VOL. E100-B NO. 10
OCTOBER 2017
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IEICE TRANS. COMMUN., VOL.E100–B, NO.10 OCTOBER 2017

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PAPER

Improving Fairness in Wireless Ad Hoc Networks by
Channel Access Sensing at Link Layer and Packet Rate Control
Nguyen Minh TUAN†a) , Nonmember, Kohei WATABE† , Member, Pham Thanh GIANG†† , Nonmember,and Kenji NAKAGAWA† , Member

SUMMARY Wireless Ad hoc networks have been rapidly developed inrecent years since they promise a wide range of applications. However, theirstructures, which are based on the IEEE 802.11 standard, cause a severe
unfairness problem in bandwidth sharing among different users. This is anextreme drawback because in wireless ad hoc networks, all users need to betreated fairly regardless of their geographical positions. In this paper, we
propose a method to improve the fairness among flows by sensing channel
access of other nodes based on the information obtained at the link layer
and then, controlling the packet sending rate from the link layer to the MAC
layer and the dequeue rate from the queue. Simulation results show thatthe proposed method achieves a better fairness with a good total throughput
compared to conventional methods.
key words: fairness, throughput, wireless ad hoc network, channel utilization, dequeue rate, Round Robin, link layer

1.

Introduction

Recently, wireless ad hoc networking has emerged and been
largely studied with many interesting problems such as routing, Quality of Service and security. Although wireless ad
hoc networks are now diverged to many new research directions [2], the fairness problem, on which we focus in thispaper, is still important and need to be addressed.
Most of wireless ad hoc network architectures are currently based on the random access method of IEEE 802.11[5] Distributed Coordination Function (DCF) in CSMA/CA.
DCF enables to distribute channel bandwidth between nodes,
and prevent the channel from collision. However, IEEE
802.11 is not really well-suited for wireless ad hoc networks [6], [8] since it often causes unfairness situations andthroughput degradation. In particular, it usually leads to theunfairness where a few flows tend to dominate and occupy
almost all the channel bandwidth. As a result, the throughputs of these flows are remarkably high while other flows
only receive extremely low throughputs.
There are many factors causing this problem. For instance, at the MAC layer, the existence of transmission range(TR) and carrier sensing range (CR) causes a problem, whichis called a large-EIFS problem [4] where nodes that are far
Manuscript received September 20, 2016.
Manuscript revised February 17, 2017.
Manuscript publicized April 14, 2017.
† The authors are with the Graduate School of Engineering,
Nagaoka University of Technology, Nagaoka-shi, 940-2188 Japan.
†† The author is with the Department of Telematics, Institute ofInformation Technology, Vietnam Academy of Science and Technology, Vietnam.
a) E-mail: tuannmdt2k52@gmail.com
DOI: 10.1587/transcom.2016EBP3359

from the destination sometimes must wait longer time Extended Interframe Space (EIFS) than DCF Interframe Space(DIFS). As a result, the throughput of flows transmitted fromthese far nodes are lower than that of flows from nodes which
are geographically near to the destination.
In addition, the link layer buffer management plays animportant role in fairness achievement. In wireless ad hoc
network architecture, at the link layer, First In First Out(FIFO) queue is used by default. At an intermediate node,
the queue at the link layer is responsible for sending bothpackets of the direct flow generated by the node itself andalso that of the forwarding flows generated by other nodes.
A packet of forwarding flow needs some time to arrive at thequeue from its source while a packet of the direct flow canbe enqueued immediately as soon as it is generated. As aresult, packets of the direct flow may occupy almost all thebuffer space, then packets of forwarding flows are dropped
due to buffer overflow.
Besides, the fairness considerably depends on the typeof topology, or the positions of nodes. A typical example
is a three-pair topology [3] (also known as the “flow in the
middle” problem [7]) as in Fig. 1. In this topology, nodes
S1, S2 and S3 transmit their packets to R1, R2 and R3,
represented by flow 1, flow 2 and flow 3, respectively. In
Fig. 1, (0, 250) written above the node R1 shows that the Xcoordinate of R1 is 0 [m], and the Y-coordinate is 250 [m],further, G [Mbps] denotes the offered load generated by eachFig. 1Three-pair topology.

Copyright © 2017 The Institute of Electronics, Information and Communication Engineers

TUAN et al.: IMPROVING FAIRNESS IN WIRELESS AD HOC NETWORKS BY CHANNEL ACCESS SENSING AT LINK LAYER AND PACKET RATE CONTROL

node.
In the topology of Fig. 1, the unfairness of throughputs
occurs as follows. If we set the channel bandwidth B =2 [Mbps] and G = 1.6 [Mbps], then the network becomes
the saturation state, i.e., every node always has a packet totransmit. The simulation result shows that the throughputs offlow 1 and flow 3 are 1.42[Mbps] while that of flow 2 is only
0.01[Mbps]. This unfairness is caused by the asynchronoustransmission between two exterior flows, flow 1 and flow 3.
In particular, link S1-R1 and S3-R3 are active by turns andhence, S2 always senses a busy channel and has little chance
to transmit its packets. In this case, the Binary Exponential
Back-off (BEB) has no effect.
In this paper, we will propose a method for achieving agood fairness of throughputs among flows sharing the samechannel. Our method consists of two algorithms, Algorithms
1 and 2, both of which work on the link layer. In Algorithm
1, a channel access of other node is sensed by using the link
layer information to control the packet sending rate to the
MAC layer. In Algorithm 2, the packet dequeue rate fromthe link layer queue to the MAC layer is controlled by the
packet dequeue rate of each flow.
The rest of the paper is organized as follows. In Sect. 2,
we briefly show some related works in the literature. In
Sect. 3, the protocols of our proposed method are given,
followed by the simulation results in Sect. 4. Finally, Sects. 5
and 6 give a conclusion and future work.
2.

Related Work

Various reasons that cause the unfairness problem at the
MAC layer of IEEE 802.11 have been studied extensively[6], [8], [10]. One of the well known problems is the hidden
terminal problem. RTS/CTS handshake can greatly reducethe influence of hidden terminal problem, however, this problem cannot be solved completely in some situations where
the interference range is very large [9]. The radius of interference range is not a fixed value and it changes according
as the transmitter-receiver distance. The effectiveness of
RTS/CTS is not good when the transmitter-receiver distanceis larger than a certain value.
In [4], Li, et al. discovered that the fixed EIFS delay
leads to unfairness for a wireless node that is far from thedestination. Then, they proposed a dynamic EIFS adjustment
based on the location of a node to deal with such situation.
However, their core idea stands on the length of sensing
range (SR) that cannot always be identified correctly due tothe mobility and collision among wireless nodes.
Several schemes for improving the fairness of MAC
protocols have been proposed. For instance, in [11],Chakraborty, et al. introduced a new method in which Contention Window is dynamically changed based on the difference between the actual channel share and the requiredchannel share in order to achieve the proportional fairness.
Nevertheless, the actual share of a node is calculated from theinformation of overhearing data and control frames which are
not stable in the multi-hop environment. In [12], Razafind-1819

ralambo, et al. proposed a novel scheme to prevent fromcollision and monopolization by inserting a waiting timealong with the standard back-off algorithm based on sensing
activities of a node and collision experience. However, in
their proposed method, it is difficult to detect node’s monopolization in some configurations. They only can solve it
by transmitting a packet with a large contention window after many consecutive packets were transmitted successfully.
Note that all above methods [4], [11], [12] need to modify
the existing IEEE 802.11 MAC layer, which is not favored
from the viewpoint of implementation.
The unfairness problem at the link layer is also severe
and thus addressed in the literature. In [13], Nandiraju, et al.
studied the weakness of the FIFO queueing, then they suggested a fair scheduling algorithm called Dual Queue Service
Differentiation (DQSD). DQSD is implemented at intermediate nodes where the direct flow and forwarding flows coexist. It uses two queues for handling the packets from theseflows. Nevertheless, since there is only one queue used forthe forwarding packets, it is not ensure the equal handlingfor packets from a distant node. Furthermore, Shagdar, etal. [14] and Jun, et al. [15] also investigated the unfairnessof the direct flow and forwarding flows. They proposed ascheduling algorithm by using Round Robin (RR) scheduling. However, they assumed ideal MAC layer fairness whichcannot be really achieved.
Giang, et al. [16] modified the RR mechanism. They
pointed that using just the RR mechanism cannot result ingood fairness. Then, they proposed Probabilistic Control on
Round Robin Queue (PCRQ), in which the RR is applied in
combination with three algorithms for queue control. We
show the detailed algorithm of PCRQ, because we will compare our proposed method with PCRQ.
2.1

Algorithms of PCRQ

There are three algorithms in PCRQ.
The first algorithm is applied to control packets forenqueueing to the link layer queue. An arrived packet isenqueued with probability
input

Pi



 1,
=
qleni − ave 1 − ψ1,(N − 1)ave

if qleni ≤ ave,
if qleni > ave,where ψ1 is a constant, N is the number of flows, qleni is thequeue length of the flow i, and ave is the average of qleni ,i = 1, . . . , N. By this algorithm, packets of flows with heavy
offered load, usually the direct flow, are likely to be dropped.
Therefore, this algorithm maintains a good balance of thequeue length of flows.
The second algorithm controls the turn of queue reading. The reading pointer is hold at queue i with probability
N × ave


,
 ψ2
qmax
Piturn = 

 0,


if qleni = 0,
if qleni >0,

where ψ2 is a constant, qmax is the maximum queue length.

IEICE TRANS. COMMUN., VOL.E100–B, NO.10 OCTOBER 20171820

The delay time δ[s] is given as a holding time of the reading
pointer at an empty queue. By this algorithm, many packets
from a distant node can arrive at the queue during the holding
time δ, hence it solves the unfairness of bandwidth amongflows at the MAC layer.
The third algorithm controls an advantageous flow not
to send too many packets to the MAC layer by decreasing
the number of output packets from RR queues. A packet atthe head of the queue of flow i is sent to the MAC layer at
probability
output

Pi



 1,
=
 1 − ψ3 qleni − ave ,(N − 1)ave

if qleni > ave,

Proposed Method

We propose a method to achieve a good fairness amongflows by using only local information obtained at the link
layer of each node. Our method consists of two algorithms,
Algorithm 1 and Algorithm 2, both of which work on thelink layer.
In Algorithm 1, a channel access of other node is sensed
by the link layer information to control the packet sending
rate to the MAC layer by giving a delay if the node is anadvantageous node.
In Algorithm 2, the packet dequeue rate from the link
layer queue to the MAC layer is controlled by the packet
dequeue rate of each flow by skipping the service of a packet
at the queue of an advantageous flow.
3.1
3.1.1

Algorithm 1 - Channel Access Sensing and Sending
Rate Control
Channel Access Sensing

We first consider the sensing of channel access by othernodes based on the channel utilization ρ of a node that isdefined by
ρ=

λ
,
µ

A three-node chain topology.

if qleni ≤ ave,where ψ3 is a constant. Then, if a packet is not sent to the
MAC layer, it will be delayed for δ, which is the same value
as in the second algorithm. By this algorithm, the forwardingflows have more chance of sending packets.
By these three algorithms, PCRQ works effectively at
the link layer. Nonetheless, the PCRQ cannot solve the unfairness problem in some cases where the link layer congestion does not occur such as in the case of three-pair topology(Fig. 1). Moreover, to determine the optimal values of theparameters ψ1 , ψ2 , ψ3 is not an easy task.
3.

Fig. 2(1)

where λ[1/s] is the packet transmission rate and µ[1/s] isthe service rate. µ is assumed to be a constant µ = B/L,
with the channel bandwidth B[Mbps] and the constant packet
length L[bit]. λ is nearly equal to the rate that packets are

Fig. 3The change of ρ at S1 in three-node chain topology.

sent from the link layer to the MAC layer, hence, λ can beapproximated by
λ'

1
,
∆t

(2)

where ∆t[s] is the average time between two consecutive
packets sent to the MAC layer at the node, which is calculatedby∆t = α∆t(last) + (1 − α)(t(cur) − t(last)),(3)

where α is a constant with 0 < α < 1, ∆t(last) is the lastvalue of ∆t, t(cur) is the time that the current packet is sent,t(last) is the time that the last packet was sent.
Thus, from (1), (2) and (3), we have
ρ'

1
.
µ∆t

(4)

The channel utilization ρ reflects the channel access abilityof a node. The higher the channel utilization at a node is,the more smoothly the packets at that node are transmitted.
For three-node chain topology of Fig. 2, we show thechange of ρ at node S1 in Fig. 3. As we can observe from
Fig. 3, because of the MAC layer problem, S1 can transmit
packets continuously, leading to a high value of ρ that isabout 0.8. However, there are some points of time that the ρat S1 suddenly decreases. At this point, the disadvantageousnode S2 accessed the channel and caused a decrease in ρ at
S1. Hence, catching this moment, the advantageous node S1
can detect the appearance of other node, S2 in this case.
As shown in the above example, each time a node detects a decrease in its utilization, the node notices that othernode accessed the channel and transmitted a packet. Hence,
this node should defer in sending its packets so that othernodes have chance to transmit their packets. In addition to
the above example, we also show in Fig. 4 the change of ρ atnode S1 in the case of three-pair topology (Fig. 1). As shownTUAN et al.: IMPROVING FAIRNESS IN WIRELESS AD HOC NETWORKS BY CHANNEL ACCESS SENSING AT LINK LAYER AND PACKET RATE CONTROL

Table 2

Fig. 4

Table 1

The change of ρ at S1 in three-pair topology.

Algorithm 1—Channel access sensing and sending rate control.

Algorithm 1 Channel Access Sensing and Sending Rate Control
when a packet is sent to the MAC Layercalculate ∆t by (3)Let safe_interval = TDIFS , which is the period of DIFS
N is the number of flows
if ∆t > ∆t (last) + safe_interval
give delay ∆t/N
send the packet to the MAC Layer
else
immediately send the packet to the MAC Layer
end ifin Fig. 4, although the flow 2 is a disadvantageous flow, it
sometimes can transmit packets. The packet transmissionof flow 2 causes a sudden decrease in ρ at node S1 and S3.
Therefore, S1 and S3 notice that there is another flow that iscontending the channel with them.
3.1.2

Sending Rate Control

Based on the above analysis, the operation of Algorithm 1 isshown in Table 1.
A (sudden) decrease of ρ is detected by a large increaseof ∆t, which is decided by ∆t > ∆t(last) + safe_interval in
Algorithm 1. We set safe_interval = TDIFS , where TDIFS is
the period of DIFS. The reason of safe_interval is explained
as follows. At the end of a packet transmission cycle, whena packet is successfully transmitted and an ACK is received
at the sender, this sender will perform a back-off process
followed by a DIFS time before starting a new packet transmission. Hence, when comparing between ∆t(last) and the
new value of ∆t, it is necessary to add safe_interval = TDIFS .
If an increase of ∆t is detected, we give a delay before
sending the packet to the MAC layer by the command “givedelay ∆t/N” in Algorithm 1. The delay value ∆t/N aims
to the case of long chain topology where a node may be
responsible for packets from many different nodes. In thiscase, a node with a heavier load should defer a smaller timethan the others. For example, in four-node chain topology, anode with three flows should defer smaller time than a nodewith only two flows. In addition, the division by N avoids toomuch delay time, that may waste bandwidth. Note that thisis just a relative comparison because ∆t is not same among

1821

Algorithm 2—Dequeue rate control.

Algorithm 2 Dequeue Rate Control
Each time the reading pointer points to queue i
Let T = the time that the reading pointer points to queue i,calculate ηi by (7)1 PN2η̄ by (6) and variance = N
i=1 (ηi − η̄) where N is the number of flows
if ηi < η̄ and (ηi − η̄) 2 > varianceflow i is considered to be an advantageous flow
skip this queue without serving a packet
throw away the value ηielse
flow i is considered to be a disadvantageous flow
dequeue a packet from queue iput ti (cur) = T
update ηiput ηi (last) = ηiput ti (last) = ti (cur)end ifpoint the reading pointer to the next queue

nodes.
3.2

Algorithm 2 - Dequeue Rate Control

Algorithm 2 operates based on the dequeue rate control forRound Robin queue.
Buffer queue at the link layer is Round Robin mechanism where each queue serves packets from each flow separately. The dequeue rate is determined by the dequeueinterval. The dequeue interval η i [s] is defined as follows.
At every departure epoch of a packet from the queue i, thedequeue interval η i of a queue i is defined byη i = βη i (last) + (1 − β)(t i (cur) − ti (last)),(5)

where β a constant with 0 < β < 1, η i (last) is the last valueof η i , t i (cur) is the time that the current packet is dequeued atqueue i, t i (last) is the time that the last packet was dequeued
at queue i. Then, the average dequeue interval η̄ is defined
by
N

η̄ =

1X
η i,
N i=1

(6)

where N is the number of queues, η i is the value calculatedby (5).
The operation of Algorithm 2 is shown in Table 2. The
Round Robin queues are used in this Algorithm. Let T be
the time that the reading pointer points to queue i, then η i is
temporally calculated byη i = βη i (last) + (1 − β)(T − t i (last)) .

(7)

Then we check whether η i is smaller than the average dequeue interval η̄ of (6). If η i < η̄, we see that the dequeue
rate of this queue is larger than the average. Further, forthe sake of a reliable decision, we add one more condition(η i − η̄) 2 > variance, which implies that the dequeue rateof the queue i is large enough. If the above two conditions
are satisfied, we consider that the flow of this queue i is an

IEICE TRANS. COMMUN., VOL.E100–B, NO.10 OCTOBER 2017

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Table 3NS-2 parameters configuration.

Channel bandwidth
Antenna typeRadio Propagation
Transmission range
Carrier sensing range
MAC protocol
Routing protocol
Connection type
Queue type
Maximum Queue length
Packet’s size
Slot time
DIFS
EIFS
Simulation time

Fig. 5

System model of the proposed method.

advantageous flow, so reading this queue is skipped without
serving a packet to reduce the dequeue rate for achieving thefairness, and throw away the temporal value η i of (7).
If η i < η̄ or (η i − η̄) 2 > v ariance are not satisfied,
we consider that the flow of queue i is a disadvantageous
flow. Then dequeue a packet of queue i immediately at timeT, and put t i (cur) = T, update η i , and put η i (last) = η i ,t i (last) = t i (cur).
As is known, in multi-hop ad hoc network, if the queueis FIFO, flows from neighboring nodes (including the direct flow) usually occupy almost all the buffer space and as
a result, packets from the far distant nodes are frequently
dropped due to buffer overflow. Hence, it is necessary toapply the Round Robin queue technique to separate packets
from different flows. Moreover, unlike packets from the direct flow that can be enqueued immediately after generated,packets from forwarding flows need some time to arrive at
the queue. This causes unfairness when reading the queueeven if the queue is modeled as the Round Robin mechanism. Algorithm 2 is implemented to solve this problem.
This algorithm ensures the fairness among flows based on
the control of the dequeue rate of flows at intermediate nodes,
and this is done by controlling the dequeue interval of eachqueue as shown in Table 2.
The system model of Algorithms 1 and 2 is illustrated
in Fig. 5.
4.

2 MbpsOmni direction
Two-ray ground
250 m
550 m
IEEE 802.11b(RTS/CTS is enabled)
DSDV
UDP/CBR and TCPFIFO, PCRQ, proposal
100 packets
1024 Bytes
20 µs
50 µs
314 µs300 s

the network is in the saturated state and a single flow may
occupy all the channel bandwidth. We start the evaluationfrom 50 seconds to ensure that the simulation is in the stablestate. The parameter values α and β are set to α = 0.1 and
β = 0.6, which are determined by extensive simulations.
The metrics used to evaluate are the throughput and fairness
index. The fairness index (F I) [18] is defined by2NX* T hi +
, i=1
FI =
,
N
X
2
(T hi )
N

(8)

i=1

where N is the number of flows, T hi is the throughput of flow
i. The fairness index ranges from 1/N to 1, where F I = 1/N
indicates the worst fairness and F I = P
1 the perfect fairness.
N
We also evaluate the total throughput i=1
T hi .
In the following subsections, we will show the resultsof performance evaluation for four fundamental scenarios.
Scenario 1 is a typical MAC unfairness topology called “flowin the middle” that is studied in [3], [12]. Scenario 2 is a wellknown multi-hop wireless network called “chain topology”
that is studied in [4], [12], [13], [14], [16]. Actually, it isa typical topology of wireless network where a destination
station acts as a gateway connecting to the Internet. Scenario
3 is a mixed topology of above scenarios 1 and 2. Scenario
4 is evaluated to show the effectiveness of our proposed
method in case of chain topology with the mixture of UDP
and TCP flows.

Performance Evaluation

In this section, we compare our proposed method with PCRQ
[16] and standard 802.11 by the simulator NS-2 version 2.35
[17]. NS-2 simulation parameters are shown in Table 3. In
the simulation, the channel bandwidth is set to 2[Mbps], andthe effective bandwidth becomes about 1.4[Mbps] due to theoverhead in IEEE 802.11 [4]. For each flow, we gradually
increase the number of packets per second from 1 to 250
packets. In other words, the offered load at a node increases
from 8[kbps] to 2[Mbps]. If the offered load is large enough,4.1

Scenario 1

Scenario 1 is the three-pair topology shown in Fig. 1. In
this type of topology, the central flow is starved due to theasynchronous transmission of two external flows. The fairness and throughput results are presented in Fig. 6, Fig. 7 andFig. 8.
In scenario 1, the channel access contention occurs but
there is no buffer contention, thus only Algorithm 1 works in
our method. Fig. 6 shows that our proposed method yields

TUAN et al.: IMPROVING FAIRNESS IN WIRELESS AD HOC NETWORKS BY CHANNEL ACCESS SENSING AT LINK LAYER AND PACKET RATE CONTROL

Fig. 6

Fig. 7

Fairness index—Three-pair topology.

Per-flow throughput—Three-pair topology.

larger fairness index compared to both PCRQ and 802.11
standard. When the offered load is large, that is, the network
is in the saturation state, in PCRQ and 802.11, the nodeS2 rarely senses the idle state of channel because of thecontinuous transmission of nodes S1 and S3. In particular,
in this scenario, PCRQ doesn’t work because forwardingflows do not exist. Hence, the throughput of flow 2 is almostzero.
On the other hand, in our proposed method, catching the
moments that S2 transmits packets, S1 and S3 can detect theexistence of S2 by the decrease of their channel utilization.
Then, S1 and S3 decrease their own utilization by giving adelay before sending their packets to the MAC layer so that
S2 can get more chance to access the channel. As a result,in our proposed method, the throughput of flow 2 increases,
leading to a better fairness index. This is also confirmed by
the per-flow throughput in Fig. 7. In this figure, the offeredload G is set to the channel bandwidth 2[Mbps]. From thisfigure, the central flow’s throughput in the case of PCRQ
and 802.11 standard are almost zero, while in our proposed
method, it can be improved remarkably.
The total throughput is shown in Fig. 8. From thisfigure, it can be seen that the total throughput in our proposed
method is smaller than the others. This can be explained asfollows. Assume the channel bandwidth is B. Then, in theworst case of fairness, i.e., the two external flows transmit at
the maximum throughput that equals B and the central flow
0 throughput. Hence, in this case, the total throughput is 2B.

Fig. 81823

Total throughput—Three-pair topology.

On the other hand, in the case of the perfect fairness where
3 flows share the channel bandwidth equally, each flow canobtain a throughput of B/2. Thus, in this case, the total
throughput is 3B/2, which is smaller than 2B. So, we cansee that there exists the tradeoff between the fairness and thetotal throughput.
Now, let us compare the throughput reduction ratio ofIEEE 802.11 with that of our proposed method. In thecase of IEEE 802.11, the ideal total throughput is 4[Mbps](= 2B = 2 × 2[Mbps]) and the experimental result (in Fig. 7)
is 1.42 + 1.42 + 0.01 = 2.85[Mbps]. Hence, the throughput reduction ratio is 2.85/4 = 0.71. Then, in the caseof proposed method, the ideal total throughput is 3[Mbps](= 3B/2 = 1.5 × 2[Mbps]) and the experimental result is
0.86 + 0.85 + 0.36 = 2.07[Mbps]. Thus, the throughput
reduction ratio is 2.07/3 = 0.69, which is similar to thatof IEEE 802.11. Therefore, there is no degradation of thethroughput reduction ratio in our method.
4.2

Scenario 2

Scenario 2 is a five-node chain topology shown in Fig. 9.
Four nodes S1-S4 generate UDP packets to the destination
R. This type of topology causes the unfairness at both MAC
and link layers. Thus, the far distant flows suffer very small
throughput.
The fairness index and throughput comparison are
shown in Fig. 10, Fig. 11 and Fig. 12. It can be clearly seenfrom Fig. 10 that in our proposed method the fairness index is greatly improved compared to both PCRQ and 802.11
standard. In the conventional protocol 802.11, because of
the weakness of FIFO queue as analyzed in [16], in the FIFO
queue at S1, packets of the direct flow occupy almost all thebuffer space. Hence, packets from other flows cannot be enqueued because the queue is full of packets of the direct flow.
In addition, due to the effect of MAC unfairness problems
like large-EIFS [4], the nodes S2, S3, and S4 only have alittle chance for packet transmission.
On the other hand, PCRQ works by comparing queue
lengths of the flows, then it can achieve better fairness than802.11. However, when the offered load value is from 0.2
to 1.2 in Fig. 10, i.e., the link utilization is small, the queue
lengths are short, thus the difference of queue lengths among

IEICE TRANS. COMMUN., VOL.E100–B, NO.10 OCTOBER 2017

1824

Fig. 9Fig. 10

A five-node chain topology.

Fig. 13

Fairness index—Five-node chain topology.

A grid topology.

proposed method achieves good fairness. This result comesfrom a good combination of Algorithm 1 and Algorithm
2. Algorithm 1 contributes to the fairness among nodes bychannel utilization adjustment. In addition, in Algorithm 2,
we use packet dequeue rate that can reflect the MAC layer
state more quickly and more accurately than queue length.
Fig. 11 shows that our proposed method achieves better per-flow fairness than the others. In Fig. 12, the total
throughput in our proposed method is slightly smaller thanthe others. The reason for this is because our algorithm
gives some delay time which causes a slight reduction in
bandwidth efficiency.
Fig. 11Per-flow throughput—Five-node chain topology.

4.3

Fig. 12

Total throughput—Five-node chain topology.

flows is not so large, then PCRQ results in a medium levelof fairness. It takes time to reflect the change of the state of
the MAC layer to the change of the queue length. Therefore,a real time control is difficult by queue length.
When the offered load is larger than 0.2 in Fig. 10, our

Scenario 3

Scenario 3 in Fig. 13 is a combination of three-pair topology
and chain topology. This topology is called a grid topology.
In this topology, the distance between columns is 300[m]which is larger than the radius of transmission range 250[m]but smaller than the radius of carrier sensing range 550[m].
Nodes S1-S6 generate the same offered load G. This scenario
has both MAC layer problems (caused by both three-pair
topology and multi-hop topology) and link layer problem(caused by multi-hop topology).
The performance results are shown in Fig. 14, Fig. 15
and Fig. 16. As shown in Fig. 14, the fairness index in thecase of 802.11 is worst due to unfairness at both MAC andlink layers. The fairness index is slightly improved by PCRQ,
yet, PCRQ cannot solve the unfairness problem caused bythree-pair topology. The proposed method achieves better
fairness index than PCRQ and 802.11 standard. In Fig. 14,
we also show the result of “Algorithm 2 Only”, which means
only Algorithm 2 is used and Algorithm 1 is not used. We
can see from Fig. 14 that the fairness index of our proposed

TUAN et al.: IMPROVING FAIRNESS IN WIRELESS AD HOC NETWORKS BY CHANNEL ACCESS SENSING AT LINK LAYER AND PACKET RATE CONTROL

Fig. 14

Fairness index—Grid topology.

Fig. 17

Fig. 15

Total throughput—Grid topology.

method is better than “Algorithm 2 Only”. Scenario 3 has
both MAC and link layer problems, then Algorithm 1 works
effectively for solving the MAC layer problem and Algorithm
2 works for the link layer problem. This result proves thegood combination of Algorithms 1 and 2. In addition, weshow in Fig. 15 the per-flow throughput result at saturation
state when the offered load G equals the effective bandwidth(1.4[Mbps]), which shows the fairness improvement of the
proposed method. The total throughput results are shownin Fig. 16. From this figure, we can see that the throughput
in the proposed method slightly decreases. This is becauseof the trade-off between fairness and throughput that was
discussed at the end of 4.1 as well as the delay time causedby Algorithm 1.
4.4

Scenario 4.

Per-flow throughput—Grid topology.

Fig. 18

Fig. 161825

Scenario 4

In scenario 4, we consider a topology in Fig. 17 where both

Throughput and fairness results—Scenario 4.

UDP and TCP flows co-exist. In this topology, one UDP
flow from S2 and two TCP flows from S3 and S4 contend
for channel access. The UDP flow’s offered load is set equal
to the channel bandwidth 2[Mbps]. The performance results
are shown in Fig. 18. Flow 2 is the central flow and hence
less advantageous than Flow 1, but Flows 2 and 3 are TCP
flows, so packet transmission control is done by TCP, thenthe result of 802.11 in Fig. 18 shows that Flows 2 and 3 got
the same throughputs. Our proposed method results in abetter fairness index than PCRQ and 802.11 standard, andthe total throughput of our method is almost equal to theothers. In other words, our proposed method is effective
also for the case of mixture of UDP and TCP flows.
5.

Conclusion

In this paper, we proposed a method to solve the unfairness
problem in wireless ad hoc networks by using only local
information at the link layer of each node. Our method
consists of two algorithms. Algorithm 1 is effective for the
MAC layer fairness, and Algorithm 2 is effective for the link
layer fairness. The effectiveness of our proposed method
was verified by various simulation results.

IEICE TRANS. COMMUN., VOL.E100–B, NO.10 OCTOBER 2017

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6.

Future WorkIn this paper, we investigated the unfairness problem which
occurs at the MAC and link layers. However, the sameproblem occurs at other layers like transport layer [19], [20].
Therefore, it is necessary to study the unfairness problem atthose layers to build a good algorithm.
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Nguyen Minh Tuan
received his B.E. degreefrom Hanoi University of Science and Technology, Vietnam, in 2012. He worked for Samsung
Electronics Vietnam from 2012 to 2014. He
achieved his M.E. degree from Nagaoka University of Technology in 2016. His researchinterests are wireless ad hoc network, networkarchitecture and routing protocols.

Kohei Watabe
received his B.E. and M.E.
degrees in Engineering from Tokyo Metropolitan University, Tokyo, Japan, in 2009 and 2011,
respectively. He also received the Ph.D. degreefrom Osaka University, Japan, in 2014. He wasa JSPS research fellow (DC2) from April 2012
to March 2014. He has been an Assistant Professor of Department of Electrical Engineering,
Nagaoka University of Technology since April
2014. He is a member of the IEEE and the
IEICE.

Pham Thanh Giang
received his B.E. degree from Hanoi University of Technology, Vietnam, in 2002. He achieved M.E. and D.E. degrees from Nagaoka University of Technology,
Japan, in 2007 and 2010, respectively. He iscurrently a researcher at Institute of Information
Technology, Vietnam Academy of Science andTechnology. His interests are security, mobile ad
hoc networks, the Internet architecture in mobile
environments and Internet traffic measurement.

Kenji Nakagawa
received the B.S., M.S. and
D.S. degrees from Tokyo Institute of Technology,
Tokyo, Japan, 1980, 1982 and 1986, respectively.
In 1985, he joined NTT (Nippon Telegraph andTelephone Corp.). In 1992, he became an associate professor of Nagaoka University of Technology, and since 2012 he has been a professor.
He received 2012 IEICE Paper Award. His research interests include performance evaluationof network, queueing theory, and informationgeometry. He is a member of IEEE, Operations
Research Society of Japan, Mathematical Society of Japan and JapaneseSociety of Engineering Education.