# Two-Tier Cooperation Based High-Reliable and Lightweight Forwarding Strategy in Heterogeneous WBAN
## 4 Experiments Analysis and Discussion
### Performance Evaluation and Analysis  
This paper measures the adaptability and effectiveness of the TTCF mechanism based on the STPCSE model by evaluating the performance of data reconstruction accuracy, cumulative transmission delay, energy consumption and throughput under various conditions. Figure 4 describes the relationship between the signal length and signal value when the measurement matrix and auxiliary matrix are changed, respectively. The abscissa and ordinate are the signal length and signal value, respectively. Figure 4 shows two sets of data of the original signal and reconstructed signal. The sparsity is 20, that is, there are 20 non-zero elements in the 240-length signal. In Figure 4a, the auxiliary matrix is kept as the Tent chaotic matrix, and the measurement matrix adopts the Gauss matrix and Logistic chaotic matrix, respectively. According to Equation (17), their reconstruction errors are, respectively, 1.19 \(\times\) 10\({}^{-14}\) and 1.1019 \(\times\) 10\({}^{-15}\). Since it is specified in advance that the reconstruction is successful if the reconstruction error is less than 10\({}^{-6}\), obviously, for these two different measurement matrices, the original signal can be reconstructed correctly, and the reconstruction error is very small. Figure 4b shows the changed relationship between the reconstructed signal and the original signal when the measurement matrix is the Logistic chaotic matrix and the auxiliary matrix is the Gaussian matrix and Logistic chaotic matrix, respectively. Obviously, in this case, the difference between the reconstructed signal and the original signal is also very small. Their reconstruction errors are, respectively, 1.1719 \(\times\) 10\({}^{-15}\) and 1.5119 \(\times\) 10\({}^{-15}\) based on Equation (17), which are significantly smaller than the predetermined reconstruction errors, and the signal can be reconstructed successfully. Seen from the above, the STPCSE model in TTCF can effectively compress the physiological index data collected by the WBAN sensors. Figure 5 reflects the relationship between the cumulative delivery rate of data and the lifetime of packets (TTL, Time to Live) with and without the TTCF scheme. Due to the limitation of the buffer size or capacity of sensor nodes in WBAN, with the increase of TTL, the cumulative delivery rate in both cases increases first and then decreases. Moreover, the cumulative delivery rate of the algorithm without TTCF is significantly lower than that of the algorithm with TTCF. The main reason is that the latter makes use of the characteristics of the nodes to measure their forwarding capacity more scientifically and reasonably, and makes an objective evaluation of the transmission energy consumption between the nodes by using the Euclidean distance, and then combines the two to improve the path calculation method in the Dijkstra algorithm, so as to speed up the selection of the relay node sequence of the optimal path between the source-destination nodes in the data forwarding process. Thus, the time of data transmission is shortened, and the delivery rate of data is improved, which is more applicable. The reliability of the model can be verified by the size of the network cumulative delivery rate. The cumulative delivery rate of the following three time points is randomly selected, as shown in Figure 6. It can be seen that our TTCF is superior to the other two, that is to say, the reliability of the TTCF is the highest. The main cause is that CRPBA forwards data directly within and between clusters in a one-hop manner, which is easy to cause forwarding failure and has a low packet delivery rate.