Enabling the
Big Earth
Observation
Data via Cloud
Computing and
DGGS:
Opportunities
and
Challenges [1]
The sheer
volume of
BEOD poses
significant
storage and
processing
challenges.
Cloud
computing
provides a
scalable and
costeffective
solution to
handle everincreasing
data
volumes.
With cloudbased
storage and
processing
capabilities,
BEOD can
be efficiently
stored,
managed,
and
analyzed
without the
limitations of
traditional
hardware
and software
infrastructure
.
Traditional
information
system
architecture
is not able to
handle the
challenges
of big Earth
observation
data in terms
of storage,
processing,
and analysis.
Spatial cloud
computing
(SCC) was
proposed to
Qualitative
Research
Narrative
Research
Big Earth
observation data
(BEOD) is a rapidly
growing field with
the potential to
revolutionize the
way we collect,
manage, and use
Earth observation
data. However, the
challenges of
storing, processing,
and analyzing
BEOD are
significant.
Cloud computing
provides a scalable
and cost-effective
platform for
handling BEOD.
Cloud computing
platforms offer a
variety of services,
such as
infrastructure as a
service (IaaS),
platform as a
service (PaaS), and
software as a
service (SaaS),
which can be used
to develop and
deploy applications
for BEOD
processing and
analysis.
The trinity solution
of BEOD, cloud
computing, and
DGGS has the
potential to;
Improve the
efficiency and
scalability of BEOD
processing and
analysis, reduce
the costs of BEOD
The trinity of big
Earth observation
data (BEOD), cloud
computing, and
discrete global grid
systems (DGGS) is a
promising
approach to
address the
challenges of
storing, processing,
and analyzing
BEOD.
The trinity solution
has the potential to
improve the
efficiency and
scalability of BEOD
processing and
analysis, reduce the
costs of BEOD
management and
utilization, enhance
the development
and deployment of
BEOD applications,
and open up new
possibilities for
BEOD-based
research and
innovation.
The trinity solution
has the potential to
improve the
efficiency and
scalability of BEOD
processing and
analysis, reduce the
costs of BEOD
management and
utilization, enhance
the development
and deployment of
BEOD applications,
and open up new
possibilities for
BEOD-based
handle the
endemic
problems
from spatial
data model,
but it does
not have a
standard
spatiotempor
al unified
framework
management
utilization,
enhance the
development and
deployment of
BEOD applications,
open new
possibilities for
BEOD-based
research and
innovation
research and
innovation.
IoT enabled
cancer
prediction
system to
enhance the
authentication
and security
using cloud
computing [2]
•
•
•
Security and
privacy of
patient data:
The paper
proposes a
system that
uses AES
encryption to
encrypt
patient data
before it is
stored in the
cloud. This
helps to
protect
patient data
from
unauthorized
access.
Scalability
and flexibility
of the
healthcare
system: The
paper
proposes a
system that
uses cloud
computing to
store and
process
patient data.
This makes
the system
scalable and
flexible, as it
can be easily
expanded to
meet the
needs of a
growing
patient
population.
Costeffectiveness
of
healthcare:
The paper
proposes a
system that
uses cloud
computing to
reduce the
Qualitative
Research
Narrative
Research
Historical
Research
Case study
The proposed
system can collect
data from sensors
in a human body
and store it in the
cloud securely.
The proposed
system is able to
encrypt patient
data using AES
encryption.
The proposed
system can use
machine learning
algorithms to
analyze patient
data and predict
the risk of cancer.
The proposed
system is scalable
and flexible, and it
can be easily
expanded to meet
the needs of a
growing patient
population.
The proposed
system is costeffective, and it
can reduce the
costs of storing
and processing
patient data.
The combination of
IoT devices and
cloud computing
offers an approach
for enhancing
healthcare systems,
particularly in
predicting cancer.
Cloud computing
provides a secure
and scalable
platform for storing
and processing
patient data,
ensuring data
privacy and
facilitating data
analysis.
costs of
storing and
processing
patient data.
This can
make
healthcare
more
affordable
for patients.
Efficient
resource
management
and workload
allocation in
fog–cloud
computing
paradigm in
IoT using
learning
classifier
systems [3]
•
•
•
Long delays
in workload
processing:
Transmitting
large
volumes of
data to the
cloud can
cause
significant
delays in
processing
workloads.
This is
because the
data must
travel over
long
distances,
which can
introduce
latency.
High energy
consumption
at the
network
edge:
Processing
workloads at
the network
edge can
reduce
delays, but it
also
increases
energy
consumption
. This is
because
edge
devices
typically
have limited
battery
power.
Imbalance
between
delays and
power
consumption
: Existing
methods for
workload
Quantitative
Research
Experimental
Research
Balancing delays
and power
consumption is
possible: The
proposed method,
which utilizes an
extended classifier
system (XCS), can
effectively balance
delays and power
consumption in fog
computing.XCS is
an effective tool
for load
distribution:
XCS is a suitable
algorithm for
finding optimal
classifiers for
workload
allocation and
power
consumption. It
can adapt to
changing
environmental
conditions and
learn from
experience.The
proposed method
outperforms
existing methods:
The proposed
method
outperforms
existing methods in
terms of reducing
delays while
maintaining
acceptable power
consumption
levels.
The proposed
method can
recharge
renewable
batteries: The
proposed method
The proposed
method can
significantly reduce
delays in workload
processing
compared to
existing methods.
The proposed
method can
maintain
acceptable power
consumption
levels, even under
heavy workloads.
The proposed
method can
effectively recharge
renewable
batteries at the
network edge,
which can help to
reduce energy
costs and extend
battery life.
distribution
often focus
on one of
these
problems at
the expense
of the other.
For example,
methods that
prioritize
delay
reduction
may result in
excessive
energy
consumption
, while
methods that
prioritize
power
conservation
may lead to
increased
delays.
can recharge
renewable
batteries at the
network edge,
which can help to
reduce energy
costs and extend
battery life.
Transformative
effects of IoT,
Blockchain and
Artificial
Intelligence on
cloud
computing:
Evolution,
vision, trends
and open
challenges [4]
•
•
The need for
new
technologies
to enable
future cloud
applications:
Cloud
computing is
constantly
evolving,
and there is
a need for
new
technologies
to enable the
development
of nextgeneration
cloud
applications.
These
technologies
include IoT,
AI, and
Blockchain,
which are all
expected to
have a
significant
impact on
the future of
cloud
computing.
The need for
resource
optimization
and energy
efficiency:
Cloud
computing
systems
consume an
amount of
energy, and
there is a
need to
develop new
resource
scheduling
policies and
optimization
techniques
to reduce
Qualitative
Research
Narrative
Research
Phenomenolog
ical Research
Emerging
technologies like
IoT, AI, and
Blockchain will
have a significant
impact on the
future of cloud
computing. These
technologies will
enable new
applications and
services, and they
will also require
new approaches to
resource
management,
security, and
privacy.
There is a need for
new resource
optimization and
energy efficiency
techniques to
reduce the
environmental
impact of cloud
computing. Cloud
data centers
consume a large
amount of energy,
and there is a
growing concern
about their impact
on the
environment. New
techniques are
needed to reduce
energy
consumption
without
compromising
performance.
The future of cloud
computing will be
shaped by
emerging
technologies like
IoT, AI, and
Blockchain.
Optimizing
resource utilization
and achieving
energy efficiency
are crucial for
sustainable cloud
computing.
Enhancing
reliability and fault
tolerance is
paramount for
business-critical
applications.
energy
consumption
without
impacting
the quality of
service
(QoS). This
is becoming
increasingly
important as
ademand for
cloud
services
continues to
grow.