Delay-Packet-Loss-Optimized Distributed Routing Using Spiking Neural Network in Delay-Tolerant Networking

Abstract

Satellite communication is inevitable due to the Internet of Everything and the exponential increase in the usage of smart devices. Satellites have been used in many applications to make human life safe, secure, sophisticated, and more productive. The applications that benefit from satellite communication are Earth observation (EO), military missions, disaster management, and 5G/6G integration, to name a few. These applications rely on the timely and accurate delivery of space data to ground stations. However, the channels between satellites and ground stations suffer attenuation caused by uncertain weather conditions and long delays due to line-of-sight constraints, congestion, and physical distance. Though inter-satellite links (ISLs) and inter-orbital links (IOLs) create multiple paths between satellite nodes, both ISLs and IOLs have the same issues. Some essential applications, such as EO, depend on time-sensitive and error-free data delivery, which needs better throughput connections. It is challenging to route space data to ground stations with better QoS by leveraging the ISLs and IOLs. Routing approaches that use the shortest path to optimize latency may cause packet losses and reduced throughput based on the channel conditions, while routing methods that try to avoid packet losses may end up delivering data with long delays. Existing routing algorithms that use multi-optimization goals tend to use priority-based optimization to optimize either of the metrics. However, critical satellite missions that depend on high-throughput and low-latency data delivery need routing approaches that optimize both metrics concurrently. We used a modified version of Kleinrock’s power metric to reduce delay and packet losses and verified it with experimental evaluations. We used a cognitive space routing approach, which uses a reinforcement-learning-based spiking neural network to implement routing strategies in NASA’s High Rate Delay Tolerant Networking (HDTN) project.

Full text

Citation: Velusamy, G.; Lent, R.
Delay-Packet- Loss-Optimized
Distributed Routing Using Spiking
Neural Network in Delay-Tolerant
Networking. Sensors 2023,23, 310.
https://doi.org/10.3390/s23010310
Academic Editors: Raffaele Bruno,
Leopoldo Angrisani, Nikos Fotiou
and Ismail Butun
Received: 28 November 2022
Revised: 18 December 2022
Accepted: 21 December 2022
Published: 28 December 2022
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sensors
Article
Delay-Packet-Loss-Optimized Distributed Routing Using
Spiking Neural Network in Delay-Tolerant Networking
Gandhimathi Velusamy * and Ricardo Lent *
College of Technology, University of Houston, Houston, TX 77204, USA
*Correspondence: gvelusamy@uh.edu (G.V.); rlent@uh.edu (R.L.); Tel.: +1-713-743-4239 (R.L.)
Abstract:
Satellite communication is inevitable due to the Internet of Everything and the exponential
increase in the usage of smart devices. Satellites have been used in many applications to make human
life safe, secure, sophisticated, and more productive. The applications that benefit from satellite
communication are Earth observation (EO), military missions, disaster management, and 5G/6G
integration, to name a few. These applications rely on the timely and accurate delivery of space data
to ground stations. However, the channels between satellites and ground stations suffer attenuation
caused by uncertain weather conditions and long delays due to line-of-sight constraints, congestion,
and physical distance. Though inter-satellite links (ISLs) and inter-orbital links (IOLs) create multiple
paths between satellite nodes, both ISLs and IOLs have the same issues. Some essential applications,
such as EO, depend on time-sensitive and error-free data delivery, which needs better throughput
connections. It is challenging to route space data to ground stations with better QoS by leveraging the
ISLs and IOLs. Routing approaches that use the shortest path to optimize latency may cause packet
losses and reduced throughput based on the channel conditions, while routing methods that try to
avoid packet losses may end up delivering data with long delays. Existing routing algorithms that
use multi-optimization goals tend to use priority-based optimization to optimize either of the metrics.
However, critical satellite missions that depend on high-throughput and low-latency data delivery
need routing approaches that optimize both metrics concurrently. We used a modified version
of Kleinrock’s power metric to reduce delay and packet losses and verified it with experimental
evaluations. We used a cognitive space routing approach, which uses a reinforcement-learning-
based spiking neural network to implement routing strategies in NASA’s High Rate Delay Tolerant
Networking (HDTN) project.
Keywords: delay-tolerant networking; spiking neural network; satellite communication; QoS; AI; ISL
1. Introduction
Satellite communication has been used as: 1. Satellite and terrestrial integrated net-
works (STINs) to extend terrestrial network infrastructure for providing global broadband
communication; 2. Space-based information networks—satellite networks used to collect
Earth observation data and planetary exploration mission data.
1.1. Satellite and Terrestrial Integrated Networks
The network traffic has increased tremendously in recent years because of everything
connected to the Internet at anytime (IoT) [
1
] and the increased use of remote services after
the COVID-19 pandemic, which required the satellite network to be integrated with the
terrestrial network to fulfill the network demands [
2
]. Hence, satellites are used in the
communication industry by providing:
1. Service continuity in areas where terrestrial networks are not available
2. Service ubiquity to provide resilient service during terrestrial network failures
3.
Scalability to load-balance the traffic demands exceeding the terrestrial network’s
capacity [3].
Sensors 2023,23, 310. https://doi.org/10.3390/s23010310 https://www.mdpi.com/journal/sensors

Sensors 2023,23, 310 2 of 22
1.2. Space-Based Information Networks
Space-based information networks consist of satellite constellations at different Earth
orbits, including: enumerate 1. Earth Sensor Web and other satellites used for investigating
planets such as Mars and the Moon in the solar system; 2. Network infrastructure that
connects satellites to the backbone, access, and proximity networks [
4
]. enumerate Earth
observation and monitoring systems collect time-sensitive information about the Earth
using remote sensing satellites [
5
,
6
]. Constellations of a large number of small satellites are
used to collect simultaneous and distributed measurements or observations in monitoring
Earth resources, weather, and disaster situations with an increased temporal resolution of
collected data [
7
]. Besides, the National Aeronautics and Space Administration (NASA) is
ambitiously developing future space and ground architecture for deep space and planet
exploration to meet the final objectives of lunar and Mars exploration, habitation, and
colonization [
8
,
9
]. The future space architecture will provide communications, navigation,
and inter-networking services for space missions from Earth’s orbit to Mars and other
planetary exploration missions.
1.3. Challenges in Space Communication
The satellite network maintains three types of links [
10
]. Each kind of link has issues
that affect the performance of the satellite networks:
1.
Inter-satellite links (ISLs): links between satellites in the same layer; for example,
satellites in LEO have four ISLs to connect with four neighbors on the same orbit.
Satellites in MEO connect with their immediate neighbors in their orbit.
2.
Inter-orbital links (IOLs): satellites in different orbits communicate through IO; for
example, communication links between GEO and MEO, GEO and LEO, and MEO
and LEO.
3.
User data links (UDLs): communication links between satellites and ground stations,
also known as feeder links. A satellite can maintain several UDLs to multiple ground
stations, and a ground station can directly connect to many satellites in any orbit.
The data transmission from satellites at the lower Earth orbits suffer from the short
contact windows to ground stations in each pass and are subject to highly dynamic topolog-
ical changes due to the high speed of satellites. High-frequency free space optics channels
in the feeder links between satellites and ground stations help to deliver data with high
throughput. However, free-space optics channels are susceptible to attenuation due to
unpredictable weather conditions [
11
]. Site diversity in ground stations helps alleviate
problems due to short contact windows, but installing and maintaining multiple ground
stations at several geographical locations involve huge investments [6,12,13].
The introduction of ISLs provides a promising solution to solve packet loss rates and
long delays due to atmospheric attenuation and the short contact windows associated
with direct satellite-to-ground station communication by utilizing satellite hand-overs. The
higher-bandwidth ISLs between satellites on the same orbits and other orbits established by
laser and radio terminals help to transmit data between satellites at high rates [
6
]. However,
ISLs suffer from dynamic topological changes due to the inter-plane ISLs between satellites
in different orbits shutting down and reestablishing (on–off switching) in and out of polar
regions [
14
,
15
]. Besides, satellites in the 0th and (N
−
1)th planes across the seam cause the
absence of ISLs and need to detour to access satellites on the other sides, which causes
longer delays [
16
]. Another issue with ISLs is that the traffic load on the ISLs varies
according to the demographic distribution of the coverage areas on the Earth [
17
]. The
non-uniform traffic load on the celestial network is due to population dispersion, economic
status, and technology penetration status over different geographical areas [18].
The IOLs have line-of-sight issues due to the movement of satellites in their orbits
and suffer longer delays due to the distances between various orbits [
19
]. When multiple
satellites try to relay data through a single satellite in a different layer to use the shortest
path, this causes congestion and leads to packet losses [19].

Sensors 2023,23, 310 3 of 22
Hence, it is highly challenging to design routing strategies adaptable to the link
dynamics of ISLs, IOLs, and UDLs in delivering data from satellites to ground stations
with an optimized delay and packet loss rate to fulfill the performance requirements of the
applications they serve.
The existing works on satellite networks with multi-QoS optimization routing goals
use a linear combination of the metrics such as the delay, packet loss rate, and throughput
with weights assigned to each. They try to optimize any one metric according to the
requirement of the application by giving more importance through the weight. Hence, the
goal of simultaneously minimizing both the delay and packet loss will be a challenging
problem, for which we have yet to find a solution. Besides, the routing approaches that
optimize delay may use paths that give short delays due to the loss of packets and suffer
reduced throughput. This premise has been theoretically proven and validated using
simulation results by considering packet losses due to the available buffer capacity in DTN
nodes [20].
1.4. Our Routing Approach
This work investigates packet losses due to the channel characteristics while all satellite
nodes have equal buffer capacity. We tried to optimize latency and packet loss together
without any weights or priorities by using the simplified Kleinrock’s power metric [
21
] in
the snnr routing algorithm, which uses the ratio of delay and packet loss. We compared the
performance of snnr with the other routing approaches with the following goal functions:1.
Linear combination of delay and packet loss; 2. Delay-only; 3. Packet-loss-only; 4. Contact
graph routing (CGR), which uses the shortest path with the earliest available links. We
evaluated the performance of the routing approaches in a laboratory testbed using virtual
machines under various testing scenarios with different emulated delay, packet loss, and
bandwidth configurations. We implemented our routing algorithms using the CSG routing
approach with the HDTN software running on each virtual machine. Though we briefly
explain HDTN and CSG in the following subsections, the readers are encouraged to read
the references for complete insights.
1.5. HDTN
With the advancements in optical communication technology used in space communi-
cation such as with the Laser Communications Relay Demonstration mission, it is possible
to achieve data rates up to 1.244 Gbps [
22
]. However, the existing DTN implementations,
interplanetary overlay networks (IONs), are limited in processing such high rates due
to the lack of parallel processing capabilities and shared memory. To overcome the rate
asymmetries in space data communication, HDTN was developed by NASA. HDTN uses
high-performance computing elements in space gateways, which are size, weight, and
power constraint platforms (SWap), to provide DTN services to high-rate space communica-
tion links (optical, laser) by optimizing the network processing tasks [
23
]. Currently, HDTN
is used as a DTN gateway for the International Space Station to store and forward scientific
payloads. It is used for network flow management using a distributed and service-based
approach. HDTN supports optical data rates and cognitive networking capabilities [24].
1.6. CSG
The CSG approach was developed to optimize bundle routing in delay-tolerant net-
working using a machine learning approach by leveraging the cognitive networking sup-
port offered by HDTN. A cognitive network controller (CNC) continuously updates the
synapse strengths (weight) of the neurons in the spiking neural network using reinforce-
ment learning. The number of vertices of the SNN is
V=
3
n+
1, and the number of edges
is
W=n(n+
2
)
, where
n
is the number of outbounds at a satellite node. The SNN has
a core neuron for each outbound link. Each core neuron is connected to one excitatory
and one inhibitory neuron. All core neurons are connected to an inhibitory neuron using
post-synaptic connections [
25
]. The synapse weights are updated using a reward, which

Sensors 2023,23, 310 4 of 22
is mapped to the optimization goal function and updated based on the performance of
the selected outbound link. The neurons emit spikes when their membrane potential
reaches a threshold. The outbound link associated with the neuron that emits a spike at
the earliest will be selected. Further details can be found in the literature [
25
,
26
]. CSG
uses forward and backward reports to collect metrics used for helping the CNC to make
dynamic routing decisions.
1.7. Advantages of Using SNN for Satellite On-Board Routing
Reinforcement learning (RL) is suitable for highly dynamic space networks since it
does not need prior training. The SNN uses temporal encoding of the data as an internal
mechanism to learn the relationship between input variables related to spatio-temporal
patterns that need to be learned, classified, and predicted. Hence, the SNN can be used
for predicting early events such as strokes and heart failures in the medical field and
earthquakes in environment monitoring [
27
]. Therefore, it is appropriate for detecting
changes in the channel conditions and dynamic path selection in satellite routing. Since the
SNN uses spikes, which are event-driven and sparse in time, SNN algorithms consume less
energy than other neural networks [
28
]. Therefore, the SNN is advantageous to be used in
on-board routing in satellites to save battery energy.
2. Related Work
A large body of literary works have become available for routing in LEO satellite
networks used for 5G/6G integration in recent years. In contrast, our work focuses on
layer-agnostic routing in satellite networks used for space data delivery. We classified our
related work into multi-layered satellite routing, LEO satellite routing, routing in Earth
observation constellations, and multi-objective optimization routing in satellite networks.
2.1. Routing in LEO Satellite Networks
A load-balancing routing method based on the extended link states (LRES) algorithm,
which uses a congestion avoidance mechanism, has been proposed to select a path from
multiple paths between a source and a destination node in satellite constellations [
29
]. A
cooperative data downloading (CDD) routing algorithm for LEO networks that use the
inter-satellite laser link between the visible satellites to schedule feeder links’ bandwidth
resources has been proposed to optimize remote sensing data downloading [
30
]. An
autonomous on-board routing algorithm that makes routing decisions on each hop using
the link state information of the ISLs between neighboring satellites was proposed for
LEO satellite networks [
31
]. The location-assisted on-demand routing protocol (LAOR)
computes the shortest path for each communication request to estimate a better network
state and avoid congestion in the LEO satellite network [
18
]. An adaptive routing for the
non-geostationary satellites network was proposed for dynamic network topology changes
and traffic load fluctuations on the ISLs using a link cost metric comprising propagation
delay and traffic load [
17
]. A clustering routing scheme based on the Nash bargaining
solution that contains intra-cluster and inter-cluster routing phases was proposed using an
agent-based clustering framework [
32
]. An extreme-learning-machine-based distributed
routing (ELMDR) strategy was proposed to make routing decisions on LEO satellites based
on the traffic forecast at the satellite nodes using the ground traffic load [33].
2.2. Routing in Multi-Layer Satellite Networks
A multi-layered satellite routing algorithm (MLSR) was proposed for a multi-layered
satellite IP network comprised of GEO, MEO, and LEO satellites to calculate routing tables
efficiently using delay measurements [
10
]. A survivable routing protocol was presented
to provide the ability to survive under LEO or MEO satellite failures and minimum delay
routing using a topology control strategy in LEO/MEO satellite networks (LMSNs) [
34
]. A
QoS-aware load-balancing was proposed to alleviate the delays and improve throughput
in LEO satellite networks by congestion-prediction-based detouring of the traffic via MEO

Sensors 2023,23, 310 5 of 22
satellites [
35
]. A traffic distribution from the LEO to the MEO layer to minimize packet
delivery delay in multi-layered satellite networks was proposed by considering propagation
and queuing latency [
36
]. An ant-colony-based inter-layer link handoff algorithm was
proposed to reduce the number of inter-layer link handoffs by considering the inter-link
layer distance, duration, and QoS metrics [19].
2.3. Routing in Earth Observation Satellite Constellations
A novel routing method was proposed to improve the transfer ratio of the whole
observed data in a contact time by dividing the data and using them to modulate multiple
carriers to send EOS data into two or more ground stations by multi-hop relaying [
37
].
A joint space–temporal routing algorithmic framework was devised in which disruption-
tolerant-networking-based Earth-observing satellite networks with a frequently changing
topology and sparse and intermittent connectivity were modeled as a space–time graph [
38
].
A minimum-cost constrained multipath (MCMP) algorithm was used to find a feasible
set of available paths to send a certain amount of mission data to ground stations within
a tolerable delay. In the same work, the earliest arrival multipath routing policy based
on the contact graph routing was proposed. A cognitive engine architecture that used
neural-network-based reinforcement learning (RLNN) was proposed to configure space
links from NASA’s International Space Station testbed to the ground station to achieve
multiple optimization objectives such as delay, throughput, bandwidth, and power [9].
2.4. Multi-Objective Optimization Routing in Satellite Networks
Heuristic-based QoS-oriented satellite routing using a software-defined framework for
integrated space–terrestrial satellite communication was proposed with a decision factor
uafor choosing a path based on the type of service as
ua=w1BandWidth −w2Delay −w3PacketLoss,
where
w1
,
w2
, and
w3
are weights [
39
]. A sustainable heuristic algorithm was proposed to
satisfy the QoS requirements of multi-layered satellite network users to select a path based
on an evaluation function f ee defined as below.
f ee =αdelay +βutilization +γpacketLossRate,
where
α
,
β
, and
γ
are balance weights [
40
]. A software-defined space–ground-integration-
network-based deep reinforcement learning algorithm was proposed, which computes
reward rto evaluate the performance of a link as
r=αbandwidth +βthroughput +γ1
delay +δ1
packetLossRate +e1
jitt er ,
where
α
,
β
,
γ
,
δ
, and
e
are adjustment factors [
41
]. A priority-and-failure-probability-
based (PFPR) routing was proposed to fulfill the QoS requirements of different services in
LEO/MEO satellite communication links using an objective function
penalty()
that uses a
linear sum as below.
min :penalty() = ∑servicejFaultbusines(j)+γ∑δ(l)C(l),
where
servicej
is the weight of different priority services,
Faultbusiness(j)
is the failure rate,
gamma
is the weight for the delay,
δ(l)
denotes the presence or absence of a link
l
,
j
is the
service type, and
C(l)
is the delay on the link [
42
]. A centralized QoS-aware algorithm
using the software-defined networking controller was proposed with a linear cost function:
score =k1AB +k2
latency +k3(1−PDR) + k4
jitter +k5stability–f lag,

Sensors 2023,23, 310 6 of 22
where
AB
is the available bandwidth of a link,
PDR
is the packet loss rate,
stability–f la g
denotes the presence of an intra-orbit link, and
k
1,
k
2,
k
3, and
k
4 are the importance weights
for each metric [
43
]. A multi-QoS adaptive routing algorithm was proposed using an SDN-
based satellite networking architecture to satisfy the QoS requirements of different types of
services. A link’s weight is calculated using a linear combination as
W=αdelay +βlogloss,
where
al pha
or
β
will be increased to improve their proportion if the delay or loss rate
cannot be satisfied by the flow demand in the previous path [
44
]. An agent-based load
balancing and QoS routing for LEO satellite networks was proposed to use a cost function:
Ci=λTotalDel ay(pi)
∑k
i=1TotalDelay(pi)+µTotallLoss(pi)
∑k
i=1TotalLoss(pi),
where
λ
and
mu
are weights and
i
is the link in a path
p
[
45
]. A routing model based on the
membership function of uncertain links in mobile edge computing satellites networks has
been proposed and solved using a grey wolf optimization algorithm to select a link with
the minimum comprehensive value of the metrics delay, packet loss rate, and bandwidth.
The comprehensive evaluation of a link is given as
ld =q(m f td
gk −1)2+ (m f lr
gk −1)2+ (m f bd
gk −1)2,
where
td
,
lr
, and
bd
are the membership degree functions of the delay, packet loss rate,
and bandwidth [
46
]. The shortest path routing approach was proposed to service delay-
sensitive traffic and other traffic separately [
47
]. This work used a linear cost function
to compute the delay on a link using the queuing delay, average delay, and service time,
where the average delay is computed for a link between two nodes iand jas
Davg =D
1−pac ketl ossr ate ,
and
Dij =Wq1j+Davgij +1
µ,
where Wq1jis the queuing delay and µis the service time.
We noticed that most of the approaches were simulation based, whereas we performed
an experimental-based study with physical links emulated with delay, rate limiting, and
packet loss.
3. Routing Approach Used-Snnr
3.1. Latency-Packet-Loss-Optimized Routing Objective
The main goal of our work is to optimize both the delay and packet loss in delivering
satellite data using multiple paths comprised of the ISLs and IOLs between the satellite
node,
s
, and a ground station,
g
. Between
s
and
g
, multiple satellite gateway nodes are
connected by ISLs or IOLs to relay or route data bundles via multiple paths. The selection
of a link/path impacts the end-to-end response time and the packet loss percentage of
delivering data depending on the atmospheric condition on the links, the distance between
two gateway nodes, the processing time by the gateway nodes, the traffic on the links,
contact availability, and the sending rate. In the CSG approach, the routing algorithm runs
on each gateway node and selects an outbound link based on the routing goal. From a
gateway node
u
to another gateway node
v
along a path from
s
to
g
, selecting a link
i
from
a set of links
i=1, 2, . . . , N
at an instance
t
involves a cost,
costt
i
due to the delay and the

Sensors 2023,23, 310 7 of 22
packet loss at that instance. The cost of using a link
i
at a time
t
,
costt
i
, is computed as
follows. The delay on the link ito deliver a bundle from uto vat time tis computed as
delayi=transmissionTimet
i+stallTimet
i, (1)
where
transmissionTimet
i
includes the queuing delay at the egress at node
u
and
stallTimet
i
is the disruption time associated with the link
i
based on the contact plan using the shortest
path from sto gat time t. The packet loss ratio is computed as
bundleLossRatiot
i=(f orwardBundlesSentt
i−backwardReportsReceivedt
i)
f orwardBundlesSentt
i
, (2)
where
f orwardBundlesSentt
i
represents the number of data bundles sent on the link
i
from
u
to
v
at
t
and
backwardReportsReceivedt
i
represents the acknowledgment reports that specify
the number of bundles received by von the link iat t.
Our goal is to select a link
i
with a minimum cost for delivering data bundles from
u
to
v
from the set of available multiple links
i=1, 2, . . . , N
at each instance
t
. In this work,
we computed the
costt
i
of delivering each bundle using the simplified inverse Kleinrock’s
power metric proposed in [
20
]. With the routing approach of snnr, the
costt
i
of sending a
bundle on a link iat an instance tis computed using the ratio of (1) and (2) as below.
costt
i=delayt
i
(1−bundleLossRatiot
i), (3)
where the denominator (1−bundleLossRatiot
i)represents the number of bundles success-
fully delivered on the link at
t
. If the
bundleLossRatiot
i
on link
i
is high, the denominator
will be a small value, leading to an increased delay. In contrast, if the
bundleLossRatiot
i
is
small, the denominator will be a high value, leading to less delay. Thus, the denominator
of the equation
(3)
helps achieve the combined optimization of both the delay and packet
loss. The goal is to select a link iat each time instance t, i.e.,
min
1≤i≤Ncostt
i(4)
3.2. System Implementation
The CSG approach uses a spiking neural network and reinforcement learning to select
a path that delivers each bundle with optimized delay and packet loss. An SNN agent
runs on each satellite, monitors the network performance on the links, and selects a link
autonomously to deliver each bundle.
The cognizant agent observes the environment by computing the latency and the
packet loss ratio after forwarding a bundle on an outgoing link. It estimates the cost of
delivering the bundle using the metrics according to the optimization goal. The agent
compares the current cost with the moving average value of the cost up to the previous
instance and rewards the link if the current cost is less; else, it penalizes the link. The
reward/penalty is used to update the weights of a neuron’s synapse, and the neuron
that emits a spike at the earliest is used to select an outbound link next time. Thus, the
performance metrics, delay, and packet loss ratio associated with each link are encoded in
the time of emitting spikes by the neurons. The cognizant agent learns to choose a neuron
corresponding to the outbound link, which gives the minimum latency and packet loss
through repeated interactions with the environment. The interaction of the cognizant agent
with its environment is depicted in Figure 1. In our context, the environment is the space
communication network, which includes spacecraft and relay satellites on different orbits,
surface elements on planets such as Mars and the Moon, and ground stations on the Earth,
as shown in the right side of the picture.

Sensors 2023,23, 310 8 of 22
GEO Satellites
LEO Satellites
Moon Network
Mars Network
Inter Planetary Backbone Network
SNN
CNC
Controller
Environment
Action
Select Next Hop
Observed
Link delay,
packet-loss
Reward
IOL
ILL
Figure 1.
Cognizant controller running on satellites autonomously selects links at each satellite to
optimize latency and pack loss in a possible interplanetary network.
Each node computes the transmission time of a bundle’s delivery on an incoming
(ingress) link when it receives a forward report by calculating the difference between
the time stamp of the previous node, which sends the report, and the time at which
it received the report. This transmission time is sent back in a backward report to the
previous node from which it received the forward report on the same link. The SNN agent
counts the number of forward reports sent and the number of backward reports received
on each outbound link and computes the packet loss ratio on the link. We compared
the performance of snnr routing with the routing approaches that use the following cost
functions: 1. snndp: a linear sum
(5)
; 2. snnd: delay-only
(6)
; 3. snnp: packet-loss-only
(7)
;
4. static: CGR;
1.
snndp—optimizes both the response time and packet loss using a linear cost function
as below:
costt
dpi=k bundl eLoss Ratiot
i+1−bundleLossRatiot
itransmissionTimet
i+stallTimet
i, (5)
where
k
is a constant used to balance the two metrics, i.e.,
bundleLossRatio
is a decimal
value, whereas transmissionTimet
iis measured in milliseconds;
2.
snnd—optimizes only the response time in delivering data from a satellite node to a
ground station. The optimization of
costd
to optimize the delay is computed for an
outbound link iat a satellite node at t, computed as
costt
di=transmissionTimet
i+stallTimet
i; (6)
3.
snnp—optimizes only the packet loss in delivering data. The optimization
costt
pi
to
minimize the packet loss ratio is computed for an outbound link
i
at a satellite node
at tas
bundleLossRatiot
i=(f orwardBundlesSentt
i−backwardReportsReceivedt
i)
f orwardBundlesSentt
i
costt
pi=bundleLossRatiot
i;
(7)
4.
static—this is the same as CGR, which selects the earliest link in the shortest path
between two satellite nodes.
4. Experimental Methodology
The details of the satellite nodes participating in a communication mission and the
topology details are described in a JSON file named contactPlan.json. The JSON file contains

Sensors 2023,23, 310 9 of 22
information such as the source, destination, availability of the satellite contacts (line-of-
sight) to each other, data rate, start time, and end time of the contacts. This file will be
distributed to all nodes from a mission-specific control center through a separate channel
and updated at scheduled intervals [
48
]. The SNN on each node decides on an outbound
link from the set of available links to the next hop on the shortest path from the source
node to the destination node, computed using the contacts’ availability from the contact
plan file. The SNN autonomously learns to select outbound links at each node based on
their performances in the previous instances.
Experimental Setup
We emulated an Earth observation data collection scenario in our lab testbed using
virtual machines connected in two topological configurations: 1. Topology 2. These are
shown in Figure 2. The satellite h23 is collecting Earth observation data from space and
sending them to a ground station (GS) h25 on the Earth.
h26
h23
h29
h27
h25
h30
(a)
h26
h23
h29
h27
h25
h30
(b)
Figure 2. Space-based information network of satellites and a GS. (a) Topology 1 (b) Topology 2.
We used the Linux traffic control utility NetEm to emulate longer delays, channel at-
tenuation, and the bandwidth characteristics of the space network by configuring the delay,
rate limiting, and packet loss for each test scenario differently. The network topology repre-
sents the multiple paths between satellites using ISLs and IOLs. We used the Bpgen tool
to generate bundles of size 10,000 bytes and sent at rates from 1 bundle/s to
10 bundle/s,
and each experiment was run for 180 s and repeated six times to have a statistical average
of the network performance metrics. The sending rate implies the number of concurrent
users of the system. We used the UDP protocol, since it does not support re-transmissions
for lost bundles, for which we needed to calculate the packet losses strictly. We present
the performance of the SNN algorithms with different optimization goals under different
scenarios in the Results Section.

Sensors 2023,23, 310 10 of 22
5. Results
5.1. Topology 1: Results
The satellite node h23 is connected to the ground station node h25 using ISLs and
satellite gateways h26, h29, h30, and h27, as shown in Figure 2a. The configured delay, rate
limiting, and packet loss under different test scenarios are depicted in Table 1.
Table 1. Delay and packet-loss configurations tested with Topology 1.
Scenario h26-h29 h29-h27 h26-h30 h30-h27
Scenario 1 50 ms 50 ms 100 ms 100 ms
Scenario 2 50 ms 5% loss 50 ms 5% loss 100 ms 100 ms
Scenario 3 50 ms loss 5% 50 ms loss 5% 120 ms 512 Kb rate limit 120 ms 512 Kb rate limit
Scenario 4 50 ms 50 ms 120 ms 640 Kb rate limit 120 ms 640 Kb rate limit
Scenario 5 120 ms 120 ms 50 ms 640 Kb rate limit 50 ms 640 Kb rate limit
Scenario 6 200 ms 200 ms 50 ms 640 Kb rate limit 50ms 640 Kb rate limit
loss 10% loss 10%
5.1.1. Scenario 1
In Scenario 1, a delay of 100 ms was configured on each link h26–>h30–>h27, and a
delay of 50 ms was configured from h26–>h29–>h27. The static method was configured to
select the path h26–>h29–>h27.
Average response times obtained from all routing methods were identical except at
bundle rate 10 bundles/s, as depicted in Figure 3a. Since the sending rate was within the
bandwidth capacity of the links, the average response time looks identical, even though
the SNN algorithms selected 10% of the time h30 for exploring globally optimal links.
Since there was no packet losses configured, the average packet loss percentage was also
negligible and similar for all the routing methods, except at 10 bundles/s, as depicted in
Figure 3b. Throughput was also similar for all the routing methods in this scenario, as
in Figure 3c.
2 4 6 8 10
Bundle Rate
0
1
1
2
2
Average Response Time (ms)
104
static
snndp
snnd
snnp
snnr
(a)
0 2 4 6 8 10
Bundle Rate
0
1
2
3
4
5
6
7
Bundle Loss Ratio (%)
static
snndp
snnd
snnp
snnr
(b)
0 2 4 6 8 10
Bundle Rate
0
0.2
0.4
0.6
0.8
Average Throughput(Mbits/sec)
static
snndp
snnd
snnp
snnr
(c)
Figure 3.
Routing performance: Scenario 1. (
a
) Average response time; (
b
) Packet loss ratio (%);
(c) Throughput.
Figure 4depicts the link selection at Node h26 by the SNN algorithms according to
their optimization goals in Scenario 1. All SNN algorithms selected h29 more times than
h30 since the delay was better on the link connecting from h26 to h29 when compared
to h30.

Sensors 2023,23, 310 11 of 22
Bundle rate
Link Utilization
snn
2 4 6 8 10
0
0.5
1snnd
2 4 6 8 10
0
0.5
1
snnp
2 4 6 8 10
0
0.5
1snnr
2 4 6 8 10
0
0.5
1
h30 h29
Figure 4. Link selection at Node h26: Scenario 1.
5.1.2. Scenario 2
In Scenario 2, the average response times achieved with the routing methods are
depicted in Figure 5a. The average response time with snnr was 9–20% better than snndp
at lower bundle rates, but 13–27% worse at bundle rates of 9–10 bundles/s. snnr and snndp
showed 2–44% and 2–27% worse performance than snnd. The static and snnp methods
performed better at lower bundle rates, but snndp and snnr performed 50% better than
static and 25% better than snnp at 10 bundles/s.
The bundle loss percentage in shown in Figure 5b. The snnr method showed 25–43%
and 13–48% better packet loss performance than snndp and snnd at bundle rates of
8–10 bundles/s.
snnr and snndp showed 20–60% and 15–60% better performance than
static. snnp showed 270–650% superior performance over snnr and snndp.
0 2 4 6 8 10
Bundle Rate
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
Average Response Time (ms)
static
snndp
snnd
snnp
snnr
(a)
0 2 4 6 8 10
Bundle Rate
0
10
20
30
40
50
60
Bundle Loss Ratio (%)
static snndp snnd snnp snnr
(b)
0 2 4 6 8 10
Bundle Rate
0
0.2
0.4
0.6
0.8
Average Throughput (Mbits/s)
static
snndp
snnd
snnp
snnr
(c)
Figure 5.
Routing performance: Scenario 2. (
a
) Average response time; (
b
) Packet loss ratio (%);
(c) Throughput.
Figure 5c shows that the throughput with snnr was up to 24% better than snndp and
snnd at bundle rates of 8–10 bundles/s. snnr showed up to 63% better throughput than
static, and snndp showed up to 50% better throughput than static. snnp showed up to 38%
better throughput than snnr and snndp.
Figure 6depicts the link selection at the node h26 by the SNN algorithms according to
their optimization goals in Scenario 2, and snnp selected h30 the most to avoid packet loss.

Sensors 2023,23, 310 12 of 22
Bundle rate
Link Utilization
snndp
2 4 6 8 10
0
0.5
1snnd
2 4 6 8 10
0
0.5
1
snnp
2 4 6 8 10
0
0.5
1snnr
2 4 6 8 10
0
0.5
1
h30 h29
Figure 6. Link selection at Node h26: Scenario 2
5.1.3. Scenario 3
In Scenario 3, we used rate limiting in the left path from h26–>h30–>h27 to slow down
the traffic on the left path, and the right path was configured with less delay and packet
loss on the links h26–>h29–>h27.
Figure 7a shows that snnr performed worse than snndp and snnd at lower and higher
bundle rates, but performed 5–19% and 2–7% better than snndp and snnd at bundle rates
of 3–7 bundles/s. snnr showed inferior performance to snnp at lower bundles rates, but
performed 36–68% better at 7–10 bundles/s. The performances of snndp and snnr were
1–47% and 23–54% better than static at bundle rates of 9–10 bundles/s, whereas static was
better than both methods at lower bundle rates. snndp was 5–20% better than snnd at
9–10 bundles/s and 43–70% better than snnp at 7–10 bundles/s.
Figure 7b shows the packet loss percentage with snnr being 1–38% better than snndp
and snn, and 10–50% better than static. snndp was 7–32% better than static. snnp showed
11–440% and 16–758% better performance than snnr and snndp.
0 2 4 6 8 10
Bundle Rate
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
Average Response Time (ms)
static
snndp
snnd
snnp
snnr
(a)
0 2 4 6 8 10
Bundle Rate
0
10
20
30
40
50
60
Bundle Loss Rate (%)
static snndp snnd snnp snnr
(b)
0 2 4 6 8 10
Bundle Rate
0
0.1
0.2
0.3
0.4
0.5
0.6
Average Throughput (Mbits/s)
static
snndp
snnd
snnp
snnr
(c)
Figure 7.
Routing performance: Scenario 3. (
a
) Average response time; (
b
) Packet loss ratio (%);
(c) Throughput.
Figure 7c shows the throughput with snnr being up to 25% better than snndp and
snnd and 10–50% better than static. Throughput with snnp was 7–40% better than snnr
and snndp. snndp showed 7–30% better throughput than static.
Figure 8depicts the link utilization at Node h26 in Scenario 3. We can see that snnp
selected h30, the most to avoid packet loss on the link connecting h29, whereas other SNN
algorithms selected h29 more since their optimization goal includes delay.

Sensors 2023,23, 310 13 of 22
Bundle rate
Link Utilization
snndp
2 4 6 8 10
0
0.5
1snnd
2 4 6 8 10
0
0.5
1
snnp
2 4 6 8 10
0
0.5
1snnr
2 4 6 8 10
0
0.5
1
h30 h29
Figure 8. Link selection at Node h26: Scenario 3.
5.1.4. Scenario 4
In Scenario 4, the links on the right path h26–>29–>h27 were emulated with less
delay and no packet loss, whereas the left path h26–>h30–>h27 had more delay including
rate limiting.
Figure 9a shows that the average response time with snnr was slightly inferior to
snndp and snnd. snndp performed 1–11% worse than snnd and 1–20% better than snnp at
1–8 bundles/s. snndp and snnr showed 11–24% and 4–24% better performance than static
at bundle rates of 9–10 bundles/s.
Figure 9b shows the bundle loss percent was the same for all methods for bundle rates
up to 8 bundles/s. Figure 9c shows that all routing methods showed similar throughput at
all bundle rates.
2 4 6 8 10
Bundle Rate
0
1
1
2
2
Average Response Time (ms)
104
static
snndp
snnd
snnp
snnr
(a)
0 2 4 6 8 10
Bundle Rate
0
1
2
3
4
5
6
7
Bundle Loss Ratio (%)
static
snndp
snnd
snnp
snnr
(b)
0 2 4 6 8 10
Bundle Rate
0
0.2
0.4
0.6
0.8
Average Throughput(Mbits/sec)
static
snndp
snnd
snnp
snnr
(c)
Figure 9.
Routing performance: Scenario 4. (
a
) Average response time; (
b
) Packet loss ratio (%);
(c) Throughput.
Figure 10 depicts the link selection at the node h26 by the SNN algorithms according
to their optimization goals in Scenario 4.

Sensors 2023,23, 310 14 of 22
Bundle rate
Link Utilization h26
snndp
2 4 6 8 10
0
0.5
1snnd
2 4 6 8 10
0
0.5
1
snnp
2 4 6 8 10
0
0.5
1snnr
2 4 6 8 10
0
0.5
1
h30 h29
Figure 10. Link selection at Node h26: Scenario 4.
5.1.5. Scenario 5
In Scenario 5, the emulated delay on the right path h26–>h29–>27 was higher than
the left path h26–>h30–>27, but the left path was rate limited by configuring queues on
the output port of h26 and h30. The static method was configured to select the left path
h26–>h30–>h27.
Figure 11a shows the average response time with snnr being 4–65% better than static.
snnr showed 2–23% worse performance than snndp and snnp. snnr was 1–12% worse than
snnd. snndp performed 6–60% better than static and 4–19% worse than snnd and snnp.
Figure 11b shows the bundle loss with static being up to 100% higher than snnr and
snndp at bundle rates of 8–10 bundles/s. Both snnr and snndp showed the same packet loss
percentage at lower bundle rates, but snnr showed 23–100% better performance than snndp.
0 2 4 6 8 10
Bundle Rate
0
1
1
2
2
3
Average Response Time (ms)
104
static
snndp
snnd
snnp
snnr
(a)
0 2 4 6 8 10
Bundle Rate
0
5
10
15
20
25
Bundle Loss Ratio (%)
static
snndp
snnd
snnpl
snnr
(b)
0 2 4 6 8 10
Bundle Rate
0
0.2
0.4
0.6
0.8
Average Throughput(Mbits/sec)
static
snndp
snnd
snnp
snnr
(c)
Figure 11.
Routing performance: Scenario 5. (
a
) Average response time; (
b
) Packet loss ratio (%);
(c) Throughput.
Figure 11c shows that all routing methods showed similar throughput at all bun-
dle rates except at higher bundle rates, where all the SNN methods showed 20% better
performance than static.
Even though the delay from h26 to h30 was less than h29, the configured rate limiting
slowed down the bundle delivery to h30; hence, snnd, snndp, and snnr selected h29 most
of the time, whereas snnp selected h29 and h30 equally, as shown in Figure 12.

Sensors 2023,23, 310 15 of 22
Bundle rate
Link Utilization
snndp
2 4 6 8 10
0
0.5
1snndp
2 4 6 8 10
0
0.5
1
snnp
2 4 6 8 10
0
0.5
1snnr
2 4 6 8 10
0
0.5
1
h30 h29
Figure 12. Link selection at Node h26: Scenario 5.
5.1.6. Scenario 6
In Scenario 6, the links on the path h26–>h30–>27 were emulated with less delay and
rate limit and a packet loss of 10%, whereas the links on the right path h26–>h29–>27
were configured with a long delay and no packet loss. static was configured to select the
h26–>h30–>27 path.
Figure 13a shows that the average response time with snnr was 15–40% and 2–35%
better than snndp and snnd, except at the bundle rate of 10 bundles/s. Both snnr and
snndp showed worse performance than static at lower bundle rates and up to 80% better
performances for bundle rates of 8–10 bundles/s. snndp was worse than snnd at lower
bundle rates, but showed 7–41% better response at 9–10 bundles/s. snndp was 28–100%
worse than snnp.
Figure 13b shows the packet loss percentage with snnr being up to 50–93% better than
snndp and snnd. snnr showed 1–130% worse performance than snnp, but showed 20–66%
better performance at bundle rates of 1–3 bundles/s. snnr and snndp showed 65–95%
and 14–60% better packet loss percentage than static. snndp showed up to 50% and 500%
inferior performance to snnd and snnp.
Figure 13c shows that snnr had 30–200% and 15–130% better throughput than snndp
and snnd. snnp had 2–17% and 23–60% better throughput than snnr and snndp. snndp
showed up to 47% lower throughput than snnd. Both snnr and snndp showed 143–381%
and 17–267% better throughput than static.
2 4 6 8 10
Bundle Rate
0
1
1
2
2
Average Response Time (ms)
104
static
snndp
snnd
snnp
snnr
(a)
0 2 4 6 8 10
Bundle Rate
0
20
40
60
80
Bundle Loss Ratio (%)
static snndp snnd snnp snnr
(b)
0 2 4 6 8 10
Bundle Rate
0
0.2
0.4
0.6
0.8
Average Throughput(Mbits/sec)
static
snndp
snnd
snnp
snnr
(c)
Figure 13.
Routing performance: Scenario 6. (
a
) Average response time; (
b
) Packet loss ratio (%);
(c) Throughput.

Sensors 2023,23, 310 16 of 22
Figure 14 shows that snnd and snndp selected h30 most since both intended to opti-
mize delay, whereas snnr and snnp selected h29 most.
Bundle rate
Link Utilization
snndp
2 4 6 8 10
0
0.5
1snnd
2 4 6 8 10
0
0.5
1
snnp
2 4 6 8 10
0
0.5
1snnr
2 4 6 8 10
0
0.5
1
h30 h29
Figure 14. Link selection at Node h26: Scenario 6.
5.2. Topology 2
In Topology 2, h29 is connected to h30 using a bandwidth capacity of 10 Mbs without
any configured delay, as depicted in Figure 2b. The delay and packet loss configurations
are provided in Table 2.
Table 2. Delay and packet loss configurations tested with Topology 2.
Scenario h26-h29 h29-h27 h26-h30 h30-h27
Scenario 7 50 ms loss 10% 50 ms loss 10% 100 ms 100 ms
Scenario 8 50 ms loss 10% 50 ms loss 10% 100 ms 100 ms
Scenario 9 200 ms 50 ms loss 10% 50ms loss 10% 200 ms
rate 640 kbit rate 640 kbit
5.2.1. Scenario 7
In this test scenario, each link on the path h26–>h30–>h27 was configured with a delay
of 100 ms, and h26–>h29–>h27 was configured with a delay of 50 ms with a 10% packet loss.
The link between h29 to h30 had no delay or packet loss. The static method was configured
to use the path h26->h29->h27.
Figure 15a shows the average response time with snnr being 1–30% better than snndp
and 2–25% better than snnd. Both snnr and snndp performed worse than static at lower
bundles, but showed 36–70% and 36–67% better response for bundle rates of 9–10 bundles/s.
snndp showed up to 20% better performance than snnd. snndp and snnr showed up to
100% and 90% worse performance than snnp.
Figure 15b shows the packet loss percentage with snnr being 2–46% and 2–25% better
than snndp and snnd. snndp had 8% better performance than snnd. snnp showed up to
100% better packet loss performance than snnr and snndp. snnr and snndp showed 41–70%
29–43% better performance than static.
Figure 15c depicts that the throughput with snnr being up to 4–31% and 2–27% better
than snndp and snnd. snnp showed up to 30% better throughput than snnr and snndp.
snndp showed up to 10% better throughput than snnd. snnr and snndp showed 105–176%
and 92–144% better throughput than static.

Sensors 2023,23, 310 17 of 22
2 4 6 8 10
Bundle Rate
0
2,000
4,000
6,000
8,000
10,000
12,000
Average Response Time (ms)
static
snndp
snnd
snnp
snnr
(a)
0 2 4 6 8 10
Bundle Rate
20
30
40
50
60
70
80
Bundle Loss Ratio (%)
static snndp snnd snnp snnr
(b)
0 2 4 6 8 10
Bundle Rate
0
0.1
0.2
0.3
0.4
0.5
Average Throughput(Mbits/sec)
static
snndp
snnd
snnp
snnr
(c)
Figure 15.
Routing performance: Scenario 7. (
a
) Average response time; (
b
) Packet loss ratio (%);
(c) Throughput.
5.2.2. Scenario 8
In this scenario, the delay and packet loss were the same as in Scenario 7, but the
connection between h29 and h27 had interruptions every other 15 seconds. Hence, we
expected the SNN algorithms that selected h29 from h26 to prefer h30 at h29 whenever there
was a connection interruption. static was configured to use the left path h26–>h29–>h27.
The average response times achieved with the routing methods in this scenario are
depicted in Figure 16a. We can see that the average response times increased with all the
routing methods compared to Scenario 7 due to the connection interruptions between h29–
>h27. snnr showed 12–69%, 8–52%, and 17–80% better average response times than snndp,
snnd, and snnp. snnr and snndp showed 36–78% and 20–65% better performance than static.
snndp showed 8–67% better performance than snnp, but performed worse than snnd.
2 4 6 8 10
Bundle Rate
0
1
1
2
2
3
3
Average Response Time (ms)
104
static
snndp
snnd
snnp
snnr
(a)
0 2 4 6 8 10
Bundle Rate
10
20
30
40
50
60
70
Bundle Loss Ratio (%)
static snndp snnd snnp snnr
(b)
0 2 4 6 8 10
Bundle Rate
0
0.1
0.2
0.3
0.4
0.5
0.6
Average Throughput(Mbits/sec)
static
snndp
snnd
snnp
snnr
(c)
Figure 16.
Routing performance: Scenario 8. (
a
) Average response time; (
b
) Packet loss ratio (%);
(c) Throughput.
Figure 16b shows that the packet loss percentage achieved by snnr was 2–76% and
1–125% worse than snndp and snnd. snnp showed 2–140% better performance than snnr
for bundle rates of 1–8 bundles/s, whereas snnr was 18–25% better than snnp at bundle
rates of 8–10 bundles/s. Both snnr and snndp were 45% better than static. snndp showed
12–14% better performance than snnd at 1–2 bundles/s, but performed worse at the higher
bundle rates, whereas it performed worse at the lower bundle rates than snnp and 20%
better at 8–10 bundles/s.
The packet loss percentage was better than in the previous scenario due to the duplicate
transmissions during connection interruptions. The DTN caused the bundles to be stored
during connection interruptions and forwarded when the connection was restored.
Figure 16c shows that the throughput with snnr was 1–25% worse than snndp and
snnd. snnp showed up to 33% better throughput at bundle rates of 1-8 bundles/s, and snnr
showed 12–21% better throughput at higher bundle rates. snndp showed 1–20% better
throughput than static, and snnr showed 10–18% better throughput only at 9–10 bundles/s.
snndp showed 2–23% better throughput than snnp at 7–10 bundles/s.
All SNN methods showed better average response times and packet-loss percentages
than the static method because of utilizing the alternate links during connection interruptions.

Sensors 2023,23, 310 18 of 22
5.2.3. Scenario 9
In this scenario, both paths were alternatively configured with low delay, rate limiting,
and packet loss on one of the links and higher delay on the other link, as depicted in Table 2.
The static method was configured to select the h26–>h30–>h27 path, whereas the SNN
algorithms made dynamic selections at h26, h29, and h30 to choose an outbound link.
Figure 17a shows that snnr was 9–30% better than snndp, but 153% worse at 10 bun-
dles/s. snnr was 10–20% better than snnd, except at 10 bundles/s, where it was 78% worse.
snnr was 3–126% worse than snnp. The average response time with snnr was 2–79% better
than static, whereas snndp was 47–80% better at 8–10 bundles/s. snndp showed 1–29%
better performance than snnd and 14–59% worse performance than snnp.
2 4 6 8 10
Bundle Rate
0
1
1
2
2
Average Response Time (ms)
104
static
snndp
snnd
snnp
snnr
(a)
0 2 4 6 8 10
Bundle Rate
0
10
20
30
40
50
60
70
Bundle Loss Ratio (%)
static snndp snnd snnp snnr
(b)
0 2 4 6 8 10
Bundle Rate
0
0.2
0.4
0.6
0.8
Average Throughput(Mbits/sec)
static
snndp
snnd
snnp
snnr
(c)
Figure 17.
Routing performance: Scenario 9. (
a
) Average response time; (
b
) Packet loss ratio (%);
(c) Throughput.
Figure 17b shows the bundle loss percentage with snnr being 33–80% and 13–81%
better than snndp and snnd. snnp achieved the best performance by reducing 210% and
491% packet losses than snnr and snndp since it avoided the links with packet losses at h26
and h29. The snnr method showed 40–85% better performance than static, whereas snndp
showed 8–59% better packet loss performance than static. snndp performed 4–94% worse
than snnd.
Figure 17c shows the throughput with snnr being up to 17–149% and 2–105% better
than snndp and snnd. snnp showed 1–25% and 15–61% better throughput than snnr and
snndp. snnr showed 38–127% better throughput than static, whereas snndp showed 10–93%
better throughput. snndp showed 7–18% inferior throughput to snnd.
Figure 18a shows that the path length with the SNN methods increased due to selecting
the better-quality link h29->h30 through learning in scenario 7. Hence, the packet loss
percentage was less with them with an increased average path length. Figure 18b shows
the path lengths were higher with the SNN methods because of using the h29–>h30 link in
scenario 8.
We can verify that snnp showed the maximum path lengths in Figure 18c by choosing
h26–>h29–>h30–>h27 or h26–>h30–>h29–>h27 in scenario 9.
0246810
Bundle Rate
3.5
4
4.5
5
Path length
static snndp snnd snnp snnr
(a)
0246810
Bundle Rate
3.5
4
4.5
5
Path length
static snndp snnd snnp snnr
(b)
0246810
Bundle Rate
3.5
4
4.5
5
Path length
static snndp snnd snnp snnr
(c)
Figure 18.
Path lengths traveled by bundles in Topology 2. (
a
) Scenario 7; (
b
) Scenario 8; (
c
) Scenario 9.

Sensors 2023,23, 310 19 of 22
6. Discussion
In general, all SNN methods made 10% random selections for exploring the globally
optimal choices as part of the learning and selected unfavorable links randomly. The static
method was configured to choose the earliest available link from the shortest path from
h23 to h25 in all test scenarios. In snndp, we used
k=
100, which gave us better results. We
found it challenging to select a value for kto balance both metrics.
In Scenario 1, the beneficial path was h26–>h29–>h27. All routing methods selected
that path and performed equally. At higher bundle rates, the SNN algorithms preferred
h30 to load balance, which led to better performance than the static method.
In Scenario 2, a small percentage of packet loss at lower bundle rates on the less delay
path made snnr select h29 and led to a slightly increased average response time than snnd.
However, increased packet losses at higher bundle rates caused snnr to choose h30 more
at the expense of increased delay. Since static used only h29, the increased packet loss
decreased the throughput more than the SNN methods. Since snnp tried to optimize only
packet loss, it selected h30 and performed better.
In Scenario 3, configured rate limiting on the link to h30 slowed down the bundle
delivery and raised the delay more than on the link to h27. Hence, by selecting h30 to
avoid bundle losses while trying to reduce delay, snnr, snndp, and snnd performed worse
than snnp, but better than static. snnp achieved better packet loss and throughput with
increased average response time by selecting h30 more.
Since the network conditions favored h29 as the profitable link, all routing methods
selected the path to h29 in Scenario 4. All SNN methods sent some of the bundles to h30 at
random for exploration and showed better performance than static at higher bundle rates
by load sharing between the two links.
In Scenario 5, the link to h30 delayed the bundle delivery and caused a higher delay.
The snnr method selected h29 more times, similar to the other SNN methods, and showed
similar performance at lower bundle rates. However, at higher bundle rates, snnr used
h30 more than the other SNN methods and improved the throughput by increasing the
average response time. All SNN methods showed better performance than static by using
h30 randomly.
In Scenario 6, though the delay on the path h26–>h29–>h27 was high, there was
no packet loss, which caused the denominator in the snnr cost function to be high and
caused it to pick h29 more. Hence, snnr resulted in less average response time, packet loss
percentage, and high throughput.
In Scenario 7, snnr showed slightly better performance than snndp and snnd. All SNN
methods showed better packet loss and throughput performance than static.
In Scenario 8, all SNN methods showed better average response times and packet
loss percentages than static. The static method achieved the same throughput as the SNN
methods, though it was configured to select the path with losses. Interrupted connections
between h29 and h27 caused bundles to be stored and delivered later by the DTN protocol
and caused the throughput to be the same. At lower bundle rates, snnr, snndp, and snnd
preferred h29 over h26 and selected h27 or h30 according to the connection interruptions at
h29. At higher bundle rates, snnr, snndp, and snnd sent more bundles to h30 from h26, then
snndp and snnd selected h30–>h29–>h27, but snnr selected h30–>h27. Thus, snnr achieved
less average response time than snndp and snnd. snnp achieved better throughput at
lower bundle rates and decreased throughput at higher bundle rates by choosing the path
h26–>h30–>h29–> most.
In Scenario 9, snnr and snnp preferred the h26–>h29–>h30–>h27 path at all bun-
dle rates, whereas snnd and snndp preferred h26–>h30–>h27 at lower bundle rates and
h26–>h29–>h30–>h27
at higher bundle rates. Hence, snndp and snnd resulted in high
packet losses and lower throughput, whereas snnr and snnp achieved better performances.
snnp achieved the minimum packet loss in all scenarios according to its optimization
goal, but with increased response times in Scenarios 3 and 8. snnd had only latency as
its optimization goal and showed better response times in Scenarios 3, 6, and 8. The

Sensors 2023,23, 310 20 of 22
response time plots did not show much difference in general because the induced delays
on the links did not cause much difference in the response times for the sending rates and
the available capacities. The snndp and snnr algorithms had both delay and packet loss
as their optimization goals and tried to optimize both metrics concurrently through the
desired balancing.
We can see that snnr performed equally to the other SNN algorithms when the link
qualities were uniform and performed better when the links on the path had conditions for
which is was challenging to make decisions, such as in Scenario 6 and Scenario 9.
7. Conclusions
Our experimental results proved that, by using inverse Kleinrock’s power metric as a
cost function in an SNN with reinforcement learning, it is possible to achieve concurrent
optimization of both delay and packet loss percentage in a satellite network with uncertain
conditions. The CSG approach helps to make forwarding decisions autonomously adaptive
to the changing environment to deliver data with a better QoS.
Our future work will consider exploring offline learning predictions whenever on-
line feedback paths are longer, as in deep space communications such as between Earth
and Mars.
Author Contributions:
Conceptualization, R.L. and G.V.; methodology, G.V.; software, R.L. and G.V.;
validation, R.L. and G.V.; formal analysis, R.L. and G.V.; investigation, R.L. and G.V.; resources,
R.L. and G.V.; data curation, G.V.; writing—original draft preparation, G.V.; writing—review and
editing, R.L. and G.V.; visualization, R.L. and G.V.; supervision, R.L.; project administration, R.L.
and G.V.; funding acquisition, R.L. All authors have read and agreed to the published version of
the manuscript.
Funding:
This work was supported by Grant #80NSSC22K0259 from the National Aeronautics and
Space Administration (NASA).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author. The data are not publicly available due to privacy restrictions.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
AI Artificial intelligence
CGR Contact graph routing
CNC Cognitive network controller
CSG Cognizant space gateway
DTN Delay-tolerant networking
EO Earth observation
GS Ground station
HDTN High-rate delay-tolerant networking
ISL Inter satellite link
IOL Inter orbital link
SNN Spiking neural network
NASA National Aeronautics and Space Administration
QoS Quality of service

Sensors 2023,23, 310 21 of 22
References
1.
Kawamoto, Y.; Nishiyama, H.; Fadlullah, Z.M.; Kato, N. Effective data collection via satellite-routed sensor system (SRSS) to
realize global-scaled Internet of Things. IEEE Sens. J. 2013,13, 3645–3654. [CrossRef]
2.
Matasaru, P.D.; Scripcariu, L.; Diaconu, F. On the QoS for Satellite IP networks: A Follow-Up. In Proceedings of the 2019
International Symposium on Signals, Circuits and Systems (ISSCS), Ia¸si, Romania, 11–12 July 2019; pp. 1–3.
3.
Rinaldi, F.; Maattanen, H.L.; Torsner, J.; Pizzi, S.; Andreev, S.; Iera, A.; Koucheryavy, Y.; Araniti, G. Non-terrestrial networks in 5G
& beyond: A survey. IEEE Access 2020,8, 165178–165200.
4.
Du, J.; Jiang, C.; Guo, Q.; Guizani, M.; Ren, Y. Cooperative earth observation through complex space information networks. IEEE
Wirel. Commun. 2016,23, 136–144. [CrossRef]
5.
Saeed, N.; Elzanaty, A.; Almorad, H.; Dahrouj, H.; Al-Naffouri, T.Y.; Alouini, M.S. Cubesat communications: Recent advances
and future challenges. IEEE Commun. Surv. Tutorials 2020,22, 1839–1862. [CrossRef]
6.
Wang, P.; Li, H.; Chen, B.; Zhang, S. Enhancing Earth Observation Throughput Using Inter-satellite Communication. IEEE Trans.
Wirel. Commun. 2022,21, 7990–8006. [CrossRef]
7.
Burleigh, S.C.; De Cola, T.; Morosi, S.; Jayousi, S.; Cianca, E.; Fuchs, C. From Connectivity to advanced internet services: A
comprehensive review of small satellites communications and networks. Wirel. Commun. Mob. Comput.
2019
,2019, 6243505.
[CrossRef]
8.
Bhasin, K.; Hackenberg, A.; Slywczak, R.; Bose, P.; Bergamo, M.; Hayden, J. Lunar relay satellite network for space exploration:
architecture, technologies and challenges. In Proceedings of the 24th AIAA International Communications Satellite Systems
Conference, San Diego, CA, USA, 11–14 June 2006; p. 5363.
9.
Ferreira, P.V.R.; Paffenroth, R.; Wyglinski, A.M.; Hackett, T.M.; Bilen, S.G.; Reinhart, R.C.; Mortensen, D.J. Reinforcement learning
for satellite communications: From LEO to deep space operations. IEEE Commun. Mag. 2019,57, 70–75. [CrossRef]
10.
Akyildiz, I.F.; Ekici, E.; Bender, M.D. MLSR: a novel routing algorithm for multilayered satellite IP networks. IEEE/ACM Trans.
Netw. 2002,10, 411–424. [CrossRef]
11.
Kaushal, H.; Kaddoum, G. Optical communication in space: Challenges and mitigation techniques. IEEE Commun. Surv. Tutorials
2016,19, 57–96. [CrossRef]
12.
Erdogan, E.; Altunbas, I.; Kurt, G.K.; Bellemare, M.; Lamontagne, G.; Yanikomeroglu, H. Site diversity in downlink optical
satellite networks through ground station selection. IEEE Access 2021,9, 31179–31190. [CrossRef]
13.
Al-Anbagi, H.N.; Vertat, I. Cooperative Reception of Multiple Satellite Downlinks. Sensors
2022
,22, 2856. [CrossRef] [PubMed]
14.
Lu, Y.; Sun, F.; Zhao, Y. Virtual topology for LEO satellite networks based on earth-fixed footprint mode. IEEE Commun. Lett.
2013,17, 357–360. [CrossRef]
15.
Blasch, E.; Pham, K.; Chen, G.; Wang, G.; Li, C.; Tian, X.; Shen, D. Distributed QoS awareness in satellite communication network
with optimal routing (QuASOR). In Proceedings of the 2014 IEEE/AIAA 33rd Digital Avionics Systems Conference (DASC),
Colorado Springs, CO, USA, 5–9 October 2014; p. 6C3-1.
16.
Chen, Q.; Guo, J.; Yang, L.; Liu, X.; Chen, X. Topology virtualization and dynamics shielding method for LEO satellite networks.
IEEE Commun. Lett. 2019,24, 433–437. [CrossRef]
17.
Mohorcic, M.; Svigelj, A.; Kandus, G.; Hu, Y.F.; Sheriff, R.E. Demographically weighted traffic flow models for adaptive routing
in packet-switched non-geostationary satellite meshed networks. Comput. Netw. 2003,43, 113–131. [CrossRef]
18.
Papapetrou, E.; Karapantazis, S.; Pavlidou, F.N. Distributed on-demand routing for LEO satellite systems. Comput. Netw.
2007
,
51, 4356–4376. [CrossRef]
19.
Yang, W.C.; Yao, S. A Multi-path Routing Algorithm based on Ant Colony Optimization in Satellite Network. In Proceedings of
the 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE),
Singapore, 15–17 July 2021, pp. 139–144.
20.
Let, R. On the Throughput-Latency Routing Optimality of a Delay-Tolerant Network. In Proceedings of the NOMA and IEEE
802.11 NETWORKS (MWN-5) Symposium, Globecom 2022, Rio de Janeiro, Brazil, 4–8 December 2022.
21.
Kleinrock, L. Internet congestion control using the power metric: Keep the pipe just full, but no fuller. Ad. Hoc. Netw.
2018
,
80, 142–157. [CrossRef]
22.
Israel, D.J.; Edwards, B.L.; Staren, J.W. Laser Communications Relay Demonstration (LCRD) update and the path towards optical
relay operations. In Proceedings of the 2017 IEEE Aerospace Conference, Big Sky, MN, USA, 4–11 March 2017; pp. 1–6.
23.
Dudukovich, R.; LaFuente, B.; Hylton, A.; Tomko, B.; Follo, J. A distributed approach to high-rate delay tolerant networking
within a virtualized environment. In Proceedings of the 2021 IEEE Cognitive Communications for Aerospace Applications
Workshop (CCAAW), Virtual, 21–22 June 2021; pp. 1–5.
24.
Hylton, A.; Raible, D.E. High data rate architecture (hidra). In Proceedings of the 34th AIAA International Communications
Satellite Systems Conference, Cleveland, OH, USA, 18–20 October 2016; p. 5756.
25.
Lent, R. A cognitive network controller based on spiking neurons. In Proceedings of the 2018 IEEE international Conference on
Communications (ICC), Kansas City, MO, USA, 20–24 May 2018; pp. 1–6.
26.
Lent, R. Enabling Cognitive Bundle Routing in NASA’s High Rate DTN. In Proceedings of the 2022 International Wireless
Communications and Mobile Computing (IWCMC), Marrakesh, Morocco, 30 May–3 June 2022; pp. 1323–1328.

Sensors 2023,23, 310 22 of 22
27.
Kasabov, N.; Feigin, V.; Hou, Z.G.; Chen, Y.; Liang, L.; Krishnamurthi, R.; Othman, M.; Parmar, P. Evolving spiking neural
networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke.
Neurocomputing 2014,134, 269–279. [CrossRef]
28.
Nguyen, D.A.; Tran, X.T.; Iacopi, F. A review of algorithms and hardware implementations for spiking neural networks. J. Low
Power Electron. Appl. 2021,11, 23. [CrossRef]
29.
Dong, C.; Xu, X.; Liu, A.; Liang, X. Load balancing routing algorithm based on extended link states in LEO constellation network.
China Commun. 2022,19, 247–260. [CrossRef]
30.
Yang, Z.; Liu, H.; Jin, J.; Tian, F. A Cooperative Routing Algorithm for Data Downloading in LEO Satellite Network. In Proceedings
of the 2021 IEEE 21st International Conference on Communication Technology (ICCT), Tianjin, China,
13–16 October 2021;
pp. 1386–1391.
31.
Zhang, L.; Yan, F.; Zhang, Y.; Wu, T.; Zhu, Y.; Xia, W.; Shen, L. A routing algorithm based on link state information for leo satellite
networks. In Proceedings of the 2020 IEEE Globecom Workshops GC Wkshps, Taipei, Taiwan, 7–11 December 2020; pp. 1–6.
32.
Geng, S.; Liu, S.; Fang, Z.; Gao, S. An agent-based clustering framework for reliable satellite networks. Reliab. Eng. Syst. Saf.
2021
,
212, 107630. [CrossRef]
33.
Na, Z.; Pan, Z.; Liu, X.; Deng, Z.; Gao, Z.; Guo, Q. Distributed routing strategy based on machine learning for LEO satellite
network. Wirel. Commun. Mob. Comput. 2018,2018, 3026405. [CrossRef]
34.
Lu, Y.; Zhao, Y.; Sun, F.; Li, H. A survivable routing protocol for two-layered LEO/MEO satellite networks. Wirel. Netw.
2014
,
20, 871–887. [CrossRef]
35.
Nishiyama, H.; Kudoh, D.; Kato, N.; Kadowaki, N. Load balancing and QoS provisioning based on congestion prediction for
GEO/LEO hybrid satellite networks. Proc. IEEE 2011,99, 1998–2007. [CrossRef]
36.
Kawamoto, Y.; Nishiyama, H.; Kato, N.; Kadowaki, N. A traffic distribution technique to minimize packet delivery delay in
multilayered satellite networks. IEEE Trans. Veh. Technol. 2013,62, 3315–3324. [CrossRef]
37.
Tani, S.; Hayama, M.; Nishiyama, H.; Kato, N.; Motoyoshi, K.; Okamura, A. Multi-carrier relaying for successive data transfer
in earth observation satellite constellations. In Proceedings of the GLOBECOM 2017—2017 IEEE Global Communications
Conference, Singapore, 4–8 December 2017; pp. 1–5.
38.
Jiang, F.; Zhang, Q.; Yang, Z.; Yuan, P. A space–time graph based multipath routing in disruption-tolerant earth-observing satellite
networks. IEEE Trans. Aerosp. Electron. Syst. 2019,55, 2592–2603. [CrossRef]
39.
Li, T.; Zhou, H.; Luo, H.; Yu, S. SERvICE: A software defined framework for integrated space-terrestrial satellite communication.
IEEE Trans. Mob. Comput. 2017,17, 703–716. [CrossRef]
40.
Long, F.; Xiong, N.; Vasilakos, A.V.; Yang, L.T.; Sun, F. A sustainable heuristic QoS routing algorithm for pervasive multi-layered
satellite wireless networks. Wirel. Netw. 2010,16, 1657–1673. [CrossRef]
41.
Tu, Z.; Zhou, H.; Li, K.; Li, G.; Shen, Q. A routing optimization method for software-defined SGIN based on deep reinforcement
learning. In Proceedings of the 2019 IEEE Globecom Workshops (GC Wkshps), Waikoloa, HI, USA, 9–13 December 2019; pp. 1–6.
42.
Zhu, Y.; Rui, L.; Qiu, X.; Huang, H. Double-layer satellite communication network routing algorithm based on priority and
failure probability. In Proceedings of the 2019 15th International Wireless Communications & Mobile Computing Conference
(IWCMC), Tangier, Morocco, 24–28 June 2019; pp. 1518–1523.
43.
Kumar, P.; Bhushan, S.; Halder, D.; Baswade, A.M. fybrrLink: Efficient QoS-aware Routing in SDN enabled Future Satellite
Networks. IEEE Trans. Netw. Serv. Manag. 2021,19, 2107–2118. [CrossRef]
44.
Shi, X.; Li, Y.; Zhao, S.; Wang, W. Multi-QoS adaptive routing algorithm based on SDN for satellite network. In Proceedings of
the IOP Conference Series: Materials Science and Engineering, Chennai, India, 16–17 September 2020; Volume 768, p. 052035.
45.
Li, N.; Hu, L.; Deng, Z.l.; Su, T. Agent-Based Load Balancing and Qos Routing for Leo Satellite Networks. DEStech Trans. Comput.
Sci. Eng. 2018, 163–171. [CrossRef]
46.
Zhou, J.; Bo, Q.; Sun, L.; Wang, J.; Yan, X. Routing Strategy for LEO Satellite Networks Based on Membership Degree Functions.
Secur. Commun. Netw. 2020,2020, 8822985. [CrossRef]
47.
Wu, W.; Wang, S.; Zhang, R. A Routing for Delay-sensitive Traffic in Aerospace Networks. In Proceedings of the 2018 7th
International Conference on Energy and Environmental Protection (ICEEP 2018), Shenzhen, China, 14–15 July 2018; pp. 1178–1183.
48.
Fraire, J.A.; De Jonckere, O.; Burleigh, S.C. Routing in the space internet: A contact graph routing tutorial. J. Netw. Comput. Appl.
2021,174, 102884. [CrossRef]
Disclaimer/Publisher’s Note:
The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.