Indonesian
J
our
nal
of
Electrical
Engineering
and
Computer
Science
V
ol.
41,
No.
3,
March
2026,
pp.
935
∼
945
ISSN:
2502-4752,
DOI:
10.11591/ijeecs.v41.i3.pp935-945
❒
935
Satellite-based
assisted-ofoading
f
or
ener
gy-constrained
edge
netw
orks
Thembelihle
Dlamini
1
,
Mengistu
A.
Mulatu
1
,
Siso
V
ilakati
2
1
DepartmentDepartment
of
Electrical
and
Electronic
Engineering,
Uni
v
ersity
of
Esw
atini,
Kw
aluseni,
Esw
atini
2
Department
of
Biostatistics,
Uni
v
ersity
of
Free
State,
Free
State,
South
Africa
Article
Inf
o
Article
history:
Recei
v
ed
Oct
27,
2025
Re
vised
Jan
21,
2026
Accepted
Feb
27,
2026
K
eyw
ords:
F
orecasting
Green
ener
gy
Protected
areas
Remote
edge
serv
ers
Satellite
constellations
ABSTRA
CT
As
the
need
for
global
broadband
internet
connecti
vity
increases,
there
is
a
need
to
consider
the
use
of
non-terres
trial
netw
orks
(NTNs)
to
e
xtend
the
netw
ork
co
v
erage
to
protected
are
as
(e.g.,
national
parks).
Usually
,
protected
areas
are
prohibited
from
ha
ving
po
wer
lines
thus
lacking
wireless
connecti
vit
y
.
T
o
o
v
er
-
come
this
challenge,
ener
gy
can
be
pro
vided
through
the
use
of
green
ener
gy
from
a
solar
photo
v
oltaic
(PV)
system.
Then,
a
green
ener
gy-based
base
station
(BS)
can
be
deplo
yed
within
the
area
in
order
to
pro
vide
mobile
connecti
vity
to
visitors,
as
well
as
also
using
the
NTNs
to
handle
e
xcess
traf
c
or
tak
e
o
v
er
the
traf
c
in
the
e
v
ent
the
BS
does
not
ha
v
e
suf
cient
green
ener
gy
from
stor
-
age.
In
this
paper
,
a
h
ybrid
wireless
communication
system
is
proposed
to
in-
clude
BS
sites
located
in
a
protected
area
and
satellites
in
the
lo
w
earth
orbits
(LEO),
coupled
with
ne
w
of
oading
strate
gies,
with
the
main
goal
of
optimizing
the
trade-of
f
between
ener
gy
consumption
and
end-to-end
delay
for
the
green
ener
gy-based
BS
sites.
F
or
accurac
y
of
our
simulations,
we
c
onsider
real
data
from
a
solar
photo
v
oltaics
system,
traf
c
w
orkloads,
visitor’
s
location
data,
and
satellite
orbits
from
Starlink
constellations.
Our
results
demonstrate
that
the
co-
e
xistence
of
the
BS
and
satellite
achie
v
e
ener
gy
sa
vings
from
59
%
to
34
%
,
with
an
a
v
erage
sys
tem
delay
of
0
.
83
seconds
and
a
pack
et
drop
rate
that
ranges
from
8
.
3
%
to
2
.
7
%
,
when
compared
with
our
benchmark.
This
is
an
open
access
article
under
the
CC
BY
-SA
license
.
Corresponding
A
uthor:
Thembelihle
Dlamini
Department
of
Electrical
and
Electronic
Engineering,
Uni
v
ersity
of
Esw
atini
Kw
aluseni,
Kw
aluseni,
Esw
atini
Email:
tldlamini@unesw
a.ac.sz
1.
INTR
ODUCTION
The
ur
ge
for
sensing,
learning,
and
communication
services
in
future
mobile
node
(MN)
is
a
k
e
y
re-
search
area
in
6G
[1].
This
is
moti
v
ated
by
the
need
to
ha
v
e
wireless
communication
systems
in
protected
areas
(e.g.,
national
parks,
nature
reserv
es)
to
intelligently
monitor
the
en
vironment,
pro
vide
mobile
services
to
visi-
tors,
and
also
to
track
endangered
species.
Ha
ving
the
communication
infrastruct
ure
will
allo
w
data
processing
at
the
edge,
that
is,
within
a
base
station
(BS)
empo
wered
with
computation
capabilities
or
in
remote
clouds
(satellites).
The
primary
objecti
v
e
of
ha
ving
protected
areas
is
to
protect
biodi
v
ersity
and
ecosys
tem
functions,
and
through
interactions
with
natural
en
vironments,
people
deri
v
e
a
v
ariety
of
ph
ysical
and
psychological
ben-
ets
[2].
Despite
the
benets,
according
to
go
v
ernment
la
ws,
electricity
lines
are
not
permitted
thus
limiting
the
pro
visioning
of
MN
services
[3].
T
o
enable
smart
connecti
vity
in
protected
areas,
in
the
near
future,
it
is
e
xpected
that
the
combination
of
edge
serv
ers
and
green-po
wered
BS
will
pro
vide
the
capability
to
deplo
y
communication
sites
without
requiring
electrical
wiring
for
po
wer
supply
.
In
this,
the
BS
are
equipped
with
J
ournal
homepage:
http://ijeecs.iaescor
e
.com
Evaluation Warning : The document was created with Spire.PDF for Python.
936
❒
ISSN:
2502-4752
ener
gy
harv
esting
(EH)
equipments
and
computing
capabilities
[4].
The
use
of
green
ener
gy
to
po
wer
the
edge
systems
will
reduce
the
carbon
emissions
and
also
to
e
xpand
netw
ork
co
v
erage
within
protected
areas
[5].
The
pro
vision
of
netw
ork
co
v
erage
in
unserv
ed
areas,
similar
to
protected
areas,
is
also
of
great
im-
portance
despite
the
terrain
dif
culties
which
hinders
communication
to
wer
installations.
Using
non-terrestrial
netw
orks
(NTN),
which
comprise
satellites,
unmanned
aerial
v
ehicles
(U
A
Vs),
and
high
altitude
platforms
(HAPs),
to
supplement
terrestrial
netw
orks
or
of
fer
on-demand
wireless
access
to
places
without
infrastruc-
ture,
is
one
potential
remedy
for
this
[6].
High
tele
vision
(TV)
to
wers
and
lar
ge
antenna
arrays
utilizing
massi
v
e-MIMO
are
proposed
to
pro
vide
connecti
vity
to
sparse
areas
[7].
Here,
the
systems
ha
v
e
the
latest
emer
ging
antenna
technologies
and
designs
such
as
recongurable
phased/inatable/fractal
antennas
realized
with
metasurf
ace
material.
In
addition,
NTN
can
also
of
fer
a
connecti
vity
service
in
the
e
v
ent
of
a
natural
disaster
[8],
that
is,
where
the
deplo
yed
netw
ork
infrastructure
or
terrestrial
to
wers
are
out
of
service.
Similar
to
Multi-access
Edge
Computing
MEC
platforms,
NTN
can
of
fer
communication-plus
compu-
tation
services
in
addition
to
e
xpanding
netw
ork
co
v
erage
[9].
Here,
the
y
can
accept
of
oaded
delay-dependent
tasks
from
ener
gy-decient
or
capacity-constrained
BS
sites.
F
or
e
xample,
within
a
protected
area,
visitors
are
serv
ed
by
the
BS
which
are
located
along
the
perimeter
of
the
area,
which
in
turn
drains
the
battery
of
the
mo-
bile
de
vices
when
processing
an
y
data
due
to
the
lar
ger
separation
distance.
This
a
v
ails
the
opportunity
of
using
NTNs.
Re
g
arding
of
oading
tasks
to
sa
tellites
in
lo
w
earth
orbit
(LEO),
Pietro
[10]
proposed
the
use
of
in-orbit
computing
to
pro
vide
near
real-time
computing
to
areas
where
satellites
are
the
only
option
and
terrestrial
con-
necti
vity
is
lacking
in
order
to
of
oad
jobs
to
LEO
satellites.
Here,
the
author
presented
an
algorithm
for
a
LEO
satellite
constellation
to
handle
tasks
from
dif
ferent
locations
through
the
sharing
of
the
computing
plat-
form,
thus
relie
ving
ground
resources
from
computing
some
of
the
w
orkloads.
Similarly
,
Soret
[11]
proposed
the
use
of
LEO
satelli
te
constellations
for
of
oading
w
orkloads
and
also
backhauling
the
traf
c
from
remote
terrestrial
communication
sites
to
the
core
netw
ork.
Here,
the
y
e
v
aluated
their
performance
based
on
Age
of
Information,
latenc
y
,
and
collision
rate.
Performance
asses
sments
of
LEO
satellites
are
pro
vided
in
[12],
where
data
of
oading
techniques
are
suggested
within
the
frame
w
ork
of
v
ehicular
edge
computi
ng.
In
that
paper
,
the
delay-sensiti
v
e
task
is
sent
from
the
ground
de
vice
directly
to
the
satellite.
The
use
of
terrestrial
netw
orks
and
NTNs
for
ubiquitous
co
v
erage
is
proposed
as
a
solution
for
multi-connecti
vity
in
rural
areas
[13].
Here,
the
research
w
ork
focused
on
smart
agriculture
related
use
case
and
using
l
atenc
y
as
a
performance
metric.
From
the
aforementioned
papers,
it
is
noted
that
more
studies
are
required
to
measure
the
performanc
e
of
the
co-e
xistence
of
LEO
sate
llite
computing
platforms
and
green-po
wered
BSs
(empo
wered
with
computing
capa-
bilities),
in
order
to
guarantee
the
e
xpected
end-to-end
delay
.
Moreo
v
er
,
this
researc
h
article
dif
fers
from
[12]
as
the
ground
mobile
de
vices
send
information
to
the
BS
rst,
and
then
the
BS
transmit
the
information
to
the
satellite
via
a
locally
mounted
Starlink
antenna.
Managing
green-po
wered
communications
sites
is
also
important
as
the
y
are
dependent
on
the
amount
of
green
ener
gy
that
can
be
harv
ested
per
time
instance.
In
order
to
handle
dynamic
w
orkload
of
oading,
an
ef
cient
reinforcement
learning-based
resource
management
algorithm
is
proposed
and
in
this
paper
green
ener
gy
sources
were
inte
grated
into
a
MEC
system
[14].
Then,
in
our
pre
vious
paper
,
we
suggested
a
ne
w
net-
w
ork
design
where
a
controller
manages
the
EH
BSs
[4].
T
o
manage
the
computing
resources
(V
irtual
machine
(VM)
and
BS),
we
selecti
v
ely
turn
them
on
and
of
f
o
v
er
a
constrained
prediction
horizon.
By
redistrib
uting
the
netw
ork
load
among
the
BSs
and
taking
adv
antage
of
the
spatial
di
v
ersity
of
the
a
v
ailable
green
ener
gy
,
the
green-based
load
balancing
technique
is
suggested,
in
order
to
maximize
the
edge
system
performance
[15].
Here,
contai
ners
were
c
o
ns
idered
as
computing
resources
within
the
MEC
serv
er
.
Ov
erall,
it
should
be
stated
that
the
aforementioned
research
w
orks
lack
the
consideration
of
of
f-grid
BS
systems
for
protected
areas
and
the
use
of
LEO
satellite
to
complement
the
BSs
located
in
such
areas.
The
main
contrib
utions
of
this
paper
are
summarized
as
follo
ws:
(i)
we
propose
a
no
v
el
edge
com-
puting
frame
w
ork
for
ground
mobile
de
vices
that
i
nclude
mechanisms
to
dynamically
of
oad
tasks
to
LEO
satellites
via
the
BS
if
there
is
a
guarantee
of
near
real-time
communications-plus-computation
processes,
or
prioritize
the
use
of
a
local
edge
platform.
This
frame
w
ork
also
mak
e
use
of
admission
control
procedures
within
the
BS,
as
well
as
in
the
satellites;
(ii)
T
o
e
v
aluate
the
netw
ork
performance,
we
jointly
consider
the
use
of
green-po
wered
BSs
and
LEO
satellites
for
of
oading
the
delay-sensiti
v
e
tasks
from
a
protected
area.
Here,
our
main
goal
it
to
optimize
the
trade-of
f
between
ener
gy
consumption
and
the
end-to-end
delay
,
through
the
use
of
limited
liabili
ty
compan
y
(LLC)
principles
and
the
use
of
green
ener
gy
as
a
performance
metric.
Real-w
orld
harv
ested
ener
gy
,
traf
c
load
traces,
visitor’
s
location
data,
orbital
traces
and
parameters,
are
used
to
e
v
aluate
the
performance
of
the
proposed
optimization
strate
gy
.
The
numerical
results
obtained
through
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
41,
No.
3,
March
2026:
935–945
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
937
simulation
sho
w
that
the
proposed
optim
ization
strate
gies
are
able
to
ef
ciently
manage
the
communication
sites,
as
well
as
allo
wing
processing
of
tasks
in
LEO
satellites.
The
remainder
of
this
article
is
or
g
anized
as
follo
ws:
Section
2
presents
our
sys
tem
model,
sec
tion
3
pro
vides
the
mathematical
formulation
of
the
problem
and
the
of
oading
strate
gies,
section
4
discuss
the
simulation
results,
and
section
5
summarizes
our
conclusion.
2.
SYSTEM
MODEL
This
section
describes
the
scenario
section
2.1,
the
satellite
orbit
and
channel
model
section
2.2,
the
ener
gy
consumption
model
for
the
BS
site
section
2.3,
and
the
delay
model
section
2.4.
2.1.
Scenario
description
Our
considered
scenario
is
depicted
in
Figure
1.
Here,
we
consider
n
∈
N
BSs
deplo
yed
in
a
protected
area
(in
our
instance,
a
national
park
is
considered)
and
each
is
equipped
with
computing
capabilities
(i.e.,
each
EH
BS
has
a
local
computing
platform
that
runs
containers).
A
set
of
u
ground
mobile
de
vices,
from
visit
ors
who
prefer
w
alking
within
the
park,
of
oad
t
heir
delay-sensiti
v
e
or
delay-tolerant
tasks
to
BS
n
,
(in
our
case
direct
of
oading
to
the
satellite
is
not
allo
wed).
The
BS
are
within
the
co
v
erage
of
LEO
Starlink
satellites.
V
isitor’
s
current
locat
ions
(mobility
patterns)
are
kno
wn
through
the
location
service
application
programming
interf
ace
(LS
API)
[16],
which
is
a
service
that
supports
the
mobile
de
vice
location
retrie
v
al
mechanism
and
then
passing
the
information
to
authorized
applications
within
the
MEC
platform.
Here,
we
emulate
the
user
equipment
(UE)
location
lookup
procedure
between
the
edge
serv
er
the
subscribed
mobile
users
using
the
location
dataset
from
[17].
Figure
1.
The
BS
system
being
complemented
by
a
satellite
The
BS
infrastructure
is
shared
between
mobile
operators,
in
order
to
a
v
oid
cro
wding
the
prot
ected
site,
and
it
is
po
wered
by
ener
gy
harv
ested
from
a
solar
photo
v
ol
taic
(PV)
system.
Here,
the
use
of
wind
turbines
as
a
source
of
ener
gy
is
ne
glected
due
to
its
noise
pollution
which
has
an
ef
fect
to
wildlife
[18].
The
obtained
ener
gy
then
supplies
the
computing
platform,
BS
communication
infrastructure,
as
well
as
Starlink
antenna
mounted
onsite
to
pro
vide
satellite-based
Internet
services.
An
y
traf
c
that
is
going
to
be
of
oaded
to
the
satellite,
will
pass
through
the
mounted
Starlink
antenna.
In
addition,
the
battery
bank
of
size
Υ
max
stores
the
e
xcess
ener
gy
,
and
for
the
satellite
we
assume
that
there
is
enough
ener
gy
within
its
battery
bank
as
there
e
xist
a
direct
line
of
sight
(LOS)
with
t
he
sun.
On
the
MEC
serv
er
of
capacity
C
lo
c
,
there
is
an
access
control
application
that
is
responsible
for
admitting
and
forw
arding
w
orkloads
to
its
acti
v
e
G
computing
resources
(containers)
or
to
a
set
of
s
visible
LEO
Starlink
satellites
that
are
each
running
G
containers
onboard,
with
each
ha
ving
a
capacity
C
sat
,
for
processing
the
of
oaded
data
taking
into
account
the
delay
constraint
σ
.
2.2.
Orbital
and
channel
model
Generic
orbital
model:
The
ground
mobile
de
vices
of
oad
their
tasks
to
the
BS,
and
the
BS
loc
ation
is
determined
by
its
latitude
l
u
and
longitude
L
u
,
and
the
satellite’
s
position
in
space
is
dened
by
its
altitude
Satellite-based
assisted-of
oading
for
ener
gy-constr
ained
edg
e
networks
(Thembelihle
Dlamini)
Evaluation Warning : The document was created with Spire.PDF for Python.
938
❒
ISSN:
2502-4752
l
h
,
latitude
l
s
and
longitude
L
s
.
T
o
accurately
determine
the
location
of
a
Starlink
satellite
on
its
orbit,
we
mak
e
use
of
the
tw
o-line
element
(TLE)
data
from
[19],
which
pro
vides
an
up-to-date
trajectory
and
orbital
parameters.
Similar
to
[12],
the
separation
distance
d
(in
km)
from
the
BS
and
the
generic
satellite
as
follo
ws,
d
=
(
l
h
+
r
e
)
"
1
+
r
e
l
h
+
r
e
2
−
2
r
e
l
h
+
r
e
cos(
ψ
)
#
1
/
2
(1)
where
r
e
is
the
radius
of
the
earth
(i.e.,
6378
.
137
km),
and
ψ
represents
the
angle
between
the
BS
and
the
satellite
as
observ
ed
from
the
earth’
s
center
,
and
it
is
related
using
the
follo
wing
equation,
cos(
ψ
)
=
cos(
l
s
)
cos(
l
u
)
cos(
L
u
−
L
s
)
+
s
i
n(
l
u
)
sin(
L
s
)
(2)
From
[20],
the
angle
of
ele
v
ation
θ
is
obtained
from
the
follo
wing
relationship,
cos(
θ
)
=
(
l
h
+
r
e
)
sin(
ψ
)
d
(3)
F
or
a
satellite
to
be
visible
to
the
BS,
its
ele
v
ation
angle
must
be
abo
v
e
some
minimum
v
alue
and
it
is
upper
bounded
by
some
v
alue,
i.e.,
θ
≤
81
.
3
◦
[20].
W
ir
eless
channel
model:
The
channel
model
for
the
BS
to
satellite
connecti
vity
can
be
obtained
from
the
3GPP
specications
[21],
where
we
assume
the
LOS
and
let
the
signal-to-noise
ratio
(dB)
between
BS
n
and
the
visible
satellite
(in
log
)
to
be,
Γ
n,s
=
EIRP
n
+
(
G/T
)
n
−
P
n
−
k
−
B
n
,
(4)
where
EIRP
n
is
the
ef
fecti
v
e
isotropic
radiated
po
wer
of
t
he
transmitter
in
W
,
(
G/T
)
n
is
the
recei
v
ed
antenna
g
ain
to
noise
temperature
ratio
(sometimes
called
gure
of
merit),
P
n
is
the
path
loss
which
constitute
of
free
space
path
loss,
pointing
loss,
polarization
loss,
and
loss
due
to
the
atmosphere,
k
is
the
Boltzmann
constant
and
B
n
is
the
bandwidth
is
Hz.
The
free
space
path
loss,
P
FS
n
,
is
gi
v
en
by
[20],
P
FS
n
=
92
.
45
+
20
log
(
f
c
)
+
20
log
(
d
)
(5)
where
f
c
is
the
carrier
frequenc
y
in
GHz.
2.3.
BS
consumption
model
The
total
ener
gy
consumed
[J]
by
each
BS
site,
denoted
by
β
site
n
(
t
)
,
consists
of
the
wireless
com-
munications
processes,
denoted
by
β
bs
n
(
t
)
,
and
the
edge
serv
er
processes,
which
includes
computing,
caching,
and
communication
acti
vities
within
itself,
is
denoted
by
β
edg
n
(
t
)
.
Thus,
at
slot
t
,
the
ener
gy
consumed
can
be
formulated
as
follo
ws
[15],
β
site
n
(
t
)
=
β
bs
n
(
t
)
+
β
edg
n
(
t
)
(6)
The
transmission
process
drains
ener
gy
from
the
BS
site.
Here,
we
let
β
0
represent
the
operating
ener
gy
ne-
glecting
w
orkloads,
β
l
d
(
t
)
is
the
task
dependent
transmission
po
wer
to-and-from
the
ground
mobile
de
vices
and
also
to
the
satellite
at
a
tar
get
rate
of
r
0
which
guarantees
the
lo
w
latenc
y
threshold,
β
sat
(
t
)
is
the
ener
gy
used
by
the
Starlink
antenna
for
uplink
(UL)
and
do
wnlink
(DL)
data
transfer
,
and
β
dt
(
t
)
is
the
inter
-communication
ener
gy
cost
[J/byte]
for
passing
data
to
the
edge
serv
er
int
erf
aces
for
processing.
Since
the
BS
transmission
po
wer
is
adapti
v
e,
we
let
η
(
t
)
∈
{
0
,
1
}
be
the
s
witching
s
tatus
indicator
.
Thus,
the
BS
consumption
is
as
follo
ws,
β
bs
n
(
t
)
=
η
(
t
)
β
0
+
β
l
d
(
t
)
+
β
sat
(
t
)
+
β
dt
(
t
)
(7)
The
ener
gy
consumed
by
the
softw
arized
computing
platform
(in
the
satellite
or
MEC
serv
er)
is
dependent
on
the
total
number
of
acti
v
e
containers
at
time
t
.
Th
e
CPU
utilization
share
is
denoted
by
β
cp
(
t
)
,
then
the
resource
scheduler
will
acti
v
ate
and
de-acti
v
ate
containers
based
on-demand,
through
a
reliable
intra-communication
link
operating
at
rate
of
r
g
(
t
)
bits/s,
in
order
to
allocate
tasks
of
size
γ
g
(
t
)
and
such
incurs
a
cost
denoted
by
β
sw
(
t
)
.
Then,
the
tasks
are
queued
before
processing
and
dequeue
after
processing,
thus
the
input-output
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
41,
No.
3,
March
2026:
935–945
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
939
b
uf
fer
acti
vities
incurs
an
ener
gy
cost
and
it
is
denoted
by
β
q
u
(
t
)
.
Since
some
of
the
viral
Internet
content
can
be
cached
locally
,
the
caching
process
contrib
utes
an
amount
of
ener
gy
drained
and
it
is
denoted
by
β
ch
(
t
)
.
The
total
ener
gy
drained
within
the
computing
platform
is
as
follo
ws,
β
bs
n
(
t
)
=
η
(
t
)
β
0
+
β
l
d
(
t
)
+
β
sat
(
t
)
+
β
dt
(
t
)
(8)
W
ithin
the
computing
platforms,
the
maximum
per
-slot
communication
rate
is
limited
to
a
pre-assigned
v
alue
r
max
,
thus
the
follo
wing
hard
constraint
must
hold:
P
G
(
t
)
g
=1
r
g
(
t
)
≤
r
max
.
The
ener
gy
that
is
used
for
BS
operations
is
from
a
solar
PV
system
Figure
2
for
harv
ested
ener
gy
traces
from
[22])
and
the
battery
.
At
each
time
instance,
the
amount
of
ener
gy
dra
wn
from
the
battery
,
for
communication-plus-computing
acti
vities,
must
be
less
than
the
amount
required
by
the
communication
site
and
it
is
dened
as
Υ
n
(
t
)
≥
β
site
n
(
t
)
.
Thus,
the
amount
of
the
green
ener
gy
demanded
per
site
is
as
follo
ws,
Figure
2.
Solar
ener
gy
traces
from
a
PV
system
for
day
1
(D1)
to
day
3
(D3)
υ
n
(
t
)
=
υ
c
n
(
t
)
+
υ
o
n
(
t
)
(9)
where
υ
c
n
(
t
)
is
the
fractional
ener
gy
that
is
for
char
ging
the
battery
and
υ
o
n
(
t
)
is
the
share
that
is
used
im-
mediately
for
supporting
local
operations.
Per
each
time
slot
t
,
it
is
important
to
note
that
the
actual
amount
of
ener
gy
(denoted
by
E
max
n
)
that
can
be
e
xtracted
from
the
en
vironment
is
limited,
t
hus
we
ha
v
e
an
ener
gy
harv
esting
constraint
as,
υ
c
n
(
t
)
+
υ
o
n
(
t
)
≤
E
max
n
,
∀
n,
∀
t.
(10)
During
the
day
,
the
storage
de
vice
le
v
el
uctuates
according
to
the
follo
wing
equation,
Υ
n
(
t
)
=
ζ
n
(Υ
n
(
t
−
1)
−
β
site
n
(
t
))
+
Φ(
υ
c
n
(
t
))
,
(11)
where
ζ
n
∈
(0
,
1]
represents
the
battery
self-dischar
ging
beha
vior
,
and
Φ
∈
(0
,
1]
represents
the
incurred
losses
during
the
char
ging
phase.
2.4.
Delay
model
The
input/output
(I/O)
queue
of
the
system
are
assumed
to
be
loss-free
such
that
the
time
e
v
olution
of
the
backlogs
queues
follo
ws
Lindle
y’
s
equations
[23].
F
or
intra-communication
within
the
computing
platform,
we
note
that
there
e
xist
a
tw
o-w
ay
pe
r
task
e
x
ecution
delay
(task
to-and-from
the
container)
where
each
link
delay
is
denoted
by
ρ
g
(
t
)
=
2
γ
g
(
t
)
/r
g
(
t
)
.
Then,
the
computation
processing
duration
that
depends
on
the
CPU
c
ycles
denoted
by
t
cp
=
|
G
(
t
)
|
/C
lo
c
=
|
G
(
t
)
|
/C
sat
,
queuing
delay
of
the
task
on
the
input-output
b
uf
fer
,
assuming
the
e
xistence
of
a
congestion
handling
mechanism,
denoted
by
t
q
u
.
Thus,
the
delay
on
the
computing
platform,
denoted
by
t
d
,
is
as
follo
ws,
t
d
=
ρ
g
+
t
cp
+
t
q
u
.
(12)
Satellite-based
assisted-of
oading
for
ener
gy-constr
ained
edg
e
networks
(Thembelihle
Dlamini)
Evaluation Warning : The document was created with Spire.PDF for Python.
940
❒
ISSN:
2502-4752
Since
the
tasks
are
either
of
oaded
to
the
BS
site
for
local
computation
or
forw
arded
to
the
satellite
edge
computing
platform,
the
time
delay
due
to
access
decision
making
(accepting
or
forw
arding)
is
denoted
by
t
ac
.
The
ground
mobile
de
vice
of
oad
the
task
to
the
BS,
and
the
UL
and
DL
delays
are
denoted
as
t
u
ul
and
t
u
dl
,
which
are
dependent
on
the
transmission
rate
r
0
that
guarantee
the
e
xpected
delay
threshold
and
the
size
of
the
tasks
as
t
u
ul
=
γ
g
(
t
)
/r
0
and
t
u
dl
=
γ
′
g
/r
0
,
where
γ
′
g
is
the
computed
results
from
the
BS.
Thus,
for
tasks
of
oaded
to
the
BS
for
local
computation,
the
total
delay
(denoted
by
t
l
oc
)
is
as
follo
ws,
t
l
oc
=
t
u
ul
+
t
u
dl
+
t
ac
+
t
d
+
2
t
pg
,
(13)
where
t
pg
is
the
propag
ation
delay
.
If
the
of
oaded
tasks
are
transmitted
to
the
selected
satellite
from
the
BS,
then
there
is
the
UL
and
DL
delay
due
to
the
transmission
rate
denoted
by
r
s
n
(
t
)
which
is
related
to
(4)
through
shannon
capacity
and
the
size
of
the
transmitted
tasks
(denoted
by
γ
n
(
t
)
)
as
t
ul
n,s
=
γ
n
(
t
)
/r
s
n
,
and
t
dl
n,s
=
γ
′
n
(
t
)
/r
s
n
,
where
γ
′
n
is
t
he
computed
results
from
the
satellite
to
BS
n
.
Then,
the
total
delay
for
tasks
of
oaded
to
the
satellite
(denoted
by
t
sat
)
is
gi
v
en
as
follo
ws,
t
sat
=
t
ul
n,s
+
t
dl
n,s
+
t
d
+
2
t
pg
.
(14)
T
o
guarantee
lo
w
latenc
y
for
applications
in
MNs,
we
ha
v
e
to
mak
e
sure
that
the
follo
wing
conditions
hold
for
delay-sensiti
v
e
tasks:
t
l
oc
≤
σ
and
t
sat
≤
σ
.
3.
BS-SA
TELLITE
OFFLO
ADING
FRAMEW
ORK
Data
processing
in
v
olv
es
local
BS
computation
or
satellite-based
computation,
follo
wed
by
tasks
drop-
ping
if
all
t
he
options
are
not
a
v
ai
lable.
Cases
of
dropping
t
asks
are
dependent
on
the
a
v
ailable
stored
ener
gy
and
the
loading
of
the
input-output
queue.
In
terms
of
resource
allocation
within
the
computing
platform
,
the
container
pro
visioning
and
load
allocation
o
v
er
them,
at
t
,
is
performed
similar
to
[15].
In
general,
local
computation
is
more
con
v
enient,
pro
vided
that
suf
ci
ent
green-ener
gy
i
s
a
v
a
ilable,
as
well
as
the
computing
resources.
The
ener
gy
to
be
harv
ested
and
the
tasks
to
be
of
oaded
are
accumulated
o
v
er
the
time
slot,
and
the
y
can
only
be
kno
wn
at
the
end
of
it.
This
implies
that
the
amount
of
harv
ested
ener
gy
and
the
tasks
from
the
ground
mobile
de
vices
can
only
be
estimated
using
the
LSTM
neural
netw
orks
[24],
i.e.,
ˆ
Υ
n
(
t
)
and
ˆ
γ
n
(
t
)
.
In
order
to
manage
the
of
oading
process,
using
green
ener
gy
as
a
performance
metric,
we
propose
a
frame
w
ork
that
will
identify
if
the
BS
will
compute
the
tasks
locally
or
it
will
steer
part
of
the
of
oaded
tasks
to
the
satellite
edge
system.
T
o
achie
v
e
this,
the
communication-plus-computing
interv
al
is
dened
as
the
ratio
of
the
ne
xt
time
slot
a
v
ailable
green
ener
gy
and
the
e
xpected
total
ener
gy
consumption
(recall
that
the
harv
ested
ener
gy
and
t
asks
are
forecasted),
per
BS
site,
as
J
n
(
t
)
=
Υ
n
(
t
+1)
β
site
n
(
t
+1)
≥
1
.
F
or
of
oading
decision
making,
we
emplo
y
the
follo
wing
strate
gies:
Str
ate
gy
1
(Local
of
oading
(LO)):
If
J
n
(
t
)
≥
1
and
ˆ
t
l
oc
<
σ
,
the
site
ener
gy
will
be
suf
cient
to
handle
the
e
xpected
tasks
with
the
guarantee
of
a
lo
w
latenc
y
,
otherwise
if
J
n
(
t
)
<
1
the
communication
site
will
not
be
able
to
handle
the
e
xpected
tasks,
thus
the
strate
gy
is
to
of
oad
the
tasks
to
the
visible
satellites
or
drop
them.
Here,
we
assume
the
ground
mobile
de
vices
of
oad
their
delay-sensiti
v
e
tasks
to
the
BS
with
the
highest
signal
strength.
Str
ate
gy
2
(Assisted-of
oading
(A
O)):
F
or
J
n
(
t
)
<
1
,
the
tasks
will
be
of
oaded
to
the
satellite
via
the
Starlink
antenna.
Here,
the
BS
selects
a
serving
satellite
from
a
set
of
visible
satellites.
The
set
consist
of
satellites
whose
SNR
(dened
in
(4))
is
abo
v
e
a
set
threshold
Γ
th
.
T
o
select
the
satellite
that
will
serv
e
the
BS
from
the
set,
the
BS
mak
e
use
of
a
feedback
mechanism
that
monitors
the
e
v
olution
of
each
satellite
queue
system,
in
a
round
robin
manner
.
The
feedback
pro
vides
the
state
of
the
input
queues
for
each
satellite,
and
then
the
BS
estimate
the
queuing
duration’
s
{
ˆ
t
q
u
}
follo
wed
by
checking
if
ˆ
t
sat
<
σ
.
Then,
the
satellite
with
the
least
v
alue
of
{
ˆ
t
q
u
}
from
the
set,
also
fullling
the
latenc
y
constraint,
of
oading
the
tasks
to
the
satellite,
denoted
by
s
′
,
will
be
prioritized,
otherwise
the
data
will
be
dropped
and
the
communication
with
the
satellite
will
be
deacti
v
ated.
T
o
handle
congestion
in
the
computing
platform
input
b
uf
fers,
the
proposed
strate
gies
ma
k
e
use
of
a
soft-dropping
polic
y
.
Here,
the
tasks
are
dropped
at
the
satellite
edge
s
ystem
when
ˆ
t
sat
≥
σ
and
in
the
BS
the
tasks
are
also
dropped
when
ˆ
t
l
oc
≥
σ
.
When
the
tasks
ha
v
e
been
dropped,
the
mobile
de
vices
will
back-of
f
for
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
41,
No.
3,
March
2026:
935–945
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
941
a
random
period
of
time
denoted
by
∆
,
and
when
∆
e
xpires
the
de
vice
can
retry
to
of
oad
their
tasks.
This
allo
w
the
input
b
uf
fers
to
decongest
and
pre
v
ent
the
edge
systems’
queues
from
o
v
erloading.
In
such
cases,
the
data
will
be
dropped
with
a
probability
of
q
dr
op
=
(
ˆ
t
sat
/σ
)
δ
=
(
ˆ
t
l
oc
/σ
)
δ
,
where
δ
is
a
parameter
that
describes
the
steepness
of
q
dr
op
.
The
online
algorithm:
The
distrib
uted
process
of
the
online
algorithm
is
as
follo
ws:
The
online
algo-
rithm
starts
with
the
initial
s
tate
and
b
uilds
a
tree
with
all
potential
future
states
up
to
the
prediction
depth
in
a
breadth-rst
manner
.
The
current
system
state
i
s
then
initialized
to
create
a
search
set,
which
is
then
accumulated
whil
e
the
algorithm
iterates
through
the
tree,
taking
into
consideration
predictions
(ener
gy
,
traf
c),
accumulated
w
orkloads
at
the
output
b
uf
fer
,
pre
vious
outputs,
and
controls.
The
coll
ection
of
states
that
are
reached
at
each
prediction
depth,
taking
into
account
the
performance
metric
J
n
(
t
)
.
In
order
to
generate
the
ne
xt
set
of
reachable
control
actions,
we
rst
esti
mate
the
traf
c
load,
del
ay-
dependent
tasks,
locally
acceptable
computational
load,
har
v
es
ted
ener
gy
,
and
J
n
(
t
)
.
Base
on
J
n
(
t
)
,
the
of-
oading
strate
gy
is
then
selected
and
an
y
e
xpected
delay
on
the
forecasted
BS
load
and
the
b
uf
fers
Ne
xt,
the
ener
gy
cost
β
bs
n
(
t
)
for
e
v
ery
created
state
is
calculated.
After
e
xamining
the
predi
ction
horizon,
a
series
of
achie
v
able
states
with
the
lo
west
ener
gy
use
is
found.
The
system
recei
v
es
an
input
control
action,
which
corresponds
to
the
rst
state
in
the
sequence;
the
others
are
discarded.
F
or
e
v
ery
time
slot
t
,
the
procedure
is
repeated.
4.
PERFORMANCE
EV
ALU
A
TION
4.1.
Simulation
setup
A
BS
system
empo
wered
with
computation
capabilities
deplo
yed
in
a
protected
area,
and
the
S
tarlink
constellation,
is
considered
in
this
setup.
The
pa
rameters
that
were
used
in
the
simulations
are
listed
in
T
able
1.
Our
time
slot
duration
τ
is
set
to
15
min
and
the
time
hori
zon
is
set
to
2
time
slots.
Datasets
for
traf
c
loads
from
[25],
visitor’
s
location
from
[17]
for
emulating
the
LS,
harv
ested
ener
gy
from
[22],
were
used
in
our
setup.
F
or
simulation,
Python
is
used
as
the
programming
language.
T
able
1.
System
parameters
P
arameter
V
al
ue
Figure
of
merit
(
G/T
)
n
15
.
84
dB/K
Carrier
frequenc
y
,
f
c
30
GHz
Bandwidth,
B
n
10
MHz
EIRP
Satellite
antenna,
EIRP
n
34
.
9
dBW
Earth
radius,
r
e
6378
.
137
km
Satellite
height,
l
h
350
−
600
km
SNR
for
A
O
polic
y
,
Γ
th
10
dB
Satellite
capacity
,
C
sat
1
TB
Starlink
antenna
po
wer
,
β
sat
50
W
BSs
total
,
N
20
BS
operating
po
wer
β
0
,
10.6
W
MEC
capacity
,
C
lo
c
40
GB
Number
of
containers,
G
20
Application
time
constraint,
σ
0.8
s
Battery
self-char
ging,
ζ
n
0
.
9999
Ener
gy
storage
capacity
,
Υ
max
100
kJ
Uplink
task
size
γ
n
3
MB
Do
wnlink
task
size
γ
′
n
0
.
1
MB
T
ar
get
transmission
rate,
r
0
1
Mbps
4.2.
Numerical
r
esults
In
Figure
3,
the
real
and
predicted
v
alues
for
the
harv
ested
ener
gy
are
sho
wn.
Here,
the
forecast
ing
algorithm
tracks
each
v
alue
and
predict
it
o
v
er
one-step.
From
the
obtained
results,
the
prediction
v
ariations
are
observ
ed
between
Υ(
t
)
and
ˆ
Υ(
t
)
,
the
obtained
root
mean
squared
error
(RMSE)
v
alues
are
0
.
050
for
one-step,
0
.
070
for
the
second-step.
The
obtained
accurac
y
is
good
enough
for
our
simulation
setup.
Satellite-based
assisted-of
oading
for
ener
gy-constr
ained
edg
e
networks
(Thembelihle
Dlamini)
Evaluation Warning : The document was created with Spire.PDF for Python.
942
❒
ISSN:
2502-4752
F
or
performance
e
v
aluation,
we
compare
the
tw
o
of
oading
methods:
(i)
the
ground
mobile
de
vice
directly
of
oads
the
data
to
the
satellite
(DO)
without
an
y
short-term
future
kno
wledge,
similar
to
[12],
and
(ii)
our
proposal
of
sending
data
via
the
BS
for
local
computation
(LO)
or
satellite-based
computation
(A
O),
utilizing
short-term
future
kno
wledge.
Figure
3.
Solar
ener
gy
traces
for
day
1
(D1)
and
its
predicted
v
alues
(D1(pred))
In
Figure
4,
we
compare
the
ener
gy
sa
vings
that
can
be
obtained
when
partiall
y
some
of
the
of
oaded
tasks
are
forw
arded
by
the
BS
to
the
selected
LEO
satellite
for
computation
(LO
+
A
O)
and
other
tasks
com-
puted
locally
,
and
cases
where
all
the
tasks
are
computed
locally
(LO).
It
is
observ
ed
that
in
the
early
hours
of
the
morning
(before
8
am),
there
are
fe
w
visitors
in
the
national
park
(lo
w
acti
vity),
thus
the
BS
site
is
not
much
utilized
as
the
ener
gy
is
used
for
operation
acti
vities.
Between
9
.
00
am
-
15
.
00
pm,
there
is
high
acti
vity
within
the
park.
This
period
corresponds
to
periods
where
there
is
suf
cient
amount
of
ener
gy
that
can
be
harv
ested,
as
well
as
the
arri
v
al
and
departure
of
visitors
from
the
national
park.
After
15
.
00
pm,
the
visitors
will
start
to
lea
v
e
the
park,
then
the
ener
gy
sa
vings
increases.
The
ener
gy
sa
vings
obtained
by
LO
+
A
O
ranges
from
59%
to
34%
and
LO
ranges
from
52%
to
30%.
From
the
obtained
results,
it
is
observ
ed
that
partial
of
oading
to
the
satellite
is
benecial
as
it
relie
v
e
t
he
BS
site
from
computing
e
v
erything
locally
,
that
is,
frac
tional
computing
is
better
than
computing
e
v
erything
locally
.
Figure
4.
Ener
gy
sa
vings
within
the
BS
site
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
41,
No.
3,
March
2026:
935–945
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
943
The
time-a
v
erage
system
delay
cost
is
sho
wn
in
F
igure
5,
where
the
computational
duration
is
ob-
serv
ed
o
v
er
a
number
of
time
slots.
It
is
observ
ed
that
local
computation
(LO)
bears
a
near
real-time
delay
of
0
.
83
seconds
when
compared
to
the
application
delay
requirement
of
0
.
8
s
econds
(
σ
).
In
addition,
we
observ
ed
that
assisted-of
oading
(where
some
of
the
tasks
are
partially
of
oaded
to
the
satellite)
i
s
benecial
as
it
of
fers
delays
of
0
.
82
seconds
which
is
as
close
to
local
computation
on
a
v
erage.
This
is
due
to
the
f
act
that
a
portion
of
the
tasks
are
of
oaded
to
the
satellite
if
computational
resources
are
a
v
ailable
on
the
satellite
and
congestion
is
not
e
xpected
in
the
ne
xt
time
slot.
The
performance
for
direct
of
oading
(DO)
is
lo
w
(when
compared
to
assisted-of
oading)
as
the
men
delay
is
0
.
84
seconds.
By
of
oading
some
of
the
tasks
to
the
satellite,
via
the
BS,
helps
in
that
the
BS
(acts
as
relay
or
sink)
decide
on
the
share
that
can
be
forw
arded
to
the
satellite
platform,
taking
into
account
the
amount
of
green
ener
gy
to
be
harv
ested
and
the
e
xpected
tasks.
Figure
5.
T
ime
a
v
erage
system
delay
The
drop
rate
from
the
queues
is
illustrated
in
Figure
6.
Here,
assisted-of
oading
(LO
+
A
O)
is
compared
with
full
of
oading
to
the
satellite
(DO),
and
LO
+
A
O
achie
v
es
a
maximum
drop
rate
of
8
.
3
%
and
a
minimum
of
2
.
6
%
whereas
DO
achie
v
es
a
maximum
drop
rate
of
12
.
9
%
and
minimum
of
9
.
9
%
.
It
is
observ
ed
that
our
proposed
strate
gies
perform
better
(drop
rate
of
<
10
%
)
when
compared
to
direct
of
oading
to
the
selected
satellite.
This
is
due
to
the
loading
of
the
input
b
uf
fer
of
the
satellites
in
the
case
of
forw
arding
all
the
tasks
to
the
satellite
and
the
e
xpected
harv
ested
ener
gy
.
Figure
6.
T
ask
drop
rate
o
v
er
a
number
of
time-slots
Satellite-based
assisted-of
oading
for
ener
gy-constr
ained
edg
e
networks
(Thembelihle
Dlamini)
Evaluation Warning : The document was created with Spire.PDF for Python.
944
❒
ISSN:
2502-4752
5.
CONCLUSIONS
In
this
paper
,
we
propose
a
h
ybrid
wireless
communication
system
consisting
of
a
base
station
em-
po
wered
with
computing
capabilities,
ener
gized
using
green
ener
gy
,
and
a
LEO
satellite
system
for
pro
viding
mobile
services
to
visitors
within
a
protected
area.
The
main
goal
of
this
research
w
as
to
minimize
the
ener
gy
consumption
per
communication
site
with
a
guarantee
of
the
e
xpected
end-to-end
latenc
y
.
In
this
w
ork,
we
put
forw
ard
a
ne
w
tasks
of
oading
strate
gy
whereby
the
BS
can
handle
some
of
the
delay-sensi
ti
v
e
tasks
locally
or
of
oad
the
task
to
the
visible
satellite,
using
green
ener
gy
as
a
performance
metric.
T
o
sa
v
e
ener
gy
,
the
BS
system
forecast
the
short-term
ener
gy
a
v
ailability
and
then
pro
v
i
sion
the
computing
resources
base
on
the
fore-
casted
ener
gy
,
and
to
guarantee
the
end-to-end
delay
the
access
control
application
on
the
edge
serv
er
decide
on
the
fraction
of
w
orkloads
to
be
computed
locally
or
of
oaded
to
the
satellite.
Our
numerical
results,
obtained
with
real-w
orld
datasets,
sho
w
via
simulations
that
our
proposed
of
oading
strate
gy
(LO
+
A
O),
which
mak
es
used
of
foresighted
optimization
in
terms
of
green
ener
gy
to
be
harv
ested,
pro
vision
of
computi
ng
resource,
and
the
e
xpected
tasks,
can
be
able
to
guarantee
the
end-to-end
delay
e
xpected
from
applications
when
compared
with
our
benchmark.
The
ener
gy
sa
vings
obtained
by
LO
+
A
O
ranges
from
59
%
to
34
%
and
LO
ranges
from
52
%
to
30
%
.
It
is
observ
ed
that
local
computation
(LO)
bears
a
near
real-time
delay
of
0
.
83
seconds
when
compared
to
the
application
delay
requirement
of
0
.
8
seconds
(
σ
).
In
terms
of
drop
rate,
LO
+
A
O
achie
v
es
a
maximum
drop
rate
of
8
.
3
%
and
a
minimum
of
2
.
6
%
whereas
DO
achie
v
es
a
maximum
drop
rate
of
12
.
9
%
and
minimum
of
9
.
9
%
.
It
is
observ
ed
that
our
proposed
strate
gies
perform
better
(drop
rate
of
<
10
%
)
when
compared
to
direct
of
oading
to
the
selected
satellite.
As
part
of
our
future
w
ork,
we
will
design
more
sophisticated
of
oading
strate
gies
that
include
long
term
dependencies
in
terms
of
ener
gy
forecasting,
other
forecasting
methods,
and
the
consideration
of
other
NTNs
such
as
high
altitude
platforms.
REFERENCES
[1]
G.-P
.
Nuria,
et
al.
,
“The
inte
grated
sensing
and
communication
re
v
olution
for
6G:
V
ision,
techniques,
and
applicat
ions,
”
Pr
oceed-
ings
of
the
IEEE
,
v
ol.
112,
no.
7,
pp.
676-723,
2024.
https://doi.or
g/10.1109/JPR
OC.2024.3397609.
[2]
J
.
Y
.
Kim,
T
.
K
ubo,
and
J.
Nishihiro,
“Mobile
phone
data
re
v
eals
spatiotemporal
recreational
patterns
in
conserv
ation
areas
during
the
CO
VID
pandemic,
”
Scientic
Reports
,
v
ol.
13,
no.
20282,
2023.
https://doi.or
g/10.1038/s41598-023-47326-y
.
[3]
“
BEREC
and
RSPG
joint
r
eport
on
F
acilitating
mobile
connectivity
in
”c
halleng
e
ar
eas
”,
”
BEREC,
R
¯
ıg
a,
Latvia,
T
ech.
Rep.,
Dec
2017.
[4]
T
.
Dlamini,
´
A.
F
.
Gamb
´
ın,
D.
Munaretto,
and
M.
Rossi,
“Online
supervisory
control
and
resource
management
for
en-
er
gy
harv
esting
BS
sites
empo
wered
with
computation
capabilities,
”
W
ir
eless
Communications
and
Mobile
Computing
,
2019.
https://doi.or
g/10.1155/2019/8593808.
[5]
L.
Chen,
S.
Zhou,
and
J.
Xu,
“Ener
gy
ef
cient
mobile
edge
computing
in
dense
cellular
netw
orks,
”
in
IEEE
Internati
onal
Confer
ence
on
Communications
(ICC)
,
P
aris,
France,
May
2017.
https://doi.or
g/10.1109/ICC.2017.7997128.
[6]
G.
Marco
and
Z.
Michele,
“Non-T
errestrial
netw
orks
in
the
6G
era:
Challenges
and
opportunities,
”
IEEE
Network
,
v
ol.
35,
no.
2,
pp.
244–251,
2021.
https://doi.or
g/10.1109/MNET
.011.2000493.
[7]
T
.
T
aheri,
R.
Nilsson,
and
J.
v
an
de
Beek,
“The
potential
of
massi
v
e-MIMO
on
TV
to
wers
for
cellular
co
v
erage
e
xtension,
”
W
ir
eless
Communications
and
Mobile
Computing
,
2021.
https://doi.or
g/10.1155/2021/8164367.
[8]
C.
Abdelaali,
et
al.
,
“6G
for
bridging
the
digital
di
vide:
W
ireless
connecti
vity
to
remote
areas,
”
IEEE
W
ir
eless
Communications
,
2021.
https://doi.or
g/10.1109/MWC.001.2100137.
[9]
N.
Dinh,
et
al.
,
“6G
internet
of
things:
A
comprehensi
v
e
surv
e
y,
”
IEEE
Internet
of
Things
J
ournal
,
v
ol.
9,
pp.
359–383,
2022.
https://doi.or
g/10.1109/JIO
T
.2021.3103320.
[10]
C.
Pietro,
G.
Alberto,
M.
Mario,
and
P
.
F
abio,
“Orbital
edge
of
oading
on
me
g
a-LEO
satellite
constellations
for
equal
access
to
computing,
”
IEEE
Communications
Ma
gazine
,
v
ol.
60,
no.
5,
pp.
32–36,
2022.
https://doi.or
g/10.1109/MCOM.001.2100818.
[11]
B.
Soret,
I.
Le
yv
a-Mayor
g
a,
S.
Cioni,
and
P
.
Popo
vski,
“5G
satellite
netw
orks
for
internet
of
things:
Of
oading
and
backhauling,
”
In-
ternational
journal
on
Satellite
Communications
and
Networks
,
v
ol.
39,
no.
4,
pp.
431–444,
2021.
https://doi.or
g/10.1002/sat.1394.
[12]
A.
Bonora,
A.
T
raspadini,
M.
Giordani,
and
M.
Zorzi,
“Performance
e
v
aluation
of
s
atellite-based
data
of
oading
on
Starlink
constellations,
”
in
IEEE
W
ir
eless
Communications
and
Networking
Confer
ence
(WCNC)
,
Milan,
Italy
,
Mar
2025.
https://doi.or
g/10.1109/WCNC61545.2025.10978515.
[13]
M.
L
´
opez,
S
.
Damsg
aard,
I.
Rodr
´
ıguez,
and
P
.
Mogensen,“Connecting
rural
areas:
an
empirical
assessment
of
5G
T
errestrial-LEO
Satellite
Multi-Connecti
vity,
”
IEEE
V
ehicular
tec
hnolo
gy
confer
ence
(VTC
)
,
Florence,
Italy
,
Aug
2023.
https://doi.or
g/10.1109/VTC2023-Spring57618.2023.10199206.
[14]
X.
Jie
and
R.
Shaolei,
“Online
learning
for
of
oading
and
autoscaling
in
rene
w
able-po
wered
mobile
edge
computing,
”
in
IEEE
Global
Communications
Confer
ence
(GLOBECOM)
,
W
ashington,
USA,
De
c.
2012.
https://doi.or
g/10.1109/GLOCOM.2016.7842069.
[15]
T
.
Dlamini
and
S.
V
ilakati,
“LSTM-based
traf
c
load
balancing
and
resource
allocation
for
an
edge
system,
”
W
ir
eless
Communica-
tions
and
Mobile
Computing
,
2020.
https://doi.or
g/10.1155/2020/8825396.
[16]
“Mobile
edge
computing
(MEC):
Location
API,
”
ETSI,
Sophia-Antipolis,
France,
T
ech.
Rep.,
Jul
2017.
[17]
K.
J.
Y
oon,
“Point
location
of
visitor
centers
in
National
P
arks
and
Ramsar
sites
in
Japan
(Data
set)”,
Zenodo:
https://doi.or
g/10.5281/zenodo.10066858.
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
41,
No.
3,
March
2026:
935–945
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