Optimal model of vehicular ad-hoc network assisted by unmanned aerial vehicles and information-centric networking to enhance network performance

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 14, No. 2, April 2024, pp. 1788~1796
ISSN: 2088-8708, DOI: 10.11591/ijece.v14i2.pp1788-1796  1788
Journal homepage: http://ijece.iaescore.com
Optimal model of vehicular ad-hoc network assisted by
unmanned aerial vehicles and information-centric networking
to enhance network performance
Abdeslam Houari, Tomader Mazri
Laboratory of Advanced Systems Engineering, Ibn Tofail Science University, Kenitra, Morocco
Article Info ABSTRACT
Article history:
Received Aug 16, 2023
Revised Dec 18, 2023
Accepted Jan 5, 2024
Vehicular ad-hoc network (VANET) is a promising project related to
intelligent transportation systems (ITS), which aims at connecting vehicles
and providing a set of functionalities for the efficient management of the
network. However, the high mobility of the network nodes is considered a
significant challenge for implementing a reliable, secure, and efficient
exchange system. Furthermore, VANET faces the issue of packet delivery
due to the high mobility of the nodes and packet collisions complicate the
process of sending and receiving packets. We propose to combine two
technologies which are unmanned aerial vehicle (UAV) and information
centric networks (ICN) and apply it in VANET architecture as supporting
technology. The UAV are more reliable and less affected by channel fading.
And can be used in areas where we cannot install network infrastructure.
The UAV has many advantages that we have cited in this article and can
solve many issues of VANET. Using ICN can solve some of the problems of
VANET since ICN has many strategies to capture and retrieve data. This
study proposes a new VANET model based on an UAV and ICN, to reduce
the overload of the vehicles, which in most cases require more resources and
have a limited time to process and act especially in case of an accident or
emergency.
Keywords:
Information centric networks
Network’s performance
Packet delivery
Smart city
Unmanned aerial vehicle
Vehicular ad-hoc network
This is an open access article under the CC BY-SA license.
Corresponding Author:
Abdeslam Houari
Laboratory of Electrical and Telecommunication Engineering. Ibn Tofail Science University
Kenitra, Morocco
Email: abdeslam.houari@uit.ac.ma
1. INTRODUCTION
Improving the vehicle's autonomy level has become a requirement for most car manufacturers [1].
For this reason, several functionalities have been integrated on board the vehicle such as sensors, cameras,
processing units, and storage space in order to warn the drivers of the various threats they might face and
consequently reduce the number of accidents and traffic jams, therefore, the increased interest in intelligent
transport systems (ITS). An efficient and reliable intelligent transportation system represents a fundamental
component of any smart city, especially with the increasing advances in wireless communication
technologies which facilitate its conception and deployment. Among the promising projects related to ITS,
there is the vehicular ad-hoc network (VANET) project, which is a specific case of a mobile ad-hoc network
(MANET) network where nodes are vehicles, the aim is to connect vehicles to exchange different
information about the road condition, and report possible incidents that may occur. VANET has been
deployed mainly as a user-oriented service such as road traffic monitoring [2], on-board passenger
entertainment platform [3], and driving assistance by giving multidimensional maps [4]. These features aim

Int J Elec & Comp Eng ISSN: 2088-8708 
Optimal model of vehicular ad-hoc network assisted by unmanned aerial … (Abdeslam Houari)
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to provide a safer and more enjoyable travel experience for road users. Although these different
functionalities need certain prerequisites to be used efficiently, for instance, most applications related to
intelligent systems (mainly for transportation) need a delivery guarantee and are therefore delay-sensitive,
while entertainment and location-based applications typically require significantly larger bandwidth to
support faster throughput. Despite the various advances made by manufacturers and automotive experts, most
VANET architecture faces several challenges, such as connection disruptions, unstable communication
infrastructure, and important packet collision rates.
As mentioned earlier, guaranteed delivery is a major issue for VANETs. The stability of vehicle-to-
vehicle (V2V) and vehicle-to-infrastructure (V2I) communication is negatively affected by the constant
mobility of the vehicle. The constructions, the poles, and the trees also disturb the radio waves causing path
loss. While ITS applications and programs require a reliable and stable connection for sending and receiving
messages, especially for urgent messages, which require fast-forwarding and a guaranteed delivery [5],
otherwise there is a high risk of getting into traffic jams or provoking road accidents [6].
In the V2V communication mode, vehicles communicate while maintaining a routes map to avoid
sending data packets at multiple hops. However, the high mobility of different vehicles disturbs the data
paths since the source, target, and intermediate node positions are continuously changing, which negatively
impacts the network performance. V2I mode also faces the effect of mobile nodes, provoking handovers
across base stations and roadside units (RSUs) and consequently, increases sending and receiving delays and
eventually leads in some situations to service breakdowns.
VANET network offers a static communication infrastructure through which ITS programs can
provide their own services to the different road users, this prevents the implementation of dynamic on-
demand access, as well as extending the coverage area, which remains relatively limited [7]. Even though
with multi-hop functionality provided through V2V, the coverage area can be extended and consequently can
offer on-demand access to the network infrastructure. This extension remains after all limited, and such an
operation in a dense environment based on multi-hop communication would lower the network performance
even more [8]. Furthermore, the growing number of nodes also decreases the VANET's performance, leading
to network congestion and a higher latency, which results in a larger routing table and a higher risk of
collision of transmitted packets [9]. In such a context VANET applications offered to users are unable to
provide their services on a reliable and efficient platform. In the case of ITS alert applications, they require
guaranteed delivery of urgent messages within a short delay, otherwise accidents may occur, and lives may
be lost.
To overcome the previously mentioned challenges, unmanned aerial vehicles (UAVs) can be
employed in a VANET architecture as a supporting technology [10]. Indeed, compared to VANET
communication modes, UAVs are more reliable as they have direct air-to-ground communication links to
users and are less affected by channel fading. In areas where it is difficult to deploy and maintain network
infrastructure due to the high installation costs, UAVs can be the right choice, especially as they allow the
collection of critical information from relevant areas and transmit them to the ground-based VANET. It can
also relay exchanged packets to the ground network when direct inter-node communication is not supported.
Using information-centric networks (ICN) in the context of VANETs is another approach to
overcome some of these issues. ICN has several strategies for caching and retrieving data [11], useful in
inter-vehicle communications. ICN accelerates content retrieval cached on different nodes, saving both
energy and retrieval delay. In this paper we propose a VANET architecture assisted by UAV and ICN
(uVANET_icn), a comparative study is conducted to show the effectiveness of such a model to make reliable
inter-vehicle connections to achieve a high level of delivery guarantee and improve the performance of the
network.
2. RELATED WORK
Many research studies have been conducted to solve the problem of high mobility, delivery
guarantee and unreliable connections between vehicles in VANET. In order to ensure stable connections for
vehicles, the drone assisted vehicular network model was introduced for the first time by Shi et al. [12]. In
this research the author has detailed the composition of a UAV-assisted VANET architecture, while
highlighting the potential of the provided services as well as the challenges encountered for its proper
functioning. Ahmed et al. [13] suggested a new collaborative communication model of internet of drone
(IoD) serving VANET vehicles in urban areas. The main objective is to dynamically deploy the drone to
achieve optimal coverage, however, the model does not show the impact of velocity and vehicle density on
the communication breakdown and providing alternative paths in the case of disrupted links. Oubbati et al.
[14] implemented a routing method involving a flooding technique to guarantee the reliability and delivery of
exchanged packets. In this study, a predictive technique is applied to determine the link timeout. Seliem et al.
[15] suggested a mathematical model to determine the separating distance within co-located UAVs and to

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identify the density that bounds the worst-case exchange packet transmission delay between UAV and
vehicle, the objective is to enhance VANET communication based on drone infrastructure to reach the
shortest possible packet delivery time for drone-to-vehicles mode communication. In a recent study by
Xiao et al. [16], they used UAVs to forward messages from vehicles and enhance the communication quality
and performance of VANETs against threats from intelligent jammers observing on-board units (OBUs) and
UAV communication states. Sedjelmaci et al. [17] explained the consequences of high latency, message loss,
repeated disconnection in a VANET network and proposed a solution to integrate UAVs in VANET, the idea
was to consider UAVs as relay point between the unconnected segments on the road in such situations, the
efficient communication is rather between vehicles (VV) or between vehicles and UAVs, the article proposes
a new framework using UAVs as an infrastructure of mobile nodes to enhance inter-vehicle connection.
3. PROPOSED MODEL
Inflexible communication infrastructure, high packet collision rate, inconsistent connection, and
energy consumption optimization are some of the key issues in traditional VANET architectures. The
objective of the UAV and ICN assisted VANET model is to resolve these issues to achieve a pleasant user
experience and better performance. In this part we describe in detail the role of each component of our
model, as well as its advantages.
3.1. Vehicles
The network nodes are the vehicles which are already employed in a VANET architecture. Vehicles
are equipped with OBUs allowing them to communicate with each other via V2V mode or with the road
infrastructure composed of RSUs (static or mobile) using V2I mode [18]. OBU also includes a GPS sensor to
track its position and has memory to store and retrieve information using the standard IEEE 802.11p
communication support. It is also linked to the wireless sensor implemented in the vehicle to manage the
physical vehicle condition.
3.2. Wireless communication channel
Dedicated short-range communication (DSRC) is one of the most common wireless technologies
used in V2V and V2I communication which is employed as a medium for short range communication [18].
The frequency communications commission (FCC) has assigned it a bandwidth of 75 MHZ at a frequency of
5.9 GHz [19]. This technology is mainly employed to connect vehicles to each other, but also to connect
vehicles to the network infrastructure. In our case, the network infrastructure consists of both RSUs and UAVs.
3.3. Road side unit
Road side units (RSUs) are roadside stationary nodes usually installed in a dense area. The RSU has
two different communication interfaces, one allowing it to connect with the vehicles of the network and
another to provide an internet access point for the benefit of vehicles and road users [20]. The RSU acts as a
relay node to extend the communication coverage area over several hops [21], as well as securing the
connections between vehicles and the road infrastructure, it also defines the centralized architecture for V2I
communications in VANET network.
3.4. Unmanned aerial vehicle
It is considered an important component in our model. It is designed to fly over an area at a
relatively high altitude to provide a range of communication services [22]. UAV is also equipped with an
OBU that allows it to interact with other nodes of the network, it also has an altimeter and a global
positioning system (GPS) to determine its location in the air and adjust its position. Compared to static RSUs,
the advantage of employing the UAV as an air mobile base station is its facility to overcome obstacles,
changing altitude according to the situation and enhancing the targeting of the node with which it
communicates on the ground.
3.5. Information centric networks
The information-centric network is a content-oriented network architecture proposed to replace the
classic IP protocol on which the Internet is based [23]. In order to achieve the ICN approach, several
implementations have been suggested. Named data networking (NDN) [24] is one of them (used in our
study). In a typical scenario, a user expresses an interest in a specific information or resource, a request is
broadcast over the network, and any of the nodes containing the requested information or resource will return
it. The objective is to dissociate the content from the physical medium and improve the performance of the
network.

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3.6. Advantages of the proposed model
In our model we are interested by the storage and processing capacities of the vehicles covered by
the UAVs, in order to reduce the overload of the vehicles, which in most cases require more resources and
have limited time for processing and acting in case of accident or emergency. As mentioned above, we're
going to use UAVs for data storage, which will make data access easier and retrieval faster. The data
processing aspect of the vehicle nodes will enable us to handle incoming requests from other nodes, and to
process packets transferring over the VANET network. Besides, we aim also to increase the guarantee of
delivery of emergency messages, since in case of road accident, an emergency message is delivered to all the
nodes that are getting closer to the accident location. The corresponding ratio for packet delivery success will
be called public distribution system (PDS) henceforth, calculated as (1).
𝑃𝐷𝑆 =
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑃𝑎𝑐𝑘𝑒𝑡
𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑛𝑜𝑑𝑒𝑠
∗ 100 (1)
A high value of PDS indicates that a significant number of vehicles have received the packet and
can therefore take safety measures and reduce the potential number of collisions or incidents that may occur.
Latency of the packet is a determining criterion in real time safety applications requiring a specific action to
be executed within a particular time frame, in case of non-response in the allotted time may result in
irreversible damage. It also has consequences on the network throughput as well as on the performance of
interest-oriented applications such as NDN applications allowing data caching. The processing time covers in
our study the duration from the transmission of the packet to its reception, including processing time, waiting
time as well as propagation time. The proposed model is mainly oriented towards extended V2I
communications. In classical VANETs the communications between vehicles and infrastructure are mainly
through RSUs, whereas with our model UAVs play the role of mobile RSUs providing more flexible
coverage, reaching areas where there is no network infrastructure, and using line-of-sight communications, as
well as reducing the packet traffic in condensed areas.
In order to reach a particular data, perform a complicated task or simply request additional space
thanks to the ICN paradigm, a vehicle submits its request (called an interest) to a UAV, which will then
consult the pending interest table (PIT) containing the list of interfaces where interests are received, to
determine which nodes satisfy the requirements. Once the request has generated a result, the target node will
make a response using the same sending path, in parallel the PIT table is updated with the new elements to be
used for the next requests. In addition to the interest information and the node interface, the PIT table also
contains the corresponding operation type if it is a storage space, processing unit, network data or any other
operation. In case the UAV does not host the node corresponding to the requirement, the request is
automatically sent to the neighboring UAVs so that the latter can proceed in the same way [25].
4. CASE STUDY AND RESULTS
In order to validate our model, we developed a case study for tracking a suspect vehicle as shown in
Figure 1. In our scenario the vehicles are moving in a multiline road each one follows the line associated to it.
A police vehicle initiates a query for a suspicious vehicle moving on the city in order to apprehend it, for this
purpose it emits an interest containing the registration of the vehicle which will be submitted to the UAV
which is under its coverage area. If the vehicle is not found in its area, the UAV saves first the information of
the tracked vehicle, and then it transmits the interest to the next UAV, so that it can investigate in its area. In our
case saving the vehicle registration will allow the UAV to find the vehicle in case it joins its coverage area.
For this test case, we assume that the UAVs relay messages between them as well as vehicles on
their coverage areas. In addition, each UAV has its own PIT table to locate each vehicle in the VANET
network. To simulate our scenario, we generate a message/ interest according to an alert issued by the police
vehicle, the objective of this message is to inform the other vehicles moving on the network of the existence
of a suspect vehicle. the message flow shown in algorithm 1. This case of figure is reproduced with each
report from a police patrol, the latter broadcast an interest, including the identifier of the vehicle, its direction
as well as its location. In our case the vehicle ID refers to the license plate. The suspicious vehicle is tracked
in both traffic directions and the location parameter informs about the position of the vehicle. the information
on the position of the vehicle is important because it allows to locate the searched vehicle in the city. The
police vehicle compares its position with that of the suspect vehicle to find out in which area it is and also in
which direction of traffic. In order to control the message delivery and to avoid overloading the VANET
network with several requests of the same interest, the vehicles are disabled to retransmit the message
between them (in V2V mode). It is only the UAV which is in charge of retransmitting and caching the
information of the tracked vehicle.

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Figure 1. Case study of tracking a suspect vehicle
Algorithm 1. Message flow algorithm in VANET supported by UAV and ICN
Set of Vehicles moving on the Network is V
Set of UAVs flying above the area U
Set actualU
START
Message/Interest is initiated by the POLICE PATROL
WHILE Suspect vehicle is not reached do
IF Receive Node n ∈ V then
IF message received ==YES
ELSE
drop the message
END IF
ELSE IF Receive Node n ∈ U then
actualU  n
IF PIT of actualU contains Vehicle ID of the Suspect
vehicle then
return position of Suspect vehicle
ELSE
𝑆𝑎𝑣𝑒𝐼𝐶𝑁(Vehicle ID) in PIT of actualU and broadcast message
END IF
END IF
END WHILE
Table 1 describes the simulation parameters. We used the network simulator version 2.1b9 to run the
test case described above. we used VANET, UAVs and ICN in our topology. we used BonnMotion to
generate the different movement of vehicle nodes and UAVs and pass them to NS-2, who generates the
packet and run the simulation based on the position of the vehicles and UAVs. A C++ function is used to
cache the suspect vehicle information in the PIT table on each UAV. The UAVs overfly the vehicles at a
distance of 1,000 meters. For the UAV it acts as a packet relay for V2I and UAV-to-UAV (U2U)
communications. To demonstrate the efficiency of our model in comparison with the classical VANETs and
those assisted by UAVs without ICNs, we carried out several simulations by changing the size of the road,
the location of the suspect vehicle in relation to the police vehicle and the density of the vehicles in the target
area. We simulated a total of 1,500 cases of a tracking message sent from several nodes, setting the number
of UAVs at 50 as a threshold.
Concerning RSUs, they are used as a secondary relay point, replaced by UAVs considered as mobile
RSUs, a temporal ordered routing algorithm (TORA) has been implemented for this purpose. The static RSU
infrastructure was only taken into account in few test scenarios since UAVs serve as mobile RSUs and

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facilitate better the communication within VANET. The proposed model is compared with the UAV assisted
VANET (uVANET) and also with multi-hop standard VANET. uVANET has a preference to use V2V
communications and relies on the drone as a relay point when the target vehicle is out of range. Regarding
VANET each vehicle calculates its proper waiting time to decide about the next relay node. vehicle with the
lowest waiting time will be the favorite candidate to be the relay point. The waiting time is calculated
according to (2):
𝑤 = (1 −
𝑑
𝑟
) × 𝑤𝑚𝑎𝑥 (2)
with 𝑑 is the distance separating the vehicles. 𝑟 is the transmission range. And finally, 𝑤𝑚𝑎𝑥 which is the
maximum waiting time.
Table 1. Simulation parameters
Parameter Value
Network simulator NS-2
Mobility simulator BonnMotion
Mobility simulator tracer VanetMobiSim
Number of nodes 50, 70, 90, 100
Road length 5, 10, 20, 100 km
UAVs per KM 2
Vehicles speed 20, 60, 100, 120 km/h
UAV altitude 100, 500, 1000 m
Simulation time 5 min
Packet sent 15,000
Routing protocol TORA
IEE standard 802.11p
Frequency V2U 5.2 GHz
Frequency U2U 2.2 GHz
UAV transmission power 1.5 dBm
Vehicle transmission power 1.2 dBm
Packet latency is a critical factor for real-time and security related applications, as it requires that an
action must be executed as soon as possible to avoid a collision or blocking anomaly. It also has a direct
impact on the performance of the network for data-oriented applications. The delay in our case is the time
spent from the transmission of the packet from the patrol vehicle to the reception of a reply. Including
processing time, caching time, waiting (before relaying) and propagation time.
𝑇 = 𝑇𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑖𝑛𝑔 + 𝑇𝑐𝑎𝑐ℎ𝑖𝑛𝑔 + 𝑇𝑤𝑎𝑖𝑡𝑖𝑛𝑔 + 𝑇𝑝𝑟𝑜𝑝𝑎𝑔𝑎𝑡𝑖𝑜𝑛 (3)
For a given distance between the police vehicle and the suspect vehicle in the case of a multi-hop
VANET, more hops mean more time 𝑇. Similarly, in a VANET context with dense traffic, this results in a
higher number of packet collisions and consequently an additional waiting time 𝑇𝑤𝑎𝑖𝑡𝑖𝑛𝑔. Figure 2 shows the
result of an experiment comparing the delays of time performing between the different VANET models,
VANET assisted by UAV (uVANET) as well as VANET assisted by UAV and ICN (uVVANET_ICN).
We notice that the delay has a weak correlation with both density and velocity for the case of
uVANET and uVANET_icn. For VANET case, as the number of nodes increases, so does the delay, and as
the speed increases, so does the difficulty of transferring packets between nodes, as well as the number of
collisions, which becomes more important. In opposition to our model in which there are no more collisions,
which largely decreases the waiting time. In contrast to our model, where there is no more collision, which
largely decreases the waiting time. Regarding the propagation time, it largely decreases compared to
uVANET, because caching the required information (in our case, the suspect vehicle) on the UAVs reduces
the searching time in the covered area.
The second indicator in which we were interested was the PDS indicator. As mentioned before, this
ratio is useful to check the delivery of the message propagated on the network. This ratio is especially
variable in urban areas since direct connections between UAVs and vehicles are negatively affected by road
obstacles and high buildings. To express the latter statement, Al-Hourani et al. [26] developed a formula to
describe the packet path loss from the air to the ground:
𝑃𝐿𝑜𝑠 =
1
1+𝑒
−𝑏(
180𝜃
𝜋
−𝑎)
(4)

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where θ is the elevation angle between the vehicle and the UAV, a significant value of the latter will tend the
probability of direct sighting to approximately approach 1. The ratio depends mainly on the receiver's
sensitivity, the technology used for communication, and the quality of the provided service. It has also been
observed that the theoretical optimum altitude that can be reached exceeds the atmospheric layer and that in
our case the use of the UAV avoids this constraint.
The other advantage of the new approach is that it reduces significantly the number of packet
collisions, as packets are sent simultaneously by the UAV to the nodes within its area. Using ICN in the other
side, the packet containing the requested information is stored in the UAV, making it possible for it to detect
the suspicious vehicle entering its zone and report it to the patrol police. Figure 3 shows the comparison of
the PDS of the proposed model with the two competing models.
In Figure 3, it is clear that our model far outperforms the classical VANET model in terms of PDS
ratio regardless of network density and road length. In comparison with the uVANET the two models are
practically similar with a small difference and this is mainly due to the employment of UAVs by both
models. For VANET, the density and the road length affect negatively the packet delivery, and this is mainly
explained by the high mobility of the nodes as well as the absence of the static infrastructure (RSU) in some
areas that interrupts the process of packet routing.
Figure 2. Time performing comparative study

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Figure 2. PDS result of the three models
5. CONCLUSION
In this paper we propose a new model of VANET assisted by UAV and ICN in order to improve the
network performances and to guarantee the delivery of exchanged packets. We began by presenting the
VANET network with its different communication modes and the different main challenges encountered, as
well as the role of the ICN and the UAV in our new model. Next, we review the different researches that
have been carried out for the application and relevance of UAVs and ICNs in a VANET context. Afterwards,
we present our model, its advantages compared to the classical VANET model. Lastly, we conclude the study
by simulating a case study, which demonstrates the effectiveness of our model on the two aspects of this
study which are the delivery guarantee and the improvement of the network performance in terms of packet
latency and processing time.
This study will be used in future works oriented towards the improvement of services and
performances of road networks in a smart city. The objective of the next study is to test the behavior of this
model in the case of networks without road infrastructures in order to test the continuity of the services in
non-urban areas. In future articles, we intend to prove the effectiveness of 6G technologies in solving
problems such as guaranteeing the delivery of exchanged packets and reducing data search and retrieval
delay.
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BIOGRAPHIES OF AUTHORS
Abdeslam Houari is a third year Ph.D. student at Ibn Tofail University. Holder of an
engineering degree in computer science from the National School of Computer Science and Systems
Analysis (ENSIAS) of Rabat. His research interests include soft computing, smart transportation, and
intelligent systems. He can be contacted at email: abdeslam.houari@uit.ac.ma.
Tomader Mazri professor at ENSA of Kenitra, holder of a Habilitation to Supervise
Research in Networks and Telecommunication Systems from Ibn Tofail University and a National PhD
in microelectronics and telecommunication systems from Sidi Mohammed Ben Abdellah University and
the National Institute of Posts and Telecommunications (INPT) of Rabat. Her research interests include
microwaves systems, smart antennas, NGN mobile networks smart transportation, and intelligent
systems. research axis is mainly on topics related to mobile networks and telecom systems. She can be
contacted at email: tomader.mazri@uit.ac.ma.

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