SDDV: scalable data dissemination in vehicular ad hoc networks
© Vandenberghe et al.; licensee Springer. 2014
Received: 29 November 2013
Accepted: 7 October 2014
Published: 4 November 2014
An important challenge in the domain of vehicular ad hoc networks (VANET) is the scalability of data dissemination. Under dense traffic conditions, the large number of communicating vehicles can easily result in a congested wireless channel. In that situation, delays and packet losses increase to a level where the VANET cannot be applied for road safety applications anymore. This paper introduces scalable data dissemination in vehicular ad hoc networks (SDDV), a holistic solution to this problem. It is composed of several techniques spread across the different layers of the protocol stack. Simulation results are presented that illustrate the severity of the scalability problem when applying common state-of-the-art techniques and parameters. Starting from such a baseline solution, optimization techniques are gradually added to SDDV until the scalability problem is entirely solved. Besides the performance evaluation based on simulations, the paper ends with an evaluation of the final SDDV configuration on real hardware. Experiments including 110 nodes are performed on the iMinds w-iLab.t wireless lab. The results of these experiments confirm the results obtained in the corresponding simulations.
Vehicular ad hoc networks (VANET) provide local vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications to support intelligent transportations systems (ITS) applications. Example applications are emergency electronic brake lights, slow vehicle indication, wrong way driver warning, stationary vehicle warning, lane change assistance, etc. The technology used to enable such ad hoc networking is called IEEE 802.11p . This is an amendment to the IEEE 802.11 standard that contains several enhancements to improve performance under vehicular conditions. Examples are an increased output power, dedicated channel, and reduced channel bandwidth to account for the influence of Doppler spread. In contrast with other ad hoc networking use cases, VANET communication in general does not focus on unicast transmissions. Instead, the emphasis is put on the dissemination of information to all nodes located in a certain region. Typically, this functionality is achieved by combining media access control (MAC) level broadcasting with geographic-aware forwarding schemes on the networking layer. In general, two different kinds of information messages are used: cooperative awareness messages (CAM)  and decentralized environmental notification messages (DENM) . The former is utilized to continuously exchange status information such as location, heading, and speed with the other vehicles in the immediate environment. Hence, CAM beacons are restricted to single-hop broadcasts. The latter is applied when some specific piece of information regarding the environment, e.g., tail of traffic jam, slippery spot, obstacle on the road, etc. is to be communicated to all vehicles within a given area. Therefore, DENM messages are multi-hop broadcasted in the VANET.
Several techniques have been researched to tackle the VANET scalability problem. These are situated on all layers of the Open Systems Interconnection (OSI) protocol stack. In general, previous studies focused on applying a single technique, identifying the optimal parameter configuration and indicating the VANET performance gain. However, recent work has pointed out the need for a more holistic approach, where different techniques are combined to effectively solve the VANET scalability problem [16–18]. However, this is not as straightforward as it may seem, techniques that perform well independently might very well result in mutual compensations, ruling out each others performance gains. An example is the work of Baldessari et al.  which came across such difficulties when combining a transmit power and rate control algorithm.
Summary of baseline VANET solution and the corresponding optimized techniques and parameters
Final SDDV parameters
Smax=75, Tmax=75 ms, a=2
Density based, a=25
Fixed transmission power
Fixed transmission power
Fixed transmission rate
6 Mbps (QPSK
Fixed transmission rate
12 Mbps (16 QAM
Fixed QoS class usage
C A M=V O, D E N M=V O
Fixed QoS class usage
Fixed beacon interval
CAM = 100 ms, DENM = 100 ms
100 ms, DENM = 100 ms
Standard channel estimation
Decision feedback and smoothing
Single antenna, decision feedback, and smoothing over all carriers
Standard packet capture
Preamble and data switching
In this paper, an empirical approach is adopted. Performance analyses are based on simulation results, which are gathered using the wireless network simulator NS-2 . A final validation of the solution is performed on real hardware using the iMinds w-iLabt.t wireless testbed. The applied performance metrics were chosen based on existing studies such as those introduced in Section 1. Examples of previously utilized metrics are probability of reception failure or its inverse form packet success rate (sometimes also called packet delivery probability), reachability within a given geographical area, saved rebroadcasts compared to simple flooding, and end-to-end delay. For this paper, it was chosen to focus on the end-to-end characteristics on the application level. The applied metrics are packet success rate and delay. These are analyzed both in the scope of CAM and DENM messages.
Node velocities (km/h) based on the lane in which they are positioned
The applied VANET networking stack was implemented using the Click modular router framework. This is a modular software router platform originally developed by the Massachusetts Institute of Technology (MIT) with subsequent development by a broad research community . This framework enables very efficient prototyping of networking protocols. It also supports the execution of the same implementation both within the NS-2 simulator and on real hardware. To guarantee that the performance measurements are performed equally during both kinds of experiments, the corresponding application level functionality was also implemented within the Click framework. More details regarding our specific VANET stack implementation are given in .
A final validation of the solution is performed on real hardware using the iMinds w-iLabt.t wireless testbed. This is useful because simulations of wireless networks are not entirely representative for the real-life performance. This can be caused by several factors, as explained in our previous work . We also refer to this publication for more information regarding the suitability of this testbed for VANET research. In short, the testbed cannot replace the simulator, but it is a useful tool to identify any possible issues that could arise in real-world deployments, but which are not adequately modeled by the simulator. One specific thing to mention here is that as described in , the lack of actual node movement in this testbed is compensated by the adoption of the same link impairment techniques as described above for the simulator.
3 Performance evaluation of baseline solution
Several techniques and parameter values are quite common in VANET literature. Together, they define the baseline VANET solution that is compliant with the current state of the art. In terms of forwarding strategy for multi-hop broadcasts, simple flooding is proposed by common standards such as CALM-FAST  and ETSI GeoNetworking . In this paradigm, every node will relay the received message if this node is located within the geographical destination area of the message and the hop-count limit has not been reached. In the ETSI standard, other forwarding strategies are briefly introduced; however, the standard annotates them as experimental and only obliges simple flooding. A common transmission power value is 2 W or 33 dBm, which corresponds with the maximum allowed equivalent isotropically radiated power (EIRP) for non-emergency vehicles in the IEEE 802.11p standard. As discussed in , in general, the default chosen data rate in VANETs is 6 Mbps. Because CAM and DENM messages are related to road safety applications, they are often annotated with the highest 802.11p QoS class, VO. The message generation frequency typically is 10 Hz, since this is required by several of the cooperative road safety applications defined in [29, 30]. As analyzed in previous work , the typical data size of both secure CAM and DENM messages is 300 bytes.
The illustrations in Figure 4 focus on the DENM characteristics. Again, the vehicle density rises from top to bottom, and the left column presents the PSR while the right column focuses on delay. The X-axis of the charts corresponds with the receiver node ID, the Y-axis with the PSR or average delay value. Every series on the figure corresponds with a different sender node. So one series on the figure illustrates to which degree the messages created by a single source could be received by all other nodes and with which average delay. To avoid overloading the figure, only half of the DENM sources are depicted. In case of PSR, the Y-axis adopts a linear scale, while in case of delay, a logarithmic scale is applied. The average value is also depicted to allow easy comparison.On Figures 3 and 4, the VANET scalability problem can clearly be observed. For increasing vehicle densities, the CAM PSR decreases significantly, while the delay under most intense traffic conditions become too large to support safety applications at all. Similar conclusions can be drawn regarding the DENM performance characteristics.
4 Gradual composition of the holistic SDDV solution
In this section, we present our holistic solution to the VANET scalability problem, SDDV. The starting point of our solution is an optimized approach to packet forwarding. Once this has been defined, other techniques spread across the different layers of the OSI networking stack are investigated. This allows us to gradually expand the SDDV suite of VANET techniques until a satisfactory combination is identified that effectively solves the VANET scalability problem. During this process, we will rely on extensive simulation efforts because the controlled environment of a wireless network simulator enables us to observe small differences in applied techniques and parameter values. Experiments on actual hardware are also included in this paper; for practical reasons, these kind of experiments were limited to the comparison of the completed SDDV solution with the baseline configuration described in Section 3.
4.1 Optimizations on the networking layer
4.1.1 Opportunistic forwarding
The basic idea behind opportunistic forwarding is that relay nodes are selected in such a way that the highest geographical gain is introduced per message retransmit. In other words, when a sender S broadcasts a DENM message, only the nodes located at the edge of S’ transmission domain are selected as forwarding nodes F (Figure 5). To achieve this behavior, every node has to wait a certain time before it gets the opportunity to retransmit a received message. This waiting time is dependent on the distance between the previous sender of the message and the possible relay node: the larger the distance, the shorter the waiting time. If the waiting timer of a relay candidate expires and it has not yet overheard a retransmission of the same packet by any of its neighbors, then it is allowed to retransmit the message. However, when such a retransmit is overheard during the waiting phase, the node cancels its timer and destroys the message.
In this formula, Smax is the maximum SNR value that is possible when receiving a message. S is the actual SNR value of the received message that the calculating node might relay. The suitable value for Smax can easily be determined. Taking into account IEEE 802.11p characteristics such as a maximum EIRP of 2 W, a frequency of 5.9 GHz and a noise floor of -99 dBm, together with the observation that vehicles on a highway are spaced at least 4 m apart, results in a maximum SNR value of 75 dB when taking the free space path loss into account. Another advantage of using SNR instead of distance is the fact that SNR values are always available, even under conditions with distorted GPS reception such as the urban canyon, tunnels, etc.
4.1.2 Irresponsible forwarding
The main concept of irresponsible forwarding is that when a node receives a multi-hop broadcast packet, it will retransmit that packet with a specific probability (Figure 5). Two approaches to define this probability can be found in literature. References [6, 37, 38] make the probability dependent of the distance between the previous sender and the relay candidate. In [34, 39, 40], the probability is determined based on the neighborhood density. Both approaches were experimentally validated in the scope of this paper.
4.1.3 Combined approach
In the previous sections, it was demonstrated that both opportunistic and irresponsible forwarding can significantly increase network performance compared to the simple flooding technique. However, for each technique, there is a trade-off between CAM PSR improvement and DENM performance decrease: delay in case of opportunistic forwarding, PSR in case of irresponsible forwarding. This brings up the question if both techniques can be combined in such a way that the performance gain is larger than in the case of a single technique, without jeopardizing DENM characteristics.
4.2 Transmit power control
Now that the networking layer optimizations of the SDDV protocol are defined, it can be explored if the inclusion of other techniques results in additional performance gains. Transmit power control is one of the common techniques in the context of the VANET scalability problem. The basic idea is that the load on the wireless channel is reduced by lowering the transmit power of the nodes in case of high vehicle density. In case of low vehicle density, higher transmit powers are applied to ensure VANET connectivity. A good example of such a solution is the work of Torrent-Moreno et al. , which introduces the D-FPAV algorithm. This algorithm dynamically adjusts the transmit power of CAM beacons to guarantee that a specific threshold called the maximum beaconing load (MBL) is never exceeded. DENM messages on the other hand are always transmitted at the maximum possible transmit power. To derive appropriate power values for the CAM beacons, the D-FPAV algorithm relies on the exchange of specific information between VANET nodes. This leads to additional communication overhead. This overhead has been reduced in the work of Mittag et al. , which presented SPAV, a segment-based evolution of D-FPAV that relies on a distributed vehicle density estimation protocol called DVDE.
Alternative techniques exist that do not exchange any control data to perform transmit power control. Instead, each node dynamically adjusts its transmit power based on the measured value of a specific network metric. Two different metrics are proposed: neighborhood density [44, 45] and channel busy time [19, 46].
Before implementing our own approach to transmit power control, some exploratory experiments were performed. In every experiment, the current level of SDDV was simulated (level 2). During a single simulation run, the transmit power was kept static, different simulations were executed with values gradually decreasing from 33 to 3 dBm (in steps of 5 dBm). For each transmit power value, the four different traffic intensities were simulated. The results indicated that from the CAM PSR perspective, there is no need to dynamically adjust the transmit power: the maximum value of 33 dBm corresponded with the best performance for all four considered traffic intensities. When focusing on the DENM PSR, however, it is beneficial to lower the transmit power for the highest vehicle density (10 m inter-vehicle distance). In the other cases, no distinct profit could be observed.
It was decided not to include any form of transmit power control in SDDV because (1) the exchange of power control data is in contradiction with the goal of SDDV to include only relatively simple techniques, (2) because the approach based on neighborhood density thresholds introduces distortions in the uniform nature of VANET performance, and (3) because our initial experiments indicated that no huge profits can be expected when introducing transmit power control on top of opportunistic and irresponsible forwarding. Instead, a fixed transmit power of 33 dBm is defined as the next key characteristic of SDDV (level 3).
4.3 Transmit rate control
Similar to power rate control, the technique of transmit rate control is sometimes presented as a possible means to overcome the VANET scalability problem. When applying a higher transmit rate in the VANET, the capacity of the wireless communication channel is increased, which could (partly) resolve the channel congestion under high-density traffic circumstances. However, the downside of increasing the transmit rate is that at the receiver side, the SNR value of the message has to be higher for successful packet reception. Hence, the communication range of the nodes is decreased. At higher rates, the wireless link is also more sensitive to node mobility (see Section 4.6) since in that case, the PHY layer applies more sensitive modulation techniques and less redundant levels of error correcting coding. These effects can negatively influence VANET end-to-end performance. Identifying the appropriate balance in this rate control trade-off is a challenge.
The work of Shankar et al.  and Wang et al.  proposed transmit rate control techniques to improve the quality of unicast communication links in the VANET. The former optimized the achievable goodput over a specific unicast link, while the latter achieved maximal energy efficiency. Because the focus of SDDV is on broadcasted data dissemination, these techniques are of less relevance to our solution. More closely related work is  which explained that the rate of 6 Mbps is generally considered as the default value in VANET literature, though that this assumption is not rooted on strong technical considerations. Based on a large number of simulations, it concludes that also from a technical point of view 6 Mbps is the optimal data rate. However, relatively small transmit power values were used (maximum range is 500 m), not all nodes apply the same transmit rate during the experiment, and only CAM messages are taken into account. Ma et al. also investigated a scenario with only CAM messages; their results indicate that optimal results can be achieved with with 6 to 12 Mbps in the IEEE 802.11p standard. The study of Xu et al.  on the other hand investigates the optimal data rate for different message repetition schemes on the MAC layer. They conclude that, depending on the chosen repetition algorithm, the optimal data rate varies between 6 and 12 Mbps.
A point to mention is that during the experimentations described above and in Section 4.2 (power control), only one parameter was varied at a time, or transmit power or transmit rate. Since SDDV level 4 defines a fixed combination of 33 dBm transmit power and 12 Mbps transmit rate, it could be useful to further investigate if other fixed combinations of rate and power lead to even better results. Therefore, additional experiments were performed in order to cover all possible combinations of the envisaged transmit powers (33 to 3 dBm) and rates (3, 6, 12 Mbps). Careful evaluation of the results indicated that no other combinations achieved a better performance than the combination of 33 dBm and 12 Mbps.
4.4 Optimal selection of QoS class
The IEEE 802.11p MAC layer is similar to the IEEE 802.11e enhanced distributed channel access (EDCA) extension that supports quality of service (QoS) . This scheme resembles the CSMA/CA scheme of standard IEEE 802.11 called distributed coordination function or DCF, but it can differentiate between four different application categories: background traffic (BK), best effort traffic (BE), video traffic (VI), and voice traffic (VO). Different contention window (CW) and arbitration inter frame space (AIFS) values are chosen for the different application categories, where VO has the highest priority and BK the lowest. For more information regarding the functionality of EDCA and the IEEE 802.11p MAC, we refer to our previous work .
Until now, the QoS class VO was always applied for the broadcasting of CAM and DENM messages. This is a common approach in literature, since both types of messages are safety related and VO is the highest QoS class. However, IEEE 802.11 defines that broadcasted messages should always be sent with a contention window value equal to C W min . In the highest QoS classes, the values for C W min and AIFSN are small. This leads to small waiting times during contention, with a small end-to-end delay as a result. This is important for voice and video multimedia applications. However, the downside of this approach is that the probability increases that two contending nodes will choose the same time slot to start their transmissions, resulting in packet loss. In the context of the VANET scalability problem, it would therefore be useful to investigate if the usage of the lower QoS classes could lead to better networking performance.
A few studies could be found that elaborated further on this idea. In , it is investigated how the communication performance of a single node can be improved by assigning it to a higher QoS class than all the other nodes. This is not applicable for SDDV since we are looking for techniques that improve the performance equally for all VANET nodes. The research of  pursued this goal, experimenting with two techniques to improve general VANET performance. In the first one, all VANET nodes apply the same QoS class for all their messages, and this QoS class is varied over different experiments. The second technique adjusts the QoS class on a per-packet basis, using the success rate of previous packet transmissions as the dominant decision parameter. However, the authors only managed to achieve minor performance increases. Rebai et al.  defined sub-classes within the four existing QoS classes. Based on the success rate of previous transmissions, nodes calculate the appropriate sub-class for their messages. However, since broadcasting does not provide an acknowledgment mechanism on the MAC layer, this technique cannot be applied in our envisaged scenario. In the work of Adler et al. , nodes re-calculate the appropriate value for their active contention timers after each DCF interframe space (DIFS), instead of using the value that was stored when the medium was sensed busy before. However, when only focusing on the presented PSR gains of this technique, the results are not as significant as desired for SDDV. In , the CWmin value was varied based on traffic density. However, the presented simulations only included up to 20 vehicles, which does not provide sufficient information to draw conclusions regarding the impact of the technique on the VANET scalability problem.
4.5 Adaptive beaconing
It can be derived from Figure 11 that CAM beacons introduce a significant higher load on the wireless channel than DENM messages: during the course of the experiment, every node (except for DENM source nodes) transmits 350 created CAM messages but only 1 to 50 forwarded DENM messages. Therefore, it would be useful to reduce the amount of transmitted CAM messages. The most obvious technique to achieve this goal is a dynamic adjustment of the CAM beacon interval.
Some authors have already explored this technique before. The work of ElBatt et al.  simulated CAM beaconing scenarios with high vehicle density. Lowering the beaconing frequency from 10 to 5 Hz resulted in a significant increase of the CAM PSR. In , similar scenarios were researched, and equal conclusions could be drawn: 10 Hz resulted in problematic network performance, while no issues could be identified at 2 Hz. Huang et al.  proposed to dynamically adjust the beaconing interval based on an estimation of the accuracy of the local dynamic map of the node’s neighbors. Such a local dynamic map is constructed using information retrieved from the CAM messages and indicates the exact position of all neighbors in the node’s vicinity. An estimation of the PSR is applied to estimate the accuracy of this local dynamic map. Some small scale outdoor tests were presented; however, they do not clearly indicate the actual impact under high vehicle densities. In , a similar technique was proposed: the beaconing interval was dynamically adjusted based on the channel quality. This metric combines channel collisions, SNR values on the channel, and neighborhood density. However, the proposed solution is designed for a relatively slow but reliable dissemination of non-safety related data within an often disconnected VANET. This is quite different from the scalability problem scenario studied in this paper.
Note that in this formula, v is the current speed of the vehicle. We also remark that the DENM beaconing interval is not adjusted according to the vehicle speed. The motivation behind this design choice is the fact that CAM messages are intended for the exchange of vehicle position, speed, heading, etc. between vehicles in each others vicinity. Hence, the sender and intended receivers are part of the same traffic flow, which results in a relatively small speed difference between them. Therefore, the CAM service level as calculated by the sender node will correspond with the achieved service level at the intended receivers. However, in the case of DENM messages, it can often happen that messages from slow or stationary vehicles are intended for approaching high-speed receivers. In this case, adaptive DENM beaconing would result in a low beaconing frequency at the sender, hereby failing to reach the targeted data service level at the intended receivers.
4.6 Improved channel tracking using decision feedback and smoothing
The IEEE 802.11p standard uses an orthogonal frequency division multiplexing (OFDM)-based communication scheme that is closely related to the IEEE 802.11a/g standards for wireless LAN. An essential part in such a receiver is channel estimations, which allows equalizing the OFDM signal. This is crucial for achieving a high packet success ratio. In IEEE 802.11a/g/p, most information to estimate the channel is coming from the initial part of a packet, more specifically the preamble. At urban and highway speeds, the channel varies much faster than in stationary applications. Also, insufficient explicit information is available in the signal to update the channel estimates. As a consequence, the amount of packet errors rises quickly in function of the packet length. Several techniques have been proposed to tackle this problem in a way that is compatible with the IEEE 802.11p standard. Examples are decision feedback-based tracking  and smoothing, using a spectral temporal averaging estimation . Recent progress in the state of the art shows that a combination of both decision feedback and smoothing provides an improved tracking performance compared to applying each technique separately. A further enhancement is the use of multiple antenna reception techniques. Our work focuses on the achievable signal processing gains of these proposed techniques . The following approaches are adopted:
Decision feedback and smoothing information: using the maximum amount of data carriers to derive the channel estimate results in the best performance. However, this also leads to a higher computational complexity overhead. A possible technique to reduce this computational complexity is to lower the amount of used data carriers. This results in a degradation of the accuracy of the channel estimation though. To achieve a significant decrease in computation complexity, the amount of data carriers has to be lowered in such a way that the corresponding performance decrease is unacceptable. Therefore, it is chosen to use all data carriers when estimating the channel based on decision feedback and smoothing information.
A single antenna receiver: although the usage of multiple antenna receivers could introduce additional performance gains, it was chosen to adopt the single antenna approach. This design choice was driven by the corresponding cost increase. More antennas require more wiring in the car frame, which is a sensitive cost issue in the automotive sector.These techniques were studied on the Matlab model of the IEEE 802.11p standard that was introduced in Section 2 and then applied in our NS-2 simulations. As a result, we could observe an improved packet success rate in the NS-2 simulations, as illustrated in Figure 13. The block effect that was present in our previous CAM plots has completely disappeared. As mentioned in Section 2, the simulated highway nodes are numbered sequentially from start to finish, and each successive ID corresponds with a node on the next lane. Therefore, there is a change of driving directions after every three consecutive node IDs. This explains the block effect: for a given sender, all receivers in the opposite direction will lose more packets due to node mobility, resulting in lower CAM PSR values. When applying decision feedback and smoothing, this difference disappears, and hence, the blocking effect is no longer present. As a result, the networking performance becomes more uniform, which is a desired characteristic for road safety applications. Therefore, the implementation of improved channel tracking using decision feedback and smoothing is defined as the next key characteristic of SDDV (level 7).
4.7 Preamble and data switching
Other possible PHY optimizations are preamble and data switching. They are explained in detail in . In short, preamble switching occurs when a new frame arrives at the time that the receiver node is still receiving the preamble and physical layer convergence procedure (PCLP) header of an earlier frame. The new frame can be picked and locked onto by the receiver node if it has sufficiently higher power to be heard above the earlier one. Data switching refers to the capture of a new incoming frame during the frame body reception process of another one. The result of applying these techniques on top of SDDV level 7 is depicted in part (c) of Figure 13. It can be observed that in the CAM PSR plot, a darker red area has appeared around the diagonal. This means that the CAM PSR for a node’s most direct neighbors has reached very high values. This is a desirable VANET communication characteristic. Therefore, we define the adoption of preamble and data switching as the last key characteristic of SDDV (level 8).
5 Final SDDV specification
6 Experimental validation on the iMinds w-iLab.t wireless testbed
Two experiments were performed: the baseline solution presented in Section 3 and SDDV level 8. In both experiments, the scenario was kept similar to the simulation scenario: six nodes located at one end of the building are DENM sources, all nodes forward these messages. In parallel, all nodes transmit CAM messages of their own. The same timing is adopted as in the simulations: 10 s warm-up, 20 s measurements, 5 s cool-down. To calculate accurate delay values, the clocks of all nodes were synchronized using the PTPd protocol . It was verified that during the experiments, the accuracy of the clock synchronization was always below 0.01 ms.
A peculiar effect worth mentioning is the fact that SDDV introduces two dips in the DENM PSR curves. Such a behavior could not be observed in any of our simulations. The cause of this effect is the location of the corresponding nodes: they are positioned inside concrete maintenance shafts that interconnect the different floors of the building. Hence, the signals sent to these nodes are attenuated more fiercely. In the baseline solution, no problems occur due to the high level of rebroadcasting redundancy of the simple flooding technique. However, the reduced redundancy level of SDDV fails to entirely overcome this problem. This means that in real-life situations where some vehicles are enclosed by obstacles (e.g., trucks), it might be required to tweak the SDDV parameters to achieve reliable dissemination to these nodes without sacrificing general VANET performance. This can be researched in future work.
This paper focused on the VANET issues regarding scalable data dissemination. Simulations have illustrated the severity of this problem when adopting common techniques and parameter values. Step by step, optimizations were combined to compose a holistic solution: SDDV. During the selection of each possible technique, consideration was given to its degree of complexity. The final result is a composition of eight techniques: opportunistic forwarding, irresponsible forwarding, fixed transmit power of 33 dBm, fixed transimission rate of 12 Mbps, adoption of the VO QoS class for both CAM and DENM messages, adaptive beaconing based on the vehicle speed, improved channel tracking using decision feedback and smoothing, and preamble and data switching.
Through simulation, it was evaluated that SDDV achieves its goal of providing scalable VANET data dissemination under all types of traffic intensities in the highway environment. A final validation of SDDV was performed on the iMinds w-iLab.t wireless testbed. This experiment confirmed the simulated results. However, some important topics for future work can be identified. The w-iLab.t experiment indicated that some optimization of SDDV might be required in case of vehicle obstruction. Another topic is the analytical analysis of SDDV. The holistic approach pursued in this paper led to a large research space to explore. The empirical approach adopted in this paper was considered to be the only practical way to work towards the definition of SDDV. Performing an analytical analysis of every intermediate step and every possible combination of techniques seemed unfeasible. However, now that SDDV has been defined, it would be very useful to analyze it analytically. Similar to the approach taken to validate SDDV on the iMinds w-iLab.t wireless testbed, a theoretical comparison of the baseline solution and SDDV would be vary valuable. Another possibility for the further improvement of SDDV is to extend the currently targeted highway environment with the urban setting. In our research, we focused on the highway scenario because this is the case where the scalability problem is the most apparent. It is challenging for the IEEE 802.11p signal to penetrate buildings. As a result, the urban environment is actually characterized by a lower amount of competing nodes on the wireless channel, although there is actually a higher vehicle density in the city then on the highway. Hence in this paper, it was shown that SDDV can handle the most challenging scenario in terms of scalability, and it is expected to be evenly successful in less challenging urban environments. In future work, a validation of this assumption would be valuable. If needed, SDDV could be further extended in this case with techniques that intelligently cope with forwarding decisions at intersections. Another possible direction for future work is the validation of SDDV in large-scale field operational tests (FOT). Since SDDV was deliberately designed using techniques characterized by a rather low level of complexity, it seems feasible to adopt it in any FOT within reasonable constraints regarding manpower and budget. Such a valuable validation of SDDV in a real-life circumstances should therefore be pursued in future work.
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