2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology
A Beacon Rate Control Scheme Based on Fuzzy Logic for Vehicular Ad-hoc Networks Ning Wang1, Guofeng Lei1, Xinhong Wang1,2, Ping Wang*1,2 and Fuqiang Liu1,2 1
2
Tongji University, Shanghai, P.R. China State Key Laboratory of Wireless Mobile Communications (CATT), Beijing, P.R. China *Corresponding author:
[email protected]
distributed way in vehicular communication environment. Secondly, the solution must be effective for the unfavorable characteristics of the high mobility of vehicles and the frequently changing of network topology. Thirdly, the protocols have to cope with various scenarios from sparse to dense areas, meanwhile meeting the low-delay and highreliability requirements of vehicle safety and traffic efficiency [1, 2]. Since DSRC uses the access mechanism of Carrier Sense Multiple Access with Collision Detection (CSMA/CA), it is easy to cause channel congestion when the density of vehicles on the road is large in vehicular communication environments. In this case, when emergency event happens, the emergency messages will be unable to issue due to the channel congestion. Thus, parts of the standard such as the access mechanism cannot guarantee the reliability of the vehicular connection since the safety requirement of VANETs. Studies have shown that undesirable congestion states at high density situation lead to severe deterioration of the performance of ITS applications [2]. Therefore, it is very necessary to solve this kind of congestion problem. In this paper, we proposed an efficient beacon rate control scheme based on fuzzy logic. Fuzzy logic is proved to be available to handle with imprecise and uncertain information in a control problem [3]. We use channel busy ratio (CBR), local density and mobility factor as the fuzzy factors to obtain the fuzzy result in order to control the beacon rate of vehicles. We built a realistic traffic scenario simulation via VISSIM traffic simulator which is a vehicle simulation software and realize 802.11p protocol via NS-3 network simulator. To achieve the reality of vehicle mobility, we obtained every vehicles realistic movement trace by VISSIM traffic simulator, converted them to NS-3 format and imported the trace files into NS-3 network simulator for our simulation. Simulation results clearly demonstrate the better performance of our proposed scheme based on fuzzy logic.
Abstract—According to IEEE 802.11p protocol, vehicles should periodically broadcast and exchange beacon messages to ensure the cooperative awareness of each other. However, high beacon generation rate may consume a large amount of channel bandwidth and thus lead to severe deterioration of network performance. To solve this problem, we propose an efficient beacon rate control scheme based on fuzzy logic in this paper. Fuzzy logic is proved to be available to handle with imprecise and uncertain information in control problems. Our scheme uses channel busy ratio (CBR), local density and mobility factor as fuzzy factors to obtain the fuzzy result in order to control the beacon rate of vehicles. The scheme is evaluated in our simulation platform, comprised of VISSIM traffic simulator for realistic traffic simulation and network simulator (NS)-3 network simulator for inter-vehicle communications. Simulation results clearly demonstrate the performance of our proposed scheme. Keywords-VANETs; Rate Control; Fuzzy Logic
I.
INTRODUCTION
As the number of vehicles increases rapidly, nowadays traffic accidents and congestions become severe global problems and thus draw concerns all over the world. The concept of vehicular ad hoc networks (VANETs) has been considered as an effective solution to handle this issue and attracted a large amount of research attentions. Dedicated Short-Range Communication (DSRC) technology has been accepted both by IEEE 802.11p standard and Europe European Telecommunication Standards Institute (ETSI) to support intelligent transportation system (ITS) applications in VANETs [1]. In both standards, two types of safety related messages should be sent frequently. One is eventdriven message which is driven by events and includes emergency information such as a car crash or an emergency brake. The other is periodical beacon message or called beacon message which is broadcasted periodically (every 100 ms according to IEEE 802.11p) to exchange vehicles status (speed, location, direction etc.) among neighbor vehicles in a certain range to realize cooperative awareness of environment and routing establishment. It is known that the IEEE 802.11p standard is extended from the protocols that are implemented in the wireless local area network (WLAN). However, vehicular communication environment presents its own features that are different from those of the traditional communication. Firstly, limited bandwidth and resources have to be coordinated in a
978-1-4799-7910-3/14 $31.00 © 2014 IEEE DOI 10.1109/ICAIET.2014.54
II.
RELATED WORKS
Existing congestion control approaches are divided into two parts: power control and rate control. Power control adjusts the message transmission power of vehicle nodes so as to control communication coverage, which reduces the number of vehicles occupying the channel. Rate control is to reduce the channel occupying ratio by controlling vehicles beacon message generation rate [4]. Reference [5] proposed
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the DFPAV power control method, through control of transmit power for each car, ensure the load threshold channel maxmin fairness does not exceed the system. Reference [6] used message distribution aggregation mechanism (information aggregation mechanism) reduced the [5] overhead. Other techniques [7, 8] aiming at preventing congestion via power control, as well as D-FPTV, may cause isolation of vehicles when the vehicle density decreases. So we focus on designing a beacon rate adaptation scheme to guarantee the efficiency and reliability of safety-related messages. The protocol introduced in [9] controls vehicles Tx power and beacon generation rate until a defined CBR threshold is reached. All information used for control is measured by the node itself and without information sharing. As a result, some nodes may contribute to congestion at a location without being aware of it, since their own location is not congested. And a fixed threshold may not satisfy the requirement of a high dynamic vehicular network. M. Drigo et al. introduced a dynamic beacon rate adaptation algorithm [10] in which each node locally adjusts its beacon rate according to the vehicle density estimated by counting the number of received beacons from neighbors within its communication range. However, simple statistical quantity of the receiving beacon messages (from vehicles in receivers communication range) is not accurate to estimate the vehicle density due to the congestion happens in the sensing range. Tielert, T. et al. presented a rate control scheme named PULSAR in [11], which can greatly improve the performance in the item of channel load limitation over the previous scheme. The intricate feature of CBR is addressed to assess the channel load and the transmit rates are selected based on the load condition within a maximum rate and a minimum rate. The global fairness is contained in this optimization but the authors did not take the vehicles mobility into account III.
addition, fuzzy rules are defined to conduct the final fuzzy value. By defuzzification, the final fuzzy value is converted to a numerical value. Since fuzzy membership functions and fuzzy rules can be modified to satisfy a specific environment, fuzzy logic based system is flexible [3]. 1) Factors Calculation: Calculation of multiple factors: Calculate CBR, local density and mobility factor. In this paper, we use CBR, local density and mobility factor as fuzzy factors to obtain the fuzzy result. It is known that CBR can present the channel busy situation of the network. And the local density can determine the numbers of beacon messages in a certain range. Besides, if a vehicle moves fast, it is necessary for it to broadcast more frequently to meet the safety requirement. That is to say, mobility factor is also an impact factor for rate control issue. Therefore, we need to control the beacon rate of vehicles according to these three factors. CBR: The factor CBR is defined as percentage of the fraction time in which the channel is not sensed idle in the given Observing Interval (OI) due to the sensing vehicles or its neighbors activity. And CBR is a good indicator for the collision probability and the MAC layer configuration. In IEEE 802.11p based VANETs, the node uses the method of Clear Channel Assessment (CCA) to sense whether the channel is busy. We define the channel is busy when the integration of the measured signal strength is greater than the CCA threshold during a given time window. In order to measure the ratio of the channel busy time, we divide the OI into a set of mini-slots, and the channel status will be measured in every mini-slot via CCA. The CBR would be calculated by the following equation ( cbk is 1 for the busy mini-slot and 0 otherwise): ¦ k i cbk , N T / T . (1) CBR OI min islot N Note that the fair use of the CBR to estimate the channel load condition offers practical advantages since the CCA method is an existing method in present VANETs protocol and it is convenient for confirming with good compatibility. The CBR parameter only helps to identify the load in current OI. The beacon rate must be adapted additionally with different levels of vehicle density. So the local density is introduced to hint the potentiality of message transmission and predict the channel load in the next coming OI. Local Density: Most studies assume that vehicle density is calculated through counting the number of the received beacons. However, the beacon-based density estimation is not accurate due to the ad-hoc nature of vehicular networks and the collision of packets especially in congestion-related situations. Accurate estimation mechanism is demanded to choose optimal rate parameter. Reference [12] derived an enhanced method based on the traffic flow theory for estimating local density. For a given vehicle, the resultant local density K can be approximated by (1 f s )K 1 K ( 1)1 , (2)
BEACON RATE CONTROL SCHEME BASED ON FUZZY LOGIC
In this section, the key criteria and steps of our proposed scheme are presented in detail as follows A. Fuzzy Logic As described in the Introduction, CSMA/CA based VANET is a highly dynamic ad hoc network in which the communication environment is affected by the mobility and density of nodes and channel states as well. It is hard to design a fixed or unified metrics (like using fixed CBR threshold in [9]) to control all the nodes accurately. Thus, in the research area of rate control, solutions based on uncertain information are not reliable in practice. Fuzzy logic is proved to be available to handle with imprecise and uncertain information in a control problem. Thus, we use fuzzy logic to design a beacon rate control scheme for VANETs. Fuzzy logic can process approximate data by using nonnumeric linguistic variables to express the facts. Fuzzy membership functions are used to represent the degrees of a numerical value belonging to linguistic variables. In
O
where K is a parameter that indicates the quality of service in the transportation network and O is a normalized 287
measure for the sensitivity of vehicle interaction. For example, the two parameters K | 0 and O | 0.027 in
highway scenarios [12]. The parameter f s >0,1@ relates to
the velocity of the vehicle and traffic flow. f s increases when traffic changes from free flow to congested-flow. Mobility Factor: After an investigation of previous works, as we know that mobility of vehicles in VANETs has not been taken into account in beacon rate control schemes. Let’s see a situation shown in Fig. 1 which is a snapshot of the traffic flow assuming at time t2 , the red vehicle A drawn in dash line is displayed for comparison purpose. It represents the position of the red one in full line at an earlier time t1 . The same applies to the green pair. d1 and d 2 signify respectively the distance that vehicle A and vehicle B have traveled over the same time period t2 t1 . The average speed of A is higher than that of B. Hence, vehicle A has travelled farther. Supposing that vehicle A and B are allowed to have the same beacon rate, A may suffer from a lower accuracy as A is not able to inform its knowledge timely to vehicles such as C, and that might even lead to undesirable grave consequences. Thereby we believe that the mobility of the current vehicle strongly affects the accuracy. Vehicles like A should increase the beacon rate with regarding to required accuracy, when they are at high speed, or at high relative mobility [13]. The node calculates a mobility factor (MF) as (3) and (4). MF indicates the average mobility level of all neighbour nodes. di (X) is the distance between the current node and the node X at time i . R is the maximum communication range and NB is number of neighbours. d (X) di 1 (X) M (X) 1 i , (3) R 1 (4) MF ¦ M (X). NB NB
Figure 2. CBR membership function.
Figure 3. Local density membership function.
Figure 4. Mobility factor membership function.
Figure 1. Different relative speeds in a realistic situation.
2) Fuzzification: The process of converting a numerical value to a fuzzy value using a fuzzy membership function which is called fuzzification. The fuzzy membership functions of CBR factor, local density factor and mobility factor are defined in Fig. 2 to Fig. 4. A node uses the CBR membership function to calculate what degree the CBR factor belongs to High, Medium, Low. Similarly, the sender node also calculates what degree the local density factor belongs to Large, Medium, Small and what degree the mobility factor belongs to Fast, Medium, Slow.
3) Rule Base: Based on the fuzzy values of CBR factor, mobility factor and local density factor, a node uses the IF/THEN rules (as defined in Table 1) to calculate the rank of the channel congestion state. The linguistic variables of the rank are defined as Very Low, Low, Medium, High, Very High. For there may be multiple rules applying at the same time, as shown in [14], we use the Min-Max method to combine their evaluation results. In the Min-Max method, for each rule, the minimal value of the antecedent is used as the final degree. When combining different rules, the maximal value of the consequents is used. 288
TABLE I.
RULE BASE
Rule 1
CBR Low
Local Density Small
Mobility Fast
Rank Very High
Rule 2
Low
Small
Medium
Very High
Rule 3
Low
Small
Slow
High
Rule 4
Low
Medium
Fast
Very High
Rule 5
Low
Medium
Medium
High
Rule 6
Low
Medium
Slow
High
Rule 7
Low
Large
Fast
High
Rule 8
Low
Large
Medium
Medium Medium
Rule 9
Low
Laege
Slow
Rule 10
Medium
Small
Fast
High
Rule 11
Medium
Small
Medium
Medium
Rule 12
Medium
Small
Slow
Medium
Rule 13
Medium
Medium
Fast
Medium
Rule 14
Medium
Medium
Medium
Medium
Rule 15
Medium
Medium
Slow
Low
Rule 16
Medium
Large
Fast
Medium
Rule 17
Medium
Large
Medium
Low
Rule 18
Medium
Large
Slow
Low
Rule 19
High
Small
Fast
Medium
Rule 20
High
Small
Medium
Low
Rule 21
High
Small
Slow
Low
Rule 22
High
Medium
Fast
Low
Rule 23
High
Medium
Medium
Very Low
Rule 24
High
Medium
Slow
Very Low
Rule 25
High
Large
Fast
Very Low
Rule 26
High
Large
Fast
Very Low
Rule 27
High
Large
Slow
Very Low
Figure 5. Output membership function.
where J is the threshold of decision. Through simulation, when J is 0.6, the control scheme performs best. IV.
SIMULATION AND RESULTS
In our simulation, we evaluate the performance of the proposed beacon rate control scheme in various highway scenarios with different parameters. A. Simulation Tools and Setup We built a realistic traffic scenario simulation via VISSIM traffic simulator and realize 802.11p protocol via NS-3 network simulator. We obtained every vehicles realistic movement trace by VISSIM traffic simulator, converted them to NS-3 format and imported the trace files into NS-3 network simulator in our simulation. We simulated typical schemes with fixed beacon rate (rate = 33, 10, 3.3 packets/s) for comparison. For each scenario, we performed simulation runs more than 10 times, and each run is about 60s. The parameters for simulation are listed in Table II. We rely on the VISSIM traffic simulator to obtain realistic traffic flows due to the imaginable variety of road users. Fig. 6 shows four samples of various scenarios from sparse to dense in our simulation. We evaluate vehicular scenarios in a 6km bidirectional highway with six lanes. We only collect and analyze the middle 4km segments in order to avoid fringe effect. Vehicles are mixed of kinds of types containing cars, buses, and trucks. They move with varying max speed, between 60 km/h to 120 km/h. The configuration results different conditions of fuzzy factors to provide reasonable representation of the real world. For each scenario, we performed simulation runs more than 10 runs, and each run lasts about 60s.Our proposed scheme which presents in the previous section is implemented in NS-3 network simulator instead of NS-2. Most of the existing literatures consider NS-2 as the simulation tool. But the new network generation simulator NS-3 could provide more detailed IEEE 802.11p specifications, more accurate interferences and channel models [15]. And a more appropriate radio propagation model named Nakagami radio propagation has been adopted. The Nakagami fading model has been performed in real-world tests on highways. It
4) Defuzzification: Defuzzification is the process of producing a numeric result based on an output membership function and corresponding membership degrees. The output membership function is defined in Fig. 5. Here we use the Center of Gravity (COG) method to defuzzify the fuzzy result and as follows: ¦ AllRules xi u E ( xi ) , (5) D ¦ AllRules E ( xi ) where R is the degree of decision making, xi is the fuzzy factor and E ( xi ) is its membership function. Based on this defuzzification method, the output of the beacon rate is changed to the crisp value. B. Rate Control Mechanism After estimate the congestion state by fuzzy logic, we use additive increase multiplicative decrease (AIMD) scheme to control beacon delivering rate. We initialize the beacon rate (BR) at 10 packets per second, and at each OI, beacon rate is changed as followed: BR / 2 0 R J BR ® , (6) ¯ BR 1 R t J
289
suggests that it is a suitable model for vehicular scenarios when estimating the mobile communication channels [16]. TABLE II.
rises drastically with the vehicle density. Continuous fixed beacon generation rate, especially in dense areas, results in an increasing number of collisions among the broadcast beacons and the worse deterioration of packet reception ratio. In contrast, our scheme still achieves good beacon reception ratio in the worst scenario. Our scheme turns out to have an advantage when meeting the reliability requirement for periodical beacon broadcasting.
RELATED SIMULATION PARAMETERS
Parameter
Value
Communication technology
IEEE802.11p
Central frequency
5.890 [GHz]
Channel bandwidth
10 [Hz]
Modulation scheme
OFDM
Propagation model
Nakagami
Data rate
6 [Mbps]
Beacon size
800 [bytes]
Number of Lanes
3*direction
Vehicle density (normalized)
0.1 to 0.9
Vehicle speed
60 to 120 [km/h]
Figure 7. Beacon successfully access ratio in various scenarios.
Figure 6. Various scenarios representing different time slots of one day in the same segment in our simulation.
B. Performance Analysis We use two metrics to analyze the performance of our rate control scheme. The first metric is the beacon successfully access ratio which means the average percentage of the successfully channel accessed beacon packets among all generated beacons. As shown in Fig. 7, the successfully access ratios initially are similar since the low vehicle density. With the increase of vehicle density, in our scheme vehicles start to adjust their beacon rates in various scenarios. The ratio curve of the adaptive beacon rate scheme approximates the curve of the 3.3 packets/s fixed scheme. That means we gain high successful access ratio meanwhile the sufficient efficiency to satisfy the requirement of vehicle communication. The second metric is the beacon reception ratio that means the ratio of the number of the vehicles that have successfully received the beacons among all the vehicles in the senders intended communication range (300f2.5m) and reflects the reliability of broadcasted beacons. Fig.8 shows that the reception radio decreases when the vehicle density increases. Without rate control, the packet collision ratio
Figure 8. Beacon reception ratio in various scenarios.
V.
CONCLUSION
In this paper, we proposed an effective beacon rate control scheme based on fuzzy logic to avoid congestion in IEEE 802.11p protocol. In our scheme, we analyzed and used fuzzy factors of CBR, local density factor and mobility factor to get the fuzzy result to adjust the beacon rate. Since VISSIM traffic simulator can grantee the reality of vehicle mobility and NS-3 network simulator can provide more detailed IEEE 802.11p specifications, more accurate interference and channel model, we built a realistic traffic scenario simulation via VISSIM network simulator and realize IEEE 802.11p protocol via NS-3 network simulator instead of NS-2. Simulation results show that our scheme performs well compared with the default scenarios in IEEE
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802.11p protocol. We will do some further evaluation and improvement of the proposed approach in our future work. [8]
ACKNOWLEDGMENT This work was supported by a grant from the National Natural Science Foundation of China (No.61103179) and Decentralized Congestion Control Design and 802.11p Simulation (DTinno.12.114). The corresponding author is Ping Wang.
[9]
[10]
REFERENCES [1]
[2]
[3] [4]
[5]
[6]
[7]
D. Jiang and L. Delgrossi, “IEEE 802.11 p: Towards an international standard for wireless access in vehicular environments,” in Vehicular Technology Conference, 2008. IEEE, pp. 2036–2040, 2008. M. J. Booysen, S. Zeadally, and G.-J. Van Rooyen, “Survey of media access control protocols for vehicular ad hoc networks,” IET communications, vol. 5, no. 11, pp. 1619–1631, 2011. O. LEBEDEVA, “Fuzzy set theory: foundations and applications,” in 7th LATVIAN MATHEMATICAL CONFERENCE, p. 29, 2008. M. Sepulcre, J. Mittag, P. Santi, H. Hartenstein, and J. Gozalvez, “Congestion and awareness control in cooperative vehicular systems,” Proceedings of the IEEE, vol. 99, no. 7, pp. 1260–1279, 2011. M. Torrent-Moreno, J. Mittag, P. Santi, and H. Hartenstein, “Vehicle-to-vehicle communication: fair transmit power control for safety-critical information,” Vehicular Technology, IEEE Transactions on, vol. 58, no. 7, pp. 3684–3703, 2009. J. Mittag, F. Schmidt-Eisenlohr, M. Killat, J. H ¨ arri, and H. Hartenstein, “Analysis and design of effective and low-overhead transmission power control for vanets,” in Proceedings of the fifth ACM international workshop on VehiculAr Inter-NETworking. ACM, pp. 39– 48, 2008. D. B. Rawat, D. C. Popescu, G. Yan, and S. Olariu, “Enhancing vanet performance by joint adaptation of transmission power and
[11]
[12]
[13]
[14]
[15]
[16]
291
contention window size,” Parallel and Distributed Systems, IEEE Transactions on, vol. 22, no. 9, pp. 1528–1535, 2011. J. Park, Z. Chen, L. Kiliaris, M. L. Kuang, M. A. Masrur, A. M. Phillips, and Y. L. Murphey, “Intelligent vehicle power control based on machine learning of optimal control parameters and prediction of road type and traffic congestion,” Vehicular Technology, IEEE Transactions on, vol. 58, no. 9, pp. 4741–4756, 2009. C.-L. Huang, Y. P. Fallah, R. Sengupta, and H. Krishnan, “Adaptive intervehicle communication control for cooperative safety systems,” Network, IEEE, vol. 24, no. 1, pp. 6–13, 2010. M. Drigo, W. Zhang, R. Baldessari, L. Le, A. Festag, and M. Zorzi, “Distributed rate control algorithm for vanets (drcv),” in Proceedings of the sixth ACM international workshop on VehiculAr InterNETworking. ACM, pp. 119–120, 2009. T. Tielert, D. Jiang, Q. Chen, L. Delgrossi, and H. Hartenstein, “Design methodology and evaluation of rate adaptation based congestion control for vehicle safety communications,” in Vehicular Networking Conference (VNC), IEEE, pp. 116–123, 2011. M. Artimy, “Local density estimation and dynamic transmissionrange assignment in vehicular ad hoc networks,” Intelligent Transportation Systems, IEEE Transactions on, vol. 8, no. 3, pp. 400–412, 2007. L. Liu, X. Wang, C. Wang, and F. Liu, “An efficient beacon rate adaptation scheme for vehicular networks,” in Wireless Communications Signal Processing (WCSP), 2013 International Conference on. IEEE, pp. 1–6, Oct 2013. W. Celimuge and S. Ohzahata, “Vanet broadcast protocol based on fuzzy logic and lightweight retransmission mechanism,” IEICE transactions on communications, vol. 95, no. 2, pp. 415–425, 2012. J. Benin, M. Nowatkowski, and H. Owen, “Vehicular network simulation propagation loss model parameter standardization in ns-3 and beyond,” in Southeastcon, 2012 Proceedings of IEEE, pp. 1–5, 2012. I. Sen and D. W. Matolak, “Vehicle–vehicle channel models for the 5-ghz band,” Intelligent Transportation Systems, IEEE Transactions on, vol. 9, no. 2, pp. 235– 245, 2008.
