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ARTICLE IN PRESSJID: CAEE [m3Gsc;June 15, 2017;5:42]
Computers and Electrical Engineering 000 (2017) 1–13
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Computers and Electrical Engineering
journal homepage: www.elsevier.com/locate/compeleceng
Analysis of congestion control based on Engset loss
formula-inspired queue model in wireless networksR
Kavitha N.S.a,∗
, Malathi P.b
a
Department of Computer Science and Engineering, Sri Ramakrishna Institute of Technology, Coimbatore, Tamil Nadu, India
b
Professor and Principal, Sri Ranganathar Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
a r t i c l e i n f o
Article history:
Received 18 January 2017
Revised 25 May 2017
Accepted 31 May 2017
Available online xxx
Keywords:
Engset loss formula
Birth-and-death process
Steady-state equation
Poisson process
Blocking probability
a b s t r a c t
Congestion is considered as a potential cause for increased transmission delay when pack-
ets are relayed between the source and destination of a mobile ad hoc environment.
Thus, balancing the network resources in a distributed manner remains an indispensable
consideration in facilitating effective sharing. The efficient sharing of network resources
avoids load balancing in mobile nodes, thus preventing their rapid depletion. The proposed
method improves the packet delivery rate and throughput of the network and it enables
the process of load scheduling based on steady-state equations. Initially, it estimates the
minimum queue length based on individual routes, then it sorts the node-disjoint indi-
vidual routes and finally forwards the packets through the node-disjoint paths identified
as reliable. This facilitates improved performances of about 21%, 17% and 25% in terms of
packet delivery ratio, throughput and network overhead, compared to the benchmarked
baseline queue-based load balancing techniques.
© 2017 Published by Elsevier Ltd.
1. Introduction
The process of reliable data transmission between the source and destination nodes remains an unsolved problem in
mobile ad hoc networks (MANETs) as they lack centralized control for communication [1,2]. Most of the reliable data trans-
mission techniques proposed for MANETs rely on the concept of the shortest path, but this induces maximum levels of
congestion as a result of the overloading of packets in the networks [3]. This state of congestion is caused by the dynamic
mobility that leads to frequent route breaks, resulting in repeated retransmission of packets in the network [4]. Repeated
retransmission of packets incurs maximum delay as the routing overhead drastically increases due to the systematic increase
in the number of control packets [5]. The increase in the number of control packets significantly affects the performance
of the network [6]. Hence, the quality of the paths, an individual node’s forwarding potential and its blocking probabil-
ity, all need to be computed in order to distribute the load along the identified multiple routes for reducing the overload
of mobile nodes. ELFIQM is proposed, with the motivation of designing and implementing an Engset loss model-inspired
queuing technique for ensuring a distributed load balance among the nodes of the network. ELFIQM is suitable for an ad
hoc environment when the number of packet-generating sources is infinite and the number of packets generated by them
is finite. Furthermore, ELFIQM is a novel and reliable congestion policing technique that possesses the ability to discover
R
Reviews processed and recommended for publication to the Editor-in-Chief by Associate Editor Dr. S. Smys.
∗
Corresponding author.
E-mail address: nsksrit@gmail.com (K. N.S.).
http://dx.doi.org/10.1016/j.compeleceng.2017.05.031
0045-7906/© 2017 Published by Elsevier Ltd.
Please cite this article as: K. N.S., M. P., Analysis of congestion control based on Engset loss formula-inspired queue model
in wireless networks, Computers and Electrical Engineering (2017), http://dx.doi.org/10.1016/j.compeleceng.2017.05.031

2 K. N.S., M. P. / Computers and Electrical Engineering 000 (2017) 1–13
multiple paths which are capable of evenly distributing the load of the network over the multiple routes of the network in
an optimal way.
The major contributions of ELFIQM are as follows:
(1) An Engset loss model-based queuing model is formulated to overcome the limitations posed by most of the congestion
relay policies using shortest path methods.
(2) The advantage of an Engset loss model-based queuing model is that it can be used for estimating the state of congestion
experienced by nodes.
(3) The minimum queue length, congestion blocking probability and probability rate of congestion are used for the distribu-
tion of load in both the routing paths and the individual nodes.
(4) The congestion rate of the wireless network is also identified based on the determination of the steady-state probabilities
that highlight the idle and busy periods of the nodes and paths of the network.
The remaining sections of this paper are organized as follows. A short survey of some of the predominant techniques
for load balancing that can be compared and analyzed together with ELFIQM is presented in Section 2. Section 3 describes
the suitability, operation and merits of ELFIQM with the estimation of significant parameters for distributing load through
the route discovered. Section 4 highlights the simulation setting, simulation results and the analysis for quantifying the sig-
nificance of ELFIQM over the compared baseline load balancing schemes. Conclusions and future research directions arising
from the implementation of ELFIQM are presented in Section 5.
2. Related work
In the past decade, a number of queue-based load balancing techniques for dealing with congestion in MANETs have
been proposed and studied. Some of the significant work on handling the issue of congestion is discussed below.
Initially, an ad hoc on-demand multipath distance vector (AOMDV) routing algorithm which was the most prominent
technique for multi-path routing, was proposed [7]. AOMDV protocol identifies link-disjoint routes that exist between source
and destination node. In this AMODV protocol, a single-route request packet is broadcast by the source node which can be
referred to as RREQ. The link disconnectedness is analyzed by the intermediate node by verifying the received RREQ packet.
After receiving the RREQ packet, the destination node responds to the source node through route reply (RREP) packets in
a reverse routing path. The AMODV protocol maintains a directory of link-disconnected routes and selects any one of the
routes based on the minimum number of hop count parameters. The main drawback of AMODV is that the transmission
will not take place using multiple paths simultaneously. Instead the transmission uses a single path, but if it fails then an
alternate path is chosen. Javan and Dehghan in [8] analyzed various multipath routing protocols that utilize node-disjoint
paths for route packets. Many of the algorithms have the same limitation, i.e., the overlapping of routes. The overlapping
of routes is due to the dynamic nature of the wireless medium and the fact that every node in a wireless network follows
the packet-transmission pattern of its own neighbor nodes. The overlapping routes problem was addressed by some of the
authors. One solution to this problem is to implement the zone-disjoint routing protocol. The presented protocol identifies
two paths which do not have any common neighbors. The identified paths are referred to as zone-disjoint paths.
Abbas et al. presented a solution in [9,10] for reliable routing transmission based on a node-disjoint multipath ad hoc
routing algorithm. The proposed method of the node-disjoint path is entirely different from link-disjoint paths. One of the
main advantages of the proposed approach is that the neighboring hops in the route are not overloaded, whereas in link-
disjoint algorithms like AMODV, the common neighboring nodes present in the route are overburdened with respect to
performing the job of route identification. Sambasivam et al. recommended a route discovery algorithm in [11] which iden-
tifies multiple reliable routes that exist between the source and destination nodes. Connectivity is established by sending
packets along the routes. The strength and reliability of the path is evaluated by means of sending periodic packets. The op-
timal path is selected for packet transmission. However, the energy consumption of the node is mostly due to the enormous
processing requirements of route identification.
Shin et al. presented an algorithm in [12] which overcomes the limitation of the AMODV routing protocol. The author
presented an adaptive AMODV routing protocol which can change its path based on the congestion rate in the current
path. An optimal path is chosen in such a way that every identified path in the network was given a weighting at the
time of route discovery. The weighting is re-evaluated in order to select the optimal route. Geng et al. [13] presented a
load balancing protocol which handles network congestion using its load balancing property. The proposed protocol is a
coding-aware multicast protocol which can efficiently select paths based on the nodes’ coding opportunities.
Sun et al. [14] presented QoS multipath optimized link state routing (QoS-MOLSR) which is based on an enhanced Dijk-
stra algorithm. In this protocol, the discovery of different routes between source nodes and destination nodes in the network
is carried out by means of the Dijkstra algorithm. For a densely-populated network, and also for multimedia-oriented trans-
missions, the QoS-MOLSR algorithm provides effective performance. Previously presented algorithms had failed to perform
efficiently in multimedia-based transmissions and in heavily-loaded networks. Zhou and Hassanein [15] recommended a
load balancing routing algorithm referred to as LBAR. The LBAR protocol manipulates a novel evaluation parameter which
quantifies the node behavior based on the packet transmission rate. The active nodes in the routes are identified based on
which reliable routing path has been identified.

K. N.S., M. P. / Computers and Electrical Engineering 000 (2017) 1–13 3
Lee and Riley [16] presented a novel routing algorithm which estimates a threshold value depending on which traffic
overloading scale is used. Ganjali and Keshavarzian [17] presented multicast routing approaches which efficiently perform
the load balancing activities in order to improve the performance of the network. The only limitation of the presented
approach is that it attains better load balancing only when there are large numbers of paths. This problem is overcome in
the approach presented by Al-Qassas and Ould-Khaoua in [18], where traffic-aware routing techniques were proposed. Ray
and Turuk [19] summarized energy-efficient protocols which select the optimal route based on a cost metric. The limitation
of this type of selection is that the lifetime of the network may decrease due to increased energy consumption in the
intermediate nodes. This problem is solved by using an energy-aware routing protocol.
3. Engset loss formula-inspired queue modeling
The Engset loss formula is highly suitable for applications in mobile ad hoc networks as these networks show congestion
when the number of packets forwarded by each mobile node is comparatively large with respect to its packet-forwarding
potential. In Engset Loss Formula-Inspired Queue Modeling (ELFIQM), it is assumed that the time utilized by a node for
packet forwarding is independent of the time at which the packet arrives at that mobile node. The time used for packet
forwarding and the time utilized in the queue before packet forwarding by the mobile nodes are both exponentially dis-
tributed with mean times 1
λ and 1
μ respectively. Let the number of sources from which each mobile node receives packets
be S and the potential capability of mobile nodes under active routing be k with resistive probability Rp which defines the
maximum threshold probability possessed by the mobile node for policing packets. ELFIQM is found to follow a finite-source
loss model since these models have characteristics contrary to those of M/M/k/k models. In the M/M/k/k model of queuing,
the rate of packet arrival follows a Poisson process which can represent the concept of congestion when the number of
sources involved in the congestion is infinite. However, ELFIQM is proposed for handling congestion with an infinite number
of source nodes generating a finite number of packets. Unlike the traditional M/M/1 and M/M/∞ models, in ELFIQM the
rate of packet arrival is independent of the state and relies on the number of nodes from which the packets are received
by a specific node under congestion policing. The service rate of ELFIQM issμ. Furthermore, the arrival rate of packets to
each specific node is(k−S)λ. Hence, the cumulative time event that represents the arrival and service time of packets with
S sources and k active neighbors is exponentially distributed, with factor(k−S)λ+kμ.
3.1. Steady-state equations and solutions for ELFIQM
In ELFIQM, the steady-state equations of the model are considered as a finite-state birth-and-death process [20] that
describes the evolution of the queue under the impact of S sources and k interacting active neighbors. The steady-state
equations of ELFIQM are represented as
v0Sλ = v1μ (1)
v1(S − 1)λ = v22μ (2)
v2(S − 2)λ = v33μ (3)
and in general
vn(S − k)λ = vk+1(k + 1)μ (4)
where n=0, 1, 2,…, min (S, k)−1.
The aforementioned steady-state Eqs. (1)–(4) imply that the packet-forwarding capacity of each node in each state vi+1
depends on the multiple lengths of time that the packets remain in the queue before forwarding, and that this increases
linearly with the number of neighbors and sources that are active in communication. The steady-state equation also confirms
that a decrease in the number of neighbor nodes of a mobile node increases the number of packets that are stored in the
queue before forwarding.
Applying standard algebraic manipulations, πk is represented in terms of π0 as
πk = SCk
λ
μ
k
v0 (5)
This shows that πk depends on the selection of k neighbors from S sources under the traffic intensity ρ at each state.
Using traffic flow rateρ = λ
μ , the value of πkbecomes
πk = SCk(ρ)k
v0 (6)
where the sum of steady-state probabilities is equal to 1 under the constraint of the normalizing equation
min(S.k)
j=0
πj = 1 (7)

The integration of the traffic flow rate and the normalization equation pertaining to the steady-state probabilities from
Eqs. (6) and (7) are computed using (8) as
π0 =
1
min(S,k)
j=0
SCkρj
(8)
Eq. (8) highlights the cumulative steady probability that is initially experienced by each of the mobile nodes and (9) rep-
resents the cumulative steady probability that is experienced by each of the mobile nodes under the interaction of k neigh-
bors, where the expression is multiplied by the factor SCkρk to represent the influence of k neighbors in packet forwarding.
Thus πk, which represents the probability rate of congestion influenced by each active mobile node is
πk =
SCkρk
min(S,k)
j=0
SCkρj
(9)
where n=0, 1, 2,…,min (S, k).
Eq. (9) is based on the computation of πk which relates to the congestion rate of each active node k under communica-
tion. The congestion of each mobile node based on time and packet forwarding is different from the Engset model as this
does not follow a Poisson process. The probability that describes the rate at which the mobile node exhibits resistivity to
congestion is
Rp =
λ(S − k)vk
λ
k
i=0
(S − i)vk
(10)
By substituting Eqs. (5) and (6) in (10) and by carrying out algebraic manipulations, the Engset loss formula that quanti-
fies the rate of blocking probability for each mobile node for load balance triggering is given by
Rp =
(S − 1)Ckρk
k
i=0
(S − 1)Ckρk
(11)
Based on the computation of Rp, the dynamic congestion policing rate of packets is incorporated for handling congestion.
Thus, an Engset loss model-based queuing model that can be used for estimating the state of congestion experienced by
nodes is a major contribution for two reasons:
a) The Engset model is reliable even when the number of neighbors’ packets forwarding rate is drastically increased.
b) The service rates of packets are dynamically improved and hence the state of congestion is effectively policed.
4. Simulation environment
The simulated network consists of 100 nodes, randomly distributed in a rectangular area of 1000×1000 meters. Each
simulation runs for 300 s and the collected data are averaged for each point. IEEE 802.11 is used as the underlying MAC-
layer communication model with the data rate and radio range set to 2 Mbps and 250 m respectively. This environment
also uses the random waypoint as the node mobility model with a minimum and maximum speed of 10 m/s and 30 m/s
respectively. The number of source and destination pairs varies between 10 and 40 with a packet size of 512 bytes.
To simulate the algorithms, suitable simulation parameters are identified and tabulated in Table 1.
4.1. Results and discussion
The performance of ELFIQM is evaluated through four experiments: i) varying the mobile nodes, ii) varying the transmis-
sion rate, iii) varying the node speed and iv) varying the pause time.
4.1.1. Experiment 1 – performance analysis of ELFIQM based on varying the number of mobile nodes
In experiment 1, the performance of ELFIQM is initially evaluated by comparing it with the existing benchmark con-
gestion control approaches such as QMLB, BRSR-AOMDV and LBAR. The comparative analysis is performed by varying the
number of mobile nodes from 10 to 100 in increments of 10.
Fig. 1 presents the plots of packet delivery ratio achieved versus the number of mobile nodes involved in data transmis-
sion. It is shown that the packet delivery ratio of all the implemented congestion control mechanisms decreases system-
atically with an increase in the number of mobile nodes. This decrease in packet delivery ratio is mainly due to the lack
of a reliable process for policing the enormous amount of data introduced into the network. However, ELFIQM achieves a
better packet delivery ratio than QMLB, BRSR-AOMDV and LBAR by utilizing an Engset loss formula-inspired queue model
for policing the rate of congestion. Hence, ELFIQM shows an improvement of 9% to 12% in PDR compared to QMLB, of 15% to

Table 1
Simulation parameters for ELFIQM.
Parameters Values
Mobile nodes 100
Simulation area 1000×1000m
Mobility model Random waypoint
Simulation time 300 s
Node placement Random
Transmission power 15 dbm
Pause time 10 s
Data source CBR
Minimum velocity of node 10 m/s
Maximum velocity of node 30 m/s
MAC protocol 802.11b
Propagation type Two-ray ground
Channel capacity 2 Mbps
10 20 30 40 50 60 70 80 90 100
40
45
50
55
60
65
70
75
80
85
90
NUMBER OF MOBILE NODES
PACKETDELIVERYRATIO
ELFIQM
QMLB
BRSR-AOMDV
LBAR
Fig. 1. Experiment 1. ELFIQM-packet delivery ratio.
19% compared to BRSR-AOMDV and of 21% to 24% compared to LBAR. In addition, ELFIQM shows on average a phenomenal
improvement of 18% in packet delivery ratio.
Similarly, Fig. 2 shows that the throughput of the network decreases with an increase in the number of transmitting
nodes, since the cumulative number of packets dropped per second increases gradually with an increase in the number of
transmitting nodes. Nevertheless, ELFIQM is capable of improving the throughput of the network by employing a reliable
congestion-control policy process that is eﬃcient enough to increase the rate of packet delivery through reliable nodes.
ELFIQM exhibits an increase of 6% to 9% in throughput compared to QMLB, 11% to 14% compared to BRSR-AOMDV and 17%
to 19% compared to LBAR. It is also clear that ELFIQM increases the throughput of the network by 16% on average.
The increase in the number of mobile nodes of an ad hoc network proportionally increases the number of transmissions
which in turn increases the end-to-end delay and the network overhead as shown in Figs. 3 and 4. However, ELFIQM reduces
the end-to-end delay by 11% to 15% compared to QMLB, by 17% to 20% compared to BRSR-AOMDV and by 23% to 26%
compared to LBAR, by enforcing rapid congestion-control queuing to a maximum level of 25%.
ELFIQM also demonstrates that the network overhead is reduced by 10% to 12% compared to QMLB, by 14% to 16%
compared to BRSR-AOMDV and by 18% to 20% compared to LBAR. In addition to this, the results also conﬁrm that ELFIQM
reduces the end-to-end delay and network overhead by an average of 19% and 16% respectively.
4.1.2. Experiment 2 – performance analysis of ELFIQM based on varying the transmission rate
In experiment 2, the performance of ELFIQM is evaluated by varying the transmission rate from 5 packets/sec to 50
packets/sec. Figs. 5 and 6 show the plots of packet delivery ratio and throughput for ELFIQM, QMLB, BRSR-AOMDV and
LBAR.

10 20 30 40 50 60 70 80 90 100
40
45
50
55
60
65
70
75
80
THROUGHPUT
ELFIQM
QMLB
BRSR-AOMDV
LBAR
Fig. 2. Experiment 1. ELFIQM-throughput.
10 20 30 40 50 60 70 80 90 100
0.32
0.34
0.36
0.38
0.4
0.42
0.44
0.46
0.48
0.5
END-TO-ENDDELAY(insec)
ELFIQM
QMLB
BRSR-AODMDV
LBAR
Fig. 3. Experiment 1. ELFIQM-end-to-end delay.
Figs. 5 and 6 present the packet delivery ratio and throughput of ELFIQM studied under the inﬂuence of varying transmis-
sion rates. The values of PDR and throughput decrease with an increase in the number of packets that need to be forwarded
under the impact of congestion. However, ELFIQM handles this impact by employing a reliable congestion-synchronizing
mechanism that derives beneﬁts from the Engset loss model of queueing theory. ELFIQM shows an improvement of 8% to
11% in PDR compared to QMLB, of 13% to 16% compared to BRSR-AOMDV and of 19% to 21% compared to LBAR. Likewise,
ELFIQM shows an improvement of 12% to 15% in throughput compared to QMLB, of 17% to 19% compared to BRSR-AOMDV
and of 22% to 25% compared to LBAR.
Figs. 7 and 8 show the plots relating to the end-to-end delay and the network overhead respectively versus the rate of
transmission in an ad hoc scenario. The end-to-end delay and network overhead systematically increase with an increase
in the number of data packets to be enforced for congestion. Thus, ELFIQM demonstrates that the end-to-end delay is
reduced by 14% to 17% compared to QMLB, by 19% to 21% compared to BRSR-AOMDV and by 25% to 28% compared to LBAR.

10 20 30 40 50 60 70 80 90 100
3500
4000
4500
5000
5500
6000
6500
NETWOKOVERHEAD(inbytes)
ELFIQM
QMLB
BRSR-AOMDV
LBAR
Fig. 4. Experiment 1. ELFIQM-network overhead.
5 10 15 20 25 30 35 40 45 50
40
45
50
55
60
65
70
75
80
85
TRANSMISSON RATE(packets/sec)
PACKETDELIVERYRATIO
ELFIQM
QMLB
BRSR-AOMDV
LBAR
Fig. 5. Experiment 2. ELFIQM-packet delivery ratio.
Furthermore, ELFIQM reduces the network overhead by 15% to 18% compared to QMLB, by 19% to 22% compared to BRSR-
AOMDV and by 26% to 28% compared to LBAR. In addition, the results confirm that ELFIQM reduces the end-to-end delay
and network overhead by an average of 21% and 23% respectively.
4.1.3. Experiment 3 – performance analysis of ELFIQM based on varying the node speed
In experiment 3, the performance of ELFIQM compared to QMLB, BRSR-AOMDV and LBAR is analyzed with varying node
speed. ELFIQM exhibits an improved performance in terms of throughput. However, the results indicate that the significance
of this improvement using ELFIQM is slightly higher than its performance identified at the crucial point of congestion polic-
ing. From Fig. 9, it is clear that ELFIQM increases the throughput by 8% to 13% compared to QMLB, by 16% to 20% compared
to BRSR-AOMDV and by 23% to 27% compared to LBAR. In addition, it is observed that, ELFIQM improves the throughput
on average by 19.5%. Furthermore, the results from Figs. 10 and 11 indicate that ELFIQM significantly improves the network
performance by decreasing the total overhead and energy consumption. This is due to a reduction in the number of retrans-

5 10 15 20 25 30 35 40 45 50
30
35
40
45
50
55
60
65
70
TRANSMISSION RATE(packets/sec
THROUGHPUT
ELFIQM
QMLB
BRSR-AODMDV
LBAR
5 10 15 20 25 30 35 40 45 50
0.32
0.34
0.36
0.38
0.4
0.42
0.44
0.46
0.48
0.5
TRANSMISSION RATE(packets/sec)
END-TO-ENDDELAY(insec)
ELFIQM
QMLB
BRSR-AODMDV
LBAR
Fig. 7. Experiment 2. ELFIQM-end-to-end delay.
missions and in the energy consumption at the point of congestion policing facilitated by ELFIQM. Thus, the performance of
ELFIQM is found to be slightly higher than that of QMLB, BRSR-AOMDV or LBAR.
Fig. 10 shows that ELFIQM reduces the total overhead by 15% to 18% compared to QMLB, by 20% to 23% compared to
BRSR-AOMDV and by 26% to 34% compared to LBAR. Similarly, Fig. 11 shows that ELFIQM reduces the energy consumption
rate by 11% to 14% compared to QMLB, by 17% to 20% compared to BRSR-AOMDV and by 22% to 25% compared to LBAR.
Hence, it is clear that ELFIQM is highly effective in reducing the total overhead and energy consumption by an average of
17% and 23% respectively.
4.1.4. Experiment 4 – performance analysis of ELFIQM based on varying the pause time
In this experiment, the performance of ELFIQM compared to QMLB, BRSR-AOMDV and LBAR is analyzed. ELFIQM exhibits
an improved performance in terms of throughput which is slightly lower than the performance exhibited at its maximum
point of congestion policing. From Fig. 12, it is clear that ELFIQM increases the throughput by 5% to 7% compared to QMLB,
by 10% to 13% compared to BRSR-AOMDV and by 16% to 19% compared to LBAR. In addition, it is observed that ELFIQM
improves the throughput by an average of 15%.

5 10 15 20 25 30 35 40 45 50
3500
4000
4500
5000
5500
6000
6500
TRANSMISSION RATE(packets/sec)
NETWORKOVERHEAD(inbytes)
ELFIQM
QMLB
BRSR-AODMDV
LBAR
Fig. 8. Experiment 2. ELFIQM-network overhead.
10 15 20 25 30 35 40 45 50
45
50
55
60
65
70
NODE SPEED(metre/sec)
THROUGHPUT
ELFIQM
QMLB
BRSR-AODMDV
LBAR
Fig. 13 indicates that ELFIQM also signiﬁcantly improves the network performance. This is achieved by decreasing the to-
tal overhead as the pause time varies systematically step by step. Thus Fig. 13 shows that ELFIQM reduces the total overhead
by 12% to 14% compared to QMLB, by 16% to 18% compared to BRSR-AOMDV and by 22% to 25% compared to LBAR.
5. Conclusion
In this paper, ELFIQM was proposed for handling congestion through the incorporation of a load balance approach based
on the computation of minimum queue length, congestion-blocking probability and stochastic congestion probability. The
characteristic performance of ELFIQM includes the following steps. Initially, multiple routes between the source and desti-
nation are identiﬁed, and subsequently the load is distributed among the nodes and routes of the network according to the
blocking congestion probability, congestion probability rate and minimum queue length based on the Engset loss model. The
potential performance of ELFIQM is investigated based on evaluation metrics such as end-to-end delay, network overhead
and packet delivery ratio. The performance of ELFIQM is compared with three important load balancing methods found in

10 15 20 25 30 35 40 45 50
1.3
1.35
1.4
1.45
1.5
1.55
TOTALOVERHEAD
ELFIQM
QMLB
BRSR-AODMDV
LBAR
Fig. 10. Experiment 3. ELFIQM-total overhead.
10 15 20 25 30 35 40 45 50
80
100
120
140
160
180
200
ENERGYCONSUMPTIONS(mJ)
ELFIQM
QMLB
BRSR-AODMDV
LBAR
Fig. 11. Experiment 3. ELFIQM-energy consumption.
the literature, i.e., QMLB, BRSR-AODMDV and LBAR. The simulation results confirm that ELFIQM performs better than the
QMLB, BRSR-AODMDV and LBAR load balancing approaches with respect to packet delivery ratio, throughput, control over-
head and total overhead when evaluated by dynamically varying the number of mobile nodes, the transmission rate, the
node speed and the pause time. The results also show that ELFIQM is more successful than the QMLB, BRSR-AODMDV and
LBAR load balancing techniques by demonstrating mean packet delivery rate and throughput improvements. ELFIQM also
has the potential to reduce the control overhead and total overhead by up to 23% and 16% respectively. The performance of
ELFIQM is also analyzed using significant mobility models such as the random waypoint and Markov models. In the near
future, it is planned to propose Markov-modulated queue-based modeling for effective and efficient load balancing among
the routes and nodes of the network. It is also proposed to analyze the stochastic properties of a queue such that it could
suitably be employed in various critical applications where the state behavior of the queue is the significant parameter of
greatest concern.

10 15 20 25 30 35 40 45 50
45
50
55
60
65
70
75
PAUSE TIME(sec)
THROUGHPUT
ELFIQM
QMLB
BRSR-AODMDV
LBAR
10 15 20 25 30 35 40 45 50
1.35
1.4
1.45
1.5
1.55
1.6
1.65
1.7
PAUSE TIME(sec)
TOTALOVERHEAD
ELFIQM
QMLB
BRSR-AODMDV
LBAR
Fig. 13. Experiment 4. ELFIQM-total overhead.
References
[1] Gao X, Zhang X, Shi D, Zou F, Zhu W. Contention and queue-aware routing protocol for mobile ad hoc networks. 2007 international conference on
wireless communications, networking and mobile computing 2007;2(3):121–34.
[2] Naseem M, Kumar C. Congestion-aware fibonacci sequence based multipath load balancing routing protocol for MANETs. Wireless Personal Commun.
2015;84(4):2955–74.
[3] Varaprasad G. Stable routing algorithm for mobile ad hoc networks using mobile agent. Int J Commun Syst 2012;27(1):163–70.
[4] Sun Y, Sun J, Zhao F, Hu Z. Delay constraint multipath routing for wireless multimedia ad hoc networks. Int J Commun Syst 2014;29(1):210–25.
[5] Naseem M, Kumar C. EDSDV: efficient DSDV routing protocol for MANET. 2013 IEEE international conference on computational intelligence and com-
puting research 2013;1(2):34–51.
[6] Al-Qassas R, Ould-Khaoua M, Mackenzie L. Performance evaluation of a new end-to-end traffic-aware routing in MANETs. 12th international conference
on parallel and distributed systems - (ICPADS’06) 2006;2(1):23–34.
[7] Marina MK, Das SR. Ad hoc on-demand multipath distance vector routing. ACM SIGMOBILE Mob Comput Commun Rev 2006;6(3):969–88.
[8] Javan NT, Dehghan M. Reducing end-to-end delay in multi-path routing algorithms for mobile ad hoc networks. In: Mobile ad-hoc and sensor net-
works, Vol. 4864. Beijing, China: Springer Berlin Heidelberg; 2007. p. 715–24.

[9] Abbas AM, Khandpur P, Jain BN. A disjoint alternate path routing for mobile ad hoc networks. J Internet Technol 2005;6(1):111–20.
[10] Abbas AM, Jain BN. Mitigating path diminution in disjoint multipath routing for mobile ad hoc networks. Int J Ad Hoc Ubiquit Compu.
2006;1(3):137–46.
[11] Sambasivam P, Murthy A, Belding-Royer EM. Dynamically adaptive multipath routing based on AODV. In: Proceedings of the 3rd annual mediterranean
ad hoc networking workshop, Bodrum, Turkey; 2004. p. 106–17.
[12] Shin D, Lee J, Kim J, Song J. A2 OMDV: an adaptive ad hoc on-demand multipath distance vector routing protocol using dynamic route switching. J
Eng Sci Technol 2009;4(2):171–83.
[13] Geng R, Ning Z, Ye N. A load-balancing and coding-aware multicast protocol for mobile ad hoc networks. Int J Commun Syst 2015.
[14] Sun Y, Sun J, Zhao F, Hu Z. Delay constraint multipath routing for wireless multimedia ad hoc networks. Int J Commun Syst 2014;29(1):210–25.
[15] Zhou A, Hassanein H. Load-balanced wireless ad hoc routing. In: Canadian conference on electrical and computer engineering, 2001, Toronto, Ontario,
Canada, Vol. 2; 2001. p. 1157–61.
[16] Lee YJ, Riley GF. A workload-based adaptive load-balancing technique for mobile ad hoc networks. In: Wireless communications and networking confer-
ence, New Orleans, LA, USA, 2005, Vol. 4. IEEE; 2005. p. 2002–7.
[17] Ganjali Y, Keshavarzian A. Load balancing in ad hoc networks: single-path routing vs. multi-path routing. In: INFOCOM 2004. twenty-third annual joint
conference of the IEEE computer and communications societies, Hong Kong, China, Vol. 2; 2004. p. 1120–5.
[18] Al-Qassas RS, Ould-Khaoua M. Performance comparison of end-to-end and on-the-spot traﬃc-aware techniques. Int J Commun Syst 2013;26(1):13–33.
[19] Ray NK, Turuk AK. A technique to improve network lifetime in mobile ad hoc networks. Int J Commun Syst 2014;29(5):840–58.
[20] Wolff RW. Problems of statistical inference for birth and death queueing models. Oper Res 1965;13(3):343–57.

Kavitha N.S. completed her B.E and M.E degrees in Computer Science and Engineering at Dr. Mahalingam College of Engineering and Technology and
Karunya Institute of Technology, Coimbatore, Tamilnadu, India. She is currently working as an Assistant Professor (Senior Grade) at Sri Ramakrishna Institute
of Technology. Her research interests include wireless networks and mobile ad hoc networks.
Malathi P. received her M.E degree in Applied Electronics from Coimbatore Institute of Technology, Coimbatore, Tamilnadu, India. She completed her Ph.D.
at PSG College of Technology, Coimbatore, Tamilnadu, India. She has 20 years’ teaching experience. She is a member of IEEE, ISTE and IETE. Her research
areas include OFDM, MIMO, WLAN, PLC and wireless mobile communication.

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