VII. CONCLUSIONS
We proposed a new model for incorporating vehicles as
obstacles in VANET simulation environments. First, we analyzed
the real world data collected by means of stereoscopic
aerial photography and showed that vehicles as obstacles have
a significant impact on LOS obstruction in both dense and
sparse vehicular networks, and should therefore be included
in V2V channel modeling. Then, based on the concepts of
computational geometry, we modeled the vehicles as threedimensional
objects that can act as LOS obstructions between
26 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 29, NO. 1, JANUARY 2011
Fig. 9. Distribution of the RSSI for 100 m in case of LOS (no
obstruction) and non-LOS (obstructing van) at 2.4 GHz.
other communicating vehicles. Next, we designed a mechanism
for calculating additional attenuation due to vehicles
as obstacles, and we showed that the obstructing vehicles
significantly decrease the received signal power and the packet
success rate. We also performed experimental measurements
in order to confirm the significance of the impact of obstructing
vehicles on the received signal strength. The results clearly
indicate that vehicles as obstacles have a significant impact
on signal propagation (see Fig. 5 and 8); therefore, in order
to properly model V2V communication, it is imperative to
account for vehicles as obstacles. Furthermore, the effect of
vehicles as obstacles can not be neglected even in the case
of relatively sparse vehicular networks, as the analyzed A3
highway dataset showed. Another important conclusion is that
the stochastic models, such as shadow fading [32], that aim
at averaging the additional attenuation due to vehicles, would
fail to adequately describe the complex and significant impact
of vehicles on the received signal power (depicted in Fig. 5).
Furthermore, neglecting vehicles as obstacles in VANET
simulation and modeling has profound effects on the performance
evaluation of upper layers of the communication
stack. The expected effects on the data link layer are twofold:
a) the medium contention is overestimated in models that
do not include vehicles as obstacles in the calculation, thus
potentially representing a more pessimistic situation than the
real-world with regards to contention and collision; and b) the
network reachability is bound to be overestimated, due to the
fact that the signal is considered to reach more neighbors and
at a higher power than in the real world. These results have
important implications for vehicular Medium Access Control
(MAC) protocol design; MAC protocols will have to cope with
an increased number of hidden vehicles due to other vehicles
obstructing them.
The algorithm behind the proposed model, even though
microscopically evaluating the attenuation due to vehicles
(i.e., calculating additional attenuation due to vehicles for
each communicating pair separately), remains computationally
efficient, location independent, and compatible with models
that evaluate the effect of other types of obstacles. By implementing
the proposed model in VANET simulators, significant
benefits can be obtained with respect to increased credibility
of simulation results, at the expense of a relatively small
computational overhead.
As part of our ongoing research efforts, we are performing
extensive experimental measurements to quantify the impact
of obstructing vehicles on V2V communication in various
mobile environments (e.g., urban, suburban, highway) with
different vehicular densities (low, medium, high) in both
2.4 GHz and 5.9 GHz bands. The measurements are aimed
at isolating and characterizing the effects of the obstructing
vehicles on V2V communication in order to thoroughly test
and optimize our proposed model.