Resbee Publishers
Journal of Networking and Communication Systems
Received 23 April, Revised 20 July, Accepted 13 July
Hybrid Butterfly Optimization and Particle Swarm
Optimization Algorithm for Video Transmission in VANET
Amal Abdulrahman Juma Al Raisi
Department of Process Engineering,
International Maritime College Suhar OM, Oman
amalabdulrahmanj@gmail.com
Abstract: In Vehicular Ad Hoc Network (VANET), the communication amid the vehicle plays an important role in the
enhancement of security in dangerous circumstances of road cases. The video transmission to other vehicles with the
VANET implementation is performed in an enhanced manner. In this work, a novel hybrid method named the Butterfly
Optimization (BO)- Particle Swarm Optimization (PSO) algorithm is adopted in order to decide the optimal multipath for
the video transmission from one vehicle to another in the VANET network. At first, VANET has experimented as well as
the optimal multipath is selected and it is performed by exploiting the adaptive geographic routing model on basis of fitness
metrics. The adopted model performance is calculated using the measures namely, Packet end-to-end delay, Packet
Delivery Ratio (PDR), as well as throughput. The proposed method yields the least end-to-end delay, as well as utmost
PDR and throughput that exhibit the advantage of the proposed technique in efficient video transmission.
Keywords: Optimization, Packet Delivery Ratio, Routing Model, VANET, Video Transmission.
Nomenclature
Abbreviations Descriptions
VANET Vehicular Ad Hoc Network
BO Butterfly Optimization
PDR Packet Delivery Ratio
PSO Particle Swarm Optimization
MIMO Multiple Input Multiple Output
DSR Dynamic Source Routing
ACO Ant Colony Optimization
FMO Flexible Macroblock Ordering
AODV Ad Hoc On-Demand Distance Vector
MDC Multiple Description Coding
En-AODV enhanced model of AODV
g-MMDSR game theoretic-Multipath Multimedia Dynamic Source Routing
DYMO Dynamic MANET on demand
FEC Forward Error Correction
GA Genetic Algorithm
MAC Medium Access Control
1.Introduction
VANETs are amid the up-and-coming network paradigms that aid secured measures and improve data
transfer with improved effectiveness. VANET applications comprise warning of collision, warning of pre-
crash, warning of an emergency vehicle, and warning of traffic circumstance and notification of hazardous
position. For VANETs the important suggestion is illustrious to offer verification and trust. It is required
to describe the routing as well as data dissemination approaches to enable the VANET applications. The
routing problem is a challenging task although vehicles travel within set lanes. For a vehicle to enhance
the safety metrics on highways, method named customizing channel access probabilities was presented
[1].
Resbee Publishers Vol.4 No.3 2021
https://doi.org/10.46253/jnacs.v4i3.a2
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Hybrid Butterfly Optimization and Particle Swarm Optimization Algorithm for Video Transmission In VANET
To improve video streaming quality by VANET issue, it is required to present an appropriate routing
protocol for the VANET. Few routing protocols namely DSR, DYMO, and AODV were presented for the
MANET routing were exploited to the VANET routing. Although these protocols have ensued in a better
performance they possess unsuccessful to alter to delay happening because of data packets transmission.
For the VANET, in order to develop the appropriate routing protocol, it is necessary to connect the
topology beside network factors [13]. In some recent works, the Particle Swarm Optimization [14] and
ACO were used to develop for the VANET, and few pieces of research were adopted for communication the
Geographic Routing Protocol in VANET [2].
In mobile video streaming applications, there is a current trend to exploit MDC in few studies. In
multi-source video streaming, the MDC is considered via VANET. Hence, spatial decomposition was
exploited by means of the checkerboard’s FMO, an H.264/AVC error resilience approach. MDC on the
basis of H.264/AVC was combined with MIMO transmission exploiting numerous antennas to improve
train to wayside video transmissions in tunnels. For a wavelet coded multi-stream video transmission, a
cross-layer method over multihop wireless networks [3].
The main contribution of this work is to propose an effective method for video transmission in VANET
from one vehicle to other vehicles to protect the vehicles from dangerous circumstances which happen on
highways. At first, VANET is experimented with to decide the count of routes for video transmission with
adaptive geographic routing models. By exploiting the newly adopted method, the multiple paths are
revealed that are attained from the combination of PSO and BOA technique. Using the proposed model,
the fitness metrics are computed based upon several metrics like distance, delay, as well as QoS
parameters. To transfer the videos, the optimal multipath having the least distance, delay, and fulfilled
the QoS is chosen. By exploiting the HEVC encoder, the video is encoded as well as the transmission
packet is performed using the multipath which is optimally chosen.
2. Literature Review
In 2018, Shivaprasad More and Udaykumar Naik [1] focused on both the multi-hop routing and urgent
data dissemination by means of choosing the optimal data disseminator as well as the trustworthy
forwarder. For a user request, this paper comprises effective video transmission.
In 2018, Shivaprasad More and Udaykumar L. Naik [2], proposed a multipath routing technique for
video transmission by exploiting the optimization method. To encode the HEVC, the encoding method
was used because the video files were large to make a bitstream. Subsequently, the probable paths among
the sender and receiver were identified. For video transmission, this paper uses the GA method to choose
the optimal multipaths from the path. The bitstream was transferred subsequent to the optimal
multipath recognitions via the multipath to attain the destinations.
In 2019, Shivaprasasd More et al [3], worked on the VANETs multipath routing. Here, the routing
protocols such as distance-based routing as well as location-based routing were strongly exploited. To
recognize the optimized path amid the multipath environment was an important monotonous task to
finish. By exploiting few meta-heuristic approaches, multipath routing optimization was attained. Hence,
this work exploits the multi-hop environment to choose one path between numerous paths by exploiting
the GA.
In 2011, Mónica Aguilar Igartua et al [4], developed a method named called g-MMDSR that comprises
a game-theoretic model with a cross-layer multipath routing protocol to attain a dynamic selection of the
forwarding paths. The proposed method searches to enhance the individual advantages of the users when
by exploiting the general scarce resource effectually. Here, the video frames' significance was considered
in the decoding procedures that were better than the received video quality.
In 2017, Boubakeur Moussaoui et al [5], developed an En-AODV protocol it was exploited to tackle the
routes unsteadiness problem in VANETS. In the link, En-AODV leverages cross-layer information was
integrated by means of ultimate destination knowledge of destination vehicle to set up high stable routes.
The attained experimentation outcomes verify the En-AODV effectiveness and spotlight its superiority
against AODV in several measures and cases.
In 2019, Mohamed Aymen Labiod et al [6], proposed an innovative cross-layer system to enhance
attained video quality in vehicular communications. In an MDC technique, the system was minimum
complex. Finally, it was on the basis of an adaptive mapping model and it has experimented with in the
IEEE 802.11p standard MAC layer.
3. VANET system model
For stipulation of V2I as well as V2V communication, VANET is considered an extraordinary example of
MANET. One of the most important applications of the VANET is to present safe, secure as well as
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Hybrid Butterfly Optimization and Particle Swarm Optimization Algorithm for Video Transmission In VANET
pleasant drives for the travelers who ride on the road in vehicles. Hence, every vehicle is presented with
an individual electronic device in order to connect the vehicle with the VANET. No fixed server
communication and infrastructures are present in the case of VANETS. The VANET architecture model
is exhibited in Fig 1, in that all vehicles comprise of a VANET device, which performs as a node in
VANET. By means of the wireless network, the whole vehicle communicates with each other [7] in the
network. Moreover, multimedia and internet facilities are offered to the passengers.
If any accidents or an unexpected braking issue happen in any of the vehicles, the VANET system will
transfer the emergency signal. In the VANET model, one of the main enhancements is the advancement
in the driver supporter system. From this system, the driver is permitted to help event which occurs
beyond his eye-sight. The driver accepts the information by exploiting this security system about the
events like a traffic jam, accident, traffic signal information which outcomes in high safety and effectual
driving.
Vehicle-vehicle Vehicle- Road Side
communication communication
Road side
Unit
Emergency
VANET
event
Fig. 1 System model of VANET
4. Adopted model for multipath routing in VANET
The main objective of this work is to propose a routing protocol to achieve multipath routing in order to
transmit the videos with an optimal path selection in VANETS by using an optimization technique. At
first, by exploiting the adaptive geographic routing model, the VANET has experimented as well as a
number of routes to transfer videos from the source to the destination [8]. On the basis of the several
metrics like delay, distance and QoS parameters the fitness metrics are developed, and the optimal paths
with the least delay, distances as well as fulfilled QoS are chosen for video transmission. Subsequent to
the encoding, video by use of HEVC, via the multipath the packet transmission is performed, which is
chosen optimally for the video transmission from the source to the destination. Fig 2 depicts the
architecture model of the developed multipath routing in VANET.
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Hybrid Butterfly Optimization and Particle Swarm Optimization Algorithm for Video Transmission In VANET
Video to be
VANET Source to transmitted
destination
Path selection
HEVC encoder
for video
transmission
Bit Stream
k path discovery
Selection of m factor
using adaptive
geographic concept
Optimal multipath
selection
Butterfly
Optimization
Algorithm
Butterfly
Multipath Routing
Particle swarm
Optimization Particle Swarm
Optimization
Algorithm
Fig. 2 Architecture model of multipath routing in VANET
4.1 HEVC Encoder for Video Compression
For the video compression, the High-Efficiency Video Coding (HEVC) [9] is considered as the current
standard method, which represents as a substitute of the Advanced Video Coding, called H.264/MPEG-4
AVC.
While comparing with the present de-facto video standard called H.264, the HEVC guarantees a half-
bit rate with the similar quality video exploited in several kinds of video applications which ranges from
cell phones, broadcast, video conferencing, automotive, and etc. In compression, because of the
attendance of maximized effectuality in compression, the HEVC set up the Super HD resolution which
enables the transparency quality as minimum as 20 Mbps. On the basis of the application area, the
performance of the HEVC video solution deviates. By exploiting the HEVC, the video which is required to
be encoded is subjected as input to progressive scan imagery. In the HEVC design, there happen no
coding features to aid the interlaced scanning that is exploited minimum generally for the distribution
process and not exploited in displays. Nevertheless, metadata syntax permits the encoder to show that
the interlace-scanned video is transmitted subsequent to the coding of all areas. The interlace-scanned
video is transmitted, each video frame which consists of the even and odd-numbered lines as an
individual image or it is transmitted subsequent to the coding the complete interlaced frame as an HEVC
coded image. The bitstreams are produced by the compressed video which is subjected as input to the
routing procedure to attain optimal path in order to transfer the video. In the VANET, by exploiting the
shortest path algorithm, the utmost probable number of paths is attained.
4.2 K-path Discovery
For the k-path determination, Dijkstra’s shortest path method is exploited from the sender to each and
every vehicle available in VANET. Let, k as the utmost probable paths presented in network.
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Hybrid Butterfly Optimization and Particle Swarm Optimization Algorithm for Video Transmission In VANET
4.3 Exploiting Adaptive Geographic Perception for m Factor Selection
In the urban areas, for the establishment of video transmission, the adaptive geographic routing model is
exploited in the urban areas. By exploiting this idea, a huge number of paths can be attained among the
destination vehicles and the source.
In this model, from k number of probable paths m number of paths is identified.
k
X Min Mi (1)
i 1
Mi
Y , mY (2)
X
whereas, Mi indicates the utmost bits to be transmitted, X indicates the minimum bit factor which
states the minimum bit transferring capability of the produced k-paths, m indicates a number of the
needed multipath, as well as Y denotes the least bits required to be available in chosen multipath.
4.4 Proposed model for Optimal Path Selection
Exploiting the proposed BO-PSO method, which is the combination of the BOA and the PSO [12] method
optimal m paths are chosen. The BOA technique is a new optimization model which is perfect to find the
optimal solution. Similarly, the PSO has the capability to attain the optimal solutions in an effective way
within a minimum time period. Thus, to attain the optimal solution in an improved way both the
techniques are integrated to generate the developed BO-PSO model.
i) Solution encoding
By exploiting the developed optimization approach, the solution vector indicates the optimal solution is
ascertained. Here, k count of paths is identified as well as optimal m - paths are chosen from k -paths to
attain effectual video transmission in VANET. The chosen m paths should be lesser than identified k
paths. Every path denotes the intermediary nodes occupied in communication.
ii) Fitness evaluation
In optimal path estimation, the fitness factor evaluation is significant. The fitness metric is calculated
as,
Pr,s Qr,s Rr,s
1
Fitness (3)
ac
r 1s 1
whereas, c states the count of nodes in the r th path, a indicates the number of multipath, Prs is the
delay in transmission, Rrs indicates the Quality of service Qrs indicates the distance among the vehicles
in VANET.
a) Delay: It indicates the delay related to the transmission of packets among the nodes in VANET.
b) Distance: The distance among the neighboring nodes of a path is called distance, it indicated as,
Dis tan ce, Qr,s
Q Dr,s , Dr,s 1 (4)
M
whereas, M indicates the normalization Factor.
c) QoS: It is derived on the basis of the QoS of packet transmission, sender, and receiver bytes.
Quality of Service, Rr,s
A r,s Sr,s (5)
2
whereas, A r ,s indicates the QoS concerning packet transmission, Sr,s indicates the QoS regarding the
sender and receiver bytes. The QoS concerning packet transmission is computed on basis of packet
received ratio at sth node of r th path to packet transmit sth node of r th path, it indicated as,
T
1
A rs
T
Bq,s (6)
q 1
whereas,
Ir ,s
Bq ,s (7)
J r ,s
whereas, J r ,s indicates the sth pack transmit at r th node, I r ,s indicates sth pack received at
r th node, as well as T indicates a total number of transactions. At r th node packet transmit is
indicated as,
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Hybrid Butterfly Optimization and Particle Swarm Optimization Algorithm for Video Transmission In VANET
1 Gr ,s Hr ,s
J r ,s (8)
2 L L
whereas, H r ,s indicates a number of destination bytes, G r ,s indicates a number of source bytes, as
well as L indicates transmission limit.
4.5 Hybrid BO and PSO Algorithm
PSO algorithm [14] is on the basis of the swarm of birds moving in order to search food in a
multidimensional search space. The position and velocity are the significant characteristics of PSO that
are exploited to find the optimal value. Each individual is named as a particle, and each particle is first
initialized with random position and velocity within the search space.
BO algorithm is the nature-inspired meta-heuristic algorithm [10] that simulates the foraging and
mating behavior of the butterfly. One of the most important characteristics of BO different from other
meta-heuristics is that each butterfly has its own unique scent.
A new hybrid BO-PSO method is developed in this paper that is an integration of standard PSO [11]
and BO algorithms [10] method. One of the important diverse among BOA and PSO is how novel
individuals are produced. The disadvantage of the PSO method is a restriction to cover a minute space to
solve the high-dimensional optimization issues. To integrate the benefits of the two methods, the
functionality of both techniques and do not exploit both techniques one after another. Conversely, it is
heterogeneous due to the technique included generating the ultimate outcomes of the two approaches.
The hybrid is developed as below:
U ti 1 w U ti C1 r1 pbest Yit C2 r2 pbest Yit (9)
whereas C1 C2 0.5 , w is computed by Eq. (10), r1 and r2 are an arbitrary number
in (0, 1)
wmax wmin T
wt wmax i (10)
Tmax
Yit 1 Yit V t 1 (11)
Additionally, the mathematical formulation of the local search phase as well as global search phase
in fundamental BOA is computed. Nevertheless, the global search phase of hybrid
a model can be devised as below:
Yit 1 w. Yit r 2 g best w Yit fi (12)
Yit 1 w. Yit r 2 Yik w Yit fi (13)
Whereas Yik and Yit are jth and k th butterflies selected arbitrarily from the solution space,
correspondingly.
5. Result and Discussion
In this section, the proposed experimentation setup by exploiting the simulation tool and the analysis of
the developed technique over the conventional methods was performed in order to exhibit the competence
of the developed algorithm. The efficiency of the adopted routing protocol to achieve multipath routing in
VANETs was analyzed via end-to-end delay, PDR, as well as throughput. Here, the proposed method is
compared with the conventional models such as PSO, Artificial Bee Colony (ABC), Whale Optimization
Algorithm (WOA), and Genetic Algorithm (GA).
Table 1 Analysis of the proposed model with the conventional model regarding 50 users
Techniques In the occurrence of 50 users
End-to-end delay Throughput Packet Delivery ratio
PSO 22.7208 82.8823 84.0544
ABC 20.3552 82.0827 84.7573
WOA 8.8738 82.8282 85.7877
GA 7.5802 83.3244 85.4024
Proposed Algorithm 7.8774 84.2877 87.5235
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Hybrid Butterfly Optimization and Particle Swarm Optimization Algorithm for Video Transmission In VANET
Table 2 Analysis of the proposed model with the conventional model regarding 100 users
Techniques In the occurrence of 100 users
End-to-end delay Packet Delivery ratio Throughput
PSO 8.8445 84.0558 84.5733
ABC 8.0758 84.3153 84.0143
WOA 5.4144 85.4855 84.803
GA 5.8544 85.5853 83.4881
Proposed Algorithm 4.4447 85.5085 84.4448
Table 1 summarizes the analysis of the techniques included in multipath routing in VANETs in the
attendance of 50 vehicles. The end-to-end delay produced by the techniques in Table I exhibits that the
adopted model yields minimum delay in transmission when compared with the conventional techniques.
In Table 1, the proposed method is 12% better than the PSO, 15% better than the ABC, 17% better than
the WOA, and 11% better than the GA for the throughput.
Table 2 summarizes the analysis of the techniques included in multipath routing in VANETs in
attendance of 50 vehicles. The overall analysis exhibits that the developed model exhibits enhanced
performance in video transmission in VANET.
In Table 2, the proposed method is 28% better than the PSO, 23% better than the ABC, 25% better
than the WOA, and 26% better than the GA for the throughput.
6. Conclusion
In this work, the adopted BO-PSO method was presented for the video transmission from one vehicle to
another to offer safe travel on road. Initially, the VANET was experimented with to calculate the number
of routes needed for the video transmission by exploiting the adaptive geographic routing model. On the
basis of the several metrics, the optimal paths are chosen were computed based upon the fitness
measures. The developed model performance was calculated with the measures like a packet delivery
ratio, delay, end-to-end as well as throughput. For a system to be efficient, the system should be able to
process the least delay, and the adopted model presents the least end-to-end delay and finally, the adopted
model presents the ability to transmit videos in an improved manner.
Compliance with Ethical Standards
Conflicts of interest: Authors declared that they have no conflict of interest.
Human participants: The conducted research follows the ethical standards and the authors ensured
that they have not conducted any studies with human participants or animals.
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