorter time within range and the challenges of connecting between fast-moving nodes makes it easier to find the worst cases of link characteristics in the background. Assumptions 3 and 4 describe the aggregation behavior of nodes in large sensor networks. Any association between nodes is unimportant due to the considerable number of independent sensing nodes. Some nodes may collect data from similar targets and move together, but enough data from autonomous nodes models a stochastic process through its composite effect. Assumption 5 uses the Poisson process to simulate total randomness, which reflects the randomness of the aggregate behavior of nodes in large sensor networks. Equation (46) shows that the number of sensor nodes in the network area \(D\) is equal to \(\sigma A\). Thus, \(\sigma\) stands for the average density of network nodes. In this section, simulation experiments discuss the performance of the proposed algorithm. We compare the algorithm with the client/server model, where nodes send compressed data to the processing center, and another routing algorithm based on best available hops, the Local Closest First (LCF) algorithm. We discussed the C/S model has been discussed in Section 2.4.1.