# Pipe Routing with Topology Control for UAV Networks

Shreyas Devaraju S. Devaraju is with Computational Science Research Center,
San Diego State University & University of California, Irvine, USA,
<EMAIL_ADDRESS>Shivam Garg S. Garg is with Electrical & Computer
Engineering Department, San Diego State University, San Diego, California,
USA<EMAIL_ADDRESS>Alexander Ihler A. Ihler is with School of Information &
Computer Science, University of California, Irvine, USA<EMAIL_ADDRESS>Sunil Kumar S. Kumar is with Electrical & Computer Engineering Department, San
Diego State University, San Diego, California, USA<EMAIL_ADDRESS>
###### Abstract

Routing protocols help in transmitting the sensed data from UAVs monitoring
the targets (called target UAVs) to the BS. However, the highly dynamic nature
of an autonomous, decentralized UAV network leads to frequent route breaks or
traffic disruptions. Traditional routing schemes cannot quickly adapt to
dynamic UAV networks and/or incur large control overhead and delays. To
establish stable, high-quality routes from target UAVs to the BS, we design a
hybrid reactive routing scheme called pipe routing that is mobility,
congestion, and energy-aware. The pipe routing scheme discovers routes on-
demand and proactively switches to alternate high-quality routes within a
limited region around the active routes (called the pipe) when needed,
reducing the number of route breaks and increasing data throughput. We then
design a novel topology control-based pipe routing scheme to maintain robust
connectivity in the pipe region around the active routes, leading to improved
route stability and increased throughput with minimal impact on the coverage
performance of the UAV network.

###### Index Terms:

Unmanned aerial vehicles, autonomous UAV networks, routing, topology control,
mobility model, network connectivity.

##  I Introduction

Unmanned aerial vehicles (UAVs) equipped with self-localization and sensing
capabilities have become popular for applications such as area monitoring,
surveillance, search-and-rescue, and target tracking [1, 2, 3]. We focus on
low SWaP (size, weight, and power), fixed-wing UAVs, which offer a balance of
portability and area coverage (with higher speeds and longer lifetimes than
rotor-based UAVs). However, low SWaP UAVs face some practical difficulties:
they have limited communication range and are more prone to failure than
larger UAVs. These issues motivate a decentralized and autonomous network of
UAVs, in which the nodes explore an area, perform local sensing, and
communicate with their neighbors without any global knowledge of network
topology.

We consider a network of low SWaP fixed-wing UAVs deployed in an area without
any fixed communication infrastructure, such as a cellular network. Example
targets might be buildings on fire or a damaged bridge in a disaster area, or
enemy troop movements on a battlefield, so the targets need constant
monitoring. When one or more targets of interest are found, a UAV (called the
_target UAV_) continuously monitors each target and transmits the sensed
information back to the base station (BS) in real time. The remaining UAVs
continue to collaboratively monitor the area to find new targets while
maintaining connectivity and communication routes to the BS.

Figure 1: Decentralized, autonomous UAV network performing monitoring, search
and surveillance in inaccessible disaster areas.

The goal of a routing protocol is to help establish communication routes along
which the sensed data can be transmitted from each target UAV to the BS.
However, the highly dynamic nature of an autonomous decentralized UAV network
can result in frequent changes in the network topology, causing existing
routes to break or creating traffic disruptions. The disrupted network may
experience loss of data (packet drops), increased control and computational
overhead, as more resources are devoted to the frequent route rediscoveries,
and corresponding increased delays in the transmission of the target data.
Most existing routing schemes cannot update the routing table or discover a
new route before the current route breaks. _Reactive_ routing schemes then
create significant delays, since they perform new route discovery while the
current routes are broken. On the other hand, _proactive_ routing schemes
introduce large overheads, as each node periodically transmits control
messages to maintain and update the routes in the network [4]. Another concern
in our application, in which all the target UAVs need to relay their
information to a single base station, is that the routes may converge and
create high resource usage (and congestion) at nodes along any shared routes,
especially at high data rates. These “hot-spot” nodes along shared routes can
become critical points of failure, causing communication disruptions. Low SWaP
UAVs have limited battery capacity, and their residual energy levels may vary
depending on usage (flight duration and trajectories). Selecting UAVs with low
energy reserves to participate in routes to the BS inevitably results in
disruption of the data flows when these low-energy UAVs fail.

To mitigate these issues, a good routing protocol should be mobility,
congestion, and energy-aware. We say a routing protocol is “mobility-aware” if
its selection of routes in a network is based on the trajectories of nodes
within the route; it can select long-lasting routes by considering the
estimated lifespan of a chosen route [5, 6, 7]. “Congestion-aware” routing
protocols select routes that minimize congestion by avoiding congested nodes
[4, 8, 9, 10, 11]. Finally, “energy-aware” routing involves selecting nodes
above a minimum energy level required to provide stable and long-lasting
routes.

Of course, the ability of a routing protocol to find or switch routes depends
on the availability of routes between the source and destination. Hence,
routing protocols alone cannot guarantee the stability and quality of the
routes; the nodes’ mobility model plays a crucial role. For this reason, we
use the BS-CAP mobility [12], a pheromone-based connectivity-aware mobility
model, to improve overall network and BS connectivity. Our focus is on
enhancing the stability and quality of routes between target UAVs and the BS;
to this end, we propose a pheromone-based topology control mechanism to
increase the density of UAVs around an active route. This improves the
availability of high-quality routes and enables the routing protocol to choose
the best route (long-lasting and less congested) from the available options.

We build our approach on a hybrid reactive routing protocol called MCA-AODV
[13], and design a novel energy-aware routing protocol, called Pipe routing,
as well as a complementary topology control scheme, together called TC-Pipe
routing. These schemes select a stable route between each target UAV and the
BS, while also considering the energy level of the nodes along the selected
route. A relay based routing scheme is also discussed for comparison against
the performance of both pipe based routing schemes.

  * •

We design a mobility, congestion, and energy-aware hybrid reactive routing
protocol, called Pipe routing, by modifying the MCA-AODV [13] routing
protocol. This routing protocol is able to adapt to highly dynamic UAV
networks and proactively switch or select stable and high-quality routes.

  * •

Energy awareness prevents the selection of routes with low-energy UAVs; this
avoids disruption of the data flows when these low-energy UAVs fail.

  * •

Congestion-aware routes reduce the congestion at hot-spot nodes when multiple
flows to the BS are present in the network (especially at high data rates).

  * •

A novel pheromone-based topology control scheme is designed; it works in
conjunction with the Pipe routing scheme, and is called TC-Pipe routing. The
TC-Pipe scheme improves the availability of high-quality routes between the
target UAVs and BS.

  * •

The TC-Pipe routing scheme maintains robust connectivity around the active
routes, leading to improved route stability and increased throughput with
minimal impact on the coverage performance of the UAV network.

  * •

Both the TC-Pipe and Pipe schemes’ PDR and coverage performance decrease
gracefully in proportion to the % UAVs failing in the network.

  * •

A relay based routing scheme that uses dedicated relay nodes is also discussed
for comparison against the performance of the Pipe and TC-Pipe routing schemes

_Paper Organization:_ We first review several existing routing and topology
control schemes that focus on providing robust routes (Section II). Next, we
discuss our proposed pipe routing and topology control schemes in Sections III
and IV, respectively, followed by the relay based routing scheme in Section
VI. We evaluate the performance of our approach in simulations and analyze the
results in Section VII.

##  II Related Work

Designing a resilient, robust routing protocol for autonomous, decentralized
UAV networks is challenging: the network topology is highly dynamic, and
traffic undergoes frequent fluctuations [14, 15, 11, 16]. Proactive routing
schemes, such as OLSR and its variants [17, 4], introduce large control and
computational overhead [4] and may increase congestion in the network.

On the other hand, reactive routing schemes [18, 19, 20, 16, 21, 22, 23, 24,
25, 26, 27] discover new routes only on demand, and thus incur lower control
and computational overhead. However, reactive methods create higher route
discovery delays. Route re-discovery can be especially problematic when routes
break frequently, as in a dynamic UAV network. Many reactive routing schemes
[18, 15] suffer from control packet (RREQ) flooding problems, known as
“broadcast storms” [16]. Although some schemes [16, 22, 21] explicitly try to
reduce the overhead and delay caused by frequent route discoveries, this often
comes at the cost of using the best information. For example, in [22], when a
route breaks, the intermediate nodes search for an alternate route only in
their respective k-hop neighborhood; this reduces control overhead by avoiding
network-wide route discovery by the source node. In [21], RREQ flooding is
limited by only forwarding the RREQ to certain nodes identified in different
zones based on the neighboring nodes’ location and connectivity. However,
since the full network topology is not used for route discovery, these schemes
can result in the selection of poor routes.

Many applications are time sensitive (e.g., video streaming) and demand a
certain bandwidth level from the network. Reactive routing schemes that are
QoS-aware [13, 23, 24, 28] use network statistics such as node mobility, link
stability, hop count, signal strength, node energy level, congestion, and
traffic load to select the best route. In one such scheme [23], RREQ is
rebroadcast by only those nodes that meet specific QoS thresholds like time-
to-live (TTL), bandwidth, and flow jitter. However, this protocol does not
estimate or track node mobility and link stability, leading to frequent route
breaks. In [24], an intermediate node in the route can adapt to the changes in
the network topology and traffic conditions by selecting a new downstream node
(towards the destination). This selection is based on neighbor distance,
latency, traffic load, and reliability. However, this scheme can sometimes
result in longer and less efficient routes. An energy-aware variant of AODV
[28] uses the link stability and residual energy of the nodes to select stable
routes.

Multipath reactive routing protocols [20, 25] find multiple node-disjoint or
link-disjoint paths during route discovery and cache them as backup routes.
When the current route quality degrades, the backup routes from the cache are
used instead of performing a new route discovery. However, like the current
route, the quality of the backup routes also typically decline over time. To
avoid selecting bad routes, [26, 27] periodically monitor the quality of the
backup routes. Whereas [16] performs local route repair using route recovery
messages and uses fuzzy logic route selection based on node mobility, residual
energy, and link quality. Link stability based reactive routing protocol [19]
uses a semi-proactive route switching mechanism, which reduces route discovery
overhead incurred in [16, 26, 27]. Here, the intermediate nodes notify the
destination node of the route whenever the quality of the link to their
downstream nodes degrades. Then the destination node sends a new RREP packet
(containing the updated route stability value) to the source node along each
cached route. The source node then switches to an alternate route, which
avoids the high overhead and delay of new route discovery. However, this
scheme is unable to adjust to traffic changes and congestion and also lacks a
route repair mechanism.

A scalable hybrid routing protocol called mobility and congestion-aware AODV
(MCA-AODV) [13] combines reactive and proactive routing methods to adapt to
dynamic UAV networks with lower control overhead than traditional proactive
networks. Here, the source node maintains knowledge of alternate routes and
their quality within a 2-hop region around the selected route (called a
“pipe”) to perform proactive route switching when the quality of the current
route degrades. MCA-AODV is both mobility and congestion-aware. It selects the
best route based on various factors such as hop count, interference, route
lifetime, and estimated latency to provide long-lasting and less congested
routes.

Topology Control for Robust Routes: Some schemes (e.g., [29, 30, 12, 31, 32,
33]) modify the mobility models to provide stable network connectivity. In
this section, we review topology control and routing schemes that maintain a
stable active route between the source and destination UAVs. In [34], the AODV
routing protocol [18] and a modified Boids mobility model [35] are used to
maintain the links over the route in a highly dynamic autonomous UAV network.
In [36], a global joint 3D trajectory and power allocation scheme was designed
using spectral graph theory and convex optimization techniques to form
interference-aware routes between a single source and destination (BS) using
rotary wing UAVs. In [37], a mobility control system for FANETs based on
virtual springs maintains an aerial mesh network to ensure ground-user
coverage and QoS for disaster recovery. In [38], a virtual force-based UAV
mobility model and energy-efficient mobility-aware cluster head (CH) UAV
selection is used to improve ground-user coverage and network connectivity.
Authors also propose a topology-aware routing protocol that uses Q-learning to
find stable, low-delay and energy-efficient routes between CH UAVs and BS.

Our proposed routing protocol for autonomous UAV networks combines the
advantages of both proactive and reactive schemes, along with topology
control, to establish robust and low-overhead routes that are also mobility,
congestion and energy aware. This protocol is based on our connectivity-aware
pheromone mobility model that balances maintaining connectivity with providing
coverage [12].

###  II-A Overview of UAV Mobility Models

Pheromone mobility models use digital repel pheromones to promote exploration
and fast coverage of an area with no prior information [30, 3]. We consider a
decentralized connectivity-aware pheromone-based UAV mobility such as BS-CAP
[12] in our experiments, to provide fast area coverage, strong node degree,
and base station connectivity. In BS-CAP, the UAVs move and deposit repel
pheromones in virtual maps divided into grid cells, and the trajectory of a
UAV (controlled by its next-waypoint) is decided based on the repel pheromone
in cells and the node degree at a set of candidate waypoints, while ensuring
that the next-waypoint maintains a route to the BS. The coverage and
connectivity trade-off is controlled by adjusting the weight given to the
UAVs’ node degree at the waypoints. In the sequel, we also modify the BS-CAP
model to provide topology control in the pipe, and increase the density of
UAVs around an active route (see Sections III and IV).

Another connectivity-aware UAV mobility model used in our experiments is the
ConCov model [31], which uses a modified flocking behavior to provide trade-
offs between coverage and BS connectivity. The ConCov model uses artificial
potential fields, along with 1-hop neighbor locations and their routing
information, to minimize overlap in the area sensed by neighboring UAVs but
maintain BS connectivity. The heading of a UAV is the weighted sum of
repelling forces from neighboring UAVs and an attractive force to a
neighboring UAV with a route to the BS. The coverage/connectivity trade-off
can be tuned by adjusting the relative weight of these two forces.

##  III Pipe Routing Scheme

The Pipe routing scheme selects and maintains a stable route between each
target UAV and the BS, known as an _active route_. It is based on a mobility
and congestion-aware, hybrid reactive routing scheme [13]. Since the UAVs
participating on an active route also participate in area coverage and
exploration tasks (e.g., following the BS-CAP mobility model [12]), the
resulting network topology changes over time, which can cause link breaks
and/or changes in route quality. Pipe routing proactively maintains the
network information of a narrow, 2-hop region of nodes around the active route
(called the “pipe”; see Section III-C), and the source node performs a “multi-
metric” route selection (Sections III-A and III-B) to switch to a new, higher-
quality route when required. Because it takes into account the node mobility,
available energy, and congestion, the Pipe routing scheme selects less
congested and long-lasting routes with fewer route breaks, while also
incurring little route discovery overhead. When the quality of the current
route drops below a pre-defined threshold, the source node proactively
switches to a new route within the pipe without triggering a new route
discovery.

###  III-A Desctiption of Routing Metrics

To choose a stable, low congestion route with a long lifetime, we use several
route information metrics, capturing route length, interference, anticipated
route lifetime, and available energy. In particular, we use the hop count (HC,
the number of links between the target UAV and the BS) along with the total
number of interfering links (IL) across all UAVs in the route. The route
lifetime (RLT) is the estimated time duration after which the route is likely
to break; it is the minimum estimated link lifetime (LLT) over all the links
that form the route. A UAV estimates the LLT of a link with its neighbors
using the GPS and current trajectory information found in the Hello message
from its neighbors. Additional details on computing these metrics can be found
in [13].

In addition to the given route metrics, the residual energy of the route
($En_{R}$) is computed as the minimum energy across all nodes in the route. If
$En_{i}$ is the energy level of node $i$ in the route (R), and $N_{R}$ is the
set of nodes, then

$En_{R}=\min_{i\in N_{R}}(En_{i})$ (1)

UAVs with energy levels below a threshold ($En_{th}$) are likely to run out of
battery soon, resulting in node failure and causing a route break. Therefore,
these UAVs should safely disengage from the route. For this reason, routes
with $En_{R}\leq En_{th}+\delta_{e}$ are not considered during route selection
in our scheme. Since the source node receives updated topology information
(node energy) every 2 s, we select the value of the tolerance factor
$\delta_{e}$ to be twice the per-second energy depletion rate of a UAV to
account for the delay. Moreover, preemptive route switching is performed when
the currently active route’s $En_{R}$ falls below the threshold $En_{th}$.

###  III-B Active Route Selection

During the route discovery process, if the target UAV (source node) does not
already have a route to the destination (in our setting, the BS), it
broadcasts an RREQ message towards the BS. Unlike MCA-AODV [13], which floods
RREQ messages in the entire network, our scheme takes advantage of the UAV’s
knowledge about the BS’s GPS location and broadcasts the RREQ messages only in
the direction of the BS. In response, the BS sends back RREP messages to the
target UAV over the reverse routes, identified during the RREQ forwarding
phase. To find stable routes, with a low control packet overhead, RREQ
forwarding is limited to intermediate nodes that meet both the link stability
condition (as in Eq. (1) of [13]) and the energy threshold condition,
$En_{i}>En_{th}$.

The target UAV receives RREPs containing the HC and IL values of the
respective routes. The target UAV then computes the best route $R^{*}$ (route
with least cost $C_{R}$, defined in (4)) by selecting among all routes that
satisfy a lifetime condition (2) and an energy condition (3). The selected
route is long-lasting, less-congested, and energy-sufficient. Here, the
lifetime condition is,

$RLT_{R}>TTL,$ (2)

where $TTL$ is the time-to-live value of the data packets. The energy
condition is,

$En_{R}>En_{th}+\delta_{e},$ (3)

For each satisfying route $R$, we compute the cost,

$C_{R}=w_{1}(\frac{HC_{R}}{HC_{min}})+w_{2}(\frac{IL_{R}}{IL_{min}+\alpha\,IL_{R}}),$
(4)

where $(\cdot)_{min}=\min_{R\in X}(\cdot)_{R}$ and $X$ is the set of all
routes known by the target UAV that satisfy the constraints (2) and (3). The
weights $w_{1}$, $w_{2}$ determine the balance between short paths (low HC)
and low-interference paths (low IL), with $w_{1}+w_{2}=1$; we set
$w_{1}=w_{2}=0.5$. To reduce the route computation delay, we only consider
routes with $HC_{R}\leq HC_{min}+3$. We use $\alpha=0.3$ as the scaling factor
to convert the IL cost values to a similar range as the HC cost values
111Since $HC_{min}\geq$ 2, the range of $(\frac{HC_{R}}{HC_{min}})$ is (1,
2.5]. Similarly, since $IL_{min}\geq$ 2,
$(\frac{IL_{R}}{IL_{min}+\alpha\,IL_{R}})$ can vary from 0.77 to 3.33.
However, in our simulation settings, the range of this ratio is [1, 2.5)
because $IL_{R}$ is generally greater than $IL_{min}$..

###  III-C Pipe Formation

Since the UAV network topology changes frequently, the quality of the current
active route may deteriorate over time and new, better-quality routes may
become available. Ideally, the target UAV (source node) should proactively
switch routes when the current route deteriorates. In practice, we accomplish
this by switching to a new route when the quality of the current active route
drops below a certain threshold. This proactive switching can improve
throughput, reduce interruptions in data flow, and prevent packet drops due to
route breaks.

Since network topology information is not centralized and no node knows the
entire topology, the nodes must periodically broadcast their information via
the protocol’s Hello packets. As in MCA-AODV [13], the target UAV (source
node) collects and builds limited network topology information around the
currently used active route to form a “pipe” and enable proactive route
switching. When a target is discovered, the target UAV performs route
discovery and selects the best route to BS. Also, a pipe (see Figure 2) is
established along the active route from the target UAV to the BS [13]. The
pipe comprises the nodes along the active route (red nodes) and their 2-hop
neighbors (green nodes). The topology information (including LLT, En, and IL
values) of the nodes within the pipe is transmitted periodically to the source
node as discussed in [13]. A pipe width of 2-hop neighbors around the active
route appears to give a good trade-off between the usefulness of the
neighborhood information and its incurred overhead [13]. Increasing the pipe
width correspondingly increases the amount of the known network topology;
while this may help identify more alternate routes, it comes at the cost of
more control packets and computational overhead.

Figure 2: Illustration of a pipe around the active route (red links) from the
target UAV to BS. The pipe consists of nodes (green nodes) that are up to
2-hop from the nodes along the active route (red nodes).

###  III-D Proactive Route Switching

Proactive route switching is triggered when the quality of the active route
($R^{*}$) degrades below a threshold, given by the conditions (2) and (3), or
when a shorter alternate route becomes available in the pipe. In such a
situation, the source node initiates a breadth-first search (BFS) algorithm to
explore all the available routes within the pipe to the destination node and
selects the route with least cost (calculated using (4)). When the source node
switches to a new active route, the existing pipe is abandoned and a new pipe
is formed around the new active route. If no suitable alternate route is
available, a new route discovery is triggered. By using the local pipe
topology when possible, proactive route switching reduces the number of new
route discoveries, and thus their overhead and delays, while minimizing the
interruptions in data throughput.

##  IV Topology Control

The UAVs in the network follow the BS-CAP mobility scheme for improved
coverage and connectivity [12]. In this section, we discuss a topology control
scheme to alter the trajectory of UAVs near the pipe region in order to
maintain a robust pipe and a stable route. The proposed scheme, which combines
both pipe-based routing (Section III) and topology control, is called _pipe
routing with topology control_ (TC-Pipe).

###  IV-A Need for Topology Control

Stable and high quality routes, along with proactively identified alternate
routes in the pipe, can increase data throughput and reduce the number of
route rediscoveries, thereby reducing packet loss, overhead, and delay.
Although the BS-CAP mobility model tries to provide a strong node degree as
well as BS connectivity, it can be difficult to achieve both fast area
coverage and stable connectivity for low to medium density UAV swarms. As a
result, the route between the target UAV and BS may not be stable or have
sufficient quality to meet the QoS requirements. Additionally, the advantages
of our pipe-based routing and proactive switching critically depend on
alternate routes being available in the pipe, which is also less likely at
lower densities.

The BS-CAP mobility model uses a repel pheromone, in which UAVs fly away from
already-covered areas to explore the entire map, without taking into account
the active route(s) being used for data communication. This can lead to
sections of the pipe becoming thin, where some intermediate node(s) in the
active route have very few or no neighbors. We call this effect the ‘pipe
thinning’ problem, and illustrate it in Figure 3.

Typically, existing node mobility schemes do not consider how to facilitate a
robust set of alternative routes to improve the reliability and throughput of
ongoing communication to BS [34, 29, 14]. Our proposed topology control scheme
uses the digital pheromone map and node-degree information of the nodes along
the active route between the target UAV and BS to maintain a sufficient number
of neighboring UAVs in the pipe region. Our topology control is performed by
applying a pheromone mask that negates the repel pheromone (effectively acting
like attract pheromone) to bring UAVs toward regions of each active route’s
pipe where the UAV density is low. Combined with pipe-based routing, this
reduces data flow interruptions and improves throughput. It can be
particularly helpful when the targets are located far from the BS (a long
route length) in a network of low node density.

Figure 3: Illustration of pipe thinning problem, where the node $N^{\prime}$
has no 1-hop neighbors except the upstream and downstream nodes on the active
route.

###  IV-B Topology Control Scheme

Figure 4: Applying a pheromone mask to attract UAVs.

In Figure 3, the node $N^{\prime}$ (shown in yellow) on the active route has a
low node degree, below a user-selected threshold $Th$, due to pipe thinning.
This low degree reduces the probability of finding an alternate route in the
pipe region from the target UAV to the BS. In this section, we propose a
topology control scheme that uses pheromone-based mobility control to resolve
the pipe thinning problem.

Since all UAVs in the network follow the BS-CAP mobility model (which is a
repel pheromone-based mobility scheme), their mobility is influenced by the
pheromone values of the cells [12]. The digital pheromone information is
propagated to the neighbors using the Hello messages. In our updated approach,
nodes on an active route apply a pheromone mask to nullify the nearby repel
pheromone if their node degree is below $Th$. As a result, neighboring nodes
that are not a part of the active route are induced to move into the region;
see Figure 4 for an illustration.

1 // For an active route

2 for  _each node $N^{\prime}$ in active route; except BS_ do

3 Get $d_{N^{\prime}}$ = 1-hop node degree excluding upstream and downstream
nodes;

4 //Node degree threshold, $Th=2$

5 if  _$d_{N^{\prime}}\leq Th$_ then

6 // Pipe node density low in region

7 Apply pheromone mask, $P=P_{R}\odot A$ from (5);

8

9 else

10 // Pipe node density not low in region

11 Remove pheromone mask, $P=P_{R}$;

12 end if

13

14 end for

Algorithm 1 Topology Control

Consider a node $N^{\prime}$ located in cell $(x,y)$, with repel pheromone map
$P_{R}$ [12]. If a pheromone mask is applied by node $N^{\prime}$, its
resulting pheromone map $P$ becomes

$P=P_{R}\odot A,$ (5)

where $A$ is the pheromone mask and $\odot$ represents an element-wise
product. $A$ is a rectangular region of dimension $2L$ x $L$ centered at node
$N^{\prime}$’s cell $(x,y)$, and oriented perpendicular to the vector
connecting the upstream and downstream nodes of $N^{\prime}$ (see Figure 4).
The size $L$ is taken to be the UAV’s signal transmission range, i.e., the
maximum 1-hop distance between UAVs. The pheromone values of cells in A are
set to zero. The mask update can be equivalently viewed as depositing an
attract pheromone value in each cell equivalent to its current repel pheromone
value, resulting in a net pheromone value of zero. This causes the cells under
the $2L$ x $L$ region to attract UAVs relative to cells outside the masked
region, whose repel pheromone values are generally positive.

Nodes arriving in the newly masked region provide potential alternate links
and improve the robustness of the pipe region around the active route node(s).
The pheromone mask is then retracted by the node(s) on the active route once
its number of 1-hop neighbors exceeds $Th$. The topology control algorithm is
described in Algorithm 1. We use the threshold $Th$ = 2 in our simulations.
While higher $Th$ attracts more nodes to the 1-hop regions of the active
route, doing so does not necessarily improve the routing performance and can
degrade the network’s area coverage at low node densities.

##  V Control Overhead and Computational Complexity

_Control Overhead:_ In addition to the information sent in the RREQ, RREP,
RERR and Hello packets in the standard AODV protocol, each node in the propsed
TC-Pipe routing scheme includes the following information in its Hello packet:

  * •

Its UAV ID (7 bits), its current location (18 bits with 10 m resolution),
next-waypoint cell (12 bits for the cell ID, assuming 60 x 60 cells of 100 m x
100 m resolution in a 6 km x 6 km area), and a local pheromone map (6-bit
pheromone values in the 5 x 5 cells centered at the UAV’s current cell, for
150 bits total). It also includes the hop count of the shortest route to BS (4
bits), and the pheromone mask flag/cell ID (12 bits).

  * •

Its $En$ and IL values (1 to 2 bytes).

  * •

If the node is a 1-hop neighbor of an active node (that is participating in
data packet forwarding), it broadcasts the LLT, $En$, IL, and isLinkActive
values for each of its 1-hop neighbor nodes (variable size depending on
neighbor density).

The destination node periodically sends the Notify_Source packet to the source
node, which carries the 2-hop neighborhood information (i.e., UAV ID and their
$En$, IL and LLT values) of all intermediate nodes on the route (variable size
depending on 2-hop neighbor density along the route).

_Computational Complexity_ : In the TC-Pipe routing scheme, the source node
employs the BFS algorithm to discover all possible routes to the destination
node within the pipe. This algorithm’s worst-case time complexity is
$O(TV_{p}^{2}(E_{p}+V_{p}))$, where T represents the number of route
recomputations, and $V_{p}$ and $E_{p}$ denote the number of nodes and links
within the pipe, respectively. Additionally, intermediate nodes use the Bron-
Kerbosch algorithm to compute cliques within their 2-hop neighborhood. This
algorithm’s worst-case time complexity is $O(3^{V_{p}/3})$. It is worth noting
that the clique computation is optional but aids in reducing control overhead.

##  VI Relay Routing

Our approach to mobility and routing in UAV swarms is explicitly homogeneous:
each node in the network is, individually, balancing the two needs of
exploration (area coverage) and communication (providing routes within the
network and to the BS). A possible alternative, of course, is to assign
_roles_ to nodes within the network, allowing some nodes to become solely
responsible for providing routing (“relays”), while others continue to explore
the environment. As one example, relay UAVs might be deployed as a separate
layer to provide continuous BS connectivity in a hierarchical network [39,
40].

We believe that a homogeneous approach has a number of important advantages.
While a relay-based scheme prioritizes ensuring a stable communication path,
by focusing on maintaining a specific route, it may create bottleneck nodes
that suffer from congestion, or that can become critical points of failure to
the communications structure. Moreover, these nodes may be more vulnerable to
antagonistic interference (e.g., on a battlefield) [41], since differences in
the nodes’ behavior may indicate their current role. Finally, in low-density
networks, the loss of some nodes to communication infrastructure may
significantly degrade area coverage, without an obvious mechanism to trade off
between the two.

In order to explore the scale of these tradeoffs to our approach, we implement
a simple relay-based routing scheme (called Relay) and compare its performance
with our proposed schemes. Initially, all UAVs in the network follow the BS-
CAP mobility model. When a UAV finds a target, it finds the shortest route to
the BS using AODV routing. The nodes along this route to BS are reassigned as
relay nodes. The relay UAVs in this established route start flying in a nearly
circular trajectory within or around their current grid cell to maintain the
established route. Thus, a stable and permanent route between each target UAV
and BS is established for data communication. When a relay node fails, the
above process is repeated to establish a new shortest route. Of course, in the
presence of multiple targets, this process of selecting the shortest routes to
the BS may lead to multiple active routes sharing links in common, which will
result in congestion, especially at high data rates. The relay UAVs act as a
separate layer from the rest of the UAVs, which continue performing area
search and coverage while maintaining connectivity using the BS-CAP mobility
model. Again, the more nodes are selected to act as relays, the fewer nodes
are available for search area coverage, degrading the swarm’s coverage
performance.

##  VII Simulation Results and Discussion

###  VII-A Simulation Setup

Our experimental evaluations simulate the behavior of a swarm of 30 to 50 low
SWaP fixed-wing UAVs (e.g., [42, 43]) monitoring a 6 km $\times$ 6 km area in
which fixed communication infrastructure, such as a cellular network, is not
available. The aerial BS is located at the bottom center of the map, and the
UAVs are launched from the vicinity of the BS. Each UAV is equipped with GPS
and has a transmission range of 1 km (e.g., [4, 31, 44]). We assume a multihop
topology with free space propagation (e.g., [44]). For simplicity, the UAVs
are assumed to be point masses, and their mobility is limited to the X-Y plane
flying at a constant altitude (typically from 200 m to 1 km above the ground
[43]). UAVs perform collision avoidance through trajectory modifications [45].
To facilitate representing the pheromone map, waypoints, and coverage
statistics, the area is divided into grid cells of 100 m $\times$ 100 m each,
consistent with other literature [30, 3, 31, 46]. A UAV scans the cell in
which it currently resides and deposits a repel pheromone of magnitude $1$. We
use pheromone evaporation $\lambda$ and diffusion $\psi$ rates of 0.006 each.
Table I shows the simulation parameters.

We evaluate monitoring both single and multi-target settings, across various
locations; see Figure 5. The UAVs use the BS-CAP mobility model ($\beta=1.5$)
to compute their flight trajectories. All the UAVs start out from the region
near the base station. If a UAV discovers a target, it becomes a “target UAV”
and continuously monitors the target by circling around it. Meanwhile, other
UAVs continue searching and scanning for new targets or events. The target UAV
establishes a route and transmits data packets continuously to the BS.

In our simulations, we take the channel rate to be 11 Mbps and the UAV
transmission range to 1 km. We consider a line-of-sight communication and
ignore channel fading and noise. The packet size and TTL values are 1.5 kB and
3 s, respectively. We consider a distributed TDMA MAC protocol [47] and a
large MAC queue size. Packets with the smallest time-to-expiry in the queue
are pushed to the head-of-line.

In order to study the network behavior under traffic, we first simulate the
UAV swarm for 1000 s, after which time all the targets have been found. Then
we evaluate the throughput, routing and coverage performance for 2000 s (i.e.,
from 1000 to 3000 s) as the swarm monitors the targets and continues to
search. We report performance metrics averaged over 30 simulation runs.

Low SWaP UAVs are prone to failure due to mechanical malfunctioning or energy
depletion. To understand the impact of these issues on our routing scheme, we
study the method’s performance when a fraction of nodes randomly fail during
the simulation time. In particular, we assign a fixed fraction of nodes (20%
or 30%) a failure time during the simulation, with the time selected uniformly
between 1000 and 3000s. This simulates nodes failing progressively over the
course of the deployment.

TABLE I: Simulation Parameters Parameters | Values
---|---
Simulation Time | 2000 s
Map Area |  6 km $\times$ 6 km
Transmission Range | 1 km
Number of UAVs | 30, 50
UAV Speed | 20 m/s, 40 m/s
Number of Targets | 1, 3
Channel Range | 11 Mbps
Packet size | 1.5 kB
Data Rate | 1 - 3.5Mbps
TTL | 3 s

[1.5,5]kmBS (a) Target Location: C1

[4,4]kmBS (b) Target Location: C2

[2,2]kmBS (c) Target Location: C3

BS[1,4.5]km[3,5]km[5,4.5]km (d) Target Location: C4

BS[1,4]km[3.6,2.5]km[5,4.8]km (e) Target Location: C5

BS[1,1.5]km[3,2]km[5,1.5]km (f) Target Location: C6

Figure 5: The target locations in a 6x6 $km^{2}$ map. Three different target
locations are shown for a single target in (a), (b) and (c) and for a group of
3-targets in (d), (e) and (f).

###  VII-B Performance Metrics

We measure the performance of our UAV network using several routing and
coverage metrics.

The _routing_ properties of the network are measured by:

  * •

Packet Delivery Ratio ($PDR$): The ratio of total data packets received at the
BS to the total data packets generated at the target UAVs. PDR is a normalized
throughput and can be used to calculate other metrics such as flow throughput
(PDR x data rate) and packet loss ratio (1 - PDR).

  * •

Route Breaks ($R_{b}$): The number of route breaks per data flow during the
2000 s simulation duration. Since a route break leads to new route discovery,
a lower $R_{b}$ value signifies more stable routes and a lower control
overhead.

  * •

Route Up Time ($R_{u}$): The percentage of time a target UAV has a valid route
to the BS throughout the simulation (2000 s duration). A higher value is
desired as it signifies a more stable route to BS with fewer data flow
interruptions.

The _coverage_ properties of the network are measured by:

  * •

Coverage ($C_{v}$): The average percentage of cells in the map visited by UAVs
at least once within a given duration of time; we prefer a higher $C_{v}$ in a
given duration of time.

  * •

Coverage Fairness ($F$): Represents how equally all the cells of the map are
visited during a given time period, as measured by Jain’s fairness index [48],

$F=\frac{(\sum_{i}x_{i})^{2}}{n\sum_{i}x_{i}^{2}}$ (6)

where $x_{i}$ is the number of scans of cell $i$, and $n$ is the total number
of cells in the map. A higher value of $F$ is desired.

  * •

Cell Visitation Frequency ($V_{f}$): is the average number of times a grid
cell of the area map is visited by UAVs over a given duration of time (2000 s
of simulation).

Our proposed pipe routing with topology control scheme (Section IV) is
referred to as ‘TC-Pipe’. Pipe routing without topology control (Section III)
is referred to as ‘Pipe’. The ‘AODV’ routing scheme does not use a pipe region
and topology control. The TC-Pipe, Pipe and AODV routing schemes all follow
the BS-CAP mobility model. Pipe routing under the ConCov mobility scheme [31]
is referred to as ‘ConCov-Pipe’. Finally, the relay-based routing scheme uses
the BS-CAP mobility model and is referred to as ‘Relay’ (see Section VI).
Suffix “-20” (e.g., TC-Pipe-20, AODV-20) indicates UAVs at 20 m/s, while “-40”
indicates UAVs at 40 m/s speeds.

We first discuss the routing and coverage performances of these schemes for
different single and 3-targets settings with no node failure in Sections VII-C
and VII-D, respectively. Next, we discuss the effect of 20% and 30% node
failure on the performance of these schemes in Section VII-E. Lastly, we
summarize the results in Section VII-F.

###  VII-C Performance for Single Target

We first consider the routing and coverage performance for a single flow from
a target (source) to BS. The performance of a routing scheme depends on the
number and location of the targets (sources). In our simulations, we
considered various target locations; however, for the sake of convenience, we
detail the results for three particular settings, designated $C_{1}$, $C_{2}$
and $C_{3}$, in which the target is located at [1.5 km, 5 km], [4 km, 4 km]
and [2 km, 2 km], respectively (see Figures 5(a), 5(b), and 5(c)). Here
$C_{1}$ (target located far from BS) and $C_{3}$ (target located close to BS)
represent the two extremes of the map. We see that the performance of the
network for other target locations (such as $C_{2}$) generally tends to fall
within the performance range of the two extreme settings ($C_{1}$ and
$C_{3}$).

####  VII-C1 Routing Performance:

Figures 6-9 show the routing performance (measured by average route length,
PDR, route up time and number of route breaks) of different schemes (TC-Pipe,
Pipe, ConCov-Pipe, AODV, and Relay) for a single data flow from a target to BS
for three different target locations, represented by $C_{1}$, $C_{2}$ and
$C_{3}$. Here, the UAV network consists of two different node densities (30
and 50 UAVs) and two node speeds (20 and 40 m/s).

Average Route Length: Figure 6 shows the average route length achieved by all
the routing schemes for both densities at both speeds. We observe that the
route length increases with distance between the target and BS nodes, but it
does not vary noticeably when the node density and speed are changed. A
shorter route length typically provides better data throughput and smaller
delays. For $C_{1}$ where the target is far from the BS, our TC-Pipe scheme
achieves the average route length of $\approx$8.6, while AODV achieves
$\approx$9.2. On the other hand, for $C_{3}$ where the target is relatively
close to the BS, our TC-Pipe scheme achieves the average route length of
$\approx$3.8, while AODV achieves $\approx$4.4. Overall, the TC-Pipe scheme
achieves the shortest average route length, while AODV has the longest route
length, with other routing schemes (Pipe, Relay and ConCov-Pipe) providing
average route lengths in between the two. This indicates the ability of
topology control with pipe routing to establish stable, shorter routes using
the pipe region.

Figure 6: Average route length for single target locations ($C_{1}$, $C_{2}$,
and $C_{3}$).

Average PDR: Figure 7(e) shows the average PDR achieved by all the routing
schemes for both node densities at both speeds. The average PDR achieved by a
routing scheme increases as the distance between the target and BS decreases;
this is because longer routes are more likely to break due to node mobility.
Further, the average PDR increases for a higher density because route
discovery and maintenance of pipe region is more efficient. Also, the average
PDR decreases with node speed because more route breaks occur. For different
network settings and target locations ($C_{1}$, $C_{2}$ and $C_{3}$), our
proposed TC-Pipe scheme achieves higher average PDR than the Pipe, AODV, and
ConCov-Pipe, except the Relay scheme, as discussed below (see Figure 7(e)).

For setting $C_{1}$, the PDR at different data rates for both UAV densities
and speeds are shown in Figures 7(a) and 7(d). For longer routes in Figure
7(a), the TC-Pipe scheme provides up to 63%, 105% and 129% increase in PDR
compared to Pipe, AODV and ConCov-Pipe schemes, respectively, for 30 UAVs at
20 m/s. For higher UAV speed of 40 m/s, the TC-Pipe scheme achieves up to 52%,
113% and 100% increase in PDR compared to Pipe, AODV and ConCov-Pipe schemes,
respectively. We observed similar trends for 50 nodes in Figure 7(d). Even at
higher UAV speeds where route breaks are more frequent, the TC-Pipe scheme
achieves significant improvement in PDR compared to the Pipe and other schemes
because the use of topology control in the pipe region makes the route more
robust. For settings $C_{2}$ and $C_{3}$ too, TC-Pipe scheme provides higher
PDR than Pipe, AODV and ConCov-Pipe, but the relative PDR improvement is
reduced with the distance of the target from BS.

Further, the Pipe routing scheme performs better than AODV for both densities
at both speeds for all three target locations because the use of pipe region
facilitates proactive route switching instead of route rediscovery when the
route quality degrades. Also, the Pipe routing scheme (which uses BS-CAP
mobility model with pipe routing) performs better than ConCov-Pipe (which uses
ConCov mobility model [31] with pipe routing); this demonstrates the advantage
of using the BS-CAP mobility model which provides a better node degree and BS-
connectivity than the ConCov model [12]. The Relay scheme achieves higher PDR
performance than all other schemes, as it uses dedicated relay UAVs to
establish the active route. However, as a trade-off, its coverage performance
is decreased, as discussed in Section VII-C2.

(a) PDR (Target C1, 30 UAVs)

(b) PDR (Target C2, 30 UAVs)

(c) PDR (Target C3, 30 UAVs)

(d) PDR (Target C1, 50 UAVs)

(e) PDR (Target C2, 50 UAVs) Figure 7: Routing Performance: PDR for single
target settings $C_{1}$, $C_{2}$ and $C_{3}$. Suffix“-20” (e.g., TC-Pipe-20,
AODV-20) indicate UAVs at 20 m/s, while “-40” indicates UAVs at 40 m/s speeds.

(f) PDR (Target C3, 50 UAVs)

Route Up ($R_{u}$): A Higher $R_{u}$ value indicates that the route from
target UAV to BS exists for a longer duration of time, which generally results
in a more stable route providing a higher PDR. Figure 8 shows the $R_{u}$
value of all the considered schemes for both node densities at both speeds.
The value of $R_{u}$ increases for a higher node density but decreases for a
higher node speed. Here, the Relay scheme achieves the highest $R_{u}$ (nearly
100%) because a stable route is established. The TC-Pipe scheme achieves the
highest $R_{u}$ value after the Relay scheme for both node densities at both
speeds. In Figure 8(b), TC-Pipe achieves $R_{u}$ value of 94% and 93%, for the
target location $C_{1}$ at a node density of 50 and speeds of 20 m/s and 40
m/s, respectively, compared to 87% and 83% achieved by the Pipe routing
scheme. The TC-Pipe scheme’s improved performance over the Pipe scheme is due
to its ability to maintain a robust pipe along the active route. AODV (which
uses the BS-CAP mobility model without pipe region) and ConCov-Pipe (which
uses the ConCov [31] mobility model with a lower BS connectivity performance
compared to the BS-CAP mobility model [12]) provide lower $R_{u}$ values. This
shows that Pipe routing combined with the BS-CAP mobility model provides
better routing performance (as in TC-Pipe, Pipe). A similar performance trend
is observed at 30 UAV density in Figure 8(a); however, the $R_{u}$
performances decreases considerably, especially for longer route lengths in
target setting $C_{1}$, due to a lower node density.

(a) Route Up (30 UAVs)

(b) Route Up (50 UAVs)

Figure 8: Routing Performance: Route Up for single target settings $C_{1}$,
$C_{2}$ and $C_{3}$. Figure 9: Routing Performance: Route Breaks for single
target settings $C_{1}$, $C_{2}$ and $C_{3}$.

Route Breaks ($R_{b})$: Figure 9 shows the number of route breaks achieved by
all the routing schemes for both node densities at both speeds. Proactive
route switching using the alternate route in the pipe reduces data flow
interruptions and delays in pipe routing based schemes (i.e., TC-Pipe, Pipe
and ConCov-Pipe). Due to better pipe robustness, the TC-Pipe scheme achieves
the least route breaks ($R_{b}$) among the pipe-based schemes for both
densities at both speeds and for all three target locations, reducing new
route discoveries and associated control overhead. The pipe routing based
schemes have far less route breaks than the AODV scheme for both densities at
both speeds. For example, in target setting $C_{1}$ for 50 UAVs, TC-Pipe
achieves lower $R_{b}$ value of 22 at 20 m/s compared to 40 for the Pipe
scheme. The ConCov-Pipe and AODV schemes yield higher $R_{b}$ of 52 and 355 at
20 m/s, respectively. At UAV speed of 40 m/s, more route breaks are observed
due to dynamic network topology. We find a similar performance trend across
all three target locations ($C_{1}$, $C_{2}$ and $C_{3}$) at both UAV
densities at both speeds. Since the Relay scheme establishes stable routes, it
experiences zero route breaks.

####  VII-C2 Coverage Performance:

Figures 10 \- 12 show the coverage performance (measured by coverage vs. time,
total coverage, and fairness) of different schemes (TC-Pipe, Pipe, ConCov-
Pipe, and Relay) for a single data flow from a target to BS for three
different target locations, represented by $C_{1}$, $C_{2}$ and $C_{3}$. Here,
the UAV network consists of two different node densities (30 and 50 UAVs) and
two node speeds (20 and 40 m/s). The performance of AODV scheme is not shown
because it uses the BS-CAP mobility model with coverage performance identical
to Pipe scheme.

We select the BS-CAP mobility model with $\beta=1.5$ (for TC-Pipe, Pipe, and
Relay) and ConCov mobility with $\omega=0.5$ (for ConCov-Pipe). Among the
three tuning parameter values of $\beta$ and $\omega$ experimented in [12],
the aforementioned middle values give a balanced coverage and connectivity
trade-off performance. Also, these values give relatively similar coverage
profiles (coverage vs. time curves) for the two mobility models, allowing us
to compare differences in their routing performances.

The Coverage vs. Time plots in Figure 10 represent the total map area covered
in a given time for both node densities at both speeds. The rate of area
covered increases with node density as well as speed. The Relay scheme
achieves a lower coverage performance compared to the Pipe and ConCov-Pipe
schemes, as it reassigns some of its UAVs as relay nodes, which do not
actively cover the map. This impact is more apparent at low UAV density (30
UAVs) and multiple targets (see Section VII-D2). Similarly, at low UAV
density, the TC-Pipe scheme achieves a lower coverage performance because some
of the UAVs are attracted to the pipe region. For setting $C_{1}$ in Figure 11
at 30 UAVs and 20 m/s, the TC-Pipe and Relay schemes achieve $C_{v}$ of
$\approx$ 86%, compared to $\approx$ 90% achieved by Pipe and ConCov schemes.
Note that TC-Pipe and Relay coverage performance is comparable to Pipe and
ConCov for higher node density and/or speed. For example, for 50 UAVs at 20
m/s, TC-Pipe, Pipe and ConCov achieve $C_{v}$ of 99%, compared to 96% by Relay
scheme.

(a) Coverage (Target C1, 30 UAVs)

(b) Coverage (Target C2, 30 UAVs)

(c) Coverage (Target C3, 30 UAVs)

(d) Coverage (Target C1, 50 UAVs)

(e) Coverage (Target C2, 50 UAVs)

(f) Coverage (Target C3, 50 UAVs)

Figure 10: Coverage vs. Time plots for single target settings $C_{1}$, $C_{2}$
and $C_{3}$. Figure 11: Coverage Performance: $C_{v}$ for target single
settings $C_{1}$, $C_{2}$ and $C_{3}$.

The Fairness ($F$) performance of all the schemes for both node densities at
both speeds is shown in Figure 12. Each scheme achieves a higher $F$ when the
density and/or node speed increase as all the cells in the area under
monitoring are visited more frequently. The TC-Pipe has slightly lower value
of $F$ than the Pipe and ConCov-Pipe schemes for 30 UAVs at both speeds for
all three target locations, as some UAVs are attracted to maintain a stable
pipe region. Since enough UAVs are available at node density of 50, the
fairness performance of the TC-Pipe scheme is comparable to Pipe and ConCov-
Pipe at both speeds. For example, in Figure 12(a), for 30 UAVs in setting
$C_{1}$, TC-Pipe achieves an $F$ of 0.67 and 0.75 at 20 m/s and 40 m/s,
respectively, compared to $F$ value around 0.71 and 0.77 (resp.) for Pipe and
ConCov. In Figure 12(b), for 50 UAVs in setting $C_{1}$, TC-Pipe achieves an
$F$ of 0.87 and 0.90 at 20 m/s and 40 m/s, respectively. Meanwhile, Pipe and
ConCov achieve $F$ values of 0.88 and 0.85 at 20 m/s, and 0.92 and 0.90 at 40
m/s, respectively. The Relay scheme achieves $F$ values of 0.83 and 0.88 for
50 UAVs at 20 m/s and 40 m/s, respectively. We further examine Relay scheme’s
coverage and fairness performance for three targets in Section VII-D2.

(a) Fairness (30 UAVs)

(b) Fairness (50 UAVs)

Figure 12: Coverage Performance: Fairness for single target settings $C_{1}$,
$C_{2}$ and $C_{3}$.

###  VII-D Performance for Three Targets

In this section, we consider the routing and coverage performance for multiple
flows from three targets to the BS. In our simulations, we considered various
target locations; however, for the sake of convenience, we detail the results
for three particular settings, designated $C_{4}$, $C_{5}$ and $C_{6}$, in
which the targets are located at ([1 km, 4.5 km], [3 km, 5 km], [5 km, 4.5
km]), ([1 km, 4 km], [3.6 km, 2.5 km], [5 km, 4.8 km]) and ([1 km, 1.5 km], [3
km, 2 km], [5 km, 1.5 km]), respectively (see Figures 5(d), 5(e) and 5(f)).
Here $C_{4}$ (targets are located far from BS) and $C_{6}$ (targets located
close to BS) represent two extremes of the map. We see that the performance of
the network for other target locations (such as $C_{5}$) generally tends to
fall within the performance range of the two extreme settings ($C_{4}$ and
$C_{6}$).

####  VII-D1 Routing Performance:

Figures 13 \- 16 show the routing performance (measured by average route
length, PDR, route up time and number of route breaks) of different schemes
(TC-Pipe, Pipe, ConCov-Pipe, AODV, and Relay) for three data flows (one flow
from each of the three targets to the BS), for three different settings of
target locations, represented by $C_{4}$, $C_{5}$ and $C_{6}$. Here, the UAV
network consists of two different node densities (30 and 50 UAVs) and two node
speeds (20 and 40 m/s).

Interference and congestion may occur when multiple targets with flows to the
BS exist, especially at higher data rates. The TC-Pipe and Pipe routing
schemes take into consideration the congestion and interfering links between
different routes when selecting an active route or when proactively switching
the active routes, leading to better performance. Using the TC-Pipe scheme to
maintain a robust pipe (with alternate routes) around each flow (active route)
allows for selecting high-quality routes to BS with less interference and
congestion. Whereas, the Pipe routing scheme (without topology control)
selects less interfering or less congested routes as and when they become
available in the pipe region. The AODV scheme only considers the shortest
route and can therefore select routes with congested or interfering links.

Average Route Length: Figure 13 shows the average route length achieved by all
the routing schemes for both densities at both speeds. We observe that the
route length increases with distance between the target and BS nodes, but it
does not vary noticeably when the node density and/or speed change. A shorter
route length is desired because it can provide better data throughput and
smaller delays. In setting $C_{4}$ where the targets are far from BS, our TC-
Pipe scheme achieves the average route length of $\approx$8.5, while AODV
achieves $\approx$9.1. On the other hand, for $C_{6}$ where the targets are
relatively close to the BS, our TC-Pipe model achieves the average route
length of $\approx$4.1, while AODV achieves $\approx$4.8. Overall, the TC-Pipe
scheme achieves the shortest average route length, while AODV has the longest
route length, with other routing schemes (Pipe, Relay and ConCov-Pipe)
providing average route lengths in between the two. This indicates the ability
of topology control with pipe routing to establish stable, shorter routes
using the pipe region.

Figure 13: Average route length for 3-target locations.

Average PDR: Figure 14 shows the average PDR achieved by all the routing
schemes for both node densities at both speeds. The average PDR achieved by a
routing scheme increases as the distance between the target and BS decreases;
this is because longer routes are more likely to break due to node mobility.
Further, the average PDR increases for a higher density because route
discovery and maintenance of pipe region is more efficient. Also, the average
PDR decreases with node speed because more route breaks occur. For different
network and 3-target settings ($C_{4}$, $C_{5}$ and $C_{6}$), our proposed TC-
Pipe scheme achieves higher average PDR than the Pipe, AODV, and ConCov-Pipe
schemes due to a more robust pipe which helps in selecting better quality
routes (i.e., stable routes with low interference and congestion).

For target setting $C_{4}$, the PDR at different data rates for both UAV
densities and speeds are shown in Figures 14(a) and 14(d), respectively. For
longer routes, the advantage of using TC-Pipe scheme becomes more prominent.
In Figure 14(a), the TC-Pipe scheme achieves up to 50%, 71% and 112% increase
in PDR compared to Pipe, AODV and ConCov-Pipe schemes, respectively, for 30
UAVs at 20 m/s. For higher UAV speeds of 40 m/s and 30 UAVs, the TC-Pipe
scheme achieves up to 39%, 77% and 75% increase in PDR compared to Pipe, AODV
and ConCov-Pipe schemes, respectively. We observed similar trends for 50 nodes
in Figure 14(d). Even at higher UAV speeds where route breaks are more
frequent, the TC-Pipe scheme achieves significant improvement in PDR compared
to the Pipe scheme because the use of topology control in the pipe region
makes the route more robust. For settings $C_{5}$ and $C_{6}$ too, TC-Pipe
scheme provides higher PDR than Pipe, AODV and ConCov-Pipe but the relative
PDR improvement is reduced with the distance of the target from BS. For all
three target settings and both UAV densities and speeds, the Pipe scheme
achieves higher PDR than ConCov and AODV schemes.

For all three settings $C_{4}$, $C_{5}$ and $C_{6}$ (for 50 UAVs) and for
$C_{6}$ (for 30 UAVs), the TC-Pipe achieves better PDR than the Relay scheme
for higher data rates. This is because forming shortest paths to BS without
considering congestion and interference in the Relay scheme leads to poor PDR
at higher data rates. Whereas, in Figure 14(a), for setting $C_{4}$ with
longer routes for 30 UAVs, the Relay scheme outperforms the TC-Pipe scheme.
Also, the Relay scheme for three targets requires more reassigned relay UAVs
to form the routes, resulting in a greater decrease in coverage performance
(as discussed in Section VII-D2).

(a) PDR (Targets C4, 30 UAVs)

(b) PDR (Targets C5, 30 UAVs)

(c) PDR (Targets C6, 30 UAVs)

(d) PDR (Targets C4, 50 UAVs)

(e) PDR (Targets C5, 50 UAVs)

(f) PDR (Targets C6, 50 UAVs)

Figure 14: Routing Performance: PDR for 3-targets settings $C_{4}$, $C_{5}$
and $C_{6}$.

Route Up ($R_{u}$): A Higher $R_{u}$ value indicates that the routes from
targets UAV to BS exist for a longer duration of time, which generally
provides a higher PDR. Figure 15 shows the $R_{u}$ value of all the considered
schemes for both node densities at both speeds. We observe that the value of
$R_{u}$ increases for a higher node density but decreases for a higher node
speed. The Relay scheme achieves the highest $R_{u}$ (nearly 100%) because a
stable route is established. The TC-Pipe scheme achieves the second highest
$R_{u}$ value for both node densities at both speeds. In Figure 15(b), TC-Pipe
achieves $R_{u}$ value of 95% and 92%, for the target location setting $C_{4}$
at a node density of 50 and speeds of 20 m/s and 40 m/s, respectively,
compared to 86% and 81% achieved by the Pipe routing scheme. AODV (which uses
the BS-CAP mobility model without pipe region) and ConCov-Pipe (which uses the
ConCov [31] mobility model with a lower BS connectivity performance compared
to the BS-CAP mobility model [12]) provide lower $R_{u}$ values. This shows
that Pipe routing combined with the BS-CAP mobility model provides better
routing performance (as in TC-Pipe and Pipe). A similar performance trend is
observed at 30 UAV density in Figure 15(a). For 30 UAVs, in setting $C_{4}$
where targets are located far from the BS, all schemes achieve lower $R_{u}$
values (except Relay) because there are fewer UAVs in the network to maintain
the routes.

(a) Route Up (30 UAVs)

(b) Route Up (50 UAVs)

Figure 15: Routing Performance: Route Up for 3-targets settings $C_{4}$,
$C_{5}$ and $C_{6}$. Figure 16: Routing Performance: Route Breaks for
3-targets settings $C_{4}$, $C_{5}$ and $C_{6}$.

Route Breaks ($R_{b}$): Figure 16 shows the number of route breaks achieved by
all the routing schemes for both UAV densities at both speeds. Proactive route
switching using the alternate route in the pipe reduces data flow
interruptions and delays in pipe routing based schemes (i.e., TC-Pipe, Pipe
and ConCov-Pipe). Due to better pipe robustness, the TC-Pipe scheme achieves
the least route breaks ($R_{b}$) among the pipe-based schemes for both
densities at both speeds and for all three target location settings, reducing
new route discoveries and associated control overhead. The pipe routing based
schemes have far less route breaks than the AODV scheme for both densities at
both speeds. In Figure 16, in setting $C_{4}$ for 50 UAVs, TC-Pipe achieves
lower $R_{b}$ values of 24 at 20 m/s compared to 40 in the Pipe scheme. The
ConCov-Pipe and AODV schemes yield $R_{b}$ of 50 and 348 at 20 m/s,
respectively. At UAV speed of 40 m/s, more route breaks are observed due to
dynamic network topology. We find a similar performance trend across all three
target location settings ($C_{4}$, $C_{5}$ and $C_{5}$) for both UAV densities
at both speeds. Since the Relay scheme establishes stable routes, it
experiences zero route breaks.

####  VII-D2 Coverage Performance:

Figures 17 \- 19 show the coverage performance (measured by coverage vs. time,
total coverage, and fairness) of different schemes (TC-Pipe, Pipe, ConCov-
Pipe, and Relay) for three data flows from targets to BS for three different
target location settings, represented by $C_{4}$, $C_{5}$ and $C_{6}$. Here,
the UAV network consists of two different node densities (30 and 50 UAVs) at
two node speeds (20 and 40 m/s). The performance of AODV scheme is not shown
because it uses the BS-CAP mobility model with coverage performance identical
to Pipe scheme. As explained in Section VII-C2, we select the BS-CAP mobility
model with $\beta=1.5$ (for TC-Pipe, Pipe, AODV, and Relay) and ConCov
mobility model with $\omega=0.5$ (for ConCov-Pipe).

The Coverage vs. Time plots in Figures 17 represent the total map area covered
in a given time for both node densities at both speeds. The rate of area
covered increases with node density as well as speed. The Relay scheme
achieves a lower coverage performance compared to the Pipe and ConCov-Pipe
schemes, as it reassigns some of its UAVs as relay nodes in the three routes
to BS; these relay nodes do not actively cover the map. This impact is more
apparent at low UAV density (30 UAVs). The longer the route length of these
routes, the greater the number of UAVs reassigned as relay nodes, and the
greater the decrease in coverage performance. This is observed in Figures 17
and 18, where the Relay scheme’s coverage performance is much worse in target
setting $C_{4}$ than in $C_{6}$. At low UAV density (30 UAVs), the TC-Pipe
scheme achieves a slightly lower coverage performance because some of the UAVs
are attracted to the pipe region to maintain its node density. However, the
TC-Pipe scheme allows the UAVs participating in the route to move and cover
the surrounding area, hence giving better coverage than the Relay scheme. For
setting $C_{4}$ in Figure 18 at 30 UAVs and 20 m/s, the TC-Pipe and Relay
schemes achieve $C_{v}$ of 84% and 73%, respectively, compared to $\approx$
90% achieved by Pipe and ConCov schemes. TC-Pipe’s coverage performance is
comparable to Pipe and ConCov for higher node density, whereas Relay has a
lower performance, especially for $C_{4}$ and $C_{5}$ (which correspond to
medium and long route lengths). For example, for 50 UAVs at 20 m/s, TC-Pipe,
Pipe and ConCov achieve $C_{v}$ of $\approx$ 98%, compared to $\approx$ 91%
achieved by Relay for $C_{4}$ and $C_{5}$.

(a) Coverage (Targets C4, 30 UAVs)

(b) Coverage (Targets C5, 30 UAVs)

(c) Coverage (Targets C6, 30 UAVs)

(d) Coverage (Targets C4, 50 UAVs)

(e) Coverage (Targets C5, 50 UAVs)

(f) Coverage (Targets C6, 50 UAVs)

Figure 17: Coverage vs. Time plots for 3-targets settings $C_{4}$, $C_{5}$ and
$C_{5}$. Figure 18: Coverage Performance: $C_{v}$ for 3-targets settings
$C_{4}$, $C_{5}$ and $C_{6}$

The Fairness ($F$) performance of all the schemes for both node densities at
both speeds is shown in Figure 19. Each scheme achieves a higher $F$ when the
density and/or node speed increase as all the cells in the area under
monitoring are visited more frequently. $F$ value of the TC-Pipe and Relay
schemes is lower than Pipe and ConCov-Pipe schemes for 30 UAVs at both speeds
for all three target location settings. In TC-Pipe, some UAVs cover the region
near the pipes of three flows (due to pheromone masking), leading to a
decrease in coverage fairness performance. Whereas, in Relay, more UAVs are
reassigned as relay nodes to form the routes of the three flows, leading to a
bigger decrease in the number of UAVs performing coverage and thus decreasing
coverage fairness. For example, in Figure 19(a), for 30 UAVs and setting
$C_{4}$, TC-Pipe achieves $F$ of 0.66 and 0.74 at 20 m/s and 40 m/s,
respectively, compared to $F$ values of 0.74-0.67 and 0.77-0.76 (resp.) for
Pipe and ConCov-Pipe. The Relay scheme achieves lower $F$ values of 0.58 and
0.63 for 30 UAVs at 20 m/s and 40 m/s, respectively. Since enough UAVs are
available at a density of 50 UAVs, the TC-Pipe scheme’s fairness performance
is comparable to Pipe and ConCov-Pipe, whereas the Relay scheme achieves lower
$F$ values (see Figure 12(b)).

(a) Fairness (30 UAVs)

(b) Fairness (50 UAVs)

Figure 19: Coverage performance: Fairness for 3-targets settings $C_{4}$,
$C_{5}$ and $C_{6}$

###  VII-E Effect of Node Failures on Performance

We study the impact of node failures on the routing and coverage performance
of the TC-Pipe, Pipe and Relay schemes when a fraction of nodes randomly fail
during the simulation time. We consider 20% and 30% of the UAVs progressively
failing during the simulation due to energy depletion and mechanical failures,
and observe that the PDR and coverage performances decrease gracefully in
proportion to the % UAVs failing in the network.

####  VII-E1 Effect of Node Failures for Single Target:

For single target settings ($C_{1}$, $C_{2}$ and $C_{3}$), Figures 20 and 21
show the PDR performance of TC-Pipe, Pipe, and Relay schemes for 30 and 50
UAVs at both speeds, respectively, whereas Figures 22 and 23 show other
routing and coverage metrics.

At low UAV densities and both speeds, fewer UAVs are left after node failures
which are required to maintain the pipe while also performing area coverage.
For 30 UAVs, the routing performance, including PDR (see Figure 20, see
highlighted column for $C_{2}$ at 3 Mbps data rate for illustration) and route
up and route breaks (see Figure 22, highlighted values), of TC-Pipe and Pipe
schemes decrease with increase in % node failures. The impact of node failures
is more for longer route lengths. The loss of nodes reduces the rate at which
the cells are covered. Since the coverage ($C_{v}$) represents the % cells
covered at least once during the observation period of 2000 s, the impact of
node failures on $C_{v}$ is not visible; however, fairness value $F$ decreases
with increase in % node failures. For a higher node density (50 UAVs) at both
speeds, the effect of node failures on routing and coverage performances of
Pipe and TC-Pipe schemes is relatively low (see Figures 21 and 23, highlighted
values). When a relay UAV fails in the Relay scheme, a new shortest route is
re-established using AODV routing. Therefore, its routing performance remains
almost unaffected by node failures; however, its coverage performance
decreases considerably compared to the TC-Pipe and Pipe schemes.

Figure 20: PDR values for 30 UAVs for single-target settings $C_{1}$, $C_{2}$
and $C_{3}$. Figure 21: PDR values for 50 UAVs for single-target settings
$C_{1}$, $C_{2}$ and $C_{3}$. Figure 22: Routing and Coverage metrics: 30 UAVs
for single target settings $C_{1}$, $C_{2}$ and $C_{3}$. Figure 23: Routing
and Coverage metrics: 50 UAVs for single settings $C_{1}$, $C_{2}$ and
$C_{3}$.

####  VII-E2 Effect of Node Failures for Three Targets:

In 3-target settings ($C_{4}$, $C_{5}$ and $C_{5}$ shown in Figure 5), a
separate route is established to serve the data flow from each of three
targets. Figures 24 and 25 show the PDR performance of the TC-Pipe, Pipe and
Relay schemes for 30 and 50 UAVs at both speeds, respectively, whereas Figures
26 and 27 show other routing and coverage metrics.

The overall trends representing the impact of node failures on different
routing and coverage metrics are similar to the single target settings
discussed above. Since more nodes are required to maintain the pipe region for
routes for flows from three targets, the node failures result in a relatively
greater impact on the routing and coverage performances for both node
densities and at both speeds in the TC-Pipe and Pipe schemes. For 30 UAVs in
setting $C_{5}$ with long routes, we observe decrease in PDR, coverage and
other routing metrics due to node failures (see Figures 24 and 26). For 50
UAVs at both speeds, we observe a better overall performance (due to a higher
node density) but the impact of node failures is still visible, especially for
a longer route length and higher % node failures (see setting $C_{5}$ in
Figures 25 and 27). In the Relay scheme, the relay UAVs establish stable
routes, so the PDR and other routing performance see a small decrease due to
node failures. But its coverage performance becomes much worse, especially for
the flows with longer route lengths. Since more UAVs are reassigned as relay
nodes to form routes, fewer UAVs are available for covering the map (see
$C_{v}$ performance in Figure 26).

Figure 24: PDR values for 30 UAVs for 3-targets settings $C_{4}$, $C_{5}$ and
$C_{6}$. Figure 25: PDR values for 50 UAVs for 3-targets settings $C_{4}$,
$C_{5}$ and $C_{6}$. Figure 26: Routing and Coverage metrics: 30 UAVs for
3-targets settings $C_{4}$, $C_{5}$ and $C_{6}$. Figure 27: Routing and
Coverage metrics: 50 UAVs for 3-targets settings $C_{4}$, $C_{5}$ and $C_{6}$.

###  VII-F Summary

We see that both pipe routing and topology control contribute to maintaining a
robust set of alternative routes and lead to better routing performance
(higher PDR, longer route-up time, and fewer route breaks) at all settings,
with TC-Pipe (which uses both) outperforming Pipe, AODV, and ConCov-Pipe. With
no pipe region, AODV performs worst; the pipe region allows the other methods
(TC-Pipe, Pipe, and ConCov-Pipe) to proactively switch routes within the pipe,
leading to fewer route breaks and thus fewer route discoveries. In general,
the routing and coverage performance is a trade-off [12]. TC-Pipe sees a
slight decrease in coverage ($C_{v}$, $F$) compared to Pipe, AODV and ConCov-
Pipe at lower node density (30 UAVs), but at higher density, TC-Pipe achieves
comparable coverage performance with better PDR.

The Relay scheme achieves higher PDR than TC-Pipe in single target settings
and some 3-targets settings (longer routes and lower data rates), because it
uses dedicated relay UAVs to establish stable routes. However, it provides the
worst coverage performance among the tested schemes, especially for multiple
targets with long routes at low UAV density, because significantly fewer UAVs
are available to perform area coverage. In both Pipe and TC-Pipe, the PDR and
coverage performances decrease gracefully in proportion to the % of UAVs
failing in the network. At higher density, this decrease is less than at lower
density, as there are enough UAVs to maintain a robust pipe.

##  VIII Conclusion

We considered a decentralized, multi-hop, dynamic UAV network consisting of
low SWaP UAVs, where UAVs continuously monitor the map area to find new
targets and maintain routes between UAVs monitoring targets to BS for data
communication. We designed a mobility, congestion, and energy-aware hybrid
reactive routing protocol, called the Pipe routing (Pipe), based on the MCA-
AODV [13], followed by a novel pipe routing with topology control scheme (TC-
Pipe).

In TC-Pipe routing, the 2-hop region around the active routes (the “pipe”) is
made robust using topology control; this helps to establish stable routes and
improves routing performance. Both TC-Pipe and Pipe routing schemes saw fewer
route breaks and better PDR than the AODV and ConCov-Pipe routing schemes. TC-
Pipe achieved better routing performance than Pipe but saw a slight decrease
in coverage performance at low UAV density. This is due to the inherent trade-
off between connectivity and coverage performance. Though the Relay scheme
establishes stable routes between the target UAVs and BS, the TC-Pipe scheme
achieved better PDR for certain 3-target settings at higher data rates. In
both the TC-Pipe and Pipe schemes, the PDR and coverage performances decreased
gracefully in proportion to the number of failures in the network. Thus, both
TC-Pipe and Pipe routing schemes are well suited for decentralized dynamic UAV
networks performing target monitoring and area coverage applications.

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