2019 IEEE Symposium on Computers and Communications (ISCC)
Leveraging Mobile Edge Computing on Smart
Grids Using LTE Cellular Networks
Alex F R Trajano∗ , Antonio Alex Monteiro de Sousa† , Emanuel B Rodrigues‡ ,
José Neuman de Souza§ , Arthur de Castro Calladok and Emanuel F Coutinho∗∗
Instituto Atlântico - Fortaleza - Brazil
Universidade Federal do Ceará - Fortaleza - Brazil
Email: ∗ alex_ferreira@atlantico.com.br, † alex_sousa@atlantico.com.br, ‡ emanuel@dc.ufc.br,
§ neuman@ufc.br, k arthur@ufc.br, and ∗∗ emanuel.coutinho@ufc.br
Abstract—Given the advance of the Smart Grid technology,
the electrical grid is now in a process of transformation never
seen in history. This new grid model allows customers and
providers to generate and manage electrical power efficiently.
To support such transformation, a key element of the model
is the communication network responsible for delivering measurement data from sensors on the grid to the applications that
analyze and adapt distribution according to demands. This work
proposes and evaluates a system architecture that copes with
reliability and latency demands that are of much importance
for a successful Smart Grid deployment, also considering the
internal design of distributed applications in the context of Smart
Grids. The architecture adopts a Mobile Edge Computing based
infrastructure that is suitable for 4G and 5G mobile cellular
networks. Experiments made on NS-3 shows that the proposed
system architecture is capable of handling a realistic number of
smart meters while providing low latency and network reliability,
supporting many use cases of a Smart Grid deployment.
I. I NTRODUCTION
Smart Grid (SG) is a new generation of electric power generation, transmission and distribution system that has emerged
with the purpose of making more efficient management of
electrical resources. It presents features unseen in the traditional systems, such as two-way communications, real-time
monitoring, self-healing and generalized control [1]. Several
countries that are investing in renewable and clean energy see
SG as an essential paradigm that allows a more efficient way
to implement it and some of these countries are also investing
in SG programs, initiatives and research. Despite the benefits
of using SG and its accelerated adoption, some barriers have
been found, especially the high cost of implementation.
There are some challenges in the adoption of SG such as the
requirement of processing large amounts of data generated by
numerous smart meters. A smart meter is a device responsible
for measuring several data from the electrical network of a
house, business or industry, sending the data to a Meter Data
Management System (MDMS) that is commonly under the
infrastructure of the electrical distribution company. All the
data stored at the MDMS can be used for extracting near
real-time data about customer billing, distribution efficiency,
electricity quality and other parameters [2]. Thus, it requires a
robust infrastructure with storage, processing and networking
resources, capable to cope with the demands. According to
Brazilian Institute of Geography and Statistics (IBGE), Rio
de Janeiro, the second largest Brazilian city, has around 5.2
million houses connected to the power grid, thus if each
house has a smart meter, the amount of data exchanged in the
infrastructure is high even with communication at low rates.
It would be possible to consider the smart meter a type of
Internet of Things (IoT) enabled device that can behave as a
sensor and actuator simultaneously.
Furthermore, it is also known by the electrical industry that
a robust SG deployment must also provide a communication
network with the lowest latency and the highest reliability
possible. That is a key feature from the SG because it may
improve the electrical network levels of efficiency by being
aware of events as soon as they happen. For instance, if the
SG communication network is able to provide low latency, it
enables the fast detection of short anomaly events, allowing the
distribution companies to adjust the supply chain accordingly,
helping to save energy, prevent outages and complying to legal
requirements regarding the energy quality. That also enables
use cases related to self-healing, load balancing and critical
alert notifications [3]. These features are important for efficient
management of the electricity system and contribute to the
clean and renewable energy policies that countries want to
develop to create a clean and sustainable environment.
On the other hand, Mobile Edge Computing (MEC) is an
emerging and promising technology that provides an environment in which Cloud Computing resources and capabilities
are placed at the edge of a mobile network, operating within
the Radio Access Network (RAN), much closer to devices and
end users. MEC is a new technology which is currently being
standardized by the European Telecommunications Standards
Institute (ETSI) [4]. Due to the fact that it is within the
RAN, the users’ experienced latency tends to be dramatically
reduced given the fact that the resources being consumed are
much closer than it would be if it was in a traditional data
center. Moreover, the shortest the distance between users and
resources, the better the reliability of certain services, since the
number of points of failure tends to be lower. To that extent,
MEC is envisioned as one of the key technologies for 5G
cellular networks, even though it could also be applied to 4G
cellular networks, once it is more related to the management
of resources than the RAN itself [4].
Considering the challenges of the implementation of SG and
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2019 IEEE Symposium on Computers and Communications (ISCC)
the potential of MEC, this work approaches the challenge of
obtaining a low latency and reliable communication network to
support a large amount of data generated by thousands of smart
meters in realistic SG deployments. The architecture leverages
MEC to provide SG application services at the mobile edge
on a Long Term Evolution (LTE) cellular network.
The rest of the paper is structured as follows. Section II
presents the related work. Section III introduces basic concepts
around SG and MEC. Section IV presents the proposed
architecture. Section V shows the experimental results and in
Section VI presents conclusions and ideas for future work.
the proposal is compelling. However, the scheme proposed by
the authors is only addressed to the intelligent transport system
applied on a road while our proposal is a generic architecture
that addresses broader deployments.
In summary, this work has the following topics as the main
contributions to the field:
•
•
•
II. R ELATED W ORK
In the literature, there are some works related to the use of
Edge Computing applied to SG to solve problems related to
processing a large amount of data generated by smart meters
and improve latency. They are described next.
In [5], authors suggest the adoption of MEC for communication networks, exploring the impact of MEC on the delay
of messages, response time and transfer rate to handle a large
amount of data generated in a specific SG environment. In
this work, MEC is suggested along with vehicular delaytolerant networks to transfer and to process this large volume
of data. As results obtained from the executed experiments,
there were improvements in the performance, response time
and in the delay. However, this work was limited only to
solving problems inherent to electric vehicles only, not being
a generic solution and that could cover the different complex
cases existing in a SG environment. Our proposal presents
a more generic solution that can be applied to different use
cases, however, our architecture may not be suitable for rural
areas due to poor mobile network coverage in these areas.
The authors in [6] present Fog Computing as a model to be
applied in SG to handle a large mass of data processing and
also provide a low latency network. The authors presented a
scenario and concluded that due to the geographical distribution of the model, it is possible to obtain a low latency and
other benefits provided by the technology. In this scenario, a
three-layer architecture is proposed, in which the first layer is
formed by the smart meters that are responsible for collecting
data and communicating with other devices. The second layer
consists of fog servers that have the responsibility of performing processing and connecting smart meters to the cloud.
The last layer consists of a cloud server that aggregates and
stores data. This work is a strong candidate to optimize SG,
however, no satisfactory experiments were made to assess the
performance of the proposed solution. In this paper, we present
the results of our experiments to show that our proposal offers
a low latency environment for SG deployments.
Energy efficiency and low latency for IoT applications were
the focus of another work [7]. A mobility-aware hierarchical
framework using a cellular network is proposed, in which
MEC is composed of two layers with MEC servers. When
the first tier servers are overloaded, they can unload their jobs
to the second layer that has a backup server. The experiments
returned good results in relation to the latency, showing that
A solution that provides a network that copes with SG
low latency and reliability requirements.
A generic architecture that could also be applicable to
other IoT applications with similar requirements to SG.
Simulation experiments to assess the proposal performance considering realistic parameters and environment.
III. BACKGROUND
A. Smart Grid
Smart Grid is a new paradigm for electric power systems
that use Information and Communications Technologies (ICT)
for efficient, reliable and flexible management in the generation and distribution of electricity [8]. There are three key
aspects that define a SG:
•
•
•
Distribution and transmission systems that provide transparency and control of their resources.
Renewable sources of energy, distributed energy generation and power storage on both sides of the meters.
The capacity to dynamically respond to demand.
In order to achieve these three key aspects, SG relies on
ICT to integrate the management systems and the electric
infrastructure in an automatized way. To do so, it includes the
utilization of sensors and actuators at the power lines and on
consumer premises, along with a reliable two-way communication infrastructure with wide coverage for the devices. These
devices usually communicate with the MDMS, performing
real-time monitoring of the network and adjusting the energy
production and distribution according to the current demands.
Smart meters, as shown in Figure 1, are intelligent devices
installed at the houses of end consumers that collect realtime measurement data and enable remote operations, making
management of the electrical system efficient. These data often
include the amount of consumed energy, the current voltage
and amperage, and many other parameters [9]. Communication
from smart meters to MDMS can be done with different
wired or wireless network technologies data [10]. Certain SG
functionalities require low latency and reliability [1], such
as real-time monitoring for efficient management of power
generation and distribution, energy balancing, sending of alerts
and handling of failures.
With the emergence of IoT, various devices and sensors act
on the network collecting data and triggering events for the
automation of different activities without human intervention
[11]. As mentioned earlier, smart meters are smart devices
that take measurements and can also behave like actuators, so
we can see them as IoT devices. Low latency is also seen as
an important requirement for certain functionalities in the IoT
environment [12]. Based on this, we can say that low latency
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2019 IEEE Symposium on Computers and Communications (ISCC)
Aggregator
MDMS
Wide Area Network
Aggregator
Figure 1. SG deployment scenario for electrical energy distribution for
residencies. A communication network must be able to measure data coming
directly from smart meters installed at each house and aggregator devices.
in SG would be a subproblem of IoT and that our proposal
may also benefit other IoT applications.
The latency requirements for some SG critical application
use cases such as Distribution Automation, Wide-Area Situational Awareness and Substation Automation lies between
20 and 200 milliseconds, while other use cases do not have
any latency requirement [2]. Not surprisingly, critical SG
use cases also require network reliability, which means that
no losses should be admitted. The work [13] presents some
communication technologies that could be used by SG along
with their expected latency, showing that supporting the critical
use cases is a challenging task to accomplish.
Figure 2. Overall organizational view of MEC components in a LTE mobile
cellular network [17]. The MEC server directly connected to the eNodeB
guarantees the latency improvement to UEs due to the shorter distance when
compared to the traditional data centers.
5G is being developed with significant features to meet
the high consumer demand imposed by the increase in the
number of connected devices that according to Gartner it will
reach 20.4 billion devices by 2020. For both 4G and 5G
optimization, other technologies such as Network Functions
Virtualization (NFV) and Network Slicing can be used. Network Slicing allows custom and isolated network structures to
be delivered to specific groups of devices or end users [18],
while NFV allows the implementation of multiple Virtualized
Network Functions (VNFs) over a common physical network
[19], making it a great tool for the construction of these
network slices. These technologies could also leverage the use
of MEC since different services can be offered to different
groups of end users. In the case of a SG scenario, a specific
network slice could be offered to meet the needs of the
electricity sector.
B. Mobile Edge Computing in Cellular Networks
MEC is an emerging technology that enables features found
in cloud computing and in edge computing [14]. MEC works
within the RAN [15] and due to the adoption of edge computing, it allows offering services closer to mobile devices and
end users. There is another technology similar to MEC, known
as Fog Computing, that also offers services close to end users.
However, Fog computing performs processing on a Fog node
or in an IoT gateway within a Local Area Network (LAN),
while MEC performs the processing on servers that are within
a RAN [16]. Figure 2 presents a brief description of how a
MEC server is placed in the mobile network, providing low
latency to User Equipments (UEs).
Since MEC operates within a RAN, mobile telecommunication technologies can be used. The widespread 4G already
achieves good bandwidth rates, while the incoming 5G will
allow much higher ones, thus making MEC a very good option
when high bandwidth rates are required. Therefore, MEC is
an excellent alternative to meet the low latency requirement
of SG due to the proximity of resources offered by the edge.
The use of edge servers in mobile networks can also be useful
for Narrowband Internet of Things (NB-IoT), which has also
been suggested as an option to support SG networking, even
not supporting low latency requirements of some common SG
use cases [13].
IV. P ROPOSED S YSTEM A RCHITECTURE
This work proposes a system architecture that builds a
reliable communication network to be used in a SG deployment. As mentioned earlier, SG needs a network capable of
delivering messages with low latency in the widest coverage
possible, since smart meter devices span a whole city. Given
these requirements, this work proposes the use of a mobile
cellular network of 4th or 5th generation. That seems to be
a good approach because cellular networks evolve in a fast
pace, and it is envisioned that 5th generation networks will be
able to give better support for numerous IoT devices.
Although 5th generation cellular networks are envisioned
to be an important enabler for supporting a large number of
IoT devices, SG can be extended to support 4th generation
cellular networks as well, by adopting MEC on existing LTE
infrastructure. This way, Mobile Network Operators (MNOs)
can provide support for future SG deployments, which in turn
could benefit from the advantages of using a cellular network,
as discussed in Subsection IV-A.
The proposed architecture is presented in Figure 3. It consists of a MEC architecture in which the servers are allocated
in base stations, where each base station covers a certain
geographic perimeter with a number of houses. Each house
has a smart meter that performs bidirectional communications
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2019 IEEE Symposium on Computers and Communications (ISCC)
with MEC through wireless cellular communication. Edge
servers can communicate with a cloud over a wired or wireless
network so measurement data can be sent to MDMS. In
addition, data transmitted to MEC can be stored at the edge,
making it available to users if needed.
tion interfaces to receive and send data. In this illustration,
application A in MEC Server A can communicate with the
cloud through the cloud interface, with smart meters through
the meter interface and with another application A in MEC
Server B through the side interface.
MDMS
MEC Server A
MEC Server B
Cloud Interface
Smart Meter
Cloud
Smart Grid
Application A
Side
Interface
Side
Interface
Smart Grid
Application A
Smart Meter
Smart Meter
Aggregator
Cloud Interface
Smart Meter
DB
SG
Application
DB
SG
Application
Smart Meter
Meters Interface
Meters Interface
Cloud Interface
Cloud Interface
Aggregator
Smart Grid
Application B
Side
Interface
Side
Interface
Smart Grid
Application B
Smart Meter
MEC Server
MEC Server
Meters Interface
Smart Meter
Meters Interface
Smart Meter
Figure 4. The internal design of a SG application on a MEC server.
Device
Sensor
Sensor
Device
Figure 3. Overview of the proposed SG communication network using MEC
servers in a cellular network infrastructure. Smart meters measurement data
flow to the applications within MEC servers, which in turn can be sent over
the cloud heading the MDMS.
In addition to intelligent meters, other devices, such as
measurement sensors, actuators, and even devices manipulated
by authorized users, can interact with services that can run on
the MEC. There are some examples of devices that connect
to the SG that would benefit from the proposed architecture:
• Smart meters can transmit measurement data with realtime measurement records, as well as trigger alarms and
events that can be transmitted to the MEC for storing and
processing data, as well as handling events or sending
notifications;
• Measuring sensors and actuators can collect light measurement data and transmit this data to the MEC that
can store and/or process the collected data making public
lighting management more efficient.
In the proposed architecture, applications are deployed in
MEC servers within the MNO infrastructure, which means a
new type of business for the MNOs, which can bill the electrical distribution companies that use their resources. Besides,
SG traffic would be handled in the same infrastructure that
also supports traditional mobile users, which may incur in a
huge overload of the network. Fortunately, MNOs can rely on
Network Slicing to make sure business needs can be met by
the virtual separation of resources in the mobile network.
Figure 4 shows the internal design of applications that
can run on edge servers and their communication interfaces.
Servers can run one or more applications that can communicate with smart meters or other devices, with the cloud or
with each other. The figure shows two servers, where each
server is running both applications, A and B, and applications
can execute specific processes and have specific communica-
The need for a side interface in applications is to allow
the development of more complex distributed applications
that may be used to offload the MDMS servers. There are
situations in which problems in the electrical network can
be solved locally. For instance, consider a neighborhood that
is facing minor power surges due to some local event that
happened shortly. In such example, applications in MEC
servers can directly connect to the neighborhood’s distribution
substation and notify the distribution controllers to change
its parameters accordingly so problems can be repaired in
a much shorter time than it would be by the MDMS. This
way, applications can communicate between themselves within
the mobile cellular network using the side interface, which
is the basis for supporting distributed systems on the MEC
layer. Applications deployed in such infrastructure should be
designed with the same principles that are commonly used
in cloud environments, including provisioning, elasticity and
some software architectural patterns like microservices. That
is one of the reasons that a side interface is needed since it is
an enabler for such cloud computing techniques.
A. Advantages of Using the Proposed Architecture
The architecture presents some advantages that justify its
adoption to enhance the electrical system management in SG.
1) Low latency: With MEC, services are available near
devices and users, thus achieving low latency [20]. In the
power sector, it is important to achieve low latency for certain
features, such as power balancing, real-time monitoring, fault
handling, alerts, and self-healing [2].
2) Coverage of the mobile network in urban centers: The
existing mobile network infrastructure provided by telecommunication companies allows the implementation of the proposed architecture with a wide coverage area since cellular
networks typically cover most or all of the urban areas. Moreover, good coverage also minimizes network outage problems,
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2019 IEEE Symposium on Computers and Communications (ISCC)
which increases availability and provides better support for
delivering critical events detected on the SG.
3) Costs: Capital Expenditure (CAPEX) and Operational
Expenditure (OPEX) can be reduced in electric and telecommunication companies. Since energy companies can use the
existing telecommunications infrastructure, commercial contracts with telecommunications companies can be signed; this
way, the CAPEX for the implementation of the infrastructure is reduced. Likewise, OPEX can be reduced, since the
maintenance of the infrastructure of the telecommunication
networks would be done by the suppliers of the mobile
technology solutions. Another positive factor is that companies
in the electric sector that use SG for power management
could benefit from the optimization offered by the proposed
architecture, and thus may increase operational performance
and reduce costs with maintenance teams. Telecommunication
companies could also benefit from being able to sign contracts
for infrastructure services and the use of the mobile services
they can provide, as well as the possibility of signing an
agreement with the electric companies that can reduce power
consumption costs.
B. Disadvantages of Using the Proposed Architecture
1) SLA, QoS and Concurrency: Telecommunication companies offer services to mobile end users, generating a share of
resources. By sharing telecommunication resources with many
other users, concurrency may become a problem to be handled
by the telecommunication companies on periods of high demand. QoS could be affected since once the data throughput
is not at acceptable levels, low latency is not reached and
communication can disrupt. In addition, due to its novelty,
it is still uncertain how the commercial agreements between
energy companies and telecommunications companies would
be. Network Slicing [21] is an alternative for this case since
specific infrastructures can be made available to serve different
groups of users, ensuring service availability and good QoS
levels.
2) Coverage of the mobile network in rural areas: In some
rural areas, mobile network coverage is poor, which would
make the adoption of this architecture impracticable there.
Other communication techniques should be applied in this
scenario, since it may not be economically advantageous to
install the cellular network infrastructure needed to support
MEC in distant rural areas.
3) Costs: To use the mobile network features proposed
in this architecture, telecommunication companies must have
an infrastructure capable of supporting MEC servers at base
stations. Since MEC is still an emerging concept, telecommunication companies may have to invest in upgrading their
infrastructure to support MEC.
V. P ERFORMANCE E VALUATION
In order to evaluate the use of a cellular mobile network to
support SG demands, a simulation was designed to assess if
the network is able to support a large number of SG devices. In
the simulation, a 4th generation LTE infrastructure was used
to support a varying number of smart meters in a topology
extracted from an actual LTE deployment of a neighborhood
in the city of Fortaleza, Brazil. Figure 5 shows the base stations
locations in a map.
Figure 5. Google Maps view of Fátima neighborhood used in the simulation.
All LTE base stations are represented by the antenna icons, while the blue
spot represents the approximate center location in the neighborhood area.
A. Simulation Environment
The NS-3 version 3.28 has been used to implement the
experiments due to its native LTE module, which has good
support for LTE-based simulations. A machine with an Intel
Xeon E5-2670 processor with 8 cores and 24GB of memory
has been used. Simulations were repeated 30 times in order
to present results following the Central Limit Theorem. The
entire experiment took 10 hours to finish. The LTE base
stations and the smart meter LTE devices used the NS3 ConstantPositionMobilityModel with fixed positions. The
coordinates for the LTE base stations were obtained from
actual base stations deployed in the city, while the coordinates
for the smart meters were randomly generated with at a
maximum radius of 1km from the central coordinate (see
Figure 5). The LTE network was configured with parameters
shown in Table I, which are based on the ITU-R M.2135-1
report [22], following the urban macro-cell scenario.
Table I
PARAMETERS USED IN NS-3
Parameter
Transmission mode
Uplink bandwidth
Downlink bandwidth
Base station transmission power
Smart meters transmission power
Base station noise figure
Smart meters noise figure
LTE scheduler
Frequency reuse algorithm
Radio propagation model
Antenna model
Antennas orientation
Antenna maximum gain
SIMULATION
Value
MIMO Multi-User
20 MHz
20 MHz
49 dBm
24 dBm
5 dB
7 dB
Proportional fair scheduler
Soft frequency reuse
COST-Hata-Model
Cosine antenna model
0, 120 and 240 degrees
17 dB
The simulation goal was to measure how a network with
the following characteristics performs:
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2019 IEEE Symposium on Computers and Communications (ISCC)
Each LTE base station has a MEC server with a SGenabled application deployed and running on it.
• The closest MEC server is the one directly connected to
the eNodeB the smart meter is connected to. Thus, the
server selection is directly affected by the LTE algorithms
responsible for allocating UEs to base stations.
• N smart meters are sending measurement data to their
MEC server. The data is sent through UDP datagrams.
• There might be aggregator devices capable of gathering
data from near smart meters and sending data in batch
to the closest MEC server. The number of aggregator
devices is determined by the aggregation factor aF .
• Each smart meter sends data in fixed intervals of 1
minute.
Simulations have two variables: N , the number of smart
meters that are active in the network, and aF , the aggregation
factor representing the percentage of aggregation used in SG
deployment. An aggregator device behaves as a gateway that
receives data from near smart meters and sends it to the
closest MEC server, so that aggregated smart meters no longer
need to be directly connected to the cellular network. In
order to establish communication between smart meters and
aggregators, techniques such as Power Line Communication
(PLC) could be used [8]. It is easy to notice that the larger the
number of households and company buildings, the larger the
value of N . The value of aF is directly related to the planning
of the SG deployment. For instance, if a neighborhood is
known to be more vertical than others, it means that the
number of apartment blocks is higher, then it would be easy
to have an aggregator per each apartment block, considerably
reducing the number of devices sending data through the
cellular network. That way, the aF parameter tends to be
directly proportional to the population density of an area.
According to the last demographic census from IBGE, the
Fátima neighborhood has around 7000 homes in an area of
1186677m2 , thus with a high population density. Taking that
into consideration, the simulation tried to reproduce those
numbers in terms of N and aF . Figures 6 and 7 show the
obtained results for the average perceived delay and jitter from
the smart meters standpoint, with values of N equal to 2000,
4000, 6000, 8000 and 10000, and aF equal to 0%, 25% and
50%. A 95% confidence interval is used to calculate the error
margin. The delay, jitter and loss metrics have been measured
by the FlowMonitor component of NS-3, considering the
amount of time needed to send data from either smart meters or
the aggregator devices to the closest MEC server. Each smart
meter sends UDP datagrams at random intervals between 10
and 100 milliseconds and sizes uniformly distributed between
100 and 1000 bytes.
•
B. Achieved Results
Figure 6 shows the perceived average delay in milliseconds
on the communication between the smart meters and their
closest MEC server. One can notice that the aggregation factor
is an important parameter to be considered when designing
robust SG deployment. Depending on the delay requirements
of the SG applications, a 65-millisecond delay, such as seen on
the scenario with 10000 smart meters without any aggregator
device, can be considered high to some SG use cases. However, introducing an aggregation factor of 25% or 50%, the
delay drops to less than 40 ms or around 25 ms, respectively.
Figure 6. The average delay (in milliseconds) of messages sent from a number
of smart meters to their MEC server in different aggregation percentages.
Figure 7 shows the perceived average jitter in milliseconds
on the communication between the smart meters and their
closest MEC server. One can recognize this behavior as similar
to the delay results shown in Figure 6, showing how important
is the aggregation factor in actual SG deployments.
Figure 7. The average jitter (in milliseconds) of messages sent from a number
of smart meters to their MEC server in different aggregation percentages.
During the experiments, the NS-3 FlowMon component has
not reported any packet loss from the data sent by smart meters
to their closest MEC servers, regardless of N and aF values.
Such result could be explained by the simulation settings from
ITU-R and the number of base stations used in the simulation
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2019 IEEE Symposium on Computers and Communications (ISCC)
(see Figure 5), indicating that the number of simulated devices
are not enough to saturate the spectrum. This suggests the LTE
can be considered a reliable option for SG, at least in areas
with good network coverage.
VI. C ONCLUSION AND F UTURE W ORK
This work presented a system architecture for a SG communication network that is capable of providing a reliable
infrastructure that allows electrical energy distributors to deploy SG applications over MEC servers placed in traditional
LTE mobile cellular networks. The architecture allows SG
applications to run at the mobile network edge, precisely
at LTE base stations, providing low latency communication
to smart meters. The design also enables SG applications
to run as distributed systems at the edge, which facilitates
scalable applications to be developed on top of the proposed
architecture.
Simulation results show that the proposed system architecture is capable to deliver smart meter data with low latency
and high reliability. Since latency requirements of critical use
cases range from 20 to 200 milliseconds [2], it is noticeable
that the aggregation factor plays a major role in the full
support for such applications. By understanding the achieved
results and considering the official demographic data from
the Fátima neighborhood, it is perceptible that critical use
cases could be supported by the underlying LTE infrastructure
already deployed in the location if the smart meters rely on
a 50% aggregation factor, which is a tangible value given the
population density of that place.
The rise of 5th generation mobile cellular network may
become an important factor to consider when adopting MEC to
handle SG demands. The increase in the number of antennas
and the utilization of higher frequencies in the communication can benefit SG devices since there is more room for
improvement on interference levels, thus reducing latency and
increasing the number of users. Therefore, future work should
extend experiments to use 5G protocols. Another future work
is related to security. A big concern surrounding the adoption
of a shared mobile network is the risk of cyber attacks, which
may spy on and/or tamper with data, as well as trigger undue
actions affecting the electrical system (e.g., power shutdown
in a region).
Finally, a neighborhood of one of the largest Brazilian cities
was used as a case study to analyze the effectiveness of
the proposal. In this way, future work includes an extensive
analysis of demographic data from the whole country, in order
to trace the different profiles that are the target of SG, helping
a better and broader analysis of the obtained results.
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