A Collision-based Beacon rate Adaptation scheme(CBA) for VANETs

Nader Chaabouni, Abdelhakim Hafid, Pratap Kumar Sahu Network Research Lab, University of Montreal, Canada

chaaboun@iro.umontreal.ca, ahafid@iro.umontreal.ca, sahupk@iro.umontreal.ca

Abstract—Safety applications in VANET use two types of messages (a)periodical messages/beacons: they are broadcast several times per second to exchange information with neighbors; and (b) warning (event driven) messages: they are generated when an event occurs (e.g., a car accident) and are disseminated in the network to notify nodes of interest. Although warning messages have higher priority, beacons are equally as important since a good dissemination strategy usually relies on information provided by beacons to choose forwarding nodes. However, in dense networks, beacons may cause network congestion leading to performance degradation of safety applications. In this paper, we propose CBA: a congestion control approach that uses the number of detected collisions as a metric to control the beacon generation frequency and therefore reduce the effect of congestion. Simulation results show that our proposed scheme achieves a balanced trade-off between beacon information accuracy and beacon related overhead.

Keywords— beacon rate; congestion control; e-safety; VANET.

I. INTRODUCTION The transportation sector is an important component of

economy and social development; a growing economy should rely on a transportation system that is able to move persons and goods efficiently while reducing costs (relative to accidents, to congestions, etc.) and environmental impact (e.g., energy consumption and carbon footprint). To increase the efficiency of transportation, it has been suggested to create intelligent transportations systems (ITS) by using advanced and emerging technologies (computers, sensors, wireless communications, etc.) in transportation to save lives, time and energy. The fast emerging VANET networks are expected to be a key enabling technology for ITS by enhancing safety and to offer information and entertainment services.

IEEE 802.11, a mature and an inexpensive wireless technology was selected to ensure wireless multi hop communications in vehicular Ad hoc networks. A suite of standards were developed over this technology to form Wireless Access for Vehicular Environment architecture[1] (WAVE). It defines how vehicles should communicate with one another (vehicle-to vehicle communications) and with infrastructure equipment (vehicle -to-infrastructure communications).

Since safety is the main concern of ITS, most current research work on these networks focuses mainly on e-safety. Many potential safety applications were identified[2] and studied, such as pre-crush sensing, lane changing, and early crush warning. These applications rely on vehicle-to-vehicle

(V2V) communications thus requiring a cooperative awareness mechanism; indeed, besides Event-Driven Messages (EDM) that are triggered in particular situations (e.g., to avoid a crush), each vehicle periodically broadcasts Cooperative awareness Messages (CAM) containing information about vehicle position, speed, direction and eventually other information that may be relevant for safety applications. This practice, called beaconing, provides relatively up to date data about surrounding vehicles which helps safety applications to make decisions. Beaconning service can also be used in other non-safety related applications, such as unicast routing.

To fulfill the strict reliability and delay requirements of safety applications, WAVE standards provide a control channel where safety messages (CAM and EDM) are exchanged without being disturbed by other non-safety related applications (usually exchanging an important amount of data). Collisions between safety messages remain however an issue since periodical CAMs consume a considerable part of available bandwidth and increase the risk of loss of EDMs. Also, beaconing service in high density networks can cause congestion that throttles the dissemination of warning messages and reduces beacon reception rate, thus forcing safety applications to use less accurate data when making decisions.

In this paper, we focus on congestion control to reduce collision rate and save more bandwidth for the dissemination of safety messages. Most proposed congestion control schemes focus on the adaptation of transmission power level to avoid congestion. We believe that adapting the beacon frequency is as important as power control approach for the safety applications; indeed, an adequate beacon rate would provide sufficiently accurate information to vehicles and save most of the available bandwidth for the dissemination of higher priority warning messages. Therefore, we propose a two phase scheme to adapt vehicles beacon rate: (1) monitoring: it consists of monitoring the network state to detect an eventual congestion; and (2) adaptation phase: it is launched, after detecting a congestion, to adapt the beacon rate and alleviate the congestion.

The remainder of the paper is organized as follows. Section 2 presents a brief overview of existing contributions that reduce congestions caused by beaconing in VANETs. Section 3 presents, in details, our proposed scheme (CBA) that dynamically adapts the beacon rate for efficient use of network bandwidth.. Section 4 evaluates, via simulations, the proposed scheme. Finally, Section 5 concludes the paper.

IEEE ANTS 2013 1569804677

1

II. STATE OF THE ART Congestion usually occurs when data is injected in the

network with a rate that exceeds the network capacity for relatively long periods of time. It is an important topic in wireless communications since all neighboring nodes use a shared wireless medium. In a vehicular environment, additional restrictions, related to highly dynamic topologies and frequent changes of the network size, make the congestion control more challenging. In safety applications, most existing schemes reduce congestion by reducing the number of forwarding nodes. Beaconing related congestion is however more difficult to control; three main approaches have been adopted for this type of congestion: transmit power adaptation [3-11], contention window adaptation [3, 12] and beacon rate adaptation [13-16].

A. Transmit power adaptation Transmission power adaption impacts the number of

surrounding nodes that are able to hear beacons sent by other nodes. Low transmission power would allow only the closest neighbors to hear/decode correctly the message. High transmission power would increase the transmission range, allowing more neighbors to receive the message correctly, and the number of nodes that will be involved in interferences. This technique aims to enhance spatial frequency reuse, allowing more nodes in a limited area to use the same frequency with lower risk of collisions. It is a popular congestion control schemes for VANET.

Artimy et al. [4] present a power control scheme that estimates local traffic density for each node and uses it to control the transmission range. However, they focus mainly on maintaining connectivity rather than congestion control.. Chunxiao et al. [5] propose a power control scheme based on a Delay-Bounded Dynamic Interactive Power Control module. However, this scheme requires the use of eight directional antennas; thus, it is not suitable for VANETs.

Torrent-Moreno et al. [6] propose a fair Power Adjustment algorithm for VANET (FPAV) where a vehicle adjusts its transmission power while keeping the bandwidth used for beaconing within a predefined threshold, called the maximum beaconing load (MBL); thus, a part of available bandwidth will be always available for event driven messages. The major problem of this scheme is that it requires a global knowledge of the network state making it unrealistic for VANET environment. This issue was addressed later by the same authors in [7] where the enhanced scheme, the Distributed Fair Power adjustment for Vehicular Networks (D-FPAV), was proposed to make FPAV fully distributed. They also proved formally that D-FPAV achieves fairness in power assignment. D-FPAV introduces, however, additional control overhead for neighbors’ information dissemination.

To reduce this overhead, Mittag et al. [9] proposed Distributed vehicle Density Estimation (DVDE) and Segment-based Power Adjustment for Vehicular environments (SPAV) strategies. Through simulations, authors show that DSDV/SPAV is able to reduce control overhead but cannot always satisfy the requirement of a strict maximum beacon load threshold like D-FPAV.

Hongsheng et al. [11] propose to apply the adaptive power control to all nodes at the same time. They argue that having nodes with different transmission ranges would increase hidden nodes in some areas of the network degrading the network

performance. Simulations show a better reception rate compared to other protocols, such as D-FPAV, but with a considerable overhead.

B. Contentiom window(CW) adaptation The binary exponential back off algorithm, used in unicast

communication, doubles the value of the contention window up to a maximum value after each failed transmission (i.e., no acknowledgement is received). This algorithm proves to be effective for unicast communications but cannot be used in broadcast since failed transmissions cannot be detected. Nevertheless, the variation of contention window may enhance the network performance. Although not very popular and even considered sometimes ineffective[17], contention window adaptation has been adopted by few schemes in the open literature [3, 12]. Rawat et al. [3] propose to combine the use of transmit power adaptation with contention window variation; the congestion control algorithm [3] continuously monitors the collision rate Cr and changes the transmission range according to a predefined table. It also decreases or increases minimal CW for all access categories when Cr is bigger (resp. smaller) than a max (resp. min) threshold. Simulations show better performance compared to the default scheme (i.e., no contention window adaptation); however, it is not clear whether this increase is due mostly to power adaptation or contention window adaptation. Stanica et al. [12] performed several simulations to find correlation between contention window, node density and the number of hidden nodes. They concluded that CW adaptation could improve reception probability and thus network performance. They proposed an empirical formula to adapt CW depending on the number of neighbors heard from in the last few seconds. No simulations were shown to prove the efficiency of the proposed scheme.

C. Beacon rate adaptation Beacon rate is the rate at which beacons are generated and

sent to the lower layers for transmission. Although usually considered a constant, beacon rate variation can be of a great importance to alleviate congestion and to enhance network scalability. In fact, increasing the rate would result in more beacons being exchanged and thus nodes have more accurate information about their neighbors. This accuracy would, however, increase at the cost of a higher collision probability. An illustration of such an issue can be observed especially in highly dense scenarios where decreasing beacon rate results in fewer beacons and hence fewer collisions but also in less accurate and reliable information. Schmidt et al. [13] discuss thoroughly this issue and highlight the importance of a trade-off between the exchanged information accuracy and the availability of resources for higher priority messages (e.g., warning messages). Beacon rate adaptation is becoming more popular and several recent congestion control schemes [14-16, 18] based on this type of adaptation have been proposed.

Long et al. [14] propose three congestion control algorithms for VANETS (Rate control, power control and joint rate/power control). They evaluate the channel busy time (CBTi) of the ith period which they use along with the last generation rate to adjust the beacon rate for the (i+1)th period. This approach does not have any additional communication overhead; however, CBT is hard to estimate in real world radio devices.

2

In [15] and [16], the proposed adaptation algorithms aiming to control congestion are divided in two phases: a detection phase and a regulation phase. The algorithm in [15] detects congestion using a metric that combines beacon reception rate, loss rate and average waiting time; if the variation of the metric value becomes important (i.e., higher than a threshold), a regulation algorithm will be initiated by node n that detects this variation. Node n will use neighborhood information to compute a fair bandwidth share [15] and then compute a new beacon rate to be used for all neighbors. Computing the bandwidth share requires, however, the exchange of neighboring lists which increases communication overhead.

Lv et al. [16] use a reception probability metric computed using the average distance between a node and its neighbors. Once this probability exceeds a threshold, the beacon frequency is divided by 2; this frequency will then be incremented by λ=1 after each observation period. Simulations were, however, not conclusive enough to prove the efficiency of this scheme.

Sommer et al. [18] proposed an Adaptive Traffic Beacon algorithm in which they propose to adapt beacon intervals based on a lineal combination of two parameters: channel quality used to estimate channel and message priority. The beacon interval is calculated for a new beacon right after sending the previous beacon. This scheme was, however, designed mainly for congestion alleviation in traffic information systems and therefore performance evaluation focused on traffic efficiency (e.g., CO2 emission and average speed) rather than network performance.

We conclude that existing beacon rate adaptation schemes are based on one or more metrics that are used as a feedback to adapt dynamically the beacon rate. Nevertheless, most of these metrics were either proven not to be effective for regulation or are (e.g., CBT) hard to extract from real world equipment. In this paper, we propose to use a metric that is easy to measure in real world and that can be proved effective by simulations.

III. PROPOSED SCHEME The basic idea of our proposed congestion control scheme is

that it adapts beacon generation rate dynamically based on the number of collisions detected by each node. The rationale behind our scheme is that collisions are directly correlated with the network state; indeed, congestion is the result of a considerable amount of data exchanged between nodes (exceeding network capacity) which would increase the number of collisions detected by nodes. In addition, collision feedback is easy to collect from lower layers; it does not require any overhead related to additional information exchanges between communicating nodes.

Our congestion control scheme consists of two phases: (1) a detection phase to detect the presence of a congestion; it is based on monitoring the number of collisions in the network nodes; and (2) a regulation phase, started when a congestion is detected, to adapt the beacon rate according to the current local density which is computed using an estimation of the number of neighbors in range. In the rest of the paper, nodes “in range” refer to nodes that are in communication/interference range of a given node.

A. Detection phase The basic idea of our proposed congestion control scheme is

that it adapts beacon generation rate dynamically based on the

number of collisions detected by each node. The rationale behind our scheme is that collisions are directly correlated with the network state; indeed, congestion is the result of a considerable amount of data exchanged between nodes (exceeding network capacity) which would increase the number of collisions detected by nodes. In addition, collision feedback is easy to collect from lower layers; it does not require any overhead related to additional information exchanges between communicating nodes.

Our congestion control scheme consists of two phases: (1) a detection phase to detect the presence of a congestion; it is based on monitoring the number of collisions in the network nodes; and (2) a regulation phase, started when a congestion is detected, to adapt the beacon rate according to the current local density which is computed using an estimation of the number of neighbors in range. In the rest of the paper, nodes “in range” refer to nodes that are in communication/interference range of a given node.

1iif,1iA*α)(11n*α

1iif,1niA (1)

where α is a constant that represents the degree of weight decrease for older factors. These values will be recorded in a collision vector maintained by each node.

TABLE I. DETECTION PHASE ALGORITHM (DPA)

Input: - Number of collisions at end of period: nb_collisions. - The collision vector: coll_vector - The variation threshold: Vthresh

Output: - coll_vector, Vthresh Begin A = coll_vector.new_EMA_value(nb_collisions) A1 = coll_vector.firstElement() V = | A - A1 | if (V< Vthresh) //no regulation is needed coll_vector.Append_Element(A)

else //start regulation coll_vector.clear() coll_vector.AppendElement(A) Vthresh = a*A start_Regulation() End

At the end of monitoring period j, node v checks the

variation V of Aj compared to the first value in the collision vector (V= |Aj - A1|). If V exceeds threshold Vthresh, highlighting a significant variation of recorded collisions since last regulation phase, v will conclude that the state of the network has changed and that it should initiate a beacon rate regulation (which will be detailed in next sub-section). Before starting the new regulation, N must empty its collision vector (containing (Ai), i>0, since last regulation phase) and ensure that Vthresh is always adapted to the current state of the network; Vthresh must also be set to a*Aj. Table 1 Shows the pseudo code of the proposed detection phase algorithm (DPA); it is executed, by each node in the network, at the end of each monitoring period.

3

B. Regulation phase A beacon rate regulation is initiated by node v when the

number of collisions variation exceeds Vthresh; this variation could mean that the number of collisions has significantly increased or decreased. In either way, the beacon rate needs to be recalculated; We propose Equation (2) to compute beacon rate. The rationale behind this equation is our belief that the rate should take into account the channel capacity and the number of neighboring nodes.

NbC

Ratesize *

*

where C is the capacity of the channel, bsize is the beacon size, N is the number of neighboring nodes that share the bandwidth/channel with v (in both communication and interference ranges) and β is a constant (0< β<1).

The beacon rate computed using Equation (2) will be effective only if all neighboring nodes (i.e., are in interference range of v) adapt their beacon rate using the same equation; thus, all neighbors will have a fair share of available bandwidth. A node initiating a beacon rate regulation must therefore inform all of its 1-hop neighbors by setting a “regulation flag” in its next couple of broadcast beacons. Nodes within range of the initiator start also a regulation and set their regulation flag to cause a regulation in the initiator's 2-hop neighbors that are in its interference range.

Using Equation (2) requires that each node is able to determine the number of neighbors N in range. Therefore, we propose to estimate N based on detected collisions and received packets. In our estimation, we use the following two hypothesis (1) The number of collisions resulting from 3 or more packets colliding at any instant t is negligible [20]. Therefore, the number of collisions ni detected by a 802.11 radio device at end of period i is equal to the sum of (a) ni_dec: number of collisions due to non decodable messages from senders in interference range; and (b) ni_coll: number of collisions due to “real” collisions of packets emitted from two senders that are both in range with the receiver (i.e. ni = ni_dec + ni_coll ); and (2) A constant percentage γ of recorded messages is due to real collisions (ni_coll = γ* ni , ni_dec = (1-γ)* ni).

Fig. 1. HIGHWAY SCENARIO To better understand collisions in wireless communications,

let us consider the highway scenario shown in Fig. 1 where v2 and v3 are in v1's communication range and v4 is in its interference range. All nodes are broadcasting periodical beacons. Since v4 is in interference range of v1, all beacons, it sends, cannot be decoded and will therefore be considered as collisions by v1's MAC layer. In case of simultaneous

transmissions (i.e. v2 and v3 sending beacons at the same time t), v1 would receive a collision that is interpreted as one non decodable packet. Thus, and based on the first hypothesis, N can be calculated as follows:

avg

i_colli_dec_

Rate) n*2n( recvin

N

where ni_recv is the number of beacons received during period i and Rateavg is the average beacon rate of neighbors calculated based on received beacons. Using the second hypothesis, Equation (3) can be further simplified into Equation (4):

avg

i_

Rate) n*)1(( recvin

N

IV. PERFORMANCE EVALUATION In this Section, we evaluate CBA using simulations. First,

we briefly describe the simulation environment, the approaches used to compare against our CBA and the evaluation metrics. Then, we present and analyze the simulation results.

A. Simulation scenario and parameters To perform our simulations, we used OMNeT++ [21]

integrating MIXIM/Veins [22] project as the network simulator and SUMO 15.0 [23] as the traffic simulator. Through the Veins project, both simulators are able to communicate in real time via a TCP connection, allowing bidirectional exchange of data and commands during simulations. Our scenario consists of an intersection of two bidirectional roads of two kilometers length. Vehicular traffic at the intersection is organized by traffic lights. Vehicles are generated in the four edges at equal intervals; when a vehicle arrives to the intersection, it chooses a random path (forward, left or right) and disappears at the end of the selected edge. During simulations, all nodes exchange beacon messages of 500 Bits (same size as in [14-16]) with static or variable frequency depending on the scheme used in beaconing service. In CBA, the frequency changes when the moving average (Ai) variation exceeds the threshold. We set α to 0.35 (see Equation 1) to give more weight to the 5 last values of detected collisions. β in Equation (2) has an impact on the node’s share of bandwidth; for a given node, channel reuse can be achieved at a distance 3 times the node’s transmission range. Thus, a node’s bandwidth share (assuming no contention) will be 100/3=33%; however, this upper bound cannot be achieved due contention. With extensive simulations, we found that β = 0.17 provides best results.

Simulations did show that a value of 0.05 for γ allows minimizing the error of neighbors’ estimation in Equation (4).

To evaluate our approach (CBA), we decided to compare it to (1) Static Beaconing (SB): using a constant beacon rate of 10Hz; this scheme serves as a proof of concept to show the impact of congestion on vehicular networks and the importance of congestion control; and (2) Rate Control algorithm (RC): a recently proposed congestion regulation algorithm [14] that relies on the evaluation of Channel Busy Time (CBT: the percentage of time during which a radio equipment is not in idle state). Our choice is motivated by the fact that this scheme does not generate additional control overhead and that, similarly to CBA, it uses locally collected information (CBT) for beacon

4

rate adaptation. In RC, each node calculates a new beacon rate at the end of each 1-second observation period i according to equation (5):

1* ii

Threshi Rate

CBTCBT

Rate

where CBTthresh is a predefined constant (CBTthresh=0.2 [14]), CBTi is the channel busy time and Ratei is the beacon rate at end of period i.

The evaluation metrics we used in our simulations are:

Beacon success rate: the percentage of beacons successfully received by nodes in the network. A higher success rate means that the beacon rate adaptation is more effective since most sent beacons are received correctly.

Average Received beacons per second per node: success rate is not always sufficient to determine the effectiveness of regulation beacon rate adaptation scheme. In fact, using a 1Hz constant beacon rate would probably result in a 100% success rate although it is less effective than a 5Hz rate with a success rate of 90%; indeed, the 5Hz rate is able to provide more accurate information about the network's dynamic topology. Thus, the number of received beacons per node per second is an important indicator of beacon rate adaptation performance; indeed, a higher number of received beacons per node means a better information accuracy.

Detected collisions: it represents the average number of collisions, measured by each of the network nodes, per second.

Bandwidth utilization: the percentage of bandwidth used to exchange beaconing messages.

To highlight the importance of beacon adaptation, we need to consider a dense scenario where many nodes are in range and are exchanging beacons. Therefore, we only consider nodes that are at the intersection after a warm up period.

Table 2 shows the simulation parameters and their values. TABLE II. SIMULATION PARAMETERS

Parameter Value Speed <= 50km/h

Data rate 3 Mbps Frequency band 5.9 GHz Channel width 10 MHz

Interference range ~510m Radio sensitivity -89dBm

B. Results Fig. 2 shows that network size doesn't have a significant

impact on the beacon success rate for CBA and RC. We also observe that CBA achieves not only a better success rate (Fig. 2) but also a higher number of received beacons (Fig. 3). Indeed, CBA is able to achieve a better success rate and a better accuracy while using the number of collisions as a congestion indicator; this metric is easier to compute than channel busy time (used by RC) making CBA more suitable for real world networks.

Fig. 2. BEACONS SUCCESS RATE VS. NETWORK SIZE

Fig. 3. AVERAGE RECEIVED BEACONS PER SECOND VS. NETWORK SIZE

Static Beaconing shows the best success rate for smaller networks (Fig. 2); this rate drops rapidly when network size increases. This makes SB not suitable for big size networks. Fig. 3 shows that when using SB the number of received beacons decreases, when network size increases; however, SB still outperforms CBA and RC with respect to this metric. This means that SB provides better accuracy than CBA and RC but at a high price (Fig. 5) as explained later.

Fig. 4. DETECTED COLLISIONS VS. NETWORK SIZE

Fig. 4 shows that the number of collisions, when using SB, increases with network size to exceed, in average, 300 collisions per second per node (network size>=70). This explains clearly the significant drop of beacon success rate (Fig. 2) since most beacons will be lost because of collisions. CBA outperforms RC in terms of collisions; indeed, it causes 10% to 20% less collisions. This can be explained by the fact that CBA rate adaptation is triggered by an increase (or decrease) of collisions. We also observe that the increase of collisions with the network

5

size is not significant when using CBA/RC compared to SB; both schemes do not cause more than 100 collisions per second per node even in the case of dense networks (100 nodes).

Fig. 5 shows the highest bandwidth utilization when using Static Beaconing (over 75% when network size >50). This proves that SB is not well suited for vehicular dense networks: in fact, the main purpose of using the beaconing service in e-Safety is to help nodes making decisions when forwarding waning messages. However, beacons cannot be useful when they consume most of available resources at the expense of warning message dissemination.

Fig. 5 shows also that CBA and RC keep more than 60% of available bandwidth free for warning messages. CBA consumes 5% to 8% more bandwidth than RC, but the percentage of bandwidth used for successful transmissions in our scheme is 10% higher than RC's. We conclude that CBA is more effective in bandwidth usage.

Fig. 5. USED BANDWIDTH

V. CONCLUSION Disseminating high priority messages is the most critical e-

safety service in VANET. It relies on neighborhood information provided by the underlying beaconing service. Therefore, beaconing should be effective and maintain a trade-off between information accuracy and resources consumption. In this paper, we proposed a control congestion scheme using the number of detected collisions as feedback (which is much easier to compute in real world networks compared to other metrics, such as channel busy time) to adapt the beacon rate. We also performed computer simulations to show the importance of beacon rate adaptation in e-safety applications and to prove the efficiency of the proposed scheme.

REFERENCES [1] Y. L. Morgan, "Managing DSRC and WAVE Standards Operations in a

V2V Scenario," In International Journal of Vehicular Technology, 2010. [2] H. Hartenstein and K. P. Laberteaux, "A tutorial survey on vehicular ad

hoc networks," In IEEE Communications Magazine, pp. 164-171, 2008. [3] D. B. Rawat, D. C. Popescu, Y. Gongjun, and S. Olariu, "Enhancing

VANET Performance by Joint Adaptation of Transmission Power and Contention Window Size," In IEEE Transactions on Parallel and Distributed Systems, vol. 22, pp. 1528-1535, 2011.

[4] M. M. Artimy, W. Robertson, and W. J. Phillips, "Assignment of dynamic transmission range based on estimation of vehicle density," In ACM International Workshop on Vehicular Ad hoc Networks, 2005.

[5] C. Chunxiao and L. Jialiang, "A Delay-Bounded Dynamic Interactive Power Control Algorithm for VANETs," In IEEE International Conference on Communications, pp. 5849-5855, 2007.

[6] M. Torrent-Moreno, P. Santi, and H. Hartenstein, "Fair sharing of bandwidth in VANETs," In ACM International Workshop on Vehicular Ad hoc Networks, 2005.

[7] M. Torrent-Moreno, P. Santi, and H. Hartenstein, "Distributed Fair Transmit Power Adjustment for Vehicular Ad Hoc Networks," In Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks, pp. 479-488, 2006.

[8] M. Torrent-Moreno, J. Mittag, P. Santi, and H. Hartenstein, "Vehicle-to-Vehicle Communication: Fair Transmit Power Control for Safety-Critical Information," In IEEE Transactions on Vehicular Technology, vol. 58, pp. 3684-3703, 2009.

[9] J. Mittag, F. Schmidt-Eisenlohr, M. Killat, J. Harri, and H. Hartenstein, "Analysis and design of effective and low-overhead transmission power control for VANETs," In ACM International Workshop on Vehicular Inter-networking, pp. 39-48, 2008.

[10] B. M. Mughal, A. A. Wagan, and H. Hasbullah, "Efficient congestion control in VANET for safety messaging," In International Symposium in Information Technology), pp. 654-659, 2010.

[11] L. Hongsheng and C. Poellabauer, "Balancing broadcast reliability and transmission range in VANETs," In IEEE Vehicular Networking Conference, pp. 247-254, 2010.

[12] R. Stanica, E. Chaput, and A. L. Beylot, "Broadcast communication in Vehicular Ad-Hoc Network safety applications," In IEEE Consumer Communications and Networking Conference, pp. 462-466, 2011.

[13] R. Schmidt, T. Leinmuller, E. Schoch, F. Kargl, and G. Schafer, "Exploration of adaptive beaconing for efficient intervehicle safety communication," In IEEE Network, vol. 24, pp. 14-19, 2010.

[14] L. Long, R. Baldessari, P. Salvador, A. Festag, and Z. Wenhui, "Performance Evaluation of Beacon Congestion Control Algorithms for VANETs," In IEEE Global Telecommunications Conference, pp. 1-6., 2011.

[15] S. Djahel and Y. Ghamri-Doudane, "A robust congestion control scheme for fast and reliable dissemination of safety messages in VANETs," In IEEE Wireless Communications and Networking Conference, pp. 2264-2269 , 2012.

[16] H. Lv, X. Ye, L. An, and Y. Wang, "Distributed Beacon Frequency Control Algorithm for VANETs (DBFC)," In International Conference on Intelligent System Design and Engineering Application, pp. 243-246, 2012.

[17] R. Reinders, M. van Eenennaam, G. Karagiannis, and G. Heijenk, "Contention window analysis for beaconing in VANETs," In International Wireless Communications and Mobile Computing Conference, pp. 1481-1487, 2011.

[18] C. Sommer, O. K. Tonguz, and F. Dressler, "Adaptive beaconing for delay-sensitive and congestion-aware traffic information systems," In IEEE Vehicular Networking Conference, pp. 1-8, 2010.

[19] C. Droke, Moving Average simplified: Marketplace Books, 2001. [20] R. K. Schmidt, T. Köllmer, T. Leinmüller, B. Böddeker, and G. Schäfer,

"Degradation of Transmission Range in VANETs caused by Interference," In PIK- Praxis der Informationsverarbeitung und Kommunikation: Special issue on Mobile Ad Hoc networks, 2009.

[21] (January 20, 2013). OMNeT++ the open simulator. Available: http://www.omnetpp.org

[22] C. Sommer. (January 20, 2013). Veins - Vehicles in network simulation. Available: http://veins.car2x.org/.

[23] (January 21, 2013). SUMO - Simulation of Urban MObility. Available: http://sumo.sourceforge.net.

6