Preventing Unfairness in the ETSI Distributed ... - Semantic Scholar

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IEEE COMMUNICATIONS LETTERS, VOL. 18, NO. 7, JULY 2014

Preventing Unfairness in the ETSI Distributed Congestion Control Seungho Kuk and Hyogon Kim

Abstract—The European Telecommunications Standards Institute (ETSI) framework imposes requirements on the exchange of periodic safety messages between components of intelligent transportation systems. First and foremost among these is the fair allocation of, and access to, wireless channels. The decentralized congestion control (DCC) algorithm has been standardized by ETSI to this end. However, DCC has a serious fairness problem, i.e., the beacon throughput can diverge by a factor of 100 or more, even among vehicles that are close together. In this letter, we demonstrate how the fairness problem manifests itself and propose a remedy so that DCC satisfies the fairness requirement. Index Terms—V2V communication, congestion control, fairness, proximity awareness.

I. I NTRODUCTION

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ERIODIC safety messages exchanged among neighboring vehicles are the foundation of crash avoidance in future driving environments, as they enable proximity awareness [1]. Moving vehicles continually broadcast their position, heading, acceleration, steering angle, and vehicle size, among other data [2], so that neighboring vehicles may track and predict each others’ positions, thereby reducing the chance of a collision. In the European profile standard for intelligent transport systems (ITS), vehicle-to-vehicle (V2V) and vehicle-to-roadside (V2R) communications on the 5 GHz frequency band must conform to a set of protocols and parameters called ITS-G5 [3]. In particular, ITS stations such as on-board units on vehicles must use the European Telecommunication Standards Institute (ETSI) Distributed Congestion Control (DCC) algorithm [4] to cooperatively adapt their behavior in transmitting periodic safety messages such as Cooperative Awareness Messages [5]. The first and foremost operational requirement of the DCC component is to “provide fair allocation of resources and fair channel access among all ITS stations in the same communication zone” [4]. In this letter, however, we demonstrate that DCC can cause a serious fairness problem among ITS stations in the same communication zone. Specifically, vehicles in proximity that perceive similar channel loads can react in opposite ways under DCC. As a consequence, some vehicles end up with an unfairly low beaconing frequency and a smaller messaging range, which can cause poorer awareness among Manuscript received March 24, 2014; revised April 23, 2014; accepted April 23, 2014. Date of publication April 29, 2014; date of current version July 8, 2014. This work was supported by the Ministry of Education, Science and Technology of Korea through the Mid-career Researcher Program under NRF Grant 2011-0028892. The associate editor coordinating the review of this paper and approving it for publication was A. Vinel. S. Kuk is with the College of Information and Communication, Korea University, Seoul 136-701, Korea. H. Kim is with the Department of Computer Science and Engineering, Korea University, Seoul 136-701, Korea. Digital Object Identifier 10.1109/LCOMM.2014.2320893

their “neighbors” (vehicles in the same communication zone) and potentially a higher risk of accidents. We show that the addition of a simple check against the averaged congestion control state of neighbors prevents an ITS station from developing the unfairness problem. It was recently pointed out that the DCC algorithm has two few states to deal with congestion in a throughputefficient manner [6]. Studies have considered the power control fairness of a recent congestion control proposal [7] that is considered for IEEE WAVE systems [8]. Tielert et al. [9] showed that the fairness can be improved by sharing global knowledge of the channel busy ratio (CBR) values for a different congestion control algorithm, but not for the ETSI DCC algorithm. This letter is organized as follows. Section II first describes the DCC algorithm, and defines the metric that is used to evaluate its fairness. After the fairness problem is exposed, Section III discusses a solution approach based on simple intervehicle coordination whose effect is shown through simulation. Section IV concludes the letter with the lessons for the design of future versions of DCC. II. FAIRNESS P ROBLEM OF ETSI DCC A. Description of DCC The DCC algorithm can be implemented by a state machine with three states that regulate the transmit behavior: Relaxed, Active, and Restrictive (Fig. 1). These states assign different transmitter (Tx) power levels, messaging rates, receiver (Rx) sensitivities, and physical (PHY) layer data rates for individual ITS stations. In the state machine, the Relaxed→Active and Active→Restrictive transitions are triggered when the respective CBR condition holds for 1 s. Similarly, the opposite transitions are triggered when the required condition holds for 5 s [4]. The intention is that, when the channel is congested with periodic safety messages, the messaging rate and the range of communication are reduced. The latter effect is collectively achieved by a reduction in transmission power level and receiver sensitivity, and through a higher PHY rate. B. Definition of Fairness To measure fairness, we use the Jain index [10] over the number of beacon messages delivered per transmitting vehicle per second, or “beacon throughput. ” Suppose vehicles v1 and v2 are in the Restricted and Relaxed states, respectively. According to DCC, the beacon throughput for v1 is the number of beacons successfully delivered among x vehicles, and for v2 , among y vehicles, where x < y because of the different combinations of frequency, Tx power, Rx sensitivity, and PHY data rate.  Given the number  of beacons, we compute the Jain 2 index as ( ni=1 xi ) /(n · ni=1 x2i ). This rates the fairness of

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KUK AND KIM: PREVENTING UNFAIRNESS IN THE ETSI DISTRIBUTED CONGESTION CONTROL

Fig. 1.

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The DCC state machine with default parameters.

a throughput distribution when there are n vehicles and xi is the throughput for the ith vehicle. It ranges from 1/n (worst case) to 1 (best case) when all vehicles have the same throughput. C. Fairness Problem of DCC To show that the DCC algorithm can exhibit a serious fairness problem, we perform a simulation experiment. We use the Qualnet 4.5 simulator, in which we implement the DCC algorithm as specified by the standard, i.e., Fig. 1. The medium access control (MAC) and PHY layers follow the IEEE 802.11p standard [8], and the wireless channel is subject to the Nakagami-m fading, which is one of the models frequently used for vehicular communication channels. We set m = 1 in this letter, since the highway environment is measured to have a value of m between 0.5 and 1 [11]. Note that with m = 1, Nakagami-m is equivalent to the Rayleigh distribution, which is appropriate since most vehicles are not in the line-of-sight in congested areas. The path loss exponent is set to 2.0. To illustrate the fairness problem, we run 560 vehicles on a threelane circular track of radius 1 km, and apply an exponential inter-vehicle distance distribution with an average of 35 m. The vehicles exchange periodic safety messages of 250 bytes each (including the security certificate [12]) while they move along the circular track in the inner, middle, or outer lane at a speed of 23, 17, or 11 m/s, respectively. Fig. 2 shows the state evolution among vehicles in the 11 m/s lane. States are numbered “0” (Restrictive), “1” (Active), and “2” (Relaxed). As the DCC standard stipulates, vehicles start from the same (Active) state. Initially, many venture into Relaxed, but the vehicles soon diverge into three different states. Fig. 3 shows the vehicular traffic density in terms of the number of vehicles within a 300 m radius. The correlation coefficient between the density and the average state of the vehicles is only −0.15, showing that the state diversity in Fig. 2 is not completely explained by the given vehicle density. In Fig. 2, it is remarkable that even the vehicles in close proximity can diverge to two extreme states (i.e., Relaxed and Restricted, see arrows). Although not shown for legibility, this phenomenon persists over longer periods of time, and can be observed across the entire vehicle population. In the DCC standard, these two extreme states have a factor of 25 difference in the beaconing rate, with further differences created by Tx

Fig. 2. State evolution in DCC (subset of vehicles shown for legibility.

Fig. 3. Vehicular traffic density at each vehicle in the experiment.

power, Rx sensitivity, and the PHY data rate, leading to an even larger asymmetry. The problem is that the asymmetry is an artifact of the algorithm, not a natural consequence of the given traffic conditions. The implication of this unfairness is that some vehicles will have “weaker” presence among their neighbors, undermining the proximity awareness that is critical to preventing collisions while driving or hurting trustworthiness of the proximity information [13]. The state divergence phenomenon between neighbors is schematically explained in Fig. 4. Suppose the CBR fluctuates around an action threshold e.g., CBR value of 0.15 or 0.4 in DCC). Vehicle A observes that the CBR has exceeded the threshold, so decides to decrease the state (i.e., beaconing activity). Once this action pushes the CBR below the threshold, vehicle B adjusts its state. Since vehicle B sees that the CBR is below the threshold, it decides to venture into a higher state, possibly pushing the CBR above the threshold again. Fig. 5 takes an example of the problem described from the simulation described above. Initially, two vehicles (with IDs of 3 and 4) perceive a similar congestion level, being only 35 m

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IEEE COMMUNICATIONS LETTERS, VOL. 18, NO. 7, JULY 2014

Fig. 4. Undesirable feedback cycle between neighboring vehicles A and B.

Fig. 5. State divergence of neighboring vehicles.

apart. However, vehicle 3 first satisfies the condition to make the Active→Relaxed transition, i.e., CBR is less than 0.15 for 5 consecutive seconds (from t = 13 to 17). As a result of a nearby vehicle increasing its beaconing rate from 2 Hz to 25 Hz, however, vehicle 4 sees the CBR around its location exceed the 0.4 threshold at t = 18, thus changing its state to Restricted. This pattern is repeated, because there is no provision to prevent this in DCC. Although being closest neighbors, these vehicles receive very different beacon throughputs. This corroborates that the DCC algorithm can allow neighboring vehicles to diverge into different states, even though they start from similar congestion level observations. III. S OLUTION A PPROACH The solution to the problem discussed above is strikingly simple: add a check against the average of the neighbor states before committing to the state change. Below, we will denote this enhanced scheme by DCC. To compute the neighbor state average, we numerically designate the states as 0, 1, and 2, as shown in Fig. 1. Then, in the periodic safety message, each vehicle piggybacks its current state number. Next, by summarizing the states from the received messages, vehicles can determine the “average” state of their neighbors. Finally, vehicles check the average to see if the existing conditions for a

Fig. 6.

State evolution in DCC.

Fig. 7.

Fairness index for delivered beacons and CBR.

state transition have been met. Namely, for vehicle v in state s ¯ DCC dictates: whose neighbors have an average state of S, s−1 1) If ρv ≥ θs , execute s → max(s − 1, 0) only if s ≥ S¯ 2) If ρv ≤ θss+1 , execute s → min(s + 1, k) only if s ≤ S¯ where ρv is the CBR observed at v, θαβ denotes the threshold value used in the transition from state α to state β, and s values of 0, 1, and 2 represent Relaxed, Active, and Restrictive states, respectively. k is the numerical designation for the state having the highest beacon activity, and k = 2 in the current DCC standard. To understand how these two rules apply in vehicles’ decisions, we revisit Fig. 5 for an example. At t = 18, vehicle 4 sees the CBR exceed 0.4. Under DCC, the state would decrease from 1 to 0. However, in DCC, we additionally check whether the average state is less than 1, the current state of vehicle 4. If not, the state decrease does not happen based on rule (1), because other vehicles that contribute to the higher average state value (among others, vehicle 3) should take action to decrease their state first. The above two rules essentially work as a Minimum Variance Control, vehicle works to  since¯ each 2 . The effect is shown reduce s − S¯ in the variance s (s − S) in Fig. 6. For the same simulation configuration as in Fig. 2, DCC obtains a significantly more homogeneous state evolution. Above all, around those vehicles in the Relaxed state, no vehicles have been unfairly downgraded to the Restricted state. Fig. 7 compares the fairness index of DCC (marked “+check”) and DCC, with the same simulation configuration as used above. The traffic density D in vehicles/lane/km varies from 5 (not congested) to 130 (bumper-to-bumper). In the figure, “FI” is the Jain index. Both DCC and DCC achieve

KUK AND KIM: PREVENTING UNFAIRNESS IN THE ETSI DISTRIBUTED CONGESTION CONTROL

the target CBR values intended by the standard, i.e., 0.15–0.4, for all tested vehicular traffic densities. However, DCC achieves it at the cost of poor fairness unless most vehicles are pushed into the Active state at over D = 100 (not shown due to page limitations). In the worst case (D = 90), the ratio of the minimum and maximum beacon throughput between neighboring vehicles is nearly 120 i.e., 12 vs. 1438 beacons (not shown for brevity) under DCC, whereas it is less than 3 under DCC. The dip in the fairness index under DCC at around D = 25 is due to the non-negligible fraction of Relaxed vehicles, as the Relaxed state delivers a far greater number of beacons than the other two states. Starting from D = 50, however, DCC leaves few vehicles in the Relaxed state, and places most in the Active state. But under DCC, the Relaxed nodes are only completely eliminated once D = 100, when a significant number of Restricted vehicles also move to the Active state. Finally, DCC restricts the beacon transmit rate, meaning that the average beacon throughput when the fairness value is low is significantly lower than with DCC. IV. C ONCLUSION In this letter, we demonstrated that the ETSI DCC algorithm exhibits a fairness problem in the number of periodic safety messages received by neighbors. This unfair beaconing disturbs the proximity awareness of some vehicles, which can raise a driving safety issue in an environment that is heavily reliant on wireless beaconing. By adding a simple check against the neighbors’ average state, however, we showed that the unfairness problem can be significantly mitigated. We believe that a more fundamental lesson of our investigation is that safety is not only affected by the messaging intensity itself, but also its fairness. This must be factored into any messaging architecture design for future ITS environments. Finally, the fairness and stability aspects of the DCC algorithm should be further studied for systematic and robust improvements.

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ACKNOWLEDGMENT The authors thank Prof. T.-W. Yoon for his valuable comments on the control-theoretical aspects of DCC.

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