Dynamically Adjusting the Min-Max Contention Window for Providing Quality of

Service in Vehicular Networks

Chrysostomos Chrysostomou

Department of Computer Science and Engineering

Frederick University

Nicosia, Cyprus

ch.chrysostomou@frederick.ac.cy

Constantinos Djouvas, Lambros Lambrinos

Department of Communication and Internet Studies

Cyprus University of Technology

Limassol, Cyprus

{costas.tziouvas, lambros.lambrinos}@cut.ac.cy

Abstract—This paper proposes a novel, intelligent medium

access control mechanism for vehicular ad hoc networks. In

particular, the minimum and maximum contention window

parameters are dynamically tuned based on network

observations, concerning all applications’ access categories.

The simple, generic, and efficient nonlinear control law built, is

based on fuzzy logic control and although we focus on vehicle-

to-infrastructure (V2I) communication, the proposed scheme

can be easily adapted for vehicle-to-vehicle (V2V)

communication. We demonstrate, via simulative evaluation of

dense and dynamic conditions, that the proposed scheme offers

enhanced differentiation among different applications’ access

categories; therefore, offering sufficient Quality of Service

(QoS) in terms of throughput performance, in contrast with

the IEEE 802.11p standard MAC protocol we compared

against.

Keywords-vehicular ad hoc networks; medium access

control; contention window; fuzzy logic control

I. INTRODUCTION

Vehicular ad hoc networks (VANETs) characterize a principally demanding class of mobile (ad hoc) networks that enable vehicles to communicate with each other (vehicle-to-vehicle (V2V) communication) and/or with roadside infrastructure (vehicle-to-infrastructure (V2I) communication). Traffic safety, traffic efficiency and management, as well as infotainment are prime examples of the plethora of applications whose development and deployment is based upon the exploitation of VANETs.

Due to the diversity in requirements between these types of applications,, the perceived Quality of Service (QoS) varies; a timing failure might have no effect (non-real-time), might compromise service quality (soft real-time) or might lead to a disaster (hard real-time). For this, the Wireless Access for the Vehicular Environment (WAVE) standards have been proposed [1-4].

The IEEE 802.11 standard body has processed to a new amendment, notably, the IEEE 802.11p [5, 8]. The allocation of the Dedicated Short Range Communications (DSRC) spectrum band [6, 10] to be used exclusively for V2V and V2I communications was followed by the IEEE 802.11p WAVE standardization process [9].

The IEEE 802.11p is based on the carrier sense multiple access with collision avoidance (CSMA/CA) MAC protocol. It uses the enhanced distributed channel access (EDCA) mechanism originally provided by IEEE 802.11e [11] that

differentiates traffic types based upon different static MAC parameters values.

The performance of IEEE 802.11p has been a centric point of research concerning VANETs. The capabilities and the limitations of the technology were the subject of research studies [12- 15], whereas relevant enhancements have been proposed [16-18]. However, up to date, to the best of our knowledge, no attempt has been made to dynamically tune the minimum and maximum contention window parameters, to avoid having predefined static/fixed values.

In this paper, we propose a novel, adaptive medium access control mechanism, in which the minimum and maximum contention window parameters are dynamically tuned based on network measurements, concerning all applications’ access categories. Using a linguistic model of the system under control, a simple, effective, and efficient nonlinear control law is built, which can easily be adopted in different network environments (V2V and V2I).

Focusing on V2I simulative environment, we show that the proposed mechanism offers significant improvements over the IEEE 802.11p standard MAC protocol in controlling access to the medium, under dense and dynamic conditions. Specifically, it provides acceptable QoS, with respect to throughput performance, and effective differentiation among differently prioritized traffic types.

The paper is organized as follows. Section II gives a brief outline of the WAVE system architecture, and the IEEE 802.11 MAC protocol. In Section III, the related work is presented, and in Section IV, we briefly introduce the characteristics of fuzzy logic control and its application in network control problems. In Section V, we describe how the proposed fuzzy logic based MAC mechanism can be applied in VANETs. Then, Section VI discusses the simulative evaluation of the proposed scheme compared with the IEEE 802.11p standard protocol and the paper ends with our conclusions (Section VII).

II. WAVE SYSTEM ARCHITECTURE

The IEEE has developed the WAVE system architecture [1-4] to provide wireless access in vehicular environments; the IEEE 802.11p [5] and IEEE 1609.x [1-4] standards are the building blocks of this architecture as their goal, as a whole, is to aid the provision of wireless access in vehicular environments [9].

A WAVE system consists of two main entities:

2012 The 11th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net)

978-1-4673-2039-9/12/$31.00 ©2012 IEEE 16

Figure 1. Generic system architecture of the MAC [4]

TABLE I. DEFAULT EDCA PARAMETER SET USED ON THE CCH [4]

AC

Ind

ex

AC CWmin

CWmax

AIF

SN

1 Background aCWmin aCWmax 9

0 Best effort (aCWmin + 1)/2 -1 aCWmin 6

2 Video (aCWmin + 1)/4-1 (aCWmin + 1)/2-1 3

3 Voice (aCWmin + 1)/4-1 (aCWmin + 1)/2-1 2

TABLE II. DEFAULT EDCA PARAMETER SET USED ON THE SCH [4]

AC

Ind

ex

AC CWmin

CWmax

AIF

SN

1 Background aCWmin aCWmax 7

0 Best effort aCWmin aCWmax 3

2 Video (aCWmin + 1)/2-1 aCWmin 2

3 Voice (aCWmin + 1)/4-1 (aCWmin + 1)/2-1 2

• Roadside units (RSUs) include equipment located along highways, at traffic intersections and at other locations where timely communication with vehicles is needed [7].

• Onboard units (OBUs) are processing and communication facilities located inside a vehicle, providing an application runtime environment, positioning, security and communication functions along with human machine interfaces and interfaces with other vehicles [7].

WAVE units operate independently exchanging information over the control channel (CCH) which is a fixed radio channel. Additionally, they may also organize themselves in small networks called WAVE basic service sets (WBSSs) which can consist of OBUs only or a mix of OBUs and RSUs. All the members of a particular WBSS (the provider that initiates the communication and the possible users) exchange information through one of several radio channels known as service channels (SCHs) [9]. Generally, the CCH is reserved for system control and safety messages, whereas the SCHs are used to exchange non-safety data.

The IEEE 802.11p is based on the carrier sense multiple access with collision avoidance (CSMA/CA) MAC protocol. It uses the enhanced distributed channel access (EDCA) mechanism originally provided by IEEE 802.11e [11] to distinguish between different traffic types through different values for the static MAC parameters.

Four applications’ access categories (ACs) are defined in the WAVE standards. The differentiation in priority between ACs for channel access parameters is implemented using the appropriate EDCA parameter set values (see Table I and Table II), which are defined as follows [4]:

• Arbitration inter-frame space (AIFS): the minimum time interval between the wireless medium becoming idle and the start of the next frame transmission.

• Contention window (CW): An interval out of which a random number is drawn to implement the random back-off mechanism.

• aCWmin and aCWmax, which are static values (15 and 1023, respectively), as specified in [5].

Note, that the back-off mechanism is used in case the medium is sensed busy, and a back-off time is chosen uniformly at random from the interval [0, CW+1], where the initial CW is equal to the CWmin. The interval size gets doubled, until it’s equal to CWmax, if the subsequent transmission attempt fails/collides. However, the minimum and maximum values of the contention window for each AC remain static/fixed, irrespective of the dynamic network conditions.

Figure 1 presents a generic system architecture of the MAC [4] and indicates that the internal contention algorithm calculates the back-off independently for each AC based on access parameters. The AC with the smallest back-off wins the internal contention; the winning AC then contends externally for the wireless medium [4].

III. RELATED WORK

A number of enhancements on the IEEE 802.11p MAC protocol have been proposed (e.g. [16-18]); a detailed description and review can be found in [19]. Up to date, to the best of our knowledge, no attempt has been made to dynamically tune the minimum and maximum contention window parameters, to avoid having predefined static/fixed values, in a VANET environment.

For example, in [16] two schemes are presented that adapt the contention window, based on the number of transmitting vehicles. The first proposal is a centralized approach; it assumes prior knowledge on the number of transmitting vehicles something which is difficult to obtain due to the variability and high dynamics of the environment. The second proposal is a distributed approach: vehicles use local channel information to adapt the contention window size. A linear updating method is used to change the back-off window size based on channel busy measurements; the high dynamics of the environment imply that such a linear updating method is not robust enough.

17

The evaluation of these has taken into consideration only one application access category for each scenario, ignoring the mixture of prioritized traffic types expected to be present.

A decentralized self-organizing time division multiple access (STDMA) scheme is presented in [17]. Although this scheme has its merits, the arguably limited capabilities of TDMA MAC schemes will be a serious impediment in deploying a wide range of applications in VANETs. Furthermore, the provision of effective differentiation is not examined as in the evaluation of the proposed scheme all data packets have the same priority.

In [18], a fuzzy logic based enhanced 802.11p mechanism is proposed which applies a non-linear control law to adapt the back-off/contention window parameter for each transmitting node; using the channel traffic occupancy, in combination with the applications’ access categories, it aims to be adaptive to the high environment dynamics. Through simulative evaluation, in V2V and V2I, this technique has achieved adequate QoS provision in terms of throughput performance, by providing effective differentiation among different applications’ access categories.

However, none of these techniques have attempted to adjust dynamically the minimum and maximum contention window parameters; rather these parameters are kept fixed irrespective of the dynamic network conditions.

IV. FUZZY LOGIC AND ITS APPLICATION IN NETWORK

CONTROL

Fuzzy logic is a logical system and is part of Computational Intelligence (CI); it is an extension and generalization of multivalued logic systems [20]. FLC [21] is commonly encountered in product design, manufacturing and control due to its capacity to control highly non-linear, complex systems. The idea of FLC was initially introduced by Zadeh [22] and first applied by Mamdani [23] in an attempt to control systems that are difficult to model mathematically and hence design controllers.. FLC is capable of qualitatively capturing the attributes of a control system based on observable phenomena; if it is designed with a good (intuitive) understanding of the system to be controlled, the limitations due to the complexity the system’s parameters introduce on a mathematical model can be avoided.

Thus, there is a lot of interest from the academic research community (e.g. [24]) to apply fuzzy logic, due to its successful deployment in the field, controlling complex, difficult to control systems (e.g. [25]).

Since early 90’s, a number of research papers were published that investigated the use of fuzzy logic to provide solutions for congestion control issues in networking, especially in Asynchronous Transfer Mode (ATM) networks. For example, in [24], [26], [27] and [28], researchers have successfully used the concept of FLC for congestion control in ATM, as an alternative to conventional counterparts. FLC was also used in the IP world (e.g. [29] and [30]). Recently, fuzzy logic has been successfully used in MAC protocol in VANETs [18].

V. FUZZY LOGIC-BASED MAC FRAMEWORK IN VANETS

A. Motivation - Contribution

In [18], we have successfully proposed a fuzzy logic based MAC scheme in VANETs, where the contention window parameter is adapted, based on the channel traffic occupancy. Thus, for example, the output of the fuzzy logic controller gives a very large value for the contention window in case of very high density. This is in contrast with what the standard suggests; that is, to just have this parameter doubled when subsequent transmissions fail/collide, without taking into account the density of the medium. Note that as the range of values for the AC2 and AC3 are much smaller than the ranges of the AC0 and AC1, the scheme proposed in [18] prioritizes the higher ACs (AC2 and AC3), over the lower (AC0 and AC1); this effectively differentiates traffic types that belong to different applications’ access categories. Morevoer, an identical fuzzy controller is used for each AC and only the range of values of the output is changed according to the standard (see Table I and Table II).

Note that the range of values of the controller’s output in [18] lies between CWmin and CWmax, as defined in Table I and Table II. However, we have kept the CWmin and CWmax EDCA parameters fixed (as specified by the standard), irrespective of the dynamic network conditions. Even though the simulative evaluation in [18] shows significant improvements over the standard MAC protocol, we further need to examine the adoption of such methodology [18] in adjusting the potential value range of the contention window for all applications’ access categories.

Thus, the proposed fuzzy logic based MAC framework is designed to maintain the adaptation of the back-off/contention window parameter value originally proposed in [18], but at the same time to dynamically adjust the minimum and maximum contention window parameter values; this allows it to react better to the dynamic network conditions.

To the best of our knowledge, fuzzy logic is yet to be considered in the development of such MAC framework in VANETs.

B. Proposed Fuzzy Logic-Based MAC Scheme (FLM)

We present a novel, adaptive MAC scheme that improves the IEEE 802.11p standard MAC protocol, operating under VANET environment. The aim is to offer adequate QoS for the wireless access in vehicular environment, by taking into account the differentiation among different traffic types that are categorized into different priorities with different EDCA MAC parameters values.

The proposed MAC protocol dynamically adjusts the CWmin and CWmax EDCA parameters (see Table I and Table II), based on network measurements. In the design of our non-linear control law we use fuzzy logic control and aim to offer inherent robustness with effective control of the system through simple and efficient operations.

Due to the high mobility and the resulting highly dynamic network environment, the adaptive medium access control mechanism needs to operate in a decentralized and self-organized way. Operating locally at each transmitting

18

node of the VANET, the new scheme uses the channel traffic occupancy in conjunction with the access categories the applications belong to in order to adapt the minimum and maximum contention window parameter values for that node; this technique allows the system to adapt to the high environment dynamics.

The perspective achievement is the provision of QoS in terms of throughput performance, by differentiating between the traffic types that belong to different applications’ access categories. It is expected that the new scheme will grant priority/preference to the higher ACs (AC2 and AC3), over the lower ones (AC0 and AC1), especially during highly dense condition periods.

The system model of the proposed fuzzy logic based MAC mechanism (FLM) is shown in Fig. 2, where all quantities are considered at the discrete instant knT:

• nT is the sampling period.

• CTO(knT) is the channel traffic occupancy, measured throughout the current sampling period, by recording the amount of time a channel is busy. The amount of busy time within each sampling period is measured by monitoring the status (idle/busy) of the physical layer channel.

• CTO(knT-nT) is the channel traffic occupancy, measured at the previous sampling period.

• Factor(knT) is the determined amount of change in CWmin and CWmax EDCA parameters.

• SGi1,2(knT) are the input scaling gains. A fuzzy inference engine (FIE) is designed to operate

locally at each VANET node in order to control the wireless access by means of linguistic rules that describe the behavior of the environment under widely differing operating conditions. As shown in Fig. 2, the FIE dynamically calculates the amount of change of CWmin and CWmax EDCA parameters, based on two network state inputs: the channel traffic occupancy for two consecutive sampling periods (can be interpreted as a prediction horizon).

Note that we have used the same inputs for the fuzzy logic controller as the ones used in [18], so as to have a uniform fuzzy logic based MAC framework that takes into account the density of the medium in order to adapt both the minimum and maximum contention window parameters, and the contention window parameter itself.

Regarding the sampling period, the adaptation of the contention window value, as proposed in [18], is performed

every kT, whereas the dynamic adjustment of CWmin and CWmax EDCA parameters is performed every n * kT, in order to avoid having too many frequent changes of the EDCA parameters that may result in oscillatory behavior. The sensitivity of the correct selection of the n value on the behavior of the FLM is kept for future work/investigation.

In fuzzy control theory, the range of values for the inputs or output of a specific controller is usually called the “universe of discourse”. To allow greater flexibility in fuzzy controller implementation, the universe of discourse for each process input is often “normalized” by means of constant scaling factors [21]. In the design of our fuzzy controller, the input scaling gains, SGi1,2(knT) are chosen so that the range of values of SGi1(knT)CTO(knT) and SGi2(knT)CTO(knT-nT) lie within the real interval [0, 1]. Thus, SGi1,2(knT) is set to be equal to 1/(nT). The range of values of the controller’s output, Factor(knT), lies between -1 and +1.

Therefore, the minimum and maximum contention window parameters are calculated dynamically through the dynamic amount of change the controller outputs, which is based on a nonlinear control law derived by the construction of the FIE, and taking into account the density of the environment. This is in contrast with the basic operation of the IEEE 802.11p, where the CWmin and CWmax EDCA parameters are kept unchanged, without taking into account the density of the medium. Further, a nonlinear control law is capable of efficiently coping with the high variability and dynamics of the system.

A design criteria of the proposed scheme is to put lower and upper bounds on the adjustments of the CWmin and CWmax parameters for all ACs, and further, to keep the distance between CWmin and CWmax parameters intact (meaning that to increase/decrease both EDCA parameters using the same amount of change), concerning the higher-priority ACs (AC3 andAC2), in order to still provide the necessary differentiation among the ACs.

Based on the above explanation, the minimum and maximum contention window parameters for all ACs are calculated dynamically using the following equations:

CWmin = CWmin + Factor * (CWmax - CWmin) (1) CWmax = CWmax + Factor * (CWmax - CWmin) (2) Note that the initial values of CWmin and CWmax parameters are the ones specified by the standard (see Table I and Table II). Table III shows the lower and upper bounds of CWmin

and CWmax parameters of all ACs, concerning the use of the service channels. However, in order to provide the necessary service differentiation, the following applies:

AC2_CWmin >= AC3_CWmax (3) AC1_CWmin >= AC2_CWmax (4) AC0_CWmin >= AC2_CWmax (5) There is no accepted systematic procedure for the design

of a fuzzy controller [21]; it must obviously meet its goals while remaining as simple and generic as possible. The most commonly used approach uses a qualitative understanding of

Fuzzy logic

Control

Factor(knT)

Plant

CTO(kT)

( )knTSGi1

Delay, nT

CTO(knT)

CTO(nkT - nT)

( )knTSGi2

Fuzzy Logic-based MAC controller

Figure 2. Fuzzy Logic based MAC system model

19

TABLE III. PROPOSED DYNAMIC ADJUSTMENT OF EDCA

PARAMETERS CWMIN AND CWMAX ON THE SCH

AC

Ind

ex

AC CWmin

CWmax

1 Background 2aCWmin aCWmax

0 Best effort 2aCWmin aCWmax

2 Video 1 2aCWmin

3 Voice 1 aCWmin

TABLE IV. FLM LINGUISTIC RULES - RULE BASE

CTO(knT-nT) Factor

(knT) Za VS S L VL

Z LD LD LD SD SD

VS SD SD SD VSD

I

VSD

I

S VSD

I

VSD

I

VS

DI

VSD

I

VSD

I

L LI SI SI SI SI

CTO

(knT)

VL LI LI LI LI LI

a. table notations: zero (Z), very-small (VS), small (S), large (L), very-large (VL),

large-decr (LD), small-decr (SD), very-small-decr-incr (VSDI), small-incr (SI), large-incr (LI)

the system together with a rule data base, to define membership functions of the inputs and output; the controller is then tested (by trial-and-error) and refined until satisfactory performance is achieved.

To calculate the amount of change that must be applied to the minimum and maximum contention window parameters, the multi-input FIE uses linguistic rules; these rules, form the control knowledge–rule base of the controller and describe how to best control the system, under differing operating conditions. Hence, linguistic expressions are needed for the inputs and the output, and their characteristics; we will use “linguistic variables” (that is, symbolic descriptions of what are in general time-varying quantities) to describe fuzzy system inputs and output. These linguistic variables take on “linguistic values” that change dynamically over time; they are used to describe specific characteristics of the variables and are generally descriptive terms such as “small”, “zero” and “large”.

The philosophy behind the knowledge base of the FLM scheme is that of being aggressive (i.e. a large positive value of the amount of change) when the density of the channel is very high over two consecutive samplings, but on the other hand being able to smoothly respond in case of low density. All other rules can represent intermediate situations, thus providing the control mechanism with a highly dynamic action. This point is illustrated in Fig. 3 which presents the visualization of the nonlinear control-decision surface of the FIE used in the FLM scheme. It is shaped by the constructed rule base and the linguistic values of the inputs and output variables. A convenient way to list all possible IF-THEN control rules is to use a tabular representation (see Table IV). As these rules reflect the particular view and experiences of the designer, they can be easily related to human reasoning processes and gathered experiences. Gaussian, triangular, or trapezoidal shaped functions are commonly used in defining the linguistic values of a fuzzy variable. For computational simplicity, we select triangular shaped membership functions for the FLM control scheme (see Fig. 4).

Figure 3. Control surface of the FIE of FLM controller

Figure 4. Membership functions of the linguistic values

representing the input and output varibles

20

TABLE V. THROUGHPUT COMPARISON IN DENSE V2I

Throughput (kbps) Sc

en

ari

os Standard 802.11p FLM

No

des

AC0 AC1 AC2 AC3 AC0 AC1 AC2 AC3

4 293 271 303 304 14 15 410 1930

12 98 93 98 102 13 13 400 1940

20 59 55 60 59 12 12 370 1970

28 41 40 43 43 10 11 260 2070

32 37 34 37 38 10 10 310 2010

TABLE VI. FLM THROUGHPUT GAIN OVER STANDARD 802.11P

Scenari

os

AC3’s

Throughput Gain

AC2’s

Throughput Gain

4 nodes 535% 35%

12 node 1802% 308%

20 nodes 3239% 517%

28 nodes 4713% 505%

32 nodes 5190% 738%

VI. PERFORMANCE EVALUATION

In this section, we use simulative evaluation to demonstrate the effectiveness and robustness of the FLM scheme focusing in V2I environments though the proposed scheme can be easily adopted in vehicle-to-vehicle (V2V) communication; the FLM scheme is also compared with the IEEE 802.11p protocol. The performance is evaluated using the NCTUns network simulator [31], a freely available simulator which among other scenarios was also used in evaluating VANETS [32].

The common network parameters used in the simulations are:

• The channel data rate is set to 3 Mbps.

• Each VANET node generates CBR traffic with 600 byte packet every 1.5 msec (used in [16]). However, we have set different priorities, to create a mixture of differently prioritized types of traffic. We assume that non-safety data is exchanged; thus, we use service channels.

• The total simulation time is 60 seconds.

• The sampling period, T, is set to 20 msec so that it is marginally longer than the maximum back-off time that could be obtained by having a contention window parameter with a value of 1023 multiplied by the slot time of 13 µsec.

• The dynamic adjustment of CWmin and CWmax EDCA parameters is performed every 3 * T (i.e., n=3).

A. Examination of dense V2I environment

We investigate the performance of the FLM scheme compared with that of the standard 802.11p as the number of

VANET nodes increase, resulting in a highly dense environment.

Five different scenarios are examined by increasing the number of vehicles (4, 12, 20, 28, and 32 vehicles) the RSU is sending data to. For each scenario, we equally split the total number of vehicles into four groups (one for each access category of Table II).

The summary of the results (Table V) clearly shows that as the number of vehicles increases, i.e., there is very high density, the proposed scheme outperforms the 802.11p in terms of throughput, but also in the differentiation of the prioritized traffic types. Thus, FLM provides much better QoS to the higher priority applications that belong to the AC2 and AC3 categories by providing most of the available bandwidth to them. On the other hand, the standard 802.11p does not seem to meet its design goals since it fails to provide any differentiation between the different ACs. Table VI shows the AC3 and AC2’s throughput gain of the FLM over the standard 802.11p. It is evident that the FLM scheme can be considered as superior over the standard 802.11p in terms of providing the most wireless access to the higher priorities traffic (thus offering the required QoS). To further illustrate this, in Fig. 5 we show the throughput obtained by the higher priorities AC3 and AC2, as the number of vehicles

AC3 and AC2 Throughput in V2I

0

500

1000

1500

2000

2500

4 Users 12 Users 20 Users 28 Users 32 Users

Number of Vehicles

Th

rou

gh

pu

t (k

bp

s)

FLM-AC3 FLM-AC2 Standard 802.11p-AC3 Standard 802.11p-AC2

Figure 5. AC3 and AC2 Throughput

Channel Utilization in V2I

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

4 Users 12 Users 20 Users 28 Users 32 Users

Uti

liza

tio

n (

%)

FLM Standard 802.11p

Figure 6. Channel Utilization

21

TABLE VII. THROUGHPUT COMPARISON UNDER DYNAMIC CHANGES

IN V2I

Throughput (kbps) Sc

en

ari

os Standard 802.11p FLM

No

des

AC0 AC1 AC2 AC3 AC0 AC1 AC2 AC3

12 100 93 98 99 12 12 410 1940

20 59 55 60 59 12 12 320 2010

28 42 39 42 44 10 11 260 2070

32 37 35 37 38 10 10 300 2020

increases. Finally, Fig. 6 shows the channel utilization obtained for both FLM and the standard 802.11p; evidence that FLM better utilizes the channel resources.

B. Dynamic changes in V2I environment

We examine the performance of the FLM scheme compared with that of the standard 802.11p under dynamic traffic changes. The network conditions apply as indicated in the previous section, however, now half the vehicles appear at the start of the simulation and the rest appear midway through the simulation; this evaluates how the MAC schemes adapt to sudden changes.

The results shown in Table VII clearly indicate that FLM again outperforms the standard 802.11p scheme by providing efficient differentiation between the differently prioritized traffic types, whereas the standard MAC scheme fails to do so. The throughput gain of the higher priority traffic is again very high; this can be seen implicitly from Table VII.

VII. CONCLUSIONS

In this paper, we proposed a novel, adaptive medium access control framework for VANETs that utilizes fuzzy logic control to outperform the standard 802.11p protocol, in terms of providing QoS in different applications’ access categories. In particular, we dynamically adjust the minimum and maximum contention window parameter values; this allows it to react better to the dynamic network conditions. We demonstrated that the nonlinear control scheme we have proposed significantly enhances controlling wireless access in VANETs under differing operating conditions, without the need for (re)tuning. Due to the high variability and dynamics experienced in VANETs, a robust and effective controller that can keep the system in a steady state under differing conditions can prove to be highly beneficial.

Specifically, we have shown that the proposed scheme is able to compensate for a varying number of active VANET nodes, as well as handle dynamic traffic changes. The fuzzy logic-based MAC mechanism outperforms the standard MAC mechanism in terms of throughput performance. At the same time, it provides a highly effective differentiation among the different applications’ access categories; it offers such applications the QoS they demand by giving precedence to the higher priority traffic they generate.

REFERENCES

[1] IEEE P1609.1, "Trial-Use Standard for Wireless Access in Vehicular Environments (WAVE) - Resource Manager", 2006.

[2] IEEE P1609.2, "Trial-Use Standard for Wireless Access in Vehicular Environments (WAVE) ― Security Services for Applications and Management Messages", 2006.

[3] IEEE Std P1609.3, "IEEE Trial-Use Standard for Wireless Access in Vehicular Environments (WAVE)-Networking Services", 2007.

[4] IEEE P1609.4, "Trial-Use Standard for Wireless Access in Vehicular Environments (WAVE) ― Multi-Channel Operation", 2006

[5] IEEE P802.11p/D3.0, "Draft Amendment to Standard for Information Technology-Telecommunications and Information Exchange Between Systems-Local and Metropolitan Area Networks- Specific requirements - Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications- Amendment 7: Wireless Access in Vehicular Environment", 2007.

[6] Dedicated Short Range Communications (DSRC), USA ITS Standards advisory, http://www.standards.its.dot.gov/Documents/ advisories/dsrc_advisory.htm

[7] G. Karagiannis, R. Wakikawa, J. Kenney, Traffic Safety Applications Requirements, IETF Draft, July 2009.

[8] D. Jiang, L. Delgrossi, IEEE 802.11p: towards an International Standard for Wireless Access in Vehicular Environments, IEEE Vehicular Conference, pp. 2036-2040, 2008.

[9] R. A. Uzcategui, WAVE: A Tutorial, IEEE Communications Magazine, pp. 126-133, May 2009.

[10] D. Jiang, V. Taliwal, A. Meier, W. Holfelder, R. Herrtwich, Design of 5.9 GHz DSRC-Based Vehicular Safety Communication, IEEE Wireless Communications, pp. 36-43, October 2006.

[11] A. Senart, M. Bouroche, V. Cahill, S. Weber, Vehicular Networks and Applications, Chapter Book, Springer, 2009.

[12] T. L. Willke, P. Tientrakool, and N.F. Maxemchuk, A Survey of Inter-Vehicle Communication Protocols and Their Applications, IEEE Communications Surveys & Tutorials, vol. 11, no. 2, pp. 3-20, 2009.

[13] S. Eichler, Performance Evaluation of the IEEE 802.11p WAVE Communication Standard, 1st IEEE Int. Symposium on Wireless Vehicular Communications, September 2007.

[14] T. Kosch, C.J. Adler, S. Eichler, C. Schroth, M. Strassberger, The Scalability Problem of Vehicular Ad Hoc Networks and How to Solve it, IEEE Wireless Communications, pp. 22-28, October 2006.

[15] L. Wischhof, H. Rohling, Congestion Control in Vehicular Ad Hoc Networks, IEEE Int. Conference on Vehicular Electronics and Safety, 2005.

[16] Y. Wang, A. Ahmed, B. Krishnamachari, K. Psounis, “IEEE 802.11p Performance Evaluation and Protocol Enhancement”, IEEE Int. Conference on Vehicular electronics and Safety, pp. 317-322, Sept. 2008.

[17] K. Bilstrub, E. Uhlemann, E.G. Strom, U. Bilstrup, “On the Ability of the 802.11p AMC Method and STDMA to Support Real-Time Vehicle-to-Vehicle communication”, EURASIP Journal on Wireless Communications and Networking, Vol 2009, 13 pages, 2009.

[18] C. Chrysostomou, C. Djouvas and L. Lambrinos, "Applying Adaptive, QoS-aware Medium Access Control in Priority-based Vehicular Ad Hoc Networks", Proceedings of the 16th IEEE Symposium on Computers and Communications (IEEE ISCC 2011), Kerkyra (Corfu), Greece, June 28 - July 1, 2011.

[19] C. Chrysostomou, L. Lambrinos, C. Djouvas, “The Need for a System Design Methodology to Address Challenges in Wireless Ad Hoc Netowrks”, 2nd International Congress on Ultra Modern Telecommunications and Control Systems 2010 (ICUMT 2010), Moscow, Russia, pp. 1226-1231, 18-20 October 2010.

[20] Zadeh, L. A., “Fuzzy Sets. Information And Control”, vol. 8,. 338-353, 1965.

22

[21] Passino, K., & Yurkovich, M., “Fuzzy Control”, Ed. Prentice Hall, ISBN 0-201-18074-X, 1998.

[22] Zadeh, L. A., “Outline of a new approach to the analysis of complex systems and decision processes”, IEEE Transactions on Systems, Man, and Cybernetics, 3(1), 28-44, 1973.

[23] Mamdani, E.H., “Applications of fuzzy algorithms for simple dynamic plant”, Proceedings of IEE, 121(12), 1585-1588, 1974.

[24] Pitsillides, A., & Sekercioglu, A, « Congestion Control », In Pedrycz, W. & Vasilakos, A. V. (Eds.), Computational Intelligence in Telecommunications Networks (pp. 109-158). Boca Raton, FL: CRC Press, ISBN: 0-8493-1075-X, 2000.

[25] Morales, E., Polycarpou, M., Hemasilpin, N., & Bissler, J., “Hierarchical Adaptive and Supervisory Control of Continuous Venovenous Hemofiltration.”, IEEE Transactions on Control Systems Technology, Vol. 9, No. 3, 445-457, 2001.

[26] Sekercioglu, A., Pitsillides, A., & Egan, G.K., “Study of an adaptive fuzzy controller based on the adaptation of relative rule weights”, Proceedings of ANZIIS’94, 204-208, Brisbane, Queensland, Australia, 1994.

[27] Pitsillides, A., Sekercioglu, A., & Ramamurthy, G., “Effective Control of Traffic Flow in ATM Networks Using Fuzzy Explicit Rate Marking (FERM)”, IEEE Journal on Selected Areas in Communications (JSAC), vol. 15, issue 2, 209-225, 1997.

[28] Douligeris, C., & Develekos, G., “A fuzzy logic approach to congestion control in ATM networks”, Proceedings of IEEE ICC’95, Washington, USA, 1969-1973, 1995.

[29] Chrysostomou, C., Pitsillides, A., Rossides, L., Polycarpou, M., & Sekercioglu, A., “Congestion Control in Differentiated Services Networks using Fuzzy-RED”, IFAC Control Engineering Practice (CEP) Journal, Vol. 11, Issue 10, 1153-1170, 2003, special Issue on "Control Methods for Telecommunication Networks".

[30] C. Chrysostomou, A. Pitsillides, A. Sekercioglu, “Fuzzy Explicit Marking: A Unified Congestion Controller for Best-effort and Diff-serv Networks”, Elsevier Computer Networks Journal (COMNET), vol. 53, isssue 5, pp. 650-667, 2009.

[31] S.Y. Wang et al., “NCTUns 4.0: An integrated simulation platform for vehicular traffic, communication and network researches,” 1st IEEE WiVec 2007 (International Symposium on Wireless Vehicular Communications), September 30-October 1, 2007, Baltimore, MD, USA. (available for download at http://NSL.csie.nctu.edu.tw/nctuns.html.)

[32] S.Y. Wang, H.L. Chao, K.C. Liu, T.W. He, C.C. Lin and C.L. Chou, “Evaluating and Improving the TCP/UDP Performances of IEEE 802.11(p)/1609 Networks”, IEEE ISCC 2008, July 6-9, 2008, Marrakech, Morocco, pp. 163-168.

23