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Journal of Discrete Mathematical Sciences and
Cryptography
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An adaptive routing protocol in flying ad hoc
networks
Orchu Aruna & Amit Sharma
To cite this article: Orchu Aruna & Amit Sharma (2022) An adaptive routing protocol in flying ad
hoc networks, Journal of Discrete Mathematical Sciences and Cryptography, 25:3, 757-770, DOI:
10.1080/09720529.2021.2016223
To link to this article: https://doi.org/10.1080/09720529.2021.2016223
Published online: 14 Jun 2022.
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An adaptive routing protocol in flying ad hoc networks
Orchu Aruna †
Department of Computer Science & Engineering
Lovely Professional University
Phagwara 144001
Punjab
India
Amit Sharma *
Department of Computer Applications
Lovely Professional University
Phagwara 144001
Punjab
India
Abstract
Nowadays, ad hoc networks play a vital role in wireless communication. It is a temporary
network to relay the data between nodes. Ad hoc network is divided into different types such
as MANET, VANET & FANET. FANET is a combination of MANET and VANET. In FANET,
routing is a big challenging issue due to unique characteristics like dynamic topology, frequent
changes of link quality, mobility of UAV nodes, etc. Reliability of routes is also a real challenge
due to the very high mobility in FANET. In this research work, we have introduced an adaptive
routing protocol, PF-WGTR - A predicted future weight-based routing scheme for FANETs
that considers the node’s existing and upcoming values of assured parameters to determine
the reliable routing path. This proposed routing protocol assigns weight to every node in the
network by calculating the node’s future. Depending on the predicted future weight of every
node in a network, the communicating nodes can establish a reliable path that persists for a
long time. This adaptive, future prediction-based routing scheme ensures better data delivery
with minimum overhead and optimized energy consumption in all conditions compared with
the existing routing protocols. The NS-2 simulator is used to compare the proposed routing
protocol with previous protocols in terms of FANET parameters. Finally, the simulation results
show better performance than the existing works.
Subject Classification: 68M18 Wireless sensor networks as related to computer science.
Keywords: UAV, FANET, FSQOS, QLF-MOR, PF-WGTR, Routing.
† E-mail: arunasri52@gmail.com
*E-mail: amit.25076@lpu.co.in (Corresponding Author)
Journal of Discrete Mathematical Sciences & Cr yptography
ISSN 0972-0529 (Print), ISSN 2169-0065 (Online)
Vol. 25 (2022), No. 3, pp. 757–770
DOI : 10.1080/09720529.2021.2016223
758 O. ARUNA AND A. SHARMA
1. Introduction
To monitor the disaster situations or reaching to hard-to-reach areas,
new wireless communication mechanisms have gained much attention
in recent years because of their extensive range of applications in aerial
technologies which is a kind of new technology or configuration. FANETs
or flying ad hoc networks are related to a kind of configuration of ad hoc
networks that consist of unmanned aerial vehicles (UAVs). Based on the
collected images and sending them to a base ground station [1], UAVs are
operated and helped to monitor a particular area in a method termed as
UAV-to-ground (U2G) communication.
A special practice of VANET and MANET can be considered as
FANET. Various distinctive challenges of design [2] have been included in
the FANETs although they have common characteristics. When compared
to the MANET and VANET [21], each node’s degree of mobility is much
higher in the FANET. The speed with the value of 30-460 km/h has been
included in AUAV [3]. From moment to moment, the UAVs’ locations can
be changed in other words. Among UAVs, the links are destroyed and
established intermittently.
A. FANET Routing Protocols
The determination of an appropriate path for the transmission
of data is the main purpose of routing protocols. There are a variety of
procedures for multiple applications in wireless communications. FANET
[6] protocols have been categorized into different types known as:
• Static Routing Protocols
• Hands-on Routing Protocols
• Reactive Routing Protocols
• Hybrid Routing Protocols
Static Routing Protocols (SRP): Before the UAV node’s operation
and cannot be changed until the operation ends [7], a static routing
protocol’s routing table should be evaluated and loaded. Each UAV node
interconnects with its neighbour node or with ground locations and stocks
its information. This requires the task to be finished before updating the
routing table in case of a disaster. This is the reason, SRP procedures are
not fault-tolerant.
AN ADAPTIVE ROUTING PROTOCOL 759
Hands-on Routing Protocols (HRP): All of the tables of a particular
region can be managed by the proactive routing protocols which can also
collect the information of routing in a network. Different types of driven
protocols are involved in FANET and they are unique to each other. Based
on the changes in topology, the routing tables can be updated by the nodes.
The nodes with the latest information can be carried out by the routing
protocol and it’s not required to choose and wait for the path between
receiver and sender. For large communication networks, this protocol
will not be concerned if in case bandwidth is not utilized effectively i.e.
more traffic between nodes. If the topology changes or failure occurs, this
protocol seems to be slow.
Reactive Routing Protocols (RRP): These type of protocols are also
called ‘On Demand Protocols’. In this process, the source can establish a
connection when it is needed to relay the data to destination. Otherwise,
it is not necessary to maintain routing information in a routing table. The
drawback of on demand routing protocols is it takes more time to discover
the route. This is the reason it faces bandwidth efficiency problem [8].
Hybrid Routing Protocols (HRP): To overcome the restrictions of on
demand and hands-on routing protocols, Hybrid Routing Protocols (HRP)
are exploited. However, the on demand routing protocols are limited in
requiring extra time to determine the paths whereas Hands-on protocols
have the limitation in control overhead. Different regions are categorized
in the hybrid routing. For intra region routing and inter-region routing,
proactive protocol and reactive protocol are used respectively [9].
AODV: In this protocol, the source node finds out the path for
relaying data to the destination node. The correspondent node sends a
route request message to its nearest neighbor nodes, various messages of
route requests might have existed in the topology. The correspondent node
is sent a distinctive request-id to avoid the mixing of the correspondent
node. In AODV protocol, each node maintains route cache. Only one entry
for each destination is included in AODV and the next hop information to
each data communication [10] is stored. Three phases are contained in the
AODV routing protocol such as route maintenance, packet transmission,
and route discovery [22]. However, the routing messages types of AODV
are included route error, route reply, and route request [23].
To find out the communication routes, it’s very essential to consider
the UAV’s mobility and their spatial arrangement. The rearrangement
of these routes is done as a result of the movement. It will lead to the
continuation of interconnection between the UAVs. With the increment in
760 O. ARUNA AND A. SHARMA
the UAVs, the routing should be performed dynamically and the delay is
reduced in the delivery of data between source and destination nodes [12].
II. Literature survey
Lin et al. [13] have proposed a design for a strategy of routing for
UAVs that allows the utilization of the geographical location of nodes for
seamless routing. For navigation, GPS is used by this approach and the
UAV’s mobility is predicted.
Bilal et al. [14] utilized the multi-cluster-based approach where a
fixed number of UAVs are contained in each cluster and the election of one
UAV is considered as a CH. Among neighboring UAVs, the information
message of a node is exchanged initially. According to the “zone ID” field
of node data, different clusters are grouped by UAVs. A link quality table
is maintained by each node in the cluster. Here, the table includes the
delays, SNR, and distance to the neighbors. According to the information
on link quality, CH is elected and the node with the best link quality is
chosen as a CH.
Shi et al. [15] proposed CBLADSR. According to the factors like the
degree of connectivity, energy level, and relative velocity, the CHs are
elected by CBLADSR. For intra-cluster and inter-cluster communications,
CBLADSR has used the short range and long range transmission
communications correspondingly.
An algorithm of mobility prediction clustering [16] was proposed by
Zang and Zang for UAVs. A neighbor table is maintained by each node
for its one-hop neighbors. The probability of a node that will persist in its
table is contained in the neighbor table. With the use of a dictionary tree
structure, this probability is determined. By using the neighbor node’s
moment and probability, Link expiration time (LET) is anticipated. By
taking the assistance of degree and LET probability of the neighbor, each
node’s weight is computed. A node that has the highest weight will be
selected as a CH.
In [17, 18], the authors have demonstrated the MDA-AODV routing
protocol that is an extension for the AODV protocol. To build the routes
with stability and efficiency between the source and destination nodes,
the intermediate node’s speed and direction are used by the MDA-AODV
protocol in route reply and route discovery phases.
For enabling a communication path between UAVs without
compromising on efficiency (a process is called UAV-to-UAV
communication – U2U), a routing protocol was proposed by the authors
AN ADAPTIVE ROUTING PROTOCOL 761
in [19] and it was adapted and applied for these created situations based
on the fuzzy system. Based on the longest durability and best connection,
the efficient route will be determined by the new routing protocols. Thus,
the performance of a network will be improved.
A routing algorithm that integrates both reinforcement learning and
fuzzy logic algorithms in FANETs and was proposed by an author in [20].
By considering the successful packet delivery time (SPDT), hop count
(HC), energy drain rate, residual energy, and transmission rate (TR), the
routing path is determined by connecting the multiple UAVs. For deriving
the reliable links between two UAV nodes, a fuzzy system is utilized and it
is supported by the Q-learning with the providing of a reward on the path.
III. Proposed system
A. PF-WGTR - A predicted future weight-based routing scheme
PF-WGTR assigns weight to every node by incorporating several
parameters into the AODV routing protocol. The Total Weight of the Route
(TWGHT) between the source and destination nodes is defined as the
mathematical representation of the following parameters.
• Flyting node Speed and Acceleration
• Node flying Direction
• Link Quality Between nodes
Flying node Speed and Acceleration: For a larger TWGHT between
the two flying nodes, the larger differences of speed and accelerations
between two nodes must be considered. Owing to the flying nodes’ speed
difference, the logical reason is to predict the link breakage. In the radio
communication range, nodes will stay much longer when they are flying
in the same acceleration and velocity relatively. A lower TWGHT must be
assigned and such types of conditions are highly desirable.
Vehicle Movement Direction: In the range of radio communication,
vehicles will stay much longer in the same direction logically. In the
determination of TWR to the target, a direction vector is also playing an
important role. The selection of routes is determined with the use of a
direction parameter and will be demonstrated later.
Node flying Direction: The flying nodes in a similar direction will stay
in the radio communication range for a longer time. However, a direction
vector can be used to compute the TWGHT to the target. The route choice
762 O. ARUNA AND A. SHARMA
can be evaluated also with the use of a direction parameter. By computing
the angle between two driving directions, this value can be retrieved.
Link Quality Between nodes: In the TWGHT, another parameter
is also taken into account i.e. the link quality between nodes to the
destination in the route. The quality of a link between the flying nodes
might be impacted by the neighboring nodes, obstructions, and buildings
in FANETs. In the calculation of TWGHT, the factor of link quality must
be included.
So based on the above parameters, the TWGHT can be defined as
follows,
( )
n s s nnn
TWGHT S NH DN A D Q
=+ - + + +
Here, Sn denotes node speed, NHs & DNs denotes next-hop speed
& destination node speed respectively, An denotes node acceleration, Dn
denotes direction vector, and Qn denotes the parameter of the link quality
between the source and next-hop node.
When compared to the destination node, the best next-hop node
that has the least TWGHT and it includes similar direction, acceleration,
and speed is observed from the aforementioned equation. The better link
quality is chosen that exists between a source node and the next-hop node.
Predict node Speed and Acceleration: From the current time to
the next time, the interval is very short. During this period, the node’s
acceleration is considered a constant. This can be calculated as follows
( _)
nn n
PS S A CURRENT TIME=+ *
Here, PSn denotes the predicted speed of the node n, Sn denotes the
current speed of the node n, An denotes the node n acceleration.
Predict Node Movement Direction: Node flying movement &direction
can be calculated as follows
( _)
nn n
PD P S CURRENT TIME=+ *
Here, PDn denotes the predicted direction of the node n, Pn denotes
the node’s current position n, Sn denotes the speed of the node n.
Predict Link Quality Between nodes: The distance of which node
flying towards which direction next time is computed after determining
the information involving direction, speed, and acceleration. By using the
current coordinate, the node’s next coordinate is computed and the link
quality between vehicles can be estimated as mentioned below-equation:
AN ADAPTIVE ROUTING PROTOCOL 763
1
1 1
n
n
n
LQ R
Tx
=
æö
-
ç÷
+
èø
Here, LQn denotes the link quality of the node n, Rn denotes the radius
of the node n, Txn denotes the maximum transmission range of the node.
Through the above-said calculations and estimations, the node’s
future TWGHT can be estimated. To know a node is well-suited for a relay
node, this value will be utilized like a parameter. The relay selection rule
can be stated as follows:
Table 1
Route Selection Relay
Node TWGHT Node STATE Future TWGHT Result
Optimal Unstable Better Select the relay
Optimal Stable Not better Select the relay
Suboptimal Unstable Better Select the relay
Suboptimal Stable Not better Select the relay
Not optimal Unstable Not better Don’t select the relay
Here, the definition of Node STATE can be stated as the difference
between the Node’s current TWGHT and future TWGHT. If it is above the
threshold then the node state is STABLE. Otherwise, it is UNSTABLE.
B. PF-WGHTR Algorithm:
Sn = Speed of the node n; An = Acceleration of the node n;
Dn = Direction of the node n; Qn = link quality of node n
PSn = Predicted speed of the node n; PDn = Predicted direction of the
node n
LQn = Predicted link quality of node n
TWGHT = Total weight of the node n;
FWGHT = future weight of the node n;
For all nodes n
Calculate Sn, An, Dn, Qn
Estimate TWGHT
764 O. ARUNA AND A. SHARMA
Calculate PSn, PDn, LQn
Estimate FWGHT
End for
For all nodes n
If (TWGHTn < TWGHTn+1)
If (|TWGHTn – FWGHTn)| ≥ threshold)
If (FWGHTn < TWGHTn)
Relay = n
Else
Relay = n+1
End if
End if
End if
End for
IV. Result and discussion
The UAVs are deployed in the network area with the size of 1000
x 1000 and are represented in figure 1. Each UAV is assigned a unique
number for easy identification. The UAVs are connected through wireless
links. The nodes can fly (move) across the network area randomly.
After deployment, the UAVs start flying (moving) in the network area
in a random direction. The routing protocol establishes the connectivity
between the nodes. Nodes start transmitting the data of size roughly 1020
bytes through the established links and shows in figure 2.
The nodes obtained a new position due to flying and continue the
data transmission irrespective of their location and represented in figure3.
The proposed protocol predicts the node movement, speed, link quality
and establishes or maintains the connectivity through reliable nodes,
despite the nodes are moving.
During mobility, it is better to monitor the vital parameters of the
nodes to maintain uninterrupted connectivity, and shows in figure4. The
proposed protocol maintains the parameters list by frequently exchanging
the control packets and update the routing table periodically despite the
nodes are in constant mobility.
The energy depletion happens in each node when the node participates
in the network activity. Initially, all of the nodes are incorporated with
AN ADAPTIVE ROUTING PROTOCOL 765
equal energy and the energy starts depleting for every activity the
node performs. The node should possess a decent amount of energy to
participate in the activity. Here log file is represented in figure5.
The proposed PF-WGTR protocol predicts the node’s future direction,
mobility, acceleration and, link quality frequently and assigns the weight
to every node. The more weight the node has, the more likely to be selected
for data transmission. The future prediction-based weight assigning
strategy ensures the selection of quality nodes for data transmission
Figure 1
UAV initial deployment, start to
flying
Figure 2
UAVs start transmitting the data with
each other using a wireless medium
Figure 3
A data unit of 1020 bytes is shared
between the pair of flying UAV’s
through the newly established path
Figure 4
AODV keep tracking of the routing
parameters using control packets
throughout the communication
period
766 O. ARUNA AND A. SHARMA
and assures the reliable data path for the entire communication. The
parameters routing table is represented in figure 6.
The consideration of the vital parameters like speed, acceleration, and
link quality always selects the shortest as well as interference-free path.
However, it ensures the data can be delivered to the target node within the
estimated time. This nature of the proposed protocol (PF-WGTR) provides
minimum delay compared with the previous protocols (FSQOS and QLF-
MOR). Figure 7 represents the end-to-end delay versus simulation time.
The proposed protocol predicts the node’s future parameters and
takes the routing decision accordingly. The path could be the shortest and
Figure 5
Logfile of energy consumption of
every node during a communication
period
Figure 6
PREDICTED WEIGHT of every
UAV at every round of communica-
tion concerning proposed routing
parameters
Figure 7
Performance on Delay
Figure 8
Energy Consumption
AN ADAPTIVE ROUTING PROTOCOL 767
interrupt free paths. This will reduce the improper retransmission of data
and reduces the overall energy consumption in every flying node. The
results prove that the proposed protocol (PF-WGTR) achieves high energy
efficiency compared with previously proposed protocols (FSQOS and
QLF-MOR). Figure 8 illustrates the energy conservation and it shows an
energy level ratio versus simulation time.
The proposed protocol selects the routing path by predicting the future
parameters and suggests the path which is reliable for the entire round of
communication. This will helps to improve the network throughput than
the other contemporary protocols. Figure 9 represents the performance of
a network and simulation time versus throughput is also presented.
But the proposed protocol always selects the reliable path by
predicting the node’s future behavior and reduces the routing overhead.
The performance comparison graph proves that the proposed protocol
(PF-WGTR) reduces the overhead than the previously proposed protocols
Figure 9
Network Throughput
Figure 10
Routing Overhead
Figure 11
Packet Delivery Ratio
768 O. ARUNA AND A. SHARMA
(FSQOS and QLF-MOR). Figure 10 represents the routing overhead versus
simulation time.
The proposed protocol takes consideration of the node’s future
factors rather than considering the present factors alone and suggests the
path which is optimal and reliable in the future also. This ensures the path
is interference-free and will not be affected by the factors which impact
PDR. The comparison results show that the PDR rate is comparatively
high than the previous protocols. Figure 11 represents the packet delivery
ratio versus simulation time.
IV. Conclusion
FANET’s have special nature than the other networks. The nodes in
the FANETs are flying around the deployment area unlike node mobility
in MANETs or VANETs. This special nature of the FANETs makes
the conventional routing protocols such as AODV do not fit the flying
networks. So, there is a need for a new routing protocol that can work well
according to the flying nature of the nodes. In this paper, a new routing
adaptive protocol is demonstrated that considers the node’s special factors
like speed, acceleration, link quality, etc. The proposed protocol not only
considers the current factors of the nodes, but also predicts the node’s
future parameters such as future speed, acceleration. Etc. Based on these
predictions, the proposed protocol assigns weight to every node, and the
routing decision is made according to node weight. This routing strategy
ensures the transmission path is reliable and interference-free which
results in improved throughput and reduced overhead.
When compared to the previously proposed protocols, better
performance is achieved by the proposed protocol through the simulation
results based on the consumption of energy, PDR, and other essential
parameters.
References
[1] E. Cruz, “A comprehensive survey in towards to future FANETs,”
IEEE Latin America Transactions, vol. 16, no. 3, 2018.
[2] Bekmezcia, İ., Sahingoza, O. K., &Temelb, Ş. (2013). Flying ad-hoc
networks (FANETs): A survey.Ad Hoc Networks, 11, 1254–1270.
AN ADAPTIVE ROUTING PROTOCOL 769
[3] Li, X., & Yan, J. (2017). LEPR: Link stability estimation-based
preemptive routing protocol for flying ad hoc networks. In Proceedings
of IEEE symposium on computers and communications (ISCC) (pp.1–6).
[4] Omar Sami Oubbati, AbderrahmaneLakas, Fen Zhou, MesutGüne,
Mohamed BachirYagoubi, A survey on position-based routing
protocols for Flying Adhoc Networks (FANETs), Veh. Commun. 10
(2017) 29–56.
[5] J. Sánchez-García, J.M. García-Campos, M. Arzamendia, D.G. Reina,
S.L. Toral, D. Gregor, A survey on unmanned aerial and aquatic
vehicle multi-hop networks: wireless communications, evaluation
tools and applications, Comput. Commun. 119 (April 2018) 43–65.
[6] Qianqian Sang, Honghai Wu, Ling Xing and Ping Xie, Review
and Comparison of Emerging Routing Protocols in Flying Ad Hoc
Networks, Symmetry 2020, 12, 971; doi:10.3390/sym12060971.
[7] Adnan Nadeem, Turki Alghamdi, Ali Yawar, Amir Mehmood,
Muhammad Shoaib Siddiqui, A Review and Classification of Flying
Ad-Hoc Network (FANET) Routing Strategies, ISSN 2090-4304 Journal
of Basic and Applied Scientific Research, 2018.
[8] Subramanya Bhat.M, Shwetha.D, Devaraju. J.T, A Performance Study
of Proactive, Reactive and Hybrid Routing Protocols using Qualnet
Simulator, International Journal of Computer Applications (0975 – 8887)
Volume 28– No.5, August 2011.
[9] Muhammad Asghar Khan 1,*, Inam Ullah Khan 1, Alamgir Safi 1,
and Ijaz Mansoor Quershi, Dynamic Routing in Flying Ad-Hoc
Networks Using Topology-Based Routing Protocols, Drones 2018, 3,
27; doi:10.3390/drones2030027.
[10] Rob van Glabbeek · Peter H¨ofner · Marius Portmann · Wee Lum Tan,
Modelling and Verifying the AODV Routing Protocol, Distributed
Computing Accepted December 1, 2015.
[11] W. Zafar and B. Khan, “Flying ad-hoc networks: technological and
social implications,” IEEE Technology and Society Magazine, vol. 35, no.
2, pp. 67–74, 2016.
[12] Yang, M. Liu, and Z. Li, “Rendezvous on the fly: efficient neighbor
discovery for autonomous UAVs,” IEEE Journal on Selected Areas in
Communications, vol. 36, no. 9, 2018.
770 O. ARUNA AND A. SHARMA
[13] Lin Lin, Qibo Sun, Jinglin Li, and Fangchun Yang. “A novel geographic
position mobility oriented routing strategy for UAVs.” Journal of
Computational Information Systems. v8, pp. 709-716,(2012).
[14] Zafar, W.; Khan, B.M. A reliable, delay bounded, and less complex
communication protocol for multiclusterFANETs. Digit. Commun.
Netw. 2017, 3, 30–38. [CrossRef]
[15] Shi, N.; Luo, X. A novel cluster-based location-aided routing protocol
for UAV fleet networks. Int. J. Digit.Content Technol. Appl. 2012, 6, 376.
[16] Zang, C.; Zang, S. Mobility prediction clustering algorithm for
UAV networking. In Proceedings of the Globecom workshops (GC
Wkshps), Houston, TX, USA, 5–9 December 2011; pp. 1158–1161.
[17] H.B. AbdelGhany, Tree-Based Mobility Aware Routing Algorithm
for Mobile Ad-Hoc Networks, Master Thesis, University of Jordan,
Amman, Jordan, 2016.
[18] Swidan, H.B. Abdelghany, R. Saifan, Z. Zilic, Mobility and direction
aware ad-hoc on-demand distance vector routing protocol, Proc.
Comput. Sci. 94 (2016) 49–56.
[19] Souza, Jorge &Jailton, José &Carvalho, Tássio&Araújo, Jasmine
& Frances, Carlos. (2019). A Proposal for Routing Protocol for
FANET: A Fuzzy System Approach with QoE/QoS Guarantee.
Wireless Communications and Mobile Computing. 2019. 1-10.
10.1155/2019/8709249.
[20] Yang, Qin & Jang, Sung &Yoo, Sang-Jo. (2020). Q-Learning-Based
Fuzzy Logic for Multi-objective Routing Algorithm in Flying Ad Hoc
Networks. Wireless Personal Communications. 113. 10.1007/s11277-
020-07181.
[21] Kumar, A., & Sinha, M. (2019). Design and analysis of an improved
AODV protocol for black hole and flooding attack in vehicular ad-
hoc network (VANET). Journal of Discrete Mathematical Sciences and
Cryptography,22(4), 453-463.
[22] Wadhwani, G. K., Khatri, S. K., & Mutto, S. K. (2020). Trust framework
for attack resilience in MANET using AODV. Journal of Discrete
Mathematical Sciences and Cryptography,23(1), 209-220.
[23] Hong-Song, C., Jianyu, Z., & Lee, H. W. J. (2008). A novel NP-based
security scheme for AODV routing protocol. Journal of Discrete
Mathematical Sciences and Cryptography,11(2), 131-145.
