# A Distributed Token Passing Protocol for Time Constrained Data Gathering in VANETs
## 3 Proposed Approach
### Phase I: Token Processing
When a node receives the token at the current time \(t\), it checks the elapsed time and, if \(t-TTE>AmTime\times\tau\), it changes the L field and sends the token to the next selected TO. 3. The proposed TT approach is previously characterised in Section 3. 4. The CGP protocol is already addressed in Section 2.1. 5. The optimal solution is, however, affordable for a limited number of involved vehicles. The simulation campaign is performed according to a Monte Carlo approach, where \(10^{5}\) runs are repeated for each of the two investigated scenarios, whose parameters are listed in Table 1. Moreover, the information is available at each vehicle modelled as a normalised uniform random variable in the (0-1) interval. To address the behaviour of the proposed approach, in Figure 6, we preliminarily presented a snapshot of the token passing process, by depicting the information gathered by the optimum, TT, _Heu1_ and _DrkRdnWilk_ approaches as a function of the round (i.e., hops) for \(N_{c}=24\) and \(ND=0.3\)[8]. The gathered information has been normalised to the maximum amount available in the scenario. \begin{table}
\begin{tabular}{l c c} \hline _Parameter_ & _Scenario 1_ & _Scenario 2_ \\ \hline number of cars (\(N_{c}\)) & 15 & 120 \\ number of lanes (\(N_{l}\)) & 3 & 3 \\ time deadline & 3 [s] & 10 [s] \\ token transmission time & 0.1 [s] & 0.1 [s] \\ neighbours discovery time (_ND_) & [0.3–0.7] [s] & [0.3–0.7] [s] \\ mean coverage radius & 10 [m] & 10 [m] \\ \hline \end{tabular}
\end{table} 
Table 1: Parameters adopted for Scenarios 1 and 2. It can be noticed that optimal values are closely approached by the proposed TT method, which instead performs better than _DrkRdnWlk_ and _Heu1_ techniques. In Figures 7 and 8, the normalised collected information and the collection probabilities achieved by the different approaches are, respectively, sketched for the Scenario 1 and different ND timer durations, i.e., 0.3, 0.5 and 0.7 [s]. It can be preliminarily pointed out that _DrkRdnWlk_ protocol is the worst algorithm among the considered algorithms in terms of both collected information and collection probability. As a consequence, whenever the token is returned back to the TL, the collected information is extremely low. Conversely, if it does not return to the TL within the time constraint, it is considered lost and the gathered information is null. In addition, _Heu1_ and TT are able to return the token to TL, while respecting the time deadline. The percentage of gathered information depends on the ND time duration: the greater time spent for ND, the shorter the path the token travels within the network and the less information it is able to collect. Moreover, it can be pointed out that the proposed TT protocol is close enough to the optimum approach and always performing better than the other alternatives, except for the case of high ND timer duration, where CGP achieves almost equivalent performance because of the limited number of vehicles. Figure 6: Normalised cumulative gathered information as a function of time round (i.e., hops) comparison among the proposed TT and alternative approaches for \(N_{c}=24\) and \(ND=0.3\) [s]. Finally, the same investigation is repeated for Scenario 2, as reported in Figures 9 and 10, where the higher number of vehicles makes the pursuit of the optimal path unaffordable. However, despite the performance degradation w.r.t. Scenario 1, TT performs better than all the other considered alternatives for any ND values. In addition, in the same figures, we also considered the presence of a low mobility pattern where the road segment under investigation is affected by traffic congestion with an average vehicle speed equal to 50 [m/s]. It can be pointed out that the impact of mobility on TT is very limited with a relative performance decrease of about 7%. Figure 8: Collection probability comparisons among the proposed TT and alternative approaches for Scenario 1 and different ND values.