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World Journal of Gastroenterology, 16 37 Liu, B. In particular, we set as the distance from the intersection and fix the probability of reception to one exactly at the intersection distance zero , so is one and is zero. Thus, the only parameter to fit is , that is, the standard deviation. So, finally we have the following expression:. This exponential function computes the delivery ratio for a particular distance.
As the distance grows, this probability asymptotically becomes 0. The value of the constant or standard deviation will depend on the scenario and the antenna position and reflects the variation or dispersion of the data values. The resulting bar charts and fitting results are shown in Figures 9 a , 9 b , and 9 c.
If we take a look at the experimental results for Intersection 1 see Figure 9 a , we can quickly notice that there is a significant difference between the delivery ratio for the dashboard and rooftop location cases. The curve fit for the dashboard antenna location shows that, for low distances, the delivery ratio is still comparable to the one from the rooftop fit. After a distance of about 20 meters from the intersection, the bars show a quick attenuation.
Also, we observe that it loses contact after about meters. We can observe how, when the distance is of about meters, the delivery ratio suddenly drops, being followed by a moderate increase. This is an effect of the buildings present in the environment, as we can see in an aerial view of the street shown in Figure 2.
Thus, we can conclude that antenna location has a very significant impact on packet delivery success.
Concerning the second intersection, the bar chart shown in Figure 9 b clearly shows a significant difference compared to the previous one. Beyond 50 meters, the delivery ratio becomes near zero.
If we focus now on the curve fit for the rooftop scenario, we find that differences towards the dashboard case are quite clear, similarly to what occurred for Intersection 1. However, with respect to that first intersection, we now see that, at a distance of 50 meters, the packet delivery ratio for the rooftop is nearly 0. This is why we can categorize this second intersection as an urban canyon, which is a worst case scenario associated with the maximum degree of obstruction.
Regarding the results for Intersection 3, Figure 9 c shows that when the antenna is located in the dashboard, there is a loss of radio connectivity after about 70 meters.
Instead, when the antenna is on the rooftop, contact is maintained beyond meters, a much greater distance. Compared to the two previous intersections, the fittings in this scenario are indeed a situation in between intersections 1 and 2.
Overall, we consider that the obtained results are quite reasonable by considering that our experiments were made in scenarios where no interference is hindering our communications band, meaning that the channel only experiences the effect of additive white Gaussian noise. In such situation, the fitting corresponds to a standard AWGN channel model. We now focus in detail on the outcome of fitting results and the corresponding fitting errors.
Notice that 2 introduced parameter , the standard deviation of the Gaussian function, which allows adapting the fitting curve to each type of intersection and antenna location. In Table 2 , we now detail the values of this parameter for each case. It is interesting to observe that this parameter decreases for lower radio ranges at intersections, being directly related to the packet delivery ratio. In fact, the higher the parameter, the higher the packet delivery ratio for a certain distance towards the intersection.
In detail, we can see that values for the first intersection are the highest ones. On the other hand, for Intersection 2, the lowest values are obtained, with Intersection 3 characterized by intermediate values.
Also, regarding antenna locations, we find that value for the rooftop results is always more than twice those obtained with the antenna in the dashboard. The largest relative difference is detected for Intersection 1, where value for the rooftop case is more than four times greater than the one for the dashboard case. This occurs because, for this kind of intersection, packet losses are mostly related to signal power dropping due to distance, and the rooftop antenna location thereby emerges as the optimal option to mitigate such power losses.
Table 2 also shows the fitting error expressed as , the sum of the squares of the differences between the model function and the actual delivery ratios obtained from the experiments. Additionally, Figure 10 shows a box and whisker plot of the difference distribution for each scenario and antenna position.
The model fitting is clearly more accurate for the dashboard scenarios than when mounting the antenna on the rooftop. This occurs because, in the latter case, the range is not large enough to reach near-zero values. In general, a detailed channel characterization between two endpoints requires studying the signal to noise plus interference values at the receiver, which includes modeling in detail the signal propagation conditions in the target environment. In the specific case of vehicular networking environments, this includes the modeling of signal reflections and Doppler spread in the presence of various obstacles, including buildings, trees, and vehicles.
However, such a detailed signal propagation analysis is extremely complex, and so it becomes computationally prohibitive to undertake such a detailed analysis when studying traffic communications in a large area, especially for vehicular networking studies where this area can grow up to the size of an entire city or even greater.
To address such problem, empirical path loss models for urban environments have emerged e. However, these models provide a generalization of the propagation behavior, meaning that they fail to provide a detailed characterization of very specific transmission conditions, such as the intersection propagation conditions addressed in this paper.
Yet the problem of how to adapt our model to simulation environments remains, as it requires knowing in advance the actual characteristics of each specific intersection in order to adequately model it. To achieve the intersection modeling requirements enabling the adoption of our models, we propose automating the intersection classification process by analyzing the street width and the presence of buildings in a preprocessing step before the actual simulation.
This way we avoid having to manually tag each intersection manually and benefit from the models hereby derived with little additional complexity.
It is worth highlighting that widely used map providers such as OpenStreetMap [ 39 ] already include such street and building information for many relevant cities, which simplifies and makes feasible the adoption of our solution. To further validate our research work, we have also analyzed the probability of successful delivery of notifications associated with critical events. As explained earlier, such event notification dissemination typically relies on multihop broadcasting to make sure that the information arrives to all vehicles in a certain target area.
However, since such dissemination procedure is prone to cause broadcast storm problems and since urban obstacles will typically hinder dissemination towards vehicles in nearby streets, different proposals consider it optimal to perform timely broadcasts when vehicles are located at intersections to maximize reachability.
Such timely broadcasts for moving vehicles, though, rely on mapping GPS coordinates to map details, and the overall effectiveness will highly depend on the GPS error introduced at the time of broadcasting. Taking the aforementioned issues into consideration, in this section, we will use the models derived in Section 6 for the different intersection types and antenna locations to study the probability of successfully delivering an event-related message at an intersection when considering different GPS error values.
We are assuming that the vehicle intends to send a packet when located at the center of the intersection to maximize the packet delivery ratio. However, if we take the GPS error into account, we could expect that the error it introduces could impact the packet delivery ratio, especially in urban canyon scenarios. To this purpose, we define different maximum values for the GPS error which typically ranges between 5 and 50 meters and create normal distributions where of the values are inside this maximum distance rule.
Then, considering this probability distribution for the vehicle location when transmitting a packet, we combined it with the models derived in the previous section to gain awareness about the expected success ratio for the event message delivery. In Figure 11 , we evaluate the impact that the GPS error ranges will have on the delivery ratio for each scenario, with the antenna located either on the dashboard or on the rooftop.
The three intersections that have different levels of obstruction are compared. In these plots the delivery ratio from the fitted model is shown for three significative points in the error distribution: the bar corresponds to the interval from to of values , the line corresponds to , and the cross is.
If we focus on the case where the antenna is located in the dashboard see Figure 11 a , a significant difference is detected when we have a GPS error of 50 meters. In the case of Intersection 1, a GPS error of up to 50 meters still shows acceptable packet delivery levels; on the contrary, for Intersection 2 urban canyon , the delivery ratio is much worse than for the other two cases.
Figure 11 b shows that when installing the antenna on the rooftop, the impact of GPS error is now reduced as the delivery ratio in these cases, when compared to the previous ones, is much better. This means that, in general, effective propagation of messages at intersections is possible, even in urban canyons and despite GPS errors, as long as rooftop antennas are used so that their extended radio range compensates for the poor radio visibility and positioning error.
In a nutshell, again we find that the different antenna positions and the characteristics of intersections clearly affect the probability of successful packet delivery even with the presence of GPS error.
That being said, the most reliable sending process takes place when we put the antenna on the rooftop of the vehicle and the transmission occurs at an open space intersection with minimum obstructions. The worst case occurs at the urban canyon intersection maximum obstruction when the antenna is located within the vehicle, in the dashboard, thereby matching our initial hypothesis.
Recent efforts to minimize accidents in vehicular environments have led safety issues to represent one of the most important applications in the context of ITS. In this scope, intervehicular communication can play a very relevant role by allowing quickly notifying neighboring vehicles about dangerous events.
However, these notifications should be delivered quickly and reliably, which can be a strict requirement in urban environments since buildings and other obstacles are prone to hinder signal propagation. Thus, timely message delivery at the center of intersections emerges as the main solution allowing avoiding urban obstacles in a simple and straightforward manner.
In this paper we have studied the packet delivery effectiveness achieved on different types of intersections no obstacles, urban canyon, and partial obstruction and when locating the antennas on either the dashboard or the rooftop. Extensive experimental results using broadcast traffic have shown that the impact of the intersection type is significant, as differences of up to meters in transmission range were detected.
Additionally, we have modeled all the obtained results by finding the best-fitting function and then applying regression. We find that a Gaussian function offers adequate fits for all cases by just varying one parameter. This way, our model allows seamlessly representing different types of intersections and bringing these results to simulation environments.
Based on our model, we then made an analytic study to determine the probability of a successful event dissemination process at intersections, for the different types of intersection and antenna locations tested, when varying the maximum GPS error. We find that, in general, dissemination is highly effective, even in urban canyons and for high GPS error conditions, as long as rooftop antennas are used, with the more restrictive dashboard solutions being not recommended.
This way, using the previous models and assisted by real-time geolocation and maps, we can first determine the type of scenario to use and then, knowing the GPS error, determine the expected delivery ratio. As future work, we will translate our results to a simulation platform in order to achieve a more realistic simulation model able to better resemble real-life experiments.
Also, since we used the standard GPS device embedded in the smartphone for localization purposes, it would be interesting to test with more precise outdoor geolocation devices. Another consideration to be kept in mind is to evaluate the feasibility of V2V communications through the use of LTE-based intervehicle communications. The authors declare that there are no conflicts of interest regarding the publication of this paper. Hadiwardoyo et al. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article of the Year Award: Outstanding research contributions of , as selected by our Chief Editors. Read the winning articles. Journal overview. Special Issues. Academic Editor: Ilaria Thibault. Received 22 Dec Accepted 13 Mar Published 23 Mar Abstract Event warnings are critical in the context of ITS, being dependent on reliable and low-delay delivery of messages to nearby vehicles.
Related Works In the literature, we can find several works in the scope of safety applications using V2V. Table 1. Figure 1. Figure 2. Figure 3. Figure 4. Location and the trajectory for Scenario 2 buildings. Figure 5. Olaverri-Monreal, C. Gomes, P. IEEE Intell. Vinel, A. Belyaev, E.
The libstreaming android api. Accessed 5 February Schulzrinne, H. Libvlc documentation. Subhadeep Patra 1 Email author Javier H.
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