Routing Protocols in Airborne Ad-hoc Networks (AANETs) A Study

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© 2019, IJCSE All Rights Reserved 1107
International Journal of Computer Sciences and Engineering Open Access
Research Paper Vol.-7, Issue-4, April 2019 E-ISSN: 2347-2693
Routing Protocols in Airborne Ad-hoc Networks (AANETs): A Study 
Pardeep Kumar1*, Seema Verma2
1,2 Department of Electronics, Banasthali Vidyapith, Jaipur, Rajasthan, India
*Corresponding Author: pardeep.lamba@gmail.com,
DOI: https://doi.org/10.26438/ijcse/v7i4.11071113 | Available online at: www.ijcseonline.org
Accepted: 20/Apr/2019, Published: 30/Apr/2019
Abstract— With the expansion of wireless technologies, the ad-hoc networking between aircraft has become possible. This
kind of networks is known as Airborne Ad hoc Networks (AANETs). These networks have emerged as one of the highly
challenging areas of research due to the unique flight dynamics of the aircraft being used as nodes. The cruising speed of an
aircraft (around 400-500 mph) is the major reason behind the intermittent link quality between two communicating aircraft. As
a result, the routing between aircraft has emerged as a major threat for research in this field. This paper presents a study of
different routing solutions provided recently by the research community for AANETs. A simulation analysis of the two well-
known routing protocols i.e. OLSR and GPSR has also been provided here to identify their suitability in the airborne
environment.
Keywords—AANET, Cruising Speed, OLSR, GPSR .
I. INTRODUCTION
Since its evolution, wireless communication has come a long
way from its analog implementation (1G and 2G) to today's
advanced digital implementations (LTE, 4G), etc. nowadays,
it is all around us and has become an integral part of our
daily life [1]. It can be implemented in two days: -
Infrastructure based mode (Cellular and satellite network)
and infrastructure-less mode (Wireless Sensor Network and
Ad-hoc Networks). The ad-hoc networks have become more
popular due to no requirement of pre-defined infrastructure
and decentralized control. Initially, these networks were
employed for communication between computers, mobile
phones, and various hand-held devices. They are termed as
mobile ad-hoc networks (MANETS) [2, 3, 4, 5]. The major
advantage of MANETs is self-configuring capability and
independent node movements. Another kind of ad-hoc
networks is Vehicular Ad-Hoc Networks (VANETs). It is a
promising approach for the future intelligent transportation
system which provides vehicle-to-vehicle communication
(V2V) and vehicle-to-infrastructure access points (V2I) [6,
7]. With advancements in communication and sensor
technologies, nowadays, we have another emerging field of
research called Airborne Ad-hoc Networks (AANETs). It can
be considered as a sub-class of VANETs with more unique
characteristics like very high cursing speed of the aircraft,
highly dynamic topology, limited bandwidth, flight
dynamics, etc. [8, 9].
In AANETs, there can be two kinds of communication: -
aircraft-to-aircraft communication and aircraft-to-
infrastructure communication [9]. In aircraft-to aircraft
communication, different aircraft can communicate with
each other directly for target tracking and path planning. In
aircraft-to infrastructure communication, an aircraft may
communicate with ground stations, satellite, etc. However,
the fundamental idea about AANETs is same as in MANETs
and VANETs. Owing to unique characteristics like higher
aircraft cursing speed with different flight dynamics and
intermittent link quality as a result of aircraft banking; there
are so many design challenges at each layer of the network.
At the physical layer, antenna structure, radio propagation
models 3-dimensional capability integration is the major
challenges. At the MAC layer, link outage, link quality
fluctuations due to higher mobility and the implementation of
localization services with full duplex radio circuits are key
issues. At the network layer, the major issue is the need to
improve peer-to-peer communication in a multi-aircraft
environment. At the transport layer, congestion control needs
to be implemented to enhance the reliability of the link. To
cop up with the scalability issue, cross-layer architecture and
clustering algorithm are still unexplored. 
It is found that poor link quality due to very high cruising
speed (300-40 km/hr) of the aircraft is the major cause of all
the research challenges presented by AANETs. So, routing in
AANETs has emerged as the most challenging research
issue. A lot of research articles have been presented since the
last 4-5 years but still, there is no specific routing solution. In
recent articles, it is found that most of the traditional
MANET routing protocols are not able to deal with such
dynamic aeronautical environment. The recent research has

International Journal of Computer Sciences and Engineering Vol. 7(4), Apr 2019, E-ISSN: 2347-2693
© 2019, IJCSE All Rights Reserved 1108
been centralized on implementing the traditional protocol,
proposed routing solution for AANETs using 2-dimensional
memory-less mobility models (i.e. Random Mobility
Models). These 2-D random mobility models are not able to
trace the aircraft movement as in a real environment. Hence,
this is another need of the hour to analyze different proposed
routing solution with 3-dimensional mobility models. The
previous research also suggested that either geographical or
proactive protocols are capable to handle the dynamic nature
of AANETs. 
With all these major research issues, this paper presents a
study of the previously proposed routing solutions for
AANETs. At the same time, a simulation comparison of two
well-known protocols:-Optimized Link State Routing
(OLSR) and Greedy Perimeter Stateless Routing (GPSR)
using the parameters of airborne network environment have
been presented here. The comparison is done using the
transport layer protocols i.e. UDP and TCP.
The rest of the paper is organized in various sections: -
section II explaining the related work done in AANETs;
section III giving a brief about routing protocols in AANETs,
section IV dealing with simulation analysis; section V
providing simulation results. The concluding remarks have
been made in section VI.
II. RELATED WORK
Y. Wang and Y. J. Zhao have presented various fundamental
issues related to design and employability of airborne
networks in civil aviation. The authors have discussed that
high speed of air vehicles results in rapid topology updates
which directly influence the capability of individual aircraft
to accomplish the task of flight coordination and situational
awareness. The alliance of airborne networks with existing
communication networks is another major issue with their
systematic design. The key motivation to adopt airborne
networks for civil aviation is to boost up safety and
productivity. To meet up these requirements, numerous
systematic design methods to be followed at the physical
layer, data link layer, and network layer; have been discussed
herein [10].
Bekmezci et al. have found that physical layer design of
FANET is quite different from traditional MANETs in terms
of available link bandwidth, long distances between
communicating nodes, larger simulation area, distinct radio
propagation models and antenna structure, etc. As a result,
the existing physical layer protocols not able to deal with
such scenarios. New physical layer design, with 3-D
implementation and their analysis are still unexplored issues.
The link quality variation due to higher mobility and larger
distances between nodes is another unique challenge to
implement AANET's MAC layer [9]
With Brisk advancements in communication technologies,
sensor devices, and other networking concepts; unmanned
aerial vehicles can be connected in groups to create a multi-
UAV environment. A new networking model based on multi-
UAV communication is presented by Ozgur Koray Sahingoz.
The multi-UAV models can be utilized for search and rescue
operations, as a relay for MANETs, automatic fire detection
in forests, disaster monitoring, wind estimation, etc. The
author has also discussed the advantages of multi-UAV
systems over single-large UAV. Various routing schemes
like location-aware routing, hierarchical routing, and data
centric-routing, proactive and routing are discussed here with
open research issues and challenges to be faced during their
implementations in AANETs [8].
Diane Kiwior et al. tested the implementation of Quality of
Service in highly dynamic environment airborne networks
with OSPF protocol. This implementation shows that due to
fading characteristics of the Line of Sight channels in an
airborne environment, OSPF does not prove to be as
effective as in terrestrial networking scenarios. Hence, some
changes in timer settings of “HELLO” messages are
suggested by the authors to make OSPF more suitable for
airborne networks. The experimental results have
demonstrated that reconfiguring the timer settings results in
faster convergence time. Now, lesser time will be needed to
detect link outage and reroute the data traffic appropriately.
This also minimized the high priority traffic loss by up to 80
percent without any significant change on jitter and latency.
The authors have also suggested that there is a need for
further research to optimize the timer settings more precisely
[11].
Tiwari et al. have presented a mathematical foundation to
deploy an airborne network with optimization. The node
placement planning for the airborne network has been
studied. Two metrics used here are maximum data upload
capacity for when of Air to ground links and for air-to-air
links worst-case link activity percentage. The maximum
upload capacity measures provided to a single ground node
while worst-case link activity percentage is a function of the
distance between the centers of loitering orbits of airborne
nodes. This metric is helpful in delay tolerant networks [12].
Ki-II Kim has proposed the method of generating realistic
mobility trace for airborne networks. Software architecture
has been used to explain the process of generating movement
trace automatically. For real-world vehicle movement's trace,
use of flight simulator has been proposed [13].
III. ROUTING PROTOCOLS IN AANETS
Relevant After a lot of literature survey of the relevant Ad-
hoc networks, it is found that variable link quality due to the
very high mobility of aircraft nodes presents many

International Journal of Computer Sciences and Engineering Vol. 7(4), Apr 2019, E-ISSN: 2347-2693
© 2019, IJCSE All Rights Reserved 1109
challenges to quality of service (QoS) implementation in
AANETs. It is also felt that terrestrial routing protocols may
not be as effective as in traditional MANETs. So, there is a
need to design suitable routing protocols for these highly
dynamic Ad-hoc networks. Here, we present some of the
protocols which may be implemented in these networks. Few
of them are novel approaches while some existing traditional
MANET protocols are implemented with some
modifications.
Open Shortest Path First (OSPF): This protocol was
designed for relatively fixed or less dynamic networks [11].
To use OSPF for Airborne Networks some modifications of
its parameters like "Hello" message timers are needed. The
default timer settings to recognize and recover from link
outages have been resulting in time convergence which is
due to delay in detection of link failures by the "Hello"
packets. If timer settings are reduced then there will be a
decrease in packet loss during link failures. The overhead can
also be reduced to meet out the problem of scalability.
Multi-Meshed Tree (MT) Protocol: This approach is
basically a combination of clustering, reactive and proactive
routing schemes [14]. This protocol has been evaluated for
robust connectivity among highly dynamic aircraft's moving
at 200-400kmph. In reality, this is a hybrid approach in
which proactive Multi-Meshed Tree (MMT) is used for inter-
cluster routing while Reactive Multi-Mesh Tree (RMMT) is
employed for inter-cluster routing. This protocol has
outperformed other protocols in terms of success rate
percentage, End-to-End packet latency, and file transfer
delay.
Predictive-OLSR (P-OLSR): This protocol makes use of
GPS data available on board in aircraft. It is an extension of
traditional OLSR which was failed in tracking changes in
highly dynamic networks [15]. This GPS data can be easily
obtained from an aircraft. So, for highly mobile Aircrafts
Networks, geographic routing protocols can prove to be very
successful. The experimental and simulation results show
that P-OLSR outperforms OLSR for frequent topology
changes.
A-GR: This protocol is based on the automatic dependent
surveillance-broadcast (ADS-B) system [16]. It is also a
geographical routing protocol which makes use of
information about position and speed of aircraft given by the
ADS-B system. So, it eliminates the need for traditional
routing messaging and presents a new metric for new hop
determination which is velocity based. A-GR decreases the
routing overhead improves packet delivery ratio.
GPSR: The routing protocol forwards the data packets by
using the positions of routers and a packet‟s destination [17].
GPSR uses information about the immediate neighbor of the
router to make greedy forwarding decisions. When this
greedy forwarding is not possible in any region of the
network, it routes around the perimeter of the region. GPSR
uses local topology information under frequent topology
changes to find new routes. So, to make forwarding
decisions, the only the position of the packet's destination
and position of the candidate next hop are enough.
Reactive-Greedy-Reactive (RGR) Protocol: This is a
promising routing protocol for high mobility and dense
scenarios. The concept of scoped flooding and mobility
prediction will be used to improve the original RGR protocol
[18]. Along with the location information, this protocol uses
a velocity vector of nodes to predict their current location.
Due to this advantage, an overhead message gets reduced.
AeroRP: This is another geographical protocol for highly
dynamic networks. The AeroRP algorithm makes use of
velocity-dependent heuristics for improved packet delivery
[19]. The time to intercept (TTI) metric is used for making
routing decisions. This parameter tells the source node that
how soon different potential neighbor will be in the
transmission range of destination. The speed component
( ) is crucial for TTI calculation. It is the relative speed
of the neighbor w.r.t to the destination. The positive and high
means the neighbor is moving towards the destination
with high velocity and negative means the neighbor is
moving away. As we know, for AANETs geographical
information can be very helpful for improved routing.
AeroRP also is very helpful for improved accuracy, less
delay and overhead, etc.
DREAM (Distance Routing Effect Algorithm for Mobility):
Here, the frequency of sharing of location information
among the nodes is decided on the basis of inter-node
distance and how fast the individual nodes are moving. More
the nodes apart from each other, the less often position
updates need to be shared. This way DREAM optimizes the
rate of generation of control messages [20].
Location-Aided Routing (LAR): It is also based on the
concept of „wedge' as used in the DREAM. Here, the wedge
zone is referred to as the request zone. This request zone is
used to forward the route request instead of data packets [21,
22]. LAR uses flooding if route request to nodes inside the
request zone doesn't reach out the destination. In LAR, every
node needs to know if they are within the request zone. Two
different methods are used to decide if a node is in the
request zone. In the first method, the sender sends a route
request containing the coordinates of a rectangular area
which has the request zone. A node receiving this request
message will discard if it is not in the rectangle and forward
if it is. In the second method, the request zone is not defined
explicitly but instead, the packet is forwarded based on the
distance between the sender and destinations nodes.

International Journal of Computer Sciences and Engineering Vol. 7(4), Apr 2019, E-ISSN: 2347-2693
© 2019, IJCSE All Rights Reserved 1110
Optimized Link State Routing (OLSR): OLSR is a proactive
link-state protocol this routing protocol uses HELLO
messages and topology control (TC) messages to discover
neighbors [23]. The HELLO messages are employed to find
out the neighbor nodes in direct connection (i.e. one hop
neighbors). While Topology Control messages are used to
build a topology information base. This protocol can be used
for ad-hoc networks having bandwidth and neighbor
mobility. OLSR uses the Multi-point Relay (MPR) technique
to reduce control traffic overhead. There is a periodical
exchange of messages about topology information of the
entire network in case of mobility and failure both. Hello,
messages are received only by the one-hop neighbors. These
HELLO messages are forwarded further in the network only
by the selected MPR nodes. These way overhead packets are
minimized. Within the network which may be created due to
the forwarding of Link state packets by every receiving node.
IV. METHODOLOGY
To carry out the various simulations and analyze the
performance of OLSR and GPSR protocols, the ns-3.23
simulator has been used. The 3-D Gauss-Markov Mobility
Model (GMMM) is used to represent the movement of
aircraft in a more realistic manner.
Performance metrics: The performance of the two
protocols have been investigated on the basis of the
following parameters:-
Routing Overhead: These are small sized packets which are
generated to maintain a present state of the routes within a
network. 
End-to-End (E2E) Delay: It defines the total time taken by
any packet to be transmitted from any source to a defined
destination.
Packet Delivery Ratio (PDR): It refers to the ratio of
successfully delivered packets to the total number of packets
sent by the sender. It is usually represented in terms of
percentage.
Throughput: It defines the rate of successfully delivery of
packets through any network. If congestion occurs
somewhere in the network then packets may be dropped. It
decreases throughput.
Simulation Scenario
The behavior of OLSR and GPSR in the airborne
environment has been analyzed using two scenarios which
are as follow:-
 Scenario 1:- The packet size is considered as 256
bytes with varying mobility model memory
(randomness) represented by α in the range 0 to 1.
The minimum node speed is 100 m/sec and
maximum node speed is 300 m/sec.
 Scenario 2:- The packet size is considered as 512
bytes with varying mobility model memory
(randomness) represented by α in the range 0 to 1.
The node speed is the same as in scenario 1.
Table 1. Simulation Parameters Used
Parameter
Value
Simulation Area
4000m*4000m
Simulator used
ns-3(Version-3.23)
Mobility Model
3-D Gauss Markov Mobility Model
Received Signal Strength (RSS)
-90dbm
Type of channel
Wireless Channel
Protocol used
OLSR, GPSR
Simulation duration
20s
MAC Layer Protocol
802.11b
Transport Layer Protocol 
UDP, TCP
Packet Size
256, 512 Bytes
No. of Nodes
13
Packet Interval
.008 sec
Number of packets transmitted
5000
Time Step
.25 sec
Node Speed range
Minimum Node Speed=100 m/sec,
Maximum Node Speed=300 m/sec
The simulation parameters used are shown in Table 1.
V. RESULTS AND DISCUSSION
In the following simulation graphs; U-OLSR, U-GPSR
represents UDP traffic while T-OLSR, T-GPSR represents
TCP traffic.

International Journal of Computer Sciences and Engineering Vol. 7(4), Apr 2019, E-ISSN: 2347-2693
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Figure 1. Routing Overhead vs. Mobility Model Memory(α) When Packet
size=256 bytes
Figure 2. End-to-End (E2E) Delay vs. Mobility Model Memory (α) When
Packet size=256 bytes
Figure 3. PDR vs. Mobility Model Memory(α)When Packet size=256
bytes
Figure 4. Throughput vs. Mobility Model Memory(α) When Packet
size=256 bytes
Figure 5. Routing Overhead vs. Mobility Model Memory(α) When Packet
size=512 bytes
Figure 6. End-to-End (E2E) Delay vs. Mobility Model Memory(α) When
Packet size=512 bytes

International Journal of Computer Sciences and Engineering Vol. 7(4), Apr 2019, E-ISSN: 2347-2693
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Figure 7. PDR vs. Mobility Model Memory(α) When Packet size=512
bytes
Figure 8. Throughput vs. Mobility Model Memory(α) When Packet
size=512 bytes
Figure 1 shows that routing overhead is more in case of UDP
as compared to TCP for both OLSR and GPSR. OLSR creates
more overhead than GPSR for both transport protocols at
lower mobility model memory (α). But as α increase, GPSR
creates a very large amount of control packets in UDP
environment.
Figure 2 shows that the total packet transmission delay is very
low in case of UDP environment as compared to TCP. With
an increase in memory, end-to-end delay increases for TCP.
But, when memory is maximum i.e. α = 1 then delay
decreases because the new route can be calculated easily
based on previous path information. At higher mobility model
memory, GPSR provides a lesser delay in TCP
environment.
The packet delivery ratio is higher in the case of TCP as
compared to UDP for both protocols as shown in Figure 3. In
the case of TCP, GPSR is able to deliver more packets in
comparison to OLSR.
Figure 4 shows that throughput is higher in case of TCP as
compared to UDP. In the case of TCP, GPSR is better in
terms of throughput. But as α increased there is a slight
decrease in GPSR throughput. 
Figure 5 presents that overall routing overhead is more in case
of UDP protocol. GPSR is able to deliver packets to the
destination with lower overhead generation for both UDP and
TCP as compared to OLSR. This is due to regular
transmission of link state packets by OLSR .
Figure 6 shows more E2E delay in case of UDP as compared
to TCP. Because, due to large packet size there can be a state
of congestion in case of UDP. The smaller delay in TCP is
due to a defined path to the destination. When packet size 512
bytes; both the protocols take more time to deliver packets
takes more time to deliver packets to the destination, in
comparison to when packet size is 256 bytes.
Figure 7 presents that packet delivery ratio is more when
packet size is 256 bytes. OLSR is able to deliver more packets
successfully in both UDP and TCP at higher aircraft speed.
Both the protocols are able to provide higher throughput when
packet size is taken as 512 bytes as compared to 256 bytes as
shown in figure 8. . Furthermore, the throughput is higher in
case of UDP instead of TCP.
VI. CONCLUSION AND FUTURE SCOPE
This paper presents AANET as a highly emerging field of
research for the ad-hoc network community. As routing has
been found as a major concern of research in AANETs, hence
various routing protocols proposed in previous researches,
have been studied here. This study can be helpful in future
research to choose any of these routing schemes which can be
suitable for AANETs. Furthermore, this paper also presents a
simulation comparison of OLSR and GPSR protocols to
investigate their suitability in the airborne environment. It is
concluded that when packet size is 256 bytes and mobility
model memory is varied, GPSR is better than OLSR in TCP
environment in terms of routing overhead, E2E delay, PDR
and throughput. OLSR is better in UDP environment in terms
of lesser overhead, higher PDR. When packet size is 512 byte,
OLSR is better in both TCP and UDP environment in terms
of PDR and throughput. GPSR is better in terms of lesser
routing overhead. Overall, OLSR is better than GPSR if the
larger packet size is used for data transmission in highly
dynamic networks. In the future, more improvements can be
done to make these protocols more suitable for airborne
networks. There is a need to develop an AANET specific
routing protocol.
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Authors Profile
Mr. Pardeep Kumar perused Master of
Technology in Electronics and
Communication Engineering in 2011.
Currently, he is pursuing a Ph.D. in
Electronics and Communication
Engineering from Banasthali Vidyapith,
Jaipur, India. His research interests include wireless
communication and MANET routing protocols.
Dr. Seema Verma received her P.hd in
Electronics in 2003. Currently, she is a
Professor at the Department of Electronics,
Banasthali Vidyapith, Jaipur, India. Her
research interests are related to Wireless
Sensor Networks, security in Cloud
Computing, Cryptography, Wireless Ad-hoc Networks, and
VLSI design. She has authored many books. She has
published many research papers in national and international
journals, conference proceedings as well as a chapter of
books. She is also a member of editorial boards of various
journals.