the backoff mode is paused when a node uses the wireless channel. However, the PCB could not apply the network load, which dramatically changes, because ...
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE CCNC 2010 proceedings
A Novel Estimation-Based Backoff Algorithm in the IEEE 802.11 Based Wireless Network Seok-Won Kang, Jae-Ryong Cha and Jae-Hyun Kim School of Electrical Engineering, Ajou University San 5 Woncheon-Dong, Youngtong-Gu, Suwon 443-749, Korea Abstract — This paper proposes a new backoff algorithm to enhance both the delay and the throughput of the Distributed Coordination Function (DCF) in the IEEE 802.11 based wireless networks. The proposed algorithm, which is named as the Estimation-based Backoff Algorithm (EBA), observes the number of the idle slots during the backoff period in order to estimate the number of active nodes in the network. Especially, when the number of nodes or the amount of traffic dramatically varies, the proposed algorithm determines a more appropriate contention window based on the estimation algorithm. This paper evaluates the performance of the proposed EBA by using simulation, and it compares the EBA’s performance with other backoff algorithms such as the binary exponential back-off (BEB), the exponential increase exponential decrease (EIED), the exponential increase linear decrease (EILD), the pause count backoff (PCB) and the history based adaptive backoff (HBAB). The simulation results show that the EBA outperforms the other backoff algorithms because it has better adaptability to the network load variation. By comparing the performance of the EBA to that of the BEB, which is defined in the IEEE 802.11, the EBA increases the network throughput by around 25 %, and it decreases the mean packet delay by about 50 % when the number of nodes is 70. Keywords— DCF, Backoff Algorithm, EBA
I.
INTRODUCTION
The DCF is the fundamental access mechanism in the IEEE 802.11 medium access control (MAC) protocol. In the DCF, the BEB algorithm is used as a contention resolution scheme [1]. However, the performance of the BEB can deteriorate when the network is heavily loaded because the collision rate increases due to the aggressive reduction in the backoff period after a successful transmission is completed. In order to overcome this problem, several algorithms such as the EIED and the EILD [2-3], which adopt the slow reduction in the backoff period, have been proposed. The EIED doubles the size of the contention window (CW) after a collision occurs, and it cut in half the CW after a successful transmission is completed [2]. The EILD also doubles the backoff period after a collision, and it linearly decreases the backoff period after a successful transmission is completed [3]. Unfortunately, these algorithms cannot cope with the dramatic variation of the network load. For this reason, the PCB has been proposed [5]. The algorithm estimates the CW by using the number of pauses while sensing
the wireless channel. In the PCB, the countdown procedure in the backoff mode is paused when a node uses the wireless channel. However, the PCB could not apply the network load, which dramatically changes, because the PCB could not estimate the number of active nodes. For this reason, this paper proposes a new backoff algorithm, which is known as the EBA, in order to apply the CW based on the network load. The EBA estimates the number of active nodes by using the number of the idle slots in the backoff period. This paper is organized as follows. In Section II, we describe the function of the DCF, and how the related works review the DCF’s functionality within a wireless channel. In section III, we describe the proposed EBA. The simulation results and the performance analysis of the proposed EBA are discussed in section IV. Finally, section V concludes the paper and presents future works. II.
THE CONVENTIONAL ALGORITHMS
A. The IEEE 802.11 DCF with the BEB In the DCF, when a node has to transmit a data frame, it first senses the wireless link and waits until the link becomes idle. When the node finds out that the wireless link is idle during a DCF interframe space (DIFS) period, the random backoff procedure starts [1]. A node generates a random backoff interval before the transmission, and it decreases the backoff interval counter while the wireless link is idle. However, the backoff counter is paused when a transmission is detected, and it is reactivated when the wireless link is sensed as being idle during the DIFS period. If the backoff counter reaches zero, then the node starts to transmit the frame. The random integer follows a uniform distribution on [0, CW]. The CW is initially set to be CWmin. If the transmission fails n times, then the CW is increased by 2n times. If the node exceeds the maximum retransmission, then the frame is dropped. If a frame is dropped or if it is successfully transmitted, then the CW is reset to be CWmin. The BEB backoff algorithm can be expressed as follows:
⎛Transmission success : CW = CWmin ⎞⎟ ⎜⎜ ⎟. ⎜⎝Transmission fail : CW = CWold × rI ⎠⎟⎟
978-1-4244-5176-0/10/$26.00 ©2010 IEEE
(1)
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE CCNC 2010 proceedings
Backoff counter paused
ㆍSense channel during DIFS DIFS DIFS
Contention Window
PIFS Busy Medium
BackoffWindow
SIFS
Next Frame
Backoff counter paused
resume
Backoff resume counterp aused Data transmission
Node A
Slot time
Defer Access
ㆍBackoff slot reduced when channel is idle
Figure 1. Illustration of the IEEE 802.11 DCF mechanism
Node B Node C
The BEB degrades the performance of the network when the network is heavily loaded because each new packet starts with the minimum CW. This resetting behavior becomes very unstable when numerous nodes are contending within the same wireless channel. This can cause more collisions and it decreases the whole system’s utilization. Fig. 1 shows how the DCF works. B. The EIED and the EILD In the EIED [2], the CW exponentially increases by a backoff factor of rI whenever a collision occurs, and it exponentially decreases by a backoff factor of rD if a node successfully transmits a packet. The EIED can be given as. ⎛Transmission success : CW = CWold / rD ⎜⎜ ⎜⎝Transmission fail : CW = CWold × rI
⎟⎟⎞ ⎟⎟ ⎠
(2)
, ( rI > 1 and rD > 1). The EILD linearly decreases by a backoff factor of rD. The EILD can be expressed as follows: ⎛Transmission success : CW = CWold − rD ⎜⎜ ⎜⎝Transmission fail : CW = CWold × rI
⎞⎟ ⎟⎟. ⎠⎟
(3)
Node D
time Figure 2. Estimating the number of active nodes with the PCB
that is using the wireless channel, and so the traffic load of the network is determined by the number of pauses. The PCB counts the pauses during the countdown procedure and it sets an appropriate CW size for the current traffic load of the network. Fig. 2 describes the PCB. D. The HBAB The HBAB algorithm checks the last N states of the medium (N=2 in this implementation), and it determines whether to increment or decrement the CW value based on the channel's tendency to being free or busy [8]. The HBAB algorithm fixes two parameters, α and β, which are used to increase or decrease the new CW based on the old CW value. TABLE 1 shows the suggested CW values per state check (0 indicates both a busy channel and 1 indicates a free channel. III.
THE PROPOSED BACKOFF ALGORITHM
The EIED and the EILD methods are based on partial observations, such as that each node uses its own results of transmissions to represent the whole system. The results of both the transmissions and the system load may have a positive correlation, but they are not sufficient to precisely set the CW value.
The proposed algorithm has two main functions: The estimation scheme for the number of active nodes and the optimal CW allocation scheme are shown in TABLE 2. The estimation scheme exploits the number of idle slots in the backoff period in order to derive the exact number of active nodes. The optimal CW allocation scheme uses the estimated number of active users in order to enhance the system performance. The detailed description is as follows.
C. The PCB The PCB monitors the traffic load of the network, and the PCB sets an appropriate CW to match the traffic load of the network [4]. The countdown procedure in the backoff period pauses when other nodes simultaneously use the wireless channel. Therefore, each pause represents more than one node
A. Estimating the number of active nodes In step 1 in Table 2, each node obtains the average number of both the idle slots and the busy slots during the backoff period. Given N slots in the total backoff period and n nodes, the probability that r out of n nodes transmit their data during a slot is given by
TABLE I.
THE CW ESTIMATION ALGORITHM IN THE HBAB
State
CW value
Ex: CW value (with α=1 β=2)
00
CW=CWold × (α β)
2 CWold
01
CW=CWold × (α / β)
1/2 CWold
10
CW=CWold × (β / α)
2 CWold
11
CW=CWold × (1/ α β)
1/2 CWold
⎛ n⎞⎛ 1 ⎞ ⎛ ⎜ ⎟ ⎜ ⎟ ⎜1 − ⎝ r ⎠⎝ N ⎠ ⎝ r
P( X = r ) =
⎞ ⎟ N⎠ 1
n-r
.
(4)
The number r in a particular slot is called the occupancy number of the slot [7]. The expected number of slots, with the occupancy number r, is given by
⎛ n⎞⎛ 1 ⎞ ⎛ ⎟ ⎜ ⎟ ⎜1 − ⎝ r ⎠⎝ N ⎠ ⎝ r
E[ X = r ] = N ⎜
⎞ ⎟ N⎠ 1
n−r
.
(5)
To estimate the number of nodes (nest), this paper defines the average number of idle slots a0(N, n), which means the ratio
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE CCNC 2010 proceedings
of the number of the idle slots to the number of slots in the backoff period[5] is given by
n −1
n
1⎞ ⎛ a0 ( N , n) = N × E[ X = 0] = N × ⎜ 1 − ⎟ . ⎝ N⎠ log(a0 ( N , n)) − log( N ) . log( N −1) − log( N )
(7)
D( N , n) = number of retransmission × total backoff size. (8)
The probability that a node successfully transmits its data during a slot is given by n−1
,
(9)
where 1/N is the probability that a node transmits its data at the particular slot in a backoff slot. Based on (9), the probability that a node successfully transmits a frame during the total backoff period is given by
.
Psucc (k ) = Psucc , N (1− Psucc , N )k −1 .
THE EBA ALGORITHM
Step1: Estimating the number of active nodes When a channel is busy during the backoff period -. busy_count = busy_count +1 Backoff period end Calculate the parameters -. busy_slot_count=busy_count *
α, ⎛ ⎞⎟ data _ packet _ size 1 ⎟ × ⎜⎜α = ⎜⎝ transmission _ data _ rate slot _ size ⎠⎟⎟ -. total_backoff_period = idle_slot_count + busy_slot_count -. a0(N,n)= idle_slot_count
Obtain the estimated number of active nodes log(a0 ( N, n)) −log(total _ backoff _ period ) nest = log(total _ backoff _ period −1) −log(total _ backoff _ period ) Step 2: Deciding the optimal CW Obtain the optimal CW -. CWoptimal= nest
(11)
Thus, the average number of retransmissions is ∞
E ( X = k ) = ∑ kPsucc ( k ) = k =1
1 . n −1 ⎛ ⎞ 1 ⎟ ⎜⎜1 − ⎟ ⎜⎝ N ⎟⎠
(12)
Therefore, D(N, n) can be obtained from (8) and (12) as D ( N , n) =
N . n −1 ⎛ ⎞ ⎜⎜1 − 1 ⎟⎟ ⎜⎝ N ⎟⎠
(13)
In (13), D(N, n) depends on N and n. Since N is the system’s parameter, this paper drives the optimal N to minimize D(N, n). Since D(N, n) is a concave function with respect to N, the optimal N can be obtained by differentiating D(N, n) with respect to N as ∂ ∂ N D( N , n) = = 0. n −1 ∂N ∂N ⎛ ⎞ ⎜⎜1 − 1 ⎟⎟ ⎜⎝ N ⎟⎠
(14)
From (14), the optimal CW can be obtained as
CWoptimal = n . TABLE II.
(10)
Let Psucc(k) be the probability that a node successfully transmits a frame in the kth retransmission. Then Psucc(k) is
B. Deciding the optional CW This paper derives the optimal CW based on the average access delay D(N, n) which refers to the time that is needed to transmit a packet from one node to the other. D(N, n) can be obtained as follow [6].
1 ⎛⎜ 1⎞ ×⎜1− ⎟⎟ N ⎜⎝ N ⎟⎠
⎛ 1⎞ = ⎜⎜1 − ⎟⎟⎟ ⎜⎝ N⎠
(6)
After the end of the backoff period, a node can calculate the total backoff period N and the estimated number of active users, as shown in TABLE 2.
Psucc =
×N
n −1
By using (6), the number of users can be derived as
nest =
1 ⎛⎜ 1⎞ ×⎜1 − ⎟⎟⎟ N ⎜⎝ N⎠
Psucc , N =
IV.
(15)
THE SIMULATION RESULTS This section evaluates the system performance in terms of the throughput and the average access delay. This paper simulates the IEEE 802.11b based WLAN setup module as defined in the OPNET. The range of the number of nodes is within 30 ~ 70 and the simulation time is 300 seconds. All nodes are within one hop distance and they select a random destination. The parameters that were used in the simulation are listed in Table 3. The parameters rI and rD in the EIED are set to 2, as suggested in [2]. TABLE III.
THE IEEE 802.11B MAC AND THE NETWORK PARAMETERS THAT ARE USED IN THE SIMULATION
Section Data rate Slot_time SIFS DIFS CWmin CWmax Packet size Packet inter-arrival time
Value 11 Mbits/s 20 μs 10 μs 50 μs 31 1023 exponential(1024) bytes exponential(0.1) sec
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE CCNC 2010 proceedings
6
5
x 10
BEB EIED EILD PCB EBA
Throughput(bits/sec)
4.5
4
3.5
3
2.5
2 30
35
40
45 50 55 Number of nodes
60
65
70
Figure 3. The throughput vs. the number of nodes
4.5 BEB EIED EILD PCB EBA
Average access delay(sec)
4 3.5 3
B. The average access delay The variation of the end to end packet delay according to the number of active nodes is presented in Fig. 4. As expected, the delay increases as the number of nodes increases. The objective of the EBA algorithm is estimating the actual network status and setting the corresponding optimal CW to precisely minimize the overheads in the system. In Fig. 4, the EBA shows the advantage of overhead reduction and the EBA obtains the lowest delay among these backoff algorithms. The delay of the EBA is around 50% less than that of a standard DCF when n = 70.
2.5 2 1.5 1 0.5 0 30
35
40
A. The network throughput Fig. 3 indicates the throughput according to various backoff algorithms in the IEEE 802.11 WLAN. The efficiency standard of the DCF performs worse (as expected) when more stations contend for the channel. Although the EIED algorithm takes an exponential decrease in the CW policy instead of resetting to CWmin when there is a successful transmission, the curve decreases when there are more active stations in the system. This means that the stations that are applying the EIED and the DCF algorithms make decisions with an unclear system status and they quickly adjust the CW from the result of a single transmission. In contrast to the PCB, the EIED and the DCF, the throughput of the EILD and the EBA algorithms remains high with respect to various system loads. These improvements mean that the stations that are using both the EILD and the EBA algorithms adjust the CW value appropriately according to the load variation within the network. In the cases of both light and heavy loads, the EBA successfully determined the optimal backoff slot because the traffic measurement is accurate. Overall, the EBA algorithm obtains high efficiency when it is compared with the other backoff algorithms in various network conditions.
45 50 55 Number of nodes
60
65
70
Figure 4. The average access delay vs. the number of nodes
0.6
C. The fairness Fairness among stations is an important problem in the BEB study, and it has been discussed by many research projects. The Fairness index can show if a resource is fairly allocated to each station. We use Jain’s fairness index formula. Jain’s fairness index is calculated as
Fairness Index
0.5
g ( y1 , y2 ,..., yn ) = 0.4
⎛ n ⎟⎞2 ⎜⎜ y ⎟ i⎟ ⎟⎠ ⎜⎝∑ i =1 n
n ⋅ ∑ yi 2
.
(16)
i =1
0.3
BEB PCB EIED EILD EBA
0.2
0.1 30
35
40
45 50 55 Number of nodes
60
65
Figure 5. The Fairness index vs. the number of nodes
70
Jain’s fairness index always lies between 0 and 1. A fairness index of 1 indicates a throughput-fair algorithm [9]. In Fig. 5, we present the fairness index of each backoff algorithm among the stations. By using the simulation setup that was described in the previous section, we executed the simulation for 10 iterations, and we calculated the average of the results. From Fig. 5, the proposed EBA algorithm has the most stability when it is compared with the other contention algorithms. We also observe that the fairness index of the BEB, the EILD, the EIED and the PCB are both low and oscillatory. This
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE CCNC 2010 proceedings
phenomenon means that some stations occupy more channel capacity than do other stations due to the different understanding of the system status among stations. V.
CONCLUSION
The proposed EBA algorithm estimates the system status by using the idle slot counts for the backoff duration, and it determines a proper contention window size that accurately matches the current network conditions. We compared the performance of the proposed EBA with that of the conventional algorithms such as the IEEE 802.11 the DCF, the EIED, the EILD and the PCB. Our simulation results show that the EBA outperforms the previously proposed algorithms for various performance metrics, and that the EBA dynamically adapts to the variations of the amount of data traffics in the network. Based on the simulation results, we can use the proposed algorithm in the future transportation information system named as Telematics. The Telematics is a system where the information such as traffic jam, living, and emergency rescue, and etc. is exchanged between the vehicles. The Telematics needs more efficiency backoff algorithm because the variation of data traffics may be large due to the many vehicles’ existence in the heart of city. Therefore the proposed EBA may improve the performance of Telematics system. In the future, we plan to explore how to implement our algorithm in the Telematics system. ACKNOWLEDGMENT "This research was supported by the MKE(The Ministry of Knowledge Economy), Korea, under the ITRC(Information Technology Research Center) support program supervised by the NIPA(National IT Industry Promotion Agency" (NIPA2009-C1090-0902-0003) REFERENCES [1] [2]
[3] [4]
[5] [6] [7] [8] [9]
IEEE 802 Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications, IEEE Std., 1999. N. Song, B. Kwak, J. Song and M.E. Miller, “Enhancement of IEEE 802.11 distributed coordination function with exponential increase exponential decrease backoff algorithm,” in Proc. VTC 2003, Vol. 4, pp. 2775 – 2778, Orlando, USA, 22-25 April 2003. V. Bharghavan, A. Demers, S. Shenker, and L. Zhang, “MACAW: A Media Access Protocol for Wireless LAN’s,” in Proc. SIGCOMM’94, pp. 212-225, London, England, 1994. H. Liang, S. Zeadally, N. K. Chilamkurti and C. Shieh, “A Novel Pause Count Backoff Algorithm for Channel access in IEEE 802.11 based Wireless LANs,” in Proc. CSA '08 International Symposium, pp. 163 – 168, Hobart, Australia, 13-15 Oct. 2008. J. Lee, W. Kim and H. Kim. “Estimation of Number of Tags in ALOHA-based RFID Systems,” KICS, ’07-7 Vol. 32, No.7, 2007. J. R. Cha and J. H. Kim, "Dynamic Framed Slotted ALOHA Algorithm using Fast Tag Estimation method for RFID System," in Proc. CCNC2006, Las Vegas, USA, 8-10, Jan. 2006. Normal Lloyd Johnson and Samuel Kotz, Urn Models and Their Applications, Wiley, 1977. Q. Nasir and M. Albalt, “History Based Adaptive Backoff (HBAB) IEEE 802.11 MAC Protocol”, in Proc. CNSRC’08, pp. 533 – 538, Nova Sotia, Canada, 5-8, May. 2008. R. Jain, The art of computer systems performance analysis: techniques for Experimental Design, Measurement, Simulation, and Modeling, Wiley, New York, 1991.
