# Distributed Triggered Access for BSM Dissemination in 802.11 bd V2V Networks
## 4 Performance Evaluation
How quickly VSTA can obtain the information of nearby vehicles can be one of the most important performance indicators in BSM dissemination scenario. Figure 8 shows that the proposed scheme greatly improves the collecting delay compare to the conventional scheme especially for extreme condition (high vehicle density 0.12) and heavy data traffic (BSM: 400 B, 20 Hz). In Figure 7(b), the delay of the proposed scheme for the highest vehicle density is three times smaller than that of EDCA (0.92 and 2.69 s). Also, the proposed scheme with large contention window (CW: 2048) outperforms EDCA throughout the all measured vehicle density range. The figure proves that our proposed scheme deals with heavy congestion very well. It is observed that the collecting delay of EDCA scheme increases drastically as the vehicle density increases for heavy traffic case, and the maximum difference of the collecting delay between the moderate traffic case (Figure 7(a)) and the heavy traffic case (Figure 7(b)) reaches to 1.9 s. On the other hand, the maximum difference of the proposed scheme remains less than 0.7 s when the contention window size is sufficiently high. Furthermore, in low vehicle density condition, DTA allows VSTAs to collect all BSMs within very short period (58 ms), even shorter than the arrival rate of BSM (100 ms), since VSTA can obtain multiple BSMs from one TXOP. In EDCA, since BSM cannot be retransmitted, if a VSTA fails to receive a BSM it should wait until the next BSM is generated. For this reason, Thus, if a VSTA experiences consecutive transmission failures, the neighboring VSTAs might lose the tracking information for the VSTA increasing the probability of accident. By transmitting non-active BSM triggered by other VSTA, such problem can be avoided without increasing channel contention overhead. Figure 9 depicts various performance statistics to provide better understanding on the performance results shown in Figures 7 and 8. Figure 8(a) shows that DTA (CW: 2048, RU: 9) tremendously improves the transmission success probability by 0.45 compare to EDCA with the same contention window. Consequently, nevertheless the transmission delay increases, the overall BSM delivery performance can be increased. Figure 8: Average BSM collecting delay. Focusing on the delay aspect, the result seems that a major portion of the transmission delay is caused by the queuing delay in high vehicle density case. In case the vehicle density is less than 0.09, since the total transmission delay is less than the packet arrival interval, only a very small amount of queuing delay occurs for all cases (except DTA with smaller contention window). On the other hand, as the transmission delay exceeds the arrival rate, the queuing delay starts increase rapidly. In this case, the increment of the proposed scheme is relatively greater than EDCA, because in DTA, as a transmitting VSTA can detect the failure of a TF by the MU-RTS procedure, it cancels the transmission and keep the serving BSM for new backoff procedure. Despite the increased transmission delay, the improvement of the transmission success probability effectively compensates for the overall performance of the proposed scheme. When it comes to the number of RUs, the result shows a trade-off between the transmission success probability and the transmission delay. According to Figure 9,b, DTA with smaller number of RUs shows better performance in terms of transmission success probability due to shorter PPDU duration. On the other hands, DTA with greater number of RUs has shorter transmission delay, as it can serve a greater number of BSMs with one medium contention. Therefore, the number of RUs needs to be optimized based on the characteristics of the target application in real world implementation. Figure 10 depicts the ratio between DTA and EDCA among successful transmissions. The figure shows that in DTA, the number of EDCA transmission is greatly reduced as the number of RUs for  
Figure 9: Performance statistics in heavy data traffic. scheduling increases.