Slbqt-Dsr: Source - based load balancing routing algorithm under constraints of quality of transmision for manet

t the traffic
distribution matrix Fs =
[
f
(s)
ij
]
n×n according to (3);
2: Based on the information of K available routes, construct the traffic dis-
tribution matrix F ′s =
[
f
′(s)
ij
]
n×n according to (5);
3: Construct the ILP problem according to the objective function(7) subject
to the constraints of (8) and (9);
4: Solving the ILP problem;
5: Select the load balancing route based on the results of the ILP problem
solving;
6: Update the information of the found route into the route cache of S;
4. SIMULATION RESULTS AND DISCUSSION
4.1. Simulation scenarios
In order to evaluate the performance of SLBQT-DSR algorithm, we use the simula-
tion method based on OMNeT++ discrete event simulator [23]. LBQT-DSR algorithm is
compared with DSR [9] and QTA-DSR algorithms [5] in terms of the SNR, BER, blocking
probability of the data packet, and network throughput. The simulation assumptions are
presented in Table 1. In addition, for each simulation scenario, the number of source nodes
SLBQT-DSR: SOURCE-BASED LOAD BALANCING ROUTING UNDER CONSTRAINTS OF QoT 277
is set to 30% of the the number of nodes, the remaining nodes are the destination nodes. For
example, for the scenarios of 30 nodes, there are 10 source nodes and 20 destination nodes.
Table 1. Simulation parameters
Parameters Setting Parameters Setting
Network Size 1000 m × 1000 m BER threshold 10−6
Modulation technique 256-QAM Minimum Required SNR 23.5 dB
MAC protocol 802.11ac Noise model Thermal noise
Number of nodes From 20 to 50 Temperature 3000K
Transmit Power 19.5 dBm Mobility model Random - Waypoint
Receiver Sensitivity -68 dBm Speed of nodes 5 - 20 m/s
Transmission Range 250 m Time simulation 40 minutes
4.2. Performance analysis
4.2.1. Evaluation of SNR and BER
The obtained results in Figure 6 show the SNR of all routes for the cases that DSR and
SLBQT-DSR algorithms are used. In this case, the networks size is 50 nodes, the channel
bandwidth is 40 MHz and the average mobility speed is 20 m/s. According to the simulation
scenarios presented in Section 4.1, the minimum required SNR for ensuring QoT is 23.5
dB. From the curves in Figure 6a, we can observe that for DSR algorithm, there are many
routes do not satify the constraint conditions of QoT due to the fact that its SNR is less
than required SNR. These routes account for 1.58%. This is the cause of the increasing
the packet blocking probability due to the unsatisfactory constraint conditions of QoT. For
SLBQT-DSR algorithm, SNR values improved significantly compared with DSR algorithm.
This is more clearly visible from Figure 6b, where we analyzed the SNR of all routes when
SLBQT-DSR is used. Most of the routes satify the constraint conditions of QoT due to the
fact that the QoT constraints were considered during the route discovery.
Since SNR decreases in case of the SLBQT-DSR is used, BER increases as shown in
Figure 7, where, we plot the BER of the routes as a function of the simulation times. In
Figure 6. The comparison the of SNR of (a) DSR and (b) SLBQT-DSR algorithms
278 LE HUU BINH, VO THANH TU, NGUYEN VAN TAM
Figure 7. The comparison of the BER of (a) DSR and (b) SLBQT-DSR algorithms
this case, the network size is 50 nodes, the channel bandwidth is 40 MHz and the average
mobility speed is 20 m/s. We can observe that, for DSR algorithm (Figure 7a), there are
many routes that its BER is greater than 1e-6. These routes do not satify the constraint
conditions of QoT. For SLBQT-DSR algorithm (Figure 7a), As the constraint conditions of
QoT ware considered during route discovery, most of the routes are that its BER is within
allowable limits, i.e it is less than 1e-6.
4.2.2. Evaluation of blocking probability
The blocking probability of data packets (BPD) is an important parameter with regard to
the network performance. In our context, BPD is given by
BPD =
Nb
Ng
, (30)
where Ng and Nb are the number of data packets are generated and the number of data pac-
kets are blocked in the overall network respectively. Nb includes two components, blocking
due to the congestion of the traffic load and blocking due to unsatisfactory constraint con-
ditions of QoT.
The obtained results in Figure 8 show the difference in the BPD of DSR, QTA-DSR and
SLBQT-DSR, where, we plot the BPD as a function of the traffic load in Erlang. These
results are simulated for the case that the number of node is 30, the average mobility speed
of the nodes is 20 m/s and the channel bandwidth is 40 MHz. The curves in Figure 8 show
that, if the traffic load is less than 0.8 Erlang, BPD of both QTA-DSR and SLBQT-DSR are
almost similar. In case of heavy traffic load, i.e from 0.8 to 1 Erlang, BPD of LBQT-DSR
algorithm reduced significantly compared with that of QTA-DSR algorithm. Considering the
case of the traffic load is 1 Erlang, BPD of QTA-DSR and LBQT-DSR algorithms are 0.031
and 0.025, respectively. Thus BPD of LBQT-DSR algorithm reduced by 0.0053 compared
with that of QTA-DSR algorithm.
Next, we analyze the case of the variable network size. The results obtained as shown in
Figure 9, where, we compared the BPD of the algorithms DSR, QTA-DSR and SLBQT-DSR.
These results are implemented for the case that the average mobility spend of the nodes is
SLBQT-DSR: SOURCE-BASED LOAD BALANCING ROUTING UNDER CONSTRAINTS OF QoT 279
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
Traffic load (Erlang)
BP
D
DSR
QTA-DSR
SLBQT-DSR
Figure 8. The comparison BPD versus the
traffic load of DSR, QTA-DSR and SLBQT-
DSR algorithms
0
0.01
0.02
0.03
0.04
0.05
30 35 40 45 50
Network size (nodes)
BP
D
DSR
QTA-DSR
SLBQT-DSR
Figure 9. The comparison BPD versus
the network size of DSR, QTA-DSR and
SLBQT-DSR algorithms
5m/s, the channel bandwidth is 40 MHz, and the traffic load is 0.95 Erlang. We can observe
that, for all algorithms, the larger the network size, the higher the PBD, due to the fact that
QoT decreases according to the network size. However, BPD of SLBQT-DSR algorithm is
always less than the other two algorithms. The average PBD reduction rate by 53.54% and
29.26% compared with DSR and QTA-DSR algorithms, respectively.
For the cases of the variable mobility speed, PBD of all three algorithms increases accor-
ding to the mobility speed of the nodes. This is more clearly visible from Figure 10. These
results are implemented for the case that the networks size and traffic load are 40 nodes and
0.95 Erlang, respectively. We can observe that, PBD reduced significantly in the cases of
QTA-DSR and SLBQT-DSR algorithms is used. In particular, the SLBQT-DSR algorithm
has the lowest BPD value, with a mean decrease of 46.72% compared to the DSR algorithm.
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
5 10 15 20
Mobility speed (m/s)
BP
D
DSR
QTA-DSR
SLBQT-DSR
Figure 10. The comparison BPD versus
the mobility speed of DSR, QTA-DSR and
SLBQT-DSR algorithms
56E+6
58E+6
60E+6
62E+6
64E+6
66E+6
68E+6
70E+6
72E+6
74E+6
0 50 100 150 200 250 300 350 400
SLBQT-DSR
DSR
Simulation times (s)
Th
ro
ug
hp
ut
 (b
it/s
)
Figure 11. The comparison throughput
versus the simulation times of DSR and
SLBQT-DSR algorithms
280 LE HUU BINH, VO THANH TU, NGUYEN VAN TAM
4.2.3. Evaluation of throughput
In this section, we analyze the average receiving throughput at each node. In Figure 11,
we plot the average throughput of each node as a function of simulation times for the case
that the network size is 50 nodes, the average mobility spend of the nodes is 5 m/s, and the
traffic load is 0.9 Erlang. The curves in Figure 11 show that, the throughput of SLBQT-DSR
algorithm increases significantly compared with DSR algorithm. The average throughput of
SLBQT-DSR and DSR algorithms are 67.385 and 65.482 Mbps, respectively. Thus, average
throughput of SLBQT-DSR increases 1.903 Mbps compared with DSR algorithm.
5. CONCLUSION
The load balancing routing is one of the optimal routing techniques to improve the
network performance of MANET network. With load balancing routing techniques, the
local congestion at some connections as well as intermediate nodes is minimized due to
the fact that traffic is distributed evenly for all connections in the network. However, for
the wide area and high node density MANET, the load balancing routing techniques can
decrease the QoT and increase the EED due to the fact that the data transmission routes can
pass through multiple hops. In this paper, we proposed a load balancing routing algorithm
for MANET taking into account the constraint of QoT and EED. The proposed algorithm
is called SLBQT-DSR which is improved from the route discovery algorithm of the DSR
protocol. The performance of SLBQT-DSR algorithm is demonstrated by the simulation
method using OMNeT++. The simulation results have shown that the proposed algorithm
can improve the network performance in terms of SNR, BER, blocking probability and
throughput compared with DSR and QTA-DSR algorithms.
In the near future, we continue to study the load balancing routing techniques taking
into account the QoT with respect to the other routing protocols of MANET, specially for
multi-channel MANET networks.
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Received on September 09, 2018
Revised on September 28, 2018