Flying Ad hoc Networks (FANET): Performance Evaluation of Topology Based Routing Protocols

Abstract

Flying Ad hoc Networks (FANETs) has developed as an innovative technology for accessing places without permanent infrastructure. This emerging form of networking is construct of flying nodes known as unmanned aerial vehicles (UAVs) that fly at a fast rate of speed, causing frequent changes in the network topology and connection failures. As a result, there is no dedicated FANET routing protocol that enables effective communication between these devices. The purpose of this paper is to evaluate the performance of the category of topology-based routing protocols in the FANET. In a surveillance system involving video traffic, four routing protocols with varying routing mechanisms were examined. Additionally, simulation experiments were conducted to determine the influence of flying altitude. The results indicate that hybrid routing protocols outperform other types of protocols in terms of average throughput. Proactive protocols, on the other hand, have the least jitter.

Full text

Paper—Flying Ad hoc Networks (FANET): Performance Evaluation of Topology Based Routing Protocols
Flying Ad hoc Networks (FANET): Performance
Evaluation of Topology Based Routing Protocols
https://doi.org/10.3991/ijim.v16i04.28235
Ali H. Wheeb(*)
Faculty Member – Lecturer, University of Baghdad, Baghdad, Iraq
a.wheeb@coeng.uobaghdadedu.iq
Abstract—Flying Ad hoc Networks (FANETs) has developed as an inno-
vative technology for accessing places without permanent infrastructure. This
emerging form of networking is construct of ying nodes known as unmanned
aerial vehicles (UAVs) that y at a fast rate of speed, causing frequent changes
in the network topology and connection failures. As a result, there is no dedi-
cated FANET routing protocol that enables effective communication between
these devices. The purpose of this paper is to evaluate the performance of the
category of topology-based routing protocols in the FANET. In a surveillance
system involving video trafc, four routing protocols with varying routing mech-
anisms were examined. Additionally, simulation experiments were conducted to
determine the inuence of ying altitude. The results indicate that hybrid routing
protocols outperform other types of protocols in terms of average throughput.
Proactive protocols, on the other hand, have the least jitter.
Keywords—multi-UAV, ying ad hoc networks, topology-based routing
protocol, Gauss Markov, ying altitude
1 Introduction
Unmanned aerial vehicles (UAVs) have made signicant advancements and are now
extensively utilize, whereas wireless data transfer technologies have indeed made sig-
nicant advancements. All of this contributes to the emergency communications system
types. The Flying Ad-Hoc Network is one of these communication networks (FANET)
[1]. FANET is an autonomous self-organizing network. Its nodes not only link to their
neighbors, but they also relay trafc via them [2]. Only a subgroup of UAVs may com-
municate with a satellite or ground station and all ying UAVs form an ad-hoc network.
Thus, in addition to the base station, the UAVs may communicate with one another.
FANET has several advantages, including adaptability, increased accuracy, economy,
continuity, Flexibility, and speed [3] [4].
FANETs are becoming more and more popular with each passing day. Previously,
FANETs are basic remotely controlled airplanes that have been primarily employed in
military operations [5]. Nevertheless, in recent years, FANETs have been deployed in
a growing variety of civil and commercial activities, including search and rescue [6],
package delivery [7], trafc monitoring in smart cities [8], agriculture [9], engineering
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Paper—Flying Ad hoc Networks (FANET): Performance Evaluation of Topology Based Routing Protocols
education [10], mobile drone in indoor localization [11] and disaster monitoring [12].
The application of FANET is depict in Figure 1.
Fig. 1. Applications of FANET
The degree of mobility of FANET nodes is signicantly higher than that of VANET
or MANET nodes. Because of the high mobility of FANET nodes, the topology of the
network changes on a more regular basis. FANET has many unique problems, with the
routing process being one of the most signicant design considerations. To function
properly in FANET, the routing protocols used must be capable of performing an auto-
mated search for the optimum route to offer one or more subjective parameters for the
operation of data transmission and receiving [13].
The primary goal of this paper is to dene FANET as a unique ad hoc network family
and to assess the effectiveness of topological routing protocols in FANET. This article
makes three contributions: (i) presenting various routing challenges; (ii) categorizing
current topology-based routing protocols in FANET; and (iii) comparing and analyz-
ing them using various performance measures. Our comparison study will aid network
engineers in selecting optimal routing protocols for the FANET deployment scenario.
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Paper—Flying Ad hoc Networks (FANET): Performance Evaluation of Topology Based Routing Protocols
The remainder of the paper is laid out as follows: Section 2 discusses FANET routing
problems as well as the taxonomy of topology-based routing systems. Describe the
simulation setup and the mobility model in Section 3. The results of the analysis will
be provided in section 4, followed by a discussion. The conclusions are in Section 5.
2 Routing in FANET
2.1 Challenge of routing protocols in FANET
Routing protocols are in charge of discovering, creating, and maintaining communi-
cation routes between two nodes. The overhead and bandwidth usage of these protocols
should be kept to a bare minimum. Due to the high mobility of UAVs, network topol-
ogy might vary over time, making route nding and route maintenance one of the most
important challenges to solve [14]. To enhance routing performance, including better
QoS and high route setup success ratio, as well as lower energy consumption. There are
three major issues to overcome [15]:
High network dynamicity. Due to the obvious high mobility of FANETs, the
extremely dynamic network topology results in low connections between nodes. as a
result, and network partitions and link disconnections are common, increasing route
discovery and maintenance and lowering routing performance [15]. To investigate the
routing process in FANET, many UAV mobility models have been developed [16].
Residual energy. According to the comparably large distance between UAVs, UAVs
powered by batteries have limited energy (a) to conduct routing processes such as
route discovery, updates, and maintenance; (b) to provide extended transmission range;
(c) retransmit various packets when a link failure occurs. UAVs carrying heavier pay-
loads, on the other hand, consume more energy [17].
High resource costs. Frequent route discovery, updates, reestablish routes, and dif-
ferent packet retransmissions in FANETs can result in three categories of resource
costs: (a) high routing overhead or inefcient bandwidth usage); (b) computational cost
owing to route processing time; (c) excessive energy consumption. To improve UAV
connectivity, multi-UAVs can be deployed in many scenarios [18].
2.2 Topology based routing protocols in FANETs
FANET routing protocols, as shown in Figure 2, can be classied into four groups
based on the approach used and the challenges that must be overcome. Topology-based
routing protocols, which are based on link information, use IP addresses to exchange
packets between interacting nodes. This category includes four types: static, proactive,
reactive, and hybrid [19]. The following section delves into the topology-based routing
protocols category and its most important routing protocols.
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Fig. 2. Taxonomy of topology based routing protocols in FANET
In static protocols, the information for UAVs is calculated and loaded to each UAV
rst in this type of routing protocol. It is not possible to modify it during the process,
and the network topology should always be xed. As a result, the number of communi-
cation lines between UAVs or between the UAV and the ground station is reduce. This
routing technique does not offer fault tolerance in a dynamic environment if certain
UAVs fail since they must wait until the mission is over to rectify the issue.
Load Carry and Deliver (LCAD) [20] is a FANET-specic static routing protocol.
Before UAVs take off, LCAD congures the navigation path on the ground. UAVs are
thought of as connections between a source and a destination ground control station,
collecting packets of data, transporting them, and transmitting them to the destination.
If the UAVs carrying the packets of data are not heading in the proper direction, other
UAVs might take over and deliver the data packets. Figure 3 illustrates the LCAD
routing technique in a FANET. It must be noted that no routing table method is used in
this technique.
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Paper—Flying Ad hoc Networks (FANET): Performance Evaluation of Topology Based Routing Protocols
Fig. 3. LCAD mechanism in FANET
In reactive protocols, the path between nodes is establish when there is a request to
transmit packets. When a request to transmit packets is receive, a path between nodes
is construct in reactive protocols. As a result, there is no need to estimate the routes for
each node regularly. It appears to resolve the overhead issue. There are two different
kinds of messages in this mechanism: route request and route reply. The key benet of
this approach is its bandwidth efciency. However, it will be delayed due to the time
required to identify the path [21].
Dynamic Source Routing (DSR) [22] is a reactive routing technology that enables
a network to self-organize and customize themselves without the use of infrastructure.
Since DSR is reactive, a discovery procedure is only used when information is transmit-
ted. A route maintenance system is also used to keep track of any path failures. DSR’s
exibility of loop characteristics allows users to choose from numerous routes to any
target node. Because each transmitted packet must include all of the transited nodes’
addresses, it is insufcient for large-scale networks and networks. Figure 4 depicts the
DSR mechanism for FANET.
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Fig. 4. DSR mechanism in FANET
In proactive protocols, the table-based routing contains all of the information about
all nodes within the network, allowing each node to know everything there is to know
about the others in the network. This approach has one major benet: each node’s table
continuously contains the most up-to-date information about another node. However, we
must keep in mind that this technique requires bandwidth due to the cost of the updated
messages for the tables. Due to the limited bandwidth available in the FANETs network,
customized routing protocols can be utilize to modify the topology of the nodes.
Optimized Link State Routing Protocol (OLSR) [23] [24] is a link-state routing pro-
tocol that establishes a global knowledge of all current UAV-to-UAV connections. This
is accomplish by periodically exchanging Hello and Topology Control (TC) packets
between the UAVs to update the network’s topology information. OLSR chooses Multi-
Point Relay (MPR) UAVs to cover two-hop neighbors, produce link-state information,
and relay data packets to other MPRs, lowering overhead. The OLSR mechanism in
FANET displays in Figure 5.
Fig. 5. OLSR mechanism in FANET
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Hybrid protocols integrate the reactive and proactive aspects of protocols. It was
created with the goal of reducing overhead and maximizing bandwidth use. As a result,
hybrid protocols are appropriate for large-scale networks with many sub-network
regions, where intra-zone routing employs proactive mechanisms and inter-zone
routing employs reactive mechanisms. Hybrid protocol adopted in many FANET
Application [25].
Temporarily Ordered Routing Algorithm (TORA) [26] is a hybrid distributed routing
technique that performs well in highly dynamic networks such as FANETs. TORA is
exclusively responsible for updating and maintaining the communication links between
nearby UAVs. TORA’s major goal is to minimize the number of control packets sent
during topology changes. TORA constructs and maintains a Directed Acyclic Graph
(DAG) between communicating UAVs that have several paths. Furthermore, TORA
frequently chooses longer routes in order to save overhead. To summarize, TORA
employs both reactive and proactive techniques depending on the network’s state, and
it discovers alternate routes in the event of connection failures. Figure 6 depicts the
TORA mechanism in FANET.
Fig. 6. TORA mechanism in FANET
3 Simulation set up
Due to the high cost of real UAVs and the time and resources required to create a
realistic FANET environment, the network simulator was used to deploy the UAVs
and facilitate communication among them. In the simulation challenge, a network
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environment that is as near to the real world as possible must be suggested [27]. In light
of this, researchers should attempt to build realistic FANETs application situations.
3.1 Simulation of FANET
A single source UAV node 1, a single base station node 20, and 18 relaying UAV
nodes make up the FANETs simulation scenario. To match a realistic ight environ-
ment of Surveillance UAVs, the UAVs are deployed and own autonomously across a
vast region of 1 Km x 1 Km. The IEEE 802.11 wireless interface is install on each UAV.
The UAV ies 5 minutes above the ground at a speed of 30 meters per second, using a
three-dimensional mobility model as illustrated in Figure 7. After establishing a path
between the UAV and the base station, the UAV begins transmitting a sensing video
with a packet size of 1024 bytes.
Fig. 7. FANET for surveillance scenario
3.2 Mobility models 3D
The Gauss-Markov (GM) mobility model [28] is a three-dimensional time-based
model that uses several parameters to respond to varying amounts of randomness
and avoid abrupt movement changes. Each movable node is provide a current speed
and direction, as illustrated in Figure 8. Based on its previous direction and speed, its
upcoming movement is then update and describe. Consequently, GM can eliminate the
abrupt pauses and turns seen in Random models. If the right parameters are set, the
equations system, which relates prior speed and direction to future ones, enables smooth
updating. GM is used to communicate among UAVs in a variety of applications [29].
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Paper—Flying Ad hoc Networks (FANET): Performance Evaluation of Topology Based Routing Protocols
Fig. 8. Trajectory of UAVs using GM models
3.3 Performance metrics
The performance of Topology routing protocols analysis and compare based on the
following metrics.
• Jitter: The jitter of a packet is measured by the average deviation of the change in
packet interval at the receiver compared to the sender, for a pairing of packets, in a
ow of packets among a source node and a destination node.
• Average throughput: this metrics represent the total throughput of FANET divide by
number of trafc ow for UAVs
• Packet Receive rate: is the rate of the successful received packet by the Base station
to the total transmitted packets by the UAV during the mission.
4 Results and discussion
Figure 9 demonstrate the jitter results of a 20-UAV node for FANET with the a low
altitude of 60–150 m and increments of 10 m. When compared to the other protocols,
the OLSR protocol has the lowest jitter. This is owing to its proactive nature and access
to the most up-to-date information for all multi-UAV networks. It is also worth noting
that when the altitude of UAVs increases, the jitter for all routing protocols goes up as
well. Due to its static nature, LCAD, on the other hand, has the greatest Jitter.
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0
50
100
150
200
250
60 70 80 90 100 110 120 130 140 150
Jitter (msec)
Altitude (Meter)
DSROLSR LCAD TORA
Fig. 9. Jitter comparison
Figure 10 shows a comparison of the average throughput of DSR, OLSR, LCAD,
and TORA routing protocols. TORA has the best throughput due to its hybrid nature and
effective bandwidth use, whereas LCAD has poor performance in most UAV altitudes.
Nevertheless, as the altitude of the UAV increases, the average throughput of all routing
protocols drops, particularly beyond 80 meters.
0
5
10
15
20
25
30
35
60 70 80 90 100 110 120 130 140 150
Throughput (kbps)
Altitude (Meter)
DSR OLSR LCAD TORA
Fig. 10. Average throughput comparison
Figure 11 depicts the packet successful rate of four routing protocols with low alti-
tude for UAVS. It is observed that TORA and DSR protocols provides high reliability
for data routing and have highest PSR rate with all UAV Altitudes. Similarly, to other
performance metrics LCAD show poor performance and drop many packets. OLSR
protocols PSR decrease as the altitude of UAVs increase.
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70%
75%
80%
85%
90%
95%
100%
60 70 80 90 100 110 120 130 140 150
PSR(%)
Altitude (Meter)
DSR OLSR LCAD TORA
Fig. 11. Packet successful rate comparison
5 Conclusion
This research paper investigates the performance of topology-based routing proto-
cols LCAD, DSR, OLSR, and TORA to determine the optimum protocol for surveil-
lance scenarios in Flying Ad hoc Networks. Multiple network metrics, jitter, average
throughput, and packet successful rate, were used to analyze and compare the four
protocols. In terms of average throughput and DSR, TORA was determined to be the
optimal protocol for monitoring scenarios. In terms of overall performance, DSR and
OLSR are both ranked second. In simulations, it has been observed that TORA and
OLSR performance sometimes goes synonymously and that OLSR jitter delay is some-
times the shortest.
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7 Author
Ali H. Wheeb is a Faculty Member - Lecturer at University of Baghdad since
2014. His elds of research interest are ying ad hoc network, mobility models, IoT,
wireless ad hoc networking, routing protocols, networking simulation tools Ns-2 &
NS-3, transporting protocols. Further, he publish 11 research papers in high reputation
journals. Also. Lecturer Ali serve as reviewer in several journals and conferences and
reviewed 100 paper until now. Email: a.wheeb@coeng.uobaghdad.edu.iq
Article submitted 2021-11-11. Resubmitted 2021-12-17. Final acceptance 2021-12-19. Final version
published as submitted by the authors.
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