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applied
sciences
Article
Ground Control System Based Routing for
Reliable and Efficient Multi-Drone Control System
Woonghee Lee †, Joon Yeop Lee †, Jiyeon Lee, Kangho Kim, Seungho Yoo ,
Seongjoon Park and and Hwangnam Kim *
School of Electrical Engineering, Korea University, Seoul 02841, Korea; tgorevenge@korea.ac.kr (W.L.);
charon7@korea.ac.kr (J.Y.L.); jiyeon524@korea.ac.kr (J.L.); mybalam2k@korea.ac.kr (K.K.);
pen0423@korea.ac.kr (S.Y.); psj900918@korea.ac.kr (S.P.)
*Correspondence: hnkim@korea.ac.kr; Tel.: +82-2-3290-4821
† These authors contributed equally to this work.
Received: 12 September 2018; Accepted: 19 October 2018; Published: 23 October 2018
Abstract:
Various unmanned aerial vehicles (UAVs), also called drones, have developed based on
advances in hardware and software technologies. Thus, service providers in diverse areas have tried
to utilize drones to create more effective solutions. In many cases, employing multiple drones is
more effective to perform the given mission than using a single drone. To utilize multiple drones,
the drones should be strongly connected, but it is not trivial to construct reliable and efficient
networks for drones due to their high mobility. Therefore, we propose a ground control system
(GCS) routing protocol (GCS-routing) to overcome this limitation and provide reliable and efficient
multi-drone control system, where GCS-routing maximizes GCS utilization. GCS is the essential
component of flying ad-hoc network (FANET) and can obtain information about drones. Using this
information, GCS-routing can provide more effective routing, predict any topology changes, and react
immediately. GCS-routing does not issue any periodic HELLO message for neighbor discovery or link
cost estimation, which significantly enhances network performance. We implemented GCS-routing
on real drones, and applied GCS-routing to actual drone fleets, as well as simulations to evaluate
GCS-routing performance. The results clearly identify the advantages of the proposed routing
protocol for drone networks compared with current routing protocols.
Keywords:
drones; unmanned aerial vehicles (UAVs); ground control system; routing protocols;
wireless networks
1. Introduction
Development of flight controllers, batteries, network modules, embedded computers, and
multi-copter frame manufacturing has been very rapid [
1
]. Consequently, drones have been utilized in
many fields, evolving from applications requiring human monitoring into unattended autonomous
applications, such as drone assisted communications [
2
], disaster monitoring [
3
], remote sensing [
4
],
surveillance [
5
], etc. In this context, a drone fleet comprising multiple drones has many advantages
compared to a single drone, including cooperation, efficiency, and fault tolerance. However, drones
in a fleet need to be able to share tasks and communicate with each other and a ground control
system (GCS).
To improve drone teamwork, we previously proposed a multi-drone platform [
6
,
7
], comprising
two major components: GCS and a drone fleet. The GCS collects information regarding the drones in
the fleet, e.g., geographic information, and it plans and manages the drone fleet missions. The fleet
is a group of multiple drones that receive the mission from GCS and perform the mission as a
team. We considered several case studies, and in particular implemented drone assisted wireless
Appl. Sci. 2018,8, 2027; doi:10.3390/app8112027 www.mdpi.com/journal/applsci
Appl. Sci. 2018,8, 2027 2 of 24
communication for network disabled areas. In this study, drones are equipped with an additional
network interface to provide network access to users and Internet of Things (IoT) devices, even in
disaster areas or emergency situations, as shown in Figure 1. To actualize drone assisted wireless
communications, it is most important to guarantee connectivity among drones and GCS. Since
connection loss means that the GCS loses control of drones in a multi-drone control system, it is
essential to construct a reliable and efficient network. However, messages are generally exchanged
via wireless ad-hoc network, which is unreliable and frequently disconnected, and drone mobility
aggravates network instability.
Figure 1. Multi-drone system providing network services to users and IoT devices.
Many studies have modified existing routing protocols, such as the mobile ad-hoc network
(MANET), to provide a suitable network for drone systems [
8
–
10
]. However, there are fundamental
differences between existing MANET and drone system network requirements.
1.
Drones can move in any direction in three-dimensional air space, in contrast to ground nodes
that can effectively only move in two-dimensions. Thus, drones can be deployed at any
three-dimensional location forming significantly more complicated network topologies.
2.
Drones have much higher level of mobility than MANET nodes, hence network topology
frequently and significantly changes.
3.
Line of sight (LoS) is usually assured in the air since there are usually no obstacles. Thus,
communication range between drones can be significantly larger than between ground nodes,
and link cost estimation can be simplified.
4.
Guaranteed connections among drones are essential. Disconnection of any link may cause critical
problems because control messages are transmitted through multi-hop communication.
Therefore, MANET based routing protocols fundamentally have significant limitations, as follows.
•
They are not ideal for drone systems, where link quality rapidly changes and node mobility is
high, since their original target nodes have limited mobility.
•
They cannot utilize GCS, which is a significant component of drone systems, because they are
fundamentally based on MANET protocols.
•
They inevitably incorporate fundamental MANET protocol limitations, such as constant exchange
of HELLO message and repetitive path finding.
These limitations significantly degrade drone system performance, hence MANET based routing
protocols are unsuitable for drone networks.
To overcome these limitations, we focused on GCS advantages, since GCS is an essential
component of drone networks, containing drone position and movement schedules [
11
]. Taking our
preliminary work [
12
] further, we propose GCS-routing, a new routing protocol for drone networks
that maximizes GCS utilization. This provides many advantages for drone networks, with the two
most significant advantages as follows.
Appl. Sci. 2018,8, 2027 3 of 24
1.
GCS can estimate link costs using drone position and movement information. Thus, drones are
only required to detect a link failure, which can be achieved by counting beacon messages sent
from neighboring drone access point (AP) interfaces.
2.
GCS knows the drone mobility schedules, hence it can predict topology changes and notify new
routes to each drone in advance. Thus, network disconnected time, which is extremely crucial for
controlling drones due to their mobility, can be minimized.
We implemented GCS-routing on real drones and GCS, and evaluated GCS-routing performance
in various experiments, e.g., video [
13
]. In addition to evaluations using real drones, we conducted
many simulations to support experimental results and analyze GCS-routing from more diverse
perspectives. In all the experiments and simulations, GCS-routing outperformed existing protocols in
terms of average throughput, throughput stability, and disconnection time.
The major contributions of this paper can be summarized as follows.
•
Proposing a routing protocol and defining a link cost metric for drone networks to control multiple
drones as a team
•
Implementing the proposed routing protocol on actual drones and evaluating the performance of
the protocol with a fleet of drones
•
Devising a new GCS onto which the proposed routing control scheme was installed to collect
topology changes and disseminate routing tables (current commercial drones and GCS products
do not have this type of network routing function)
The remainder of this paper is organized as follows. Section 2briefly introduces related work.
Section 3provides a detailed explanation of the proposed GCS-routing design, and Section 4presents
evaluation results. Finally, Section 5summarizes and concludes the paper.
2. Related Work
Several previous studies have considered aerial networks, such as unmanned aeronautical ad-hoc
networks [
14
], airborne network [
15
], UAV ad-hoc network [
16
], etc. However, despite the different
names, these have largely only considered aerial ad-hoc networks. The flying ad-hoc network (FANET)
was subsequently defined [
17
], where all UAVs constituted an ad-hoc network but only a subset
communicated with the GCS. FANET nodes fundamentally move more freely and have higher mobility
than nodes in traditional networks such as MANETs or vehicular ad-hoc networks (VANETs). Thus, it
is essential to guarantee reliable peer-to-peer connection among FANET nodes for collaboration and
coordination between multiple UAVs.
Since FANET is a new form of MANET, most studies modified existing MANET routing
protocols, i.e., proactive (PRP) and reactive (RRP) routing protocols, to make them suitable for FANET.
For example, Alshabtat et al. proposed directional optimized link state routing protocol (DOLSR) [
8
]
based on optimized link state routing protocol (OLSR) [
18
], one of widely used PRPs, that minimized
the number of multipoint relays by using UAVs equipped with directional antennas. They verified that
DOLSR reduced both end-to-end delay and the number of overhead packets by simulation. In contrast,
Brown et al. developed a FANET testbed [
9
] based on a commonly employed RRP, dynamic source
routing (DSR) [
19
]. Forsmann et al. also proposed a routing protocol using time slot allocation to
coordinate communication between UAVs [
10
] based on ad-hoc on-demand distance vector routing
(AODV) [
20
]. Simulation results showed that the proposed routing protocol minimized packet drops
and improved communication quality of service.
Thus, the various routing protocols were not ideal for FANET where link quality rapidly changes
and node (drone) mobility is high. Previous protocols also failed to consider GCS, which is the
most significant FANET component. In a typical FANET scenario, GCS is essential to monitor UAV
information and control them [
11
]. However, previous protocols were not able to utilize GCS because
they were fundamentally based on existing MANET protocols. Consequently, the protocols inherited
MANET protocol limitations, such as constant exchange of HELLO message and repetitive path
Appl. Sci. 2018,8, 2027 4 of 24
finding, which significantly degrade FANET performance. To overcome these limitations, we propose
GCS-routing designed for FANET to maximize GCS utilization.
GCS-routing has advantages compared to previous approaches from several perspectives.
•
In contrast to previous routing protocols that were based on MANET protocols, GCS-routing was
fundamentally designed for FANET and makes full use of the GCS, enabling FANET to overcome
MANET limitations. The proposed routing can predict topology changes and react to them
immediately, since the GCS knows the drone movement schedules. Removing the requirement for
periodic HELLO messages for neighbor discovery or link cost estimation means that GCS-routing
significantly reduces current protocol overheads, making it suitable for high mobility drones.
•
Most previous protocols were only evaluated through simulations, without experiments using
real drones, even though general network simulators cannot fully support aerial conditions
and specifications [
17
]. Implementing drone networks in the real world is challenging, and
development of new routing protocols for drones is difficult. In contrast to the existing
work, we implemented GCS-routing on real drones and GCS, and verified the performance
of GCS-routing in various experiments. As discussed above, GCS-routing improves overall
network performance by reducing network overhead. Thus, it is compatible with systems or
algorithms for networks composed of multiple nodes. For example, GCS-routing can be used with
systems that require large network bandwidth, such as drone systems that incorporate continuous
video transmission [
21
–
23
], and algorithms that optimize bandwidth usage by considering the
physical characteristics of various nodes [
24
–
28
]. In addition, with an analytical model of
communication recovery probability in emergency situations [
29
], GCS-routing can be utilized for
drone assisted wireless communications more effectively.
3. GCS-Routing Design
This section explains GCS-routing design. Section 3.1 presents an overview of GCS-routing
operations, and Section 3.2 presents the route management process in detail. Section 3.3 describes
determination of link failure and link cost, and Section 3.4 analyzes transient loops and proves
GCS-routing to be fundamentally loop-free. Finally, Section 3.5 analyzes route update cost.
3.1. GCS-Routing Overview
The operations in GCS-routing were divided into two major phases: initialization and
maintenance. In the initialization phase, GCS generates routing table for each drone in a centralized
manner based on the distance between drones. Since LoS environment is usually guaranteed in FANET,
it is reasonable to estimate the link cost based on distance. In the maintenance phase, GCS updates and
sends routing tables if the network topology changes due to the drones’ mobility. Since GCS knows
future drone locations, future topology can also be predicted.
In some cases, LoS may not be guaranteed. To deal with this problem, each drone monitors
link failures between itself and its neighbors by counting the number of received beacons from the
neighboring drones’ AP interface (not their ad-hoc interface). Broadcasting beacons does not affect
routing performance in ad-hoc networks because periodic beacon broadcasts by AP interfaces are
enabled by default. When a drone detects an obstacle or a link failure, it reports the failures to GCS.
The GCS then updates the link cost and transmits the new routing table to the corresponding drones.
Figure 2shows the proposed GCS-routing architecture composed of two main components:
GCS and drones. The GCS consists fleet management and route management modules. The fleet
management module plans and generates flight commands to each drone, tracks drone position
and movement schedules, and hands this information to the route management module. The route
management module constructs routing tables and updates link cost in a centralized manner. The GCS
then sends routing tables to each corresponding drone through the ad-hoc interface.
Drones include communication and flight management modules. The communication
management module updates its routing table using route information sent from GCS,
Appl. Sci. 2018,8, 2027 5 of 24
and continuously monitors link failures between itself and neighbors. If link failure is detected,
error reports are sent to GCS through the ad-hoc interface. The flight management module relays flight
commands from GCS to the drone and reports sensor information to GCS.
Figure 2. GCS-routing architecture.
3.2. Routing Table Management
We explain how GCS manages routing tables of drones and GCS, and Algorithms 1and 2describe
GCS-routing operations.
Algorithm 1 GCS-routing operations in GCS.
Input: Drone geographic information and flight schedules
Initialization:
1: InitLinkCost (Drone geographic information)
2: tree T←GenerateRoute (GCS)
3: GCS ←applyRouteInfo (T)
Distribution:
T: Shortest paths tree for drones from GCS
M: Aggregated route message
m: Route message for one drone
4: for each subtree tof Tdo
5: for each child drone iof the subtree tdo
6: mi=GenerateRoute (i)
7: Mt←Mt+mi
8: end for
9: a root of the subtree t←sendRouteInfo (Mt)
10: end for
Maintenance:
11: while do
12: if deployment changes or failure is reported then
13: updateLinkCost (changed-links)
14: tree T←GenerateRoute (GCS)
15: for only for drones whose route has been changed do
16: Go Distribution process
17: end for
18: end if
19: end while
Appl. Sci. 2018,8, 2027 6 of 24
3.2.1. GCS-Routing Operations in GCS
Algorithm 1shows GCS-routing operations in GCS, which were classified into initialization,
distribution, and maintenance phases. In the initialization phase (Lines 1–3), i.e., drones are not
deployed yet or a route between drones is not constructed yet, GCS estimates link costs among drones
based on drone geographic information. The GCS then generates a routing table using Dijkstra’s
algorithm and applies the routing table to itself.
In the distribution phase (Lines 4–10), GCS distributes routing tables to each corresponding drone
while preventing loop construction. The GCS routing table is already constructed, hence GCS has a
tree where GCS is a root node. After generating drone routing tables, GCS creates an aggregated route
message,
Mt
, for each subtree,
t
, where the one-hop distance child node of GCS is a root node.
Mt
comprises routing tables for drones in subtree
t
. Subsequently, GCS distributes
Mt
to its child nodes
that then perform the remaining distribution process (see Algorithm 2).
Algorithm 2 GCS-routing operations in the drone.
Input: Aggregated route message M
Route update:
1: while do
2: if Mreceived then
3: m←findRouteInfo (M)
4: M←M−m
5: itself ←applyRouteInfo (m)
6: end if
7: if is not a leaf then
8: classify Minto M1,M2,· · · ,Mnfor each subtree
9: for each subtree, tdo
10: a root of subtree t←sendRouteInfo (Mt)
11: end for
12: else
13: delete M
14: end if
15: end while
Link monitoring:
16: while do
17: rc = monitorLinkFailure()
18: if rc == 1 then
19: report link failure to GCS
20: end if
21: end while
In the maintenance phase (Lines 11–19), when network topology changes due to drone movements
or link failure is reported, GCS updates the drone routing tables. As discussed above, GCS manages
and schedules drone movements, so it knows drone final destinations. Thus, before the actual
topology change, GCS can predict network topological changes and generate routes for future topology.
When drone movements are scheduled, GCS generates new route tables and issues them to the
corresponding drones using the same distribution strategy of Algorithm 1. The topology also changes
when a link failure occurs due to obstacles between drones. When GCS receives a link failure report
from a drone, it carries out the cost update process. GCS updates the failure link’s cost and regenerates
drone routing tables. However, in the maintenance phase, GCS distributes routing tables only to
drones that need to be updated, avoiding unnecessary communications and operations.
Appl. Sci. 2018,8, 2027 7 of 24
3.2.2. GCS-Routing Operations in the Drone
Algorithm 2shows that operations in a drone can be divided into route update and link
monitoring. In the route update phase (Lines 1–15), the drone parses the routing message,
m
, addressed
to itself from the received aggregated route message,
M
. It then compares new routes in
m
with old
routes and updates only changed route entries for efficiency. The drone deletes its routing table from
M
and classifies the remaining
M
into
M1
,
M2
,
· · ·
,
Mn
for its subtrees to generate new aggregated route
messages. The subtree root is the received node’s one-hop child node. The newly generated aggregated
messages are then issued, and the process is repeated until
M
reaches the leaf nodes. Consequently,
routing tables of all drones can be maintained up-to-date. Figure 3shows an example of distributing
the routing table in GCS-routing. Each drone extracts routing table information from corresponding
data in the aggregated route message, and then sends the remaining data to the child node.
In the link monitoring phase (Lines 16–21), a drone scans for link failures between itself and
neighbors periodically based on the reception rate of beacons from neighbors. As discussed above,
beacon broadcasts conducted by AP interfaces do not affect routing performance in ad-hoc networks.
If a link failure is detected, the drone broadcasts a link failure message. Neighboring drones receive
the message and forward it to the GCS.
Figure 3. Example routing table distribution in GCS-routing.
3.3. Link Cost Metric
In common MANET routing protocols, drones periodically broadcast HELLO messages to
estimate link costs and detect link failures. In contrast, drones in GCS-routing conduct link cost
estimation and link failure detection without HELLO messages. This section explains link cost
estimation and detection of link failure in GCS-routing.
3.3.1. Cost Initialization
Since GCS-routing is an aerial network routing protocol, we must consider environmental
characteristic of aerial networks. Most significantly, LoS between drones is usually guaranteed.
Thus, distance between two drones is proportional to the link cost between them. We conducted
an experiment to confirm the relationship between drone distance and throughput. One drone
hovered at 10 m altitude, while the other drone flew away from the hovering drone at constant 2 m/s.
We measured TCP and UDP throughput flows at 4, 8, and 16 Mbps. Data were transmitted through
the network interface using IEEE 802.11g. Figure 4shows that, regardless of the protocol or data
rate, throughput decreased proportionally with distance. Data transmissions fluctuated, but were
disconnected at almost the same distance, exceeding 80 m.
Appl. Sci. 2018,8, 2027 8 of 24
We normalized the data into [0, 1] to identify common features,
X(d)0=X(d)−Xmin
Xmax −Xmin
, (1)
where
X(d)
is the throughput at distance
d
; and
Xmax
and
Xmin
are the maximum and minimum
throughput, respectively. We then calculated the average of all datasets to generalize results from
various transmission rates, as shown in Figure 5. The fact that mean of normalized data is 0.8 indicates
that the best performance of link was 80% of the ideal case. Performance was approximately 50% of the
best performance when
d=
60 m, implying that it takes twice the time to deliver the same number of
data. Thus, link cost can be estimated using the reciprocal of the fitted curve in Figure 5. The resulting
cost function indicates the amount of time to deliver data through the link at the given distance.
Figure 4. Throughput dependency on distance between drones.
Figure 5. Mean normalized throughput.
There was significant difference between theoretical and experimental data rates outdoors,
consistent with many previous studies [
30
–
32
]. Thus, it is more appropriate to use the experimental
link cost estimation function rather than the theoretical result. Many studies have experimentally
established communication standards’ data rates, hence GCS-routing used suitable previous results
depending on the communication module employed. For example, estimated throughput,
Test
,
at distance dusing IEEE 802.11g can be expressed as shown in Equation (2) according to [33].
Test =−0.00001d3−0.0013d2−0.2454d+25.424. (2)
3.3.2. Cost Maintenance
Link costs for GCS-routing were initialized based on drone geographic information. To determine
the link costs correctly, we considered not only the distance but also obstacles that could exist. To detect
Appl. Sci. 2018,8, 2027 9 of 24
a link in non-LoS (NLoS), each drone counts the number of beacon messages received from neighboring
drone APs. We conducted experiments similar to that of Figure 4, but with 8 Mbps UDP flows with a
4.5 cm thick metal obstacle between drones to make the link NLoS. Each second, one drone monitored
the average number of received beacons for the past 5 s, as shown in Figure 6. Throughput decreases
more sharply in NLoS than LoS environments, with transmissions in NLoS suspended two times
earlier than in LoS. The average number of received beacons decreased to fewer than 3, although the
distance was close enough to communicate fully in LoS. Thus, we set the threshold count = 3, i.e., if the
average number of received beacons is fewer than 3, the drone reports link failure to GCS.
(a) Throughput dependence on distance.
(b) Number of received beacons dependence on distance.
Figure 6.
Throughput and number of received beacons dependence on distance in line of sight (LoS)
and non-LoS (NLoS) environments.
GCS-routing uses a beacon message broadcast in a different radio band from that used in the
ad-hoc network, and periodic beacon broadcasts by AP interfaces are enabled by default. Thus,
the process explained above does not affect ad-hoc network communication quality.
3.4. Routing Loops in GCS-Routing
This section analyzes routing loops in GCS-routing. We first consider a simple example where
a transient loop is formed and show how the network becomes loop-free in GCS-routing. Subsequently,
we theoretically prove GCS-routing to be loop-free.
3.4.1. Transient Loops in GCS-Routing
It is essential to guarantee loop-free operation for routing protocols. In GCS-routing, transient
loops may be formed during route redistribution when a node receives a new routing table but its child
Appl. Sci. 2018,8, 2027 10 of 24
nodes have not received the updated routing table yet. However, since route messages are distributed
in a hop-by-hop manner from the closest drone to the farthest drone from the GCS, transient loops
eventually disappear, and the network becomes loop-free. Figure 7shows an example scenario where
a transient loop is formed during route update. The numbers show link costs, and the network consists
of three nodes. Figure 7a shows the initial routes to node
C
from nodes
A
and
B
.
NHX(Y) = Z
means
that
Z
is the next hop from node
X
to node
Y
. Thus,
NHA(C) = C
means the next hop from node
A
to
node
C
,
NHB(C) = A
means the next hop from node
B
to node
C
, and similarly for
NHC(A) = A
and
NHC(B) = A.
(a) Initial routes. (b) Transient loop. (c) Final routes.
Figure 7. Example scenario where a transient loop is formed in GCS-routing.
In the example scenario, if the link between nodes
A
and
C
is broken, GCS-routing creates
new routing tables for each node. As described in Section 3.2, routing tables are distributed in
a hop-by-hop manner. Thus, node Areceives a new routing table from GCS and updates N HA(C)to
B
, i.e.,
NHA(C) = B
. However, node
B
’s routing table is not yet updated,
NHB(C) = A
. Figure 7b
shows that a transient loop between nodes
A
and
B
is formed if node
A
tries to transmit data to node
C
, and the presence of the transient loop means the data cannot be transmitted to node
C
. When node
B
receives the new routing table from node
A
, it updates
NHB(C)
to
C
, and the network becomes
loop-free. Then a new routing table is transmitted to node
C
, both
NHC(A)
and
NHC(B)
are updated
to B, and the network converges, as shown in Figure 7c.
3.4.2. Loop-Free GCS-Routing Proof
We formally prove that GCS-routing is loop-free. Let
G= (V
,
E
,
g)
be a weighted directed graph,
with
g
:
E(G)→R
representing a flying ad-hoc network where each vertex represents a drone or
GCS, and an edge is incident to two vertices when both vertices are in transmission range of each
other. We assume that membership in the vertex set,
V(G)
, is quasi-static and
G
is always connected.
(This assumption is reasonable since GCS controls and manages one or more drones to accomplish
a given mission.) The edge set,
E(G)
, can be changed depending on drone mobility and link state.
Since GCS-routing uses distance (based on GPS information) as the routing metric,
G
cannot have
negative weight cycles. Let
Ti
(
Ti>
0,
i=
1, 2, 3,
· · ·
) be a sequence of distinct time instants,
T1
,
T2,· · · , when the network topology changes due to mobility and link state.
Lemma 1.
GCS-routing generates a shortest paths tree rooted at each vertex
s
in
V(G)
. (“Shortest paths tree
rooted at vertex
s
” means that the GCS-routing algorithm returns a directed tree rooted at
s
, such that it contains
all vertices and every tree path to each vertex is the shortest path in the given graph.)
Proof.
GCS-routing used edge relaxation such as Dijkstra’s or Bellman–Ford’s algorithm to generate
shortest paths to all the vertices in
V(G)
for a given source vertex,
s
. Thus, from many well-known
theorems [34], GCS-routing generates a shortest paths tree rooted at sin V(G).
Then, let a shortest paths tree rooted at sbe Gt
sat an arbitrary time instant t.
Appl. Sci. 2018,8, 2027 11 of 24
Lemma 2. For any time interval [Ti,Ti+1], i ≥1, GTi
sis not changed.
Proof.
Suppose
GTi
s
is changed within the interval. Let
Ti+α
be the time instant when the topology is
changed. Then, Ti+α=Ti+1since Ti+αshould be in Ti. This contradicts the assumption.
Once a shortest paths tree,
GTi
s
, rooted at
s
, is constructed by GCS-routing after time instant
Ti
,
it is continuously used to route all the packets from node
s
to the other vertices until
Ti+1
.
Ti+1
is
when GCS initiates GCS-routing after perceiving a change in the network topology. Then GCS-routing
generates a new shortest paths tree, GTi+1
s, rooted at each s.
Lemma 3.
There exists a short interval
δ(Ti
,
Ti+1)
,
i≥
1, when the subgraph that GCS-routing generates for
vertex s is not a tree after GCS-routing is initiated, i.e., the topology is changed.
Proof.
When GCS-routing notices one or more topology changes at time instant
Ti+1
, it takes finite
time to make
GTi+1
s
different from
GTi
s
. Therefore, any subgraph
GTδ(Ti,Ti+1)
s
of
GTi
s∪GTi+1
s
can be possible
until
GTi+1
s
is completely constructed. Hence, there could be a cycle between two nodes
vi
and
vj
.
The cycle is constructed in the way that one part of the cycle comes from
GTi
s
and the other part is from
GTi+1
s, and hence GTδ(Ti,Ti+1)
sis not a tree.
Lemma 4.
Any short interval
δ(Ti
,
Ti+1)
,
i≥
1, when the subgraph that GCS-routing generates for vertex
s
is not a tree is eventually terminated if all the routing messages from GCS are eventually delivered to all the
participating nodes.
Proof.
Since we assume membership of
V(G)
is not changed and any vertex is connected to at least
one other vertex, all routing control messages generated by GCS-routing are eventually delivered to
every vertex. Therefore, vertex
s
comes to have a new shortest paths tree based on newly delivered
routing messages.
Theorem 1.
There is one shortest path from
s
to
v
for any vertex
v∈V(Gt
s)
at any time instant
t∈
any
interval [Ti+δ(Ti,Ti+1),Ti+1], i ≥1.
Proof.
Suppose there are two simple paths from
s
to vertex
v
:
p1
and
p2
. The former can be
decomposed into
s vi vj→vl v
, and the latter into
s vi vk→vl v
(see
Figure 8). Then, the predecessor vertex of vertex
vl
on
p1
from
s
to
v
is
vj
, and the vertex on
p2
is
vk
, which implies
vj=vk
since GCS-routing generates a shortest paths tree rooted at
s
. This
contradicts the assumption.
Figure 8. Supposed situation for proof of Theorem 1.
Consequently, GCS-routing generates a shortest paths tree for each vertex
s
that can be used to
deliver packets generated by
s
, and it maintains the tree even if topology changes occur, except for the
short period when all routing messages are delivered to all drones.
3.5. Complexity Analysis
In terms of scalability, low cost route update is an important requirement for routing protocols.
Common PRPs periodically issue and exchange HELLO messages, whereas GCS-routing does not issue
Appl. Sci. 2018,8, 2027 12 of 24
HELLO messages. Routing table management for GCS-routing also has much lower overhead than
other protocols in respect of the number of route update messages. Let us assume a network composed
of
N
randomly deployed nodes. Route update cost can be measured in terms of communication
complexity, i.e., the number of messages required to perform a protocol operation [35].
Suppose destination sequenced distance vector routing (DSDV) [
36
], one of the most widely
used PRPs, is applied to the network. DSDV has two types of route update messages.
One immediately updates drastically changed routes, and the other periodically updates the overall
route. Communication complexity of the former case is
O(N)
, since a route update message would be
forwarded by all nodes in the network in the worst case. Communication complexity for the periodical
route update is much larger. In a network where every node periodically broadcasts its own route
table, each table would be forwarded
N
times in the worst case. Therefore, communication complexity
is O(N2).
In contrast, OLSR [
18
] chooses one or more nodes among neighboring nodes to be multipoint
relay (MPR) nodes, and topology control (TC) packets are transmitted by MPR nodes only. Thus, OLSR
reduces the number of packet transmissions for route update. However, in the worst case, all OSLR
nodes except the end node are MPR nodes, hence communication complexity for route update in
OLSR is also O(N2), similar to DSDV.
On the other hand, consider GCS-routing applied to the same network. Two types of route
update messages are used in GCS-routing for initialization and route maintenance. The worst case
is when all node route information is updated, which is identical to initialization. As discussed
above, GCS generates a tree to issue route update messages, where GCS is the root. GCS then
issues each aggregated route update message to corresponding subtree, and each drone repeats the
operation. Therefore, every node in the network receives the aggregated route update message one
time only. Thus, communication complexity is
O(N)
, significantly lower than DSDV or OLSR, and
hence more efficient.
4. Performance Evaluation
We conducted various experiments to evaluate GCS-routing performance, and selected two
MANET PRPs: DSDV and OLSR, as comparison protocols. RRP end-to-end delay is very large due to
frequent route establishments before data transmissions [
37
]. Thus, since high control rate is essential
for drone networks, RRPs are inappropriate and were not considered for performance evaluations.
On the other hand, PRPs update routing tables periodically, so they have a relatively short end-to-end
delay compared to RRPs. We implemented GCS-routing, DSDV, and OLSR on real drones and GCS,
and evaluated GCS-routing performance in various experiments. We then conducted simulations to
support the experiment results, thoroughly verify GCS-routing performance, and evaluate GCS-routing
from more diverse perspectives. The evaluations and simulations focused on throughput stability and
network disconnection time, which significantly affect drone control through a network.
4.1. GCS-Routing Implementation
We used F550 ARF KITs from DJI as drone frames and assembled them ourselves, as shown in
Figure 9. We adopted ODROID-U3 of HardKenel for drone computing and networking operations,
and Pixhawk autopilot [
38
] of 3D Robotics as the flight controller. ODROID-U3 and Pixhawk were
connected via USB to a UART module.
Appl. Sci. 2018,8, 2027 13 of 24
Figure 9. Drone prototype and network module.
We used two ipTime N600UA modules as communication interfaces. The following section
details experiments where drones were connected with ad-hoc network using IEEE 802.11g as the
primary interface. Each drone could act as an AP using a secondary interface to provide network to
users located in the service area of the drone. The secondary interface also used IEEE 802.11g, but
used a different channel from the ad-hoc network channel to avoid influencing routing performance.
We implemented GCS-routing using C on ODROID-U3 with Linux network libraries, and used Babeld
and OLSRd as DSDV and OLSR protocols, respectively. These protocols were implementations of
RFC 6126 [
39
] and RFC 3626 [
18
], respectively. We built six drones and implemented GCS with C on a
laptop computer running Linux.
4.2. Experiments
This section details the various experiments and outcomes using the devices and drones
discussed above.
4.2.1. Route Update Time and Throughput
We evaluated GCS-routing route update time and throughput. We configured a network
comprising 1–4 drones, then added a new drone to the network and measured the time until all
drones updated the optimal route to each other. Since the HELLO interval for PRPs significantly
affects throughput and route update time, we used different HELLO intervals for evaluation. Babeld
based on DSDV dynamically adjusts the HELLO interval during data transmission, whereas dynamic
adjustment of HELLO interval is not mentioned in the OLSR protocol RFC, and OLSRd does not
dynamically adjust the interval during data transmission. Thus, we compared GCS-routing with
DSDV with different HELLO intervals, and did not consider OLSR for these evaluations.
Figure 10a shows that DSDV route update times decrease as the HELLO interval shortens.
However, they were always greatly larger than that of GCS-routing, i.e., GCS-routing immediately
reacted to topology changes. As discussed in Section 3.5, GCS-routing complexity is significantly
lower than DSDV because GCS-routing does not issue HELLO messages and hence routing table
management has much lower overhead.
In addition to the route update time, we also measured UDP data throughput between two
drones using iperf. Figure 10b shows that average DSDV throughput significantly decreases as HELLO
interval shortens, but is always significantly lower than that of GCS-routing. Thus, throughput stability
is significantly affected by shorter HELLO interval. Consequently, HELLO messages with shorter
intervals reduce route update time, but degrade network throughput. Therefore, it is more appropriate
for drone networks to use GCS-routing, which outperforms other protocols in terms of both throughput
and route update time.
Appl. Sci. 2018,8, 2027 14 of 24
(a) Route update time.
(b) Average throughput.
Figure 10. Route update time and throughput of DSDV and GCS-routing.
4.2.2. Performance Evaluation for the Mobility Scenario
We evaluated GCS-routing performance for the mobility scenario shown in Figure 11a, where
Drone 4 generating 1 Mbps UDP traffic to GCS moves along the arrow at 2 m/s. GCS-routing updated
drone routes based on the mobile drone’s location, as shown in Figure 12. GCS-routing redistributed
routing tables only twice (at 31.5 and 46 s), and network disconnection time due to the route update
was only approximately 1 s for each redistribution. Thus, GCS-routing reacted to topology changes
immediately and would be suitable for real-time applications. In contrast, after Drone 4 lost connection
with Drone 1, DSDV with 4 s HELLO interval (DSDV-4s) could not reconfigure a routing table, because
Drone 4’s velocity was too fast for DSDV-4s to configure a valid routing table before the next topology
change. For example, when a route passing through Drone 3 was configured, Drone 4 was already out
of communication range of Drone 3, so the new routing table was no longer valid. Throughput was
still unstable when HELLO interval was reduced to 2 s. Network disconnection time was significantly
longer than that of GCS-routing.
On the other hand, OLSR provided relatively stable throughput compared with DSDV. OLSR
updated routing tables four times, at 8.5, 44.5, 55, and 67.5 s, but the first and the third updates were
unnecessary. Thus, OLSR wasted more than 9 s due to frequent and unnecessary route updates. Table 1
shows that GCS-routing achieved higher average throughput and superior stability compared with
both DSDV and OLSR.
Table 1. Throughput for the mobility scenario.
Throughput (Kbps) Standard Deviation
GCS-routing 974.5 219.7
DSDV-2s 756.4 441.5
OLSR 798.7 417.1
Appl. Sci. 2018,8, 2027 15 of 24
(a) Mobility scenario. (b) Replacement scenario.
Figure 11. Drone deployments in mobility and replacement scenarios.
Figure 12. Mobility experiment throughput.
4.2.3. Performance Evaluation for the Replacement Scenario
Battery limitations mean drone flight time is generally less than 20 min [
1
]. Thus, replacement
functionality is essential to prolong drone system service time. If remaining battery energy becomes
low, GCS should send a return-to-home command to the depleted drone and assign a new drone
to that service area. If a drone experiences failure due to any problem, e.g., collision or hardware
malfunction, a new drone should be deployed to perform the designated mission. In these situations,
it is important for drones to update routing tables with minimum overhead to preserve connectivity.
Figure 11b shows the drone replacement scenario where the user located in Drone 4’s service area
sends 1 Mbps UDP data using iperf to Drone 1. Drones 5 and 6 were waiting for future replacement.
At
T1
, Drone 3 returned to the base station, and Drone 5 replaced it, and Drone 6 replaced Drone 2 at
T2. We measured UDP throughput of GCS-routing, DSDV, and OLSR, as shown in Figure 13.
Appl. Sci. 2018,8, 2027 16 of 24
Figure 13. Throughput for GCS-routing, DSDV, and OLSR for the replacement scenario.
Network topology changed at
T1
and
T2
, so connections were disconnected until new routes
were established. Table 2shows network disconnected time for each replacement. GCS-routing takes
approximately 1 s to update routes between the two drones on average, whereas DSDV takes 17.5 s,
significantly longer than both OLSR and GCS-routing. This is because the DSDV HELLO interval
was 4 s, whereas OLSR interval was 2 s. Since OLSR detects events every 0.1 s, it can minimize delay
to set up the routing table again. However, OLSR network disconnected time was 3 times that of
GCS-routing. Thus, GCS-routing immediately reacted to network topology changes and stabilized
the new network very quickly. The drone network supervisor loses drone control during network
disconnected time, which is fatal for real-time applications. If drones remain uncontrolled for a long
period, they could collide with obstacles or other drones.
Table 2. Network disconnected time.
1st Replacement 2nd Replacement Average
GCS-routing 0.5 s 1.5 s 1.0 s
DSDV 19.0 s 16.0 s 17.5 s
OLSR 3.0 s 3.5 s 3.3 s
GCS-routing also provided more uniform network service quality than the other protocols,
as shown in Table 3. DSDV and OLSR standard deviations were relatively high due to the HELLO
messages. Thus, GCS-routing exhibited superior network stability and would be more appropriate for
controlling drone networks. The video of the experiment can be found at [13].
Table 3. Throughput standard deviation for each time period.
Period A B C
GCS-routing 47.9 494.6 302.0
DSDV 187.3 506.2 342.9
OLSR 356.5 585.6 521.1
4.3. Simulations
In addition to experiments using real drones, we conducted simulations using network simulator 3
(ns-3) [
40
,
41
]. We first conducted simulations similar to the real experiments in Section 4.2 to support
the experiment results and thoroughly verify GCS-routing performance. We then performed additional
simulations to evaluate GCS-routing from more diverse perspectives.
Appl. Sci. 2018,8, 2027 17 of 24
4.3.1. Route Update Time and Throughput
It is critical that drone systems react to topology changes promptly, particularly for real-time
applications. Thus, we evaluated GCS-routing performance when topology changes using simulations
to investigate route update time and throughput. Similar to the experiment in Section 4.2.1, we
configured a network composed of drones, and then added a new drone to the network, using DSDV
with various HELLO intervals and GCS-routing. Figure 14a shows the time until all drones updated
the optimal route. This result is consistent with the previous experimental outcomes (Figure 10a,
Section 4.2.1). Route update times for DSDV were much longer than those of GCS-routing. DSDV
could not react to topology changes promptly, whereas GCS-routing reacted immediately due to
utilizing GCS and low GCS-routing complexity.
(a) Route update time. (b) Average throughput.
Figure 14. Simulated route update time and throughput for DSDV and GCS-routing.
Figure 14b shows that average DSDV throughput decreased as the HELLO interval shortened,
due to frequent HELLO message transmission consuming available throughput, hence limiting
availability for data transmission. Shorter intervals decreased the time for route update, but degraded
throughput. Therefore, GCS-routing is more suitable for drone networks, providing significantly
superior throughput and route update time.
4.3.2. Performance Evaluation for the Mobility Scenario
We conducted a simulation to verify that GCS-routing provided reliable routing and stable
throughput even with drone movement. Similar to the experiment in Section 4.2.2, Drone 4 sending
1 Mbps UDP traffic to GCS moved along the arrow shown in Figure 15a. GCS-routing updated routing
routes based on Drone 4’s geographic information, as shown in Figure 16. Simulation outcomes were
strongly consistent with the drone experiment in Figure 12. GCS-routing redistributed routing tables
appropriately without unnecessary redistribution, highlighting high efficiency of GCS-routing.
(a) Mobility simulation. (b) Replacement simulation.
Figure 15. Deployment of drones in simulation.
Appl. Sci. 2018,8, 2027 18 of 24
Figure 16. Simulated throughput for the mobility scenario.
GCS-routing did not significantly degrade data communication, and provided stable network
throughput. In contrast, DSDV-4s was unable to respond quickly to changing network topology, hence
Drone 4 frequently lost network connection, and throughput fluctuated quite sharply. DSDV with 2 s
HELLO interval provided more rapid response to network changes and stable throughput. However,
throughput fluctuation was very large compared to the GCS-routing result. There were unnecessary
redistributions of routing tables using OLSR, causing network disconnection and large fluctuation.
Table 4shows throughput of the compared protocols, and confirms that GCS-routing provides better
network performance.
We also conducted a further mobility scenario experiment. Similar to the previous case, Drone 4
sent 1 Mbps UDP traffic to GCS. However, there was an additional 0.3 Mbps UDP transmission
between Drones 1 and 2 to evaluate routing performance in the presence of background traffic in
the routing path, as shown in Table 5. GCS-routing throughput decreased compared to the previous
experiment due to the background traffic. However, this decrease was relatively very small compared
to that of DSDV and OLSR. The standard deviation of GCS-routing was also much less than that
of DSDV or OLSR despite the background traffic. Thus, this additional experiment confirmed that
GCS-routing provides more stable data communications than other routing protocols even when there
was background traffic in the routing path.
Table 4. Simulated throughput for the mobility scenario without background traffic.
Throughput (Kbps) Standard Deviation
GCS-routing 1000.1 3.1
DSDV-2s 814.0 370.9
OLSR 743.9 416.9
Table 5. Simulated throughput for the mobility scenario with background traffic.
Throughput (Kbps) Standard Deviation
GCS-routing 989.2 90.7
DSDV-2s 581.6 446.0
OLSR 669.7 452.6
4.3.3. Performance Evaluation for the Replacement Scenario
As discussed above, drone replacement is an essential function to prolong the drone system service
time. Therefore, we evaluated GCS-routing for the replacement scenario by simulation, as shown in
Appl. Sci. 2018,8, 2027 19 of 24
Figure 15b. The user located in the service area of Drone 4 sent 1 Mbps UDP data traffic to Drone 1.
At T1, Drone 3 left the fleet and was replaced by Drone 5. At T2, Drone 2 was replaced by Drone 6.
Figure 17 shows the resultant GCS-routing, DSDV, and OLSR throughputs, and
Tables 6and 7
show network disconnected time and throughput standard deviation, respectively. Similar to the
real-drone experiment case in Section 4.2.3, GCS-routing exhibited superior performance compared
with the other protocols, and immediately reacted to new network topologies.
Figure 17. Simulated throughput of GCS-routing, DSDV, and OLSR for the replacement scenario.
Table 6. Simulated network disconnected time (s) for each time period for the replacement scenario.
Period A B C
GCS-routing 0 0 0
DSDV 1 4 73
OLSR 3 66 26
Table 7. Simulated throughout standard deviation for each time period for the replacement scenario.
Period A B C
GCS-routing 160.3 157.9 154.9
DSDV 257.8 247.4 52.9
OLSR 336.8 328.7 392.2
4.3.4. Performance Evaluation for the TCP Traffic Scenario
TCP is one of the most widely used transport layer protocols along with UDP, and many
applications transmit data through TCP. Therefore, we conducted a simulation study to evaluate
GCS-routing performance with TCP. Similar to Sections 4.3.2 and 4.3.3, we evaluated GCS-routing for
the mobility and replacement scenario, but employed TCP rather than UDP.
For the mobility scenario, Drone 4 moved along the arrow and sent TCP traffic to GCS without
throughput limitation. We conducted this simulation 10 times for each routing protocol, with the
outcomes as shown in Table 8. OLSR was omitted because OLSR in ns-3 does not support TCP
data communications. GCS-routing provided better network throughput and stability than DSDV.
The standard deviations in Table 8are much larger than those in Table 4due to larger TCP throughputs
than the previous UDP case.
Table 8. Simulated throughput for the mobility scenario with TCP traffic.
Throughput (Kbps) Standard Deviation
GCS-routing 14,517.9 3900.1
DSDV 7911.1 8623.6
Appl. Sci. 2018,8, 2027 20 of 24
For the replacement scenario, the user located in Drone 4’s service area sent TCP data traffic
without throughput limitation to Drone 1. We also conducted this simulation 10 times for each routing
protocol, with outcomes as shown in Tables 9–11. Network disconnected times of GCS-routing are
much shorter than those of DSDV, and GCS-routing provides better network throughput than DSDV.
GCS-routing standard deviation was also significantly smaller than that of DSDV. Thus, GCS-routing
provided more stable network performance than DSDV.
Thus, the simulations confirmed that GCS-routing provided better network performance than
DSDV even when using TCP.
Table 9.
Simulated network disconnected time (s) for each time period for the replacement scenario
with TCP traffic.
Period A B C
GCS-routing 0.1 8.7 5.1
DSDV 20.6 75.1 134.2
Table 10.
Simulated throughput (Kbps) for each time period for the replacement scenario with
TCP traffic.
Period A B C
GCS-routing 9265.4 7354.8 7239.7
DSDV 7860.2 6257.3 6450.0
Table 11.
Simulated throughput standard deviation for each time period for the replacement scenario
with TCP traffic.
Period A B C
GCS-routing 2036.0 1621.0 1568.2
DSDV 2491.3 1899.2 2405.5
4.3.5. Performance Evaluation for the Large Scale Network Scenario
The previous experiments and simulations confirmed that GCS-routing outperforms other routing
protocols in practical situations. We performed an additional simulation for a large network scenario,
which would have been difficult to conduct using real drones and devices. Figure 18 shows a simplified
view of the scenario. Two end drones conducted 1 Mbps UDP data communication through at least a
26-hop drone network. Distance between consecutive drones on the data communication path was
50 m, so source and destination were about 1300 m apart. To reflect battery limitations of real drones,
drones between the end drones were sequentially replaced during the simulation, and 52 drones in
total participated in the simulation. As the simulation proceeded, packets were transmitted through
different paths as the drones kept moving and were replaced, and at least 24 handovers occurred in
simulation period. We conducted this simulation 10 times for each routing protocol.
Table 12 shows that GCS-routing provides superior throughput and network stability than other
protocols. Throughputs using OLSR and DSDV were relatively very low due to drone mobility, large
number of hops, and frequent handovers. GCS-routing provided stable communication in the large
network scenario similar to the previous evaluation results. Thus, GCS-routing redistributed routing
tables properly and is also appropriate for large drone networks.
Appl. Sci. 2018,8, 2027 21 of 24
Figure 18. Simplified view of large scale network scenario.
Table 12. Simulated network performance for the large scale network scenario.
Throughput (Kbps) Standard Deviation
GCS-routing 971.9 160.9
DSDV 383.6 453.4
OLSR 59.2 179.3
4.4. GCS-Routing Power Consumption
Generally, the major power consumption in a drone is throttling by the motors for flight, as shown
in Figure 19. Power consumption increases rapidly as the drone starts flying (throttling up) and falls
quickly when the drone stops flying (throttling down). Table 13 shows that throttling up the motors
consumed more than 97% of the overall drone consumption. Since flight takes most drone power
consumption, it is meaningless to consider power consumption caused by routing operations.
Figure 19. Drone power consumption during throttling phase.
Table 13. Drone average power consumption for different phases.
Non-Throttling Phase Throttling Phase
Average power consumption (W) 6.98 242.27
Appl. Sci. 2018,8, 2027 22 of 24
5. Conclusions
Rapid technological development has encouraged applications and services in various areas
to consider utilizing drones. Generally, employing multiple drones is superior to a single drone
for complex missions, and strong connectivity among the drones is essential. Most previous
studies modified earlier MANET protocols to make them suitable for FANET and provide sufficient
connectivity. However, these inevitably inherited fundamental MANET protocol limitations, which
were not ideal for FANET. To overcome these limitations, we focused on GCS advantages and proposed
GCS-routing, designed specifically for FANET. GCS-routing makes the best use of GCS to predict
topology changes and react to them immediately without requiring periodic HELLO messages for
neighbor discovery or link cost estimation. Therefore, current routing protocol overheads were
significantly reduced using GCS-routing, making it suitable for high mobility drones in FANET.
We designed and implemented GCS-routing on real drones and GCS, and conducted experiments
and simulations to thoroughly evaluate GCS-routing performance from diverse perspectives [
13
].
Evaluation results confirmed that GCS-routing provided higher and more stable throughput, with
considerably shorter network disconnected and route update times compared with other routing
protocols. Thus, GCS-routing provided better network performance in many aspects and is more
suitable for drone networks than other protocols. GCS-routing makes it possible to construct reliable
and efficient networks for multi-drone control systems.
There are several directions for future study. GCS incorporates various information that was
not considered in the current study. Therefore, we intend to extend GCS-routing to incorporate
diverse information to improve routing algorithm efficiency and performance. Furthermore, this study
considered multiple drone networks with GCS-routing, so we plan to develop a testing guideline and
benchmark for drone swarm networks.
Author Contributions:
Concept, W.L., K.K. and H.K.; Methodology, W.L., J.Y.L., K.K. and H.K.; Software, J.Y.L.
and J.L.; Validation, J.Y.L., J.L., S.Y. and S.P.; Original draft preparation, W.L. and J.L.; Review and editing, W.L.
and H.K.; and Supervision, H.K.
Funding:
This research was supported by the Unmanned Vehicles Advanced Core Technology Research and
Development Program through the Unmanned Vehicle Advanced Research Center funded by the Ministry of
Science, ICT and Future Planning, Republic of Korea (NRF-2016M1B3A1A01937599).
Acknowledgments:
A preliminary version of this paper was presented at the IEEE International Conference on
Communications (ICC), IEEE, 2016.
Conflicts of Interest: The authors declare that they have no conflicts of interest.
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