1]Instituto de Telecomunicações, 3810-193 Aveiro, Portugal, Email:
<EMAIL_ADDRESS>2]ESTGA—Universidade de Aveiro, 3750-127 Aveiro,
Portugal 3]CFisUC, Universidade de Coimbra, Coimbra, Portugal 4]MDN,1400-204
Lisboa, Portugal 5]Instituto de Telecomunicações/ISEL-IPL, 1049-001 Lisboa,
Portugal 6]Departamento de Electrónica, Telecomunicações e
Informática—Universidade de Aveiro, 3810-193 Aveiro, Portugal

# New SST Optical Sensor of Pampilhosa da Serra – studies on image processing
algorithms and multi-filter characterization of Space Debris.

B. Coelho  D. Barbosa  M. Bergano  A.C.M. Correia  J. Freitas  P. Marques  J.
Pandeirada  V. Ribeiro  [  [  [  [  [  [

###### Abstract

As part of the Portuguese Space Surveillance & Tracking (SST) System, two new
Wide Field of View (2.3ºx2.3º) small aperture (30cm) telescopes will be
deployed in 2021, at the Pampilhosa da Serra Space Observatory (PASO), located
in the center of the continental Portuguese territory, in the heart of a
certified Dark Sky area. These optical systems will provide added value
capabilities to the Portuguese SST network, complementing the optical
telescopes currently in commissioning in Madeira and Azores. These telescopes
are optimized for GEO and MEO survey operations and besides the required SST
operational capability, they will also provide an important development
component to the Portuguese SST network. The telescopes will be equipped with
filter wheels, being able to perform observations in several optical bands
including white light, BVRI bands and narrow band filters such as H(alpha) and
O[III] to study potential different object’s albedos. This configuration
enables us to conduct a study on space debris
classification$/$characterization using combinations of different colors
aiming the production of improved color index schemes to be incorporated in
the automatic pipelines for classification of space debris. This optical
sensor will also be used to conduct studies on image processing algorithms,
including source extraction and classification solutions through the
application of machine learning techniques. Since SST dedicated telescopes
produce a large quantity of data per observation night, fast, efficient and
automatic image processing techniques are mandatory. A platform like this one,
dedicated to the development of Space Surveillance studies, will add a
critical capability to keep the Portuguese SST network updated, and as a
consequence it may provide useful developments to the European SST network as
well.

###### keywords:

LaTeX; ESA; macros

##  1 Introduction

The number of pieces of space debris — including broken pieces of artificial
satellites and wreckage from rockets — has been increasing yearly with the
progress of many country’s space sectors and rise in satellite launches. This
rising population of space debris increases the potential danger to all space
vehicles and in-space infrastructure, from expensive communications
satellites, Earth Observation satellite constellations, to the International
Space Station, space shuttles and other spacecraft with humans aboard. For
this reason, it is important to create a set of preventive measures in order
to avoid any damages to space satellites.

A network of optical systems using telescopes with large fields of view (FOV)
has proven to be a most effective way of surveying and tracking the ever
increasing debris population. In particular the orbit occupancy in LEO and MEO
orbit faces big challenges with planned deployment of space mega-
constellations requiring sensors with strong surveying and tracking
capabilities. But also the strategic GEO orbits do present a growing debris
population. Since by default GEO objects are stationary or slowly evolving in
the sky, surveying sensors with large FOV and higher integration times are
better suited.

Clearly, information on the orbital debris environment is crucially needed to
determine the current and future hazards that orbital debris poses to space
operations since the orbital environment is dynamic and in constant change.
Unfortunately, this environment is difficult to accurately characterize since
only the largest of debris can be repeatedly tracked by ground-based sensors.
Several studies have shown the advantage of multiple site data integration in
improving the accuracy of orbit prediction and the possibility of cataloging
small debris. In this sense the complementary between optical and radar
sensors do offer a very high value service to deliver good performance in
monitoring and tracking debris of many sizes and in a wide orbit range.[1]

In this article we described an new optical sensor to be installed at PASO
(Pampilhosa da Serra Space Observatory), which will contribute for the
Portuguese SST network, may be also an valuable asset for the Portuguese
participation in the EU-SST, and that will constitute a crucial platform for
research and development(R&D) of image processing algorithms and to conduct
multi-filter studies for characterization of space debris.

##  2 New SST Optical Sensor at PASO

The New SST Optical Sensor to be installed at PASO site. In the center of
Portugal, PASO is located in the municipality of Pampilhosa da Serra, where
ATLAS the First Portuguese Radar Tracking Sensor for Space Debris (see
Pandeirada et al. also in this conference proceedings). Being in the heart of
a Dark Sky certified region, PASO location has excellent all sky clearance, in
a hilltop at 840 m, and surrounded by mountain ranges that allow protection
from the light pollution of the coastal regions of the country. The conditions
are among the best in continental Portugal for Optical use, where sky
background reaches mag 21 or above and with more than 200 clear nights per
year.

This sensor will have dual use, SST (main focus) and Science. The sensor
configuration with twin Optical Tube Assemblies (OTAs) with large FOV is
optimized to operate in survey mode of Geosynchronous Equatorial Orbit (GEO)
and Medium Earth Orbit objects (MEO) in the context of the Portuguese SST
network. It will be able to perform observation in white light for SST
operations, but being equipped with filters B, V, R, I, H(alpha) and O[III]
the configuration capabilities are also fully suited for other uses, namely
debris characterization studies, observing techniques development and
scientific studies. It was also considered the flexibility of the system to
accommodate future upgrades, if needed, and for example to develop the
national capability towards LEOs observation in the future, through the use of
a fast EQ mount. The system will be provided and integrated by PrimaLuceLab
and can be seen figure 1.

Figure 1: Optical Sensor to be installed at PASO, consisting of two large FOV
telescopes, equiped with filters B,V, R, I, H(alpha), O[III], here seen inside
the dome. Image courtesy by PrimaLuceLab, the astronomical system provider and
integrator. The EQ mount has a very fast speed enabling space debris
surveillance.

In figure 2 can be seen a simulation of the FOV of 4ºx2.3º obtained with the
to telescopes observing contiguous fields at the same time, this configuration
is optimized for maximum sky coverage is excellent for SST survey observation
with white light. However, the system will allow also to use both telescopes
to image the same field of 2.3ºx2.3º, this allows to increase the signal to
noise when performing observations with filters, or even to do observation of
a given source in two different filters at the same time. This twin OTA
configuration also provides redundancy in the system, and constitutes an ideal
test platform for the development of instrumentation such as cameras allowing
comparison of devices in real similar conditions of observation.

Figure 2: FOV using the moon as a reference: in purple - a 4ºx2.3º field for
the survey mode, in which the telescopes observe two different fields at same
time; in yellow – a 2.3ºx2.3º FOV corresponding to each telescope, it can be
also observed by the two telescopes at the same time to increase the signal to
noise when performing observation with filters. (simulation obtained with
astronomy.tools)

##  3 Studies on multi-filter characterization of space debris

A development component within the SST effort, is the possibility to conduct
studies on space debris classification and characterization, using multi band
measurements, and in this way trying to obtain color indexes that can be
integrated in automatic classification processes, that can add extra, and
useful information for SST operations.

There are some works that showed that multi band observations can be used to
characterize Space Debris (eg. [2], [3], [4]). We intend to increase the
samples of objects observed in order to investigate the possibility of
statistically refine color indexes for automatic characterization of the
objects, at the same time study light curves not only in white light but also
in different filters, to infer rotation parterns.

##  4 Studies on image processing algorithms

The central product of an SST system is an object catalogue, which must
contain updated orbit information for all detected objects by the entire
network. The optical sensor proposed here will operate in Survey mode of GEOs
and MEOs objects contributing for the Portuguese SST network, complementing
the optical telescopes in Madeira and Azores.

The image processing pipeline will constitute the first of the development
fronts associated with the sensor. This is a hot topic in science, due to the
modern and future large infrastructures that are being prepared at
international level. The need for fast and effective image processing
pipelines are taking techniques and computing solutions to new levels of
performance and automation, adopting the principles of machine learning and
AI.

Figure 3: Overview of the platform for development on image processing
algorithms, and to conduct studies on multi-filter characterization of Space
Debris, here we intend to increase the samples in terms of the number of
objects, but also in terms of time variability of the colors.

In figure 3 is shown a scheme of the operation and development scenarios to be
applied.

The data acquisition is made at the telescope as images that can be part of
SST operations, or for development and research use. The results of the image
processing obtained in SST operations are going to be sent to the Portuguese
SST National Operation Center (client 1 in the figure 7). Images obtained for
development and science can be processed and used for example for the studies
of multi-filter classification of Space Debris. On the other hand we can
obtain images for Science programs that can be sent directly to the client 2,
or processed by observatory staff, with the results to be sent to the client
3. All these processes will involve interaction with the different clients,
allowing pipelines and procedures to evolve to meet their needs, in the most
effective way possible.

## Acknowledgments

The team acknowledges financial support from the European Commission H2020
Programme under the grant agreement 2-3SST2018-20.

## References

  * [1] ESA’s annual space environment report. Technical Note 2, ESA Space Debris Office, May 2018.
  * [2] Cardona T., Seitzer P., Rossi A., et al., (2016), BVRI photometric observations and light-curve analysis of GEO objects, Advances in Space Research, 58, 514-527
  * [3] Xiao-Fen Z., Hui-Yan Z., Young Y., et al., (2016), Advances in Space Research, 58, 2269-2279
  * [4] Lu Y., Zhang C., Sun R., et al., (2017), Advances in Space Research, 59, 2501-2511


# Appliance-Level Monitoring with Micro-Moment Smart Plugs

Abdullah Alsalemi, Yassine Himeur, Faycal Bensaali
Department of Electrical Engineering
Qatar University
Doha, Qatar
yassine.himeur@qu.edu.qa;a.alsalemi<EMAIL_ADDRESS>
&Abbes Amira
Institute of Artificial Intelligence
De Montfort University
Leicester, United Kingdom
<EMAIL_ADDRESS>
This paper has been accepted in SCA2020: The Fifth international conference on
Smart City Applications

###### Abstract

Human population are striving against energy-related issues that not only
affects society and the development of the world, but also causes global
warming. A variety of broad approaches have been developed by both industry
and the research community. However, there is an ever increasing need for
comprehensive, end-to-end solutions aimed at transforming human behavior
rather than device metrics and benchmarks. In this paper, a micro-moment-based
smart plug system is proposed as part of a larger multi-appliance energy
efficiency program. The smart plug, which includes two sub-units: the power
consumption unit and environmental monitoring unit collect energy consumption
of appliances along with contextual information, such as temperature,
humidity, luminosity and room occupancy respectively. The plug also allows
home automation capability. With the accompanying mobile application, end-
users can visualize energy consumption data along with ambient environmental
information. Current implementation results show that the proposed system
delivers cost-effective deployment while maintaining adequate computation and
wireless performance.

_K_ eywords Smart plug $\cdot$ domestic energy usage $\cdot$ energy efficiency
$\cdot$ recommender systems $\cdot$ micro-moments $\cdot$ internet of things.

##  1 Introduction

Undoubtedly, energy saving and energy security have become major contemporary
issues. We are facing an energy shortage that not only affects the world’s
economy, environment, and growth, but also results in global warming [1]. A
set of recent developments is about to change this picture and propose
effective energy policies. These policies explicitly or indirectly create
drivers that, from the point of view of business and end-users, are perceived
to be any activity taken by the energy-efficient system created. In Qatar, the
goal of the TARSHEED initiative is to raise awareness of energy-saving
activities and the unnecessary energy use in the country as a whole [2]. This
has been achieved across a variety of advertising promotions, standards, and
competitions [3].

Smart metering is instrumental in collecting and analyzing energy market data
[4]. The smart meter is an important tool for managing the energy consumption
curve efficiently. This calls for a connection with the quantity of usage and
the quantity of production, allowing for the substitution of flat-rate prices
with better strategies [5]. There is, therefore, a need for development and
standardization of metering schemes.

One of the main elements of smart metering is tracking the volume of the
system. This enables processing of unique data linked to each device without
the need for aggregation algorithms. This is why smart plugs can play a
significant role in the monitoring and operation of domestic appliances, where
end-users mount them for each device at a fairly low cost, and thus, obtaining
the benefit of precise calculation of power usage and home automation.

Throughout this sense, we introduce micro-moments, which are time-based time-
slots where the end-user uses an appliance or occupy a space (e.g. room,
corridor, hall, etc.) [6]. This concept enables the extraction of different
moments when the end-user engages in unhealthy energy consumption patterns,
making it easier to classify and produce recommendations to enhance the energy
efficiency of the household.

Throughout this article, we are presenting a micro-moment smart plug as part
of the broader $(EM)^{3}$ project, which seeks to use artificial intelligence
to achieve high domestic energy performance. The $(EM)^{3}$ smart plug enables
real-time power measurements to be collected in addition to environmental
information, such as temperature , humidity, and occupancy.

The remainder of this paper is structured as follows. Section 2 outlines the
latest research on smart plugs. Section 3 provides a description of the
broader $(EM)^{3}$ structure and its elements. The smart plug is described in
Section 4, and data visualization scenarios are described in Section 5. System
implementation results are expounded upon in Section 6. The paper is concluded
in Section 7.

##  2 Related Work

In this section, an overview of recent developments to smart plugs is
provided. First, Ahmed et al. proposed a smart plug prototype that measures
power consumption in home energy management systems [7]. It is assisted by a
Zigbee microcontroller. It has been demonstrated, from their findings, that
the suggested plug absorbs less power and achieves better precision in
contrast with the oscilloscope. In fact, the system provides the option to
connect/disconnect the attached device from the power supply.

In addition, Hajahan and Anand have suggested an Arduino microcontroller-based
smart plug that uses ENC28J60 for communication [8]. This is powered by a
split core style current transformer for non-invasive current calculation and
an Android-based user interface.

To alleviate the problems of peak shortages, Ganu et al. have suggested a
cost-effective smart plug that monitors the synchronization (by flipping on or
off) of loads to the grid at on or off on times [9]. This is done with the
help of real-time analysis and data collection [10]. It allows off-peak and
unpredictable scheduling by addressing user choice settings in addition to
grid load conditions.

Another research relevant to in-device detection, Petrovic et al. suggested a
smart plug for electrical load detection focused on the [11] active sensing
method. The input signal produced by the smart plug is modified before the
output is calculated in such a way that more distinct real-time data is
created. An artificial neural network is used to evaluate the approach to
specify classification performance metrics; accuracy and speed.

Environmental control is another important aspect. That is why Gomes et al.
have suggested a smart plug that is mindful of the environmental factors and
awareness of the device resource background [12]. The technique adopts a
multi-agent structure strategy that helps the agent to react to any
adjustments that arise and to communicate with other agents.

To make progress on the literature studied, the main contributions of this
paper are summarized as follows:

  * •

Modern application of the concept of micro-moments in the control of energy
use.

  * •

Design and implementation of a micro-moment based smart plug for collecting
contextual details, such as temperature, humidity, brightness and room
occupancy.

  * •

The smart plug enables edge computing features, such as the identification of
several devices attached to the smart plug.

##  3 The EM3 Energy Efficiency Framework

The Consumer Engagement Towards Energy Saving Behavior by means of Exploiting
Micro Moments and Mobile Recommendation Systems $(EM)^{3}$ platform has been
developed to promote customer behavioral improvement through increasing energy
use understanding [6]. The $(EM)^{3}$ system consists of the following
components [13]:

  1. 1.

Data Collection: gathers data dependent on micro-moments for power usage and
environmental monitoring [14].

  2. 2.

Classification: detects and analyzes abnormal energy patterns [15].

  3. 3.

Suggestions and Automations: provides personalized guidance to end-users to
endorse energy saving management activities coupled with recommended actions
for environmental change [16] .

  4. 4.

Visualization: upload results, observations and feedback in an accessible and
engaging manner via a mobile application.

Sensing devices play an essential role in capturing data in a given datastore
[17]. They are used for wirelessly uploading collected data to a testing
center at Qatar University (QU). The study lab consists of a variety of
testing cubicles, some of which are installed with sensing instruments to the
$(EM)^{3}$ storage server housed in the QU building. Each cubicle contains
data aggregated from monitors, screens and table lamps.

The No-SQL CouchDB cloud platform is used to store customer micro-moments and
usage levels, user expectations and resources, energy management guidelines
and ranking ratings. The dataset used in this study, Qatar University Dataset
(QUD), is compiled in a micro-moment laboratory at QU [17]. The set-up
collects environmental sensors (indoor temperature and humidity, room
luminosity and motion sensors) and power consumption for a number of
appliances (i.e. light bulb, computer). Data is collected using wireless
sensing modules that communicate in real-time to the backend. QUD contains
data points divided by a couple of seconds each. The dataset was collected
earlier before this work.

##  4 Proposed System Design

The $(EM)^{3}$ smart plug helps to combine both energy tracking at the device
level and the environmental details of the current household space. Data on
the energy usage of equipment is assumed to be the key piece of knowledge,
whereas environmental factors such as temperature, humidity, luminosity and
occupancy provide a background under which the knowledge on use is evaluated.
The system is composed of two sub-units: the power consumption unit and the
environmental monitoring unit.

Figure 1: Overview of the micro-moment smart plug.

###  4.1 Power Consumption Unit

Fig. 1 provides a description of the $(EM)^{3}$ smart plug. The power wire
from the grid travels into the smart plug and goes to the extension socket
where the appliance(s) can be worked. Smart plug modules calculate the current
used by the equipment attached to the extension cord and compare it with the
normal voltage of the nation in which it is worked. The HLW8012 intrusive
power sensor was used to calculate current values up to 20A with 5% tolerance.

###  4.2 Environmental Monitoring Unit

The environmental unit is used to measure temperature, humidity, luminosity
and occupancy of a given household’s room. The data is transmitted in real-
time to the backend for further processing.

The HC-SR501 motion sensor is used to assess space occupancy and the DHT-22
temperature and humidity sensor operates between -40-80∘C and 0-100% for
ambient temperature and relative humidity, respectively. Also, the light
sensor TSL2591, which can sense light in the range of 0.1-40,000 Lux. The
sensors are described in Table 1.

In addition, real-time synchronization between the chosen micro-controller and
the server side is accomplished where a delay of only a millisecond is
required. Real-time synchronization is checked by matching the time stamp on
the sensor-side microcontroller with the time stamp on the server-side.

Data obtained from various data collection programs were stored on the server
side. Data fusion algorithms are used to obtain data and provide a full
overview of the world [18]. In fact, it provides scalability if further
sensors are to be installed. Instead, machine learning classifiers are
educated on cumulative data where they can be used to grasp how energy use
looks like [19]. The efficiency of such classifiers can be checked by
measuring precision, memory, responsiveness, F1 score and accuracy, along with
a confusion matrix [20]. The recommendation framework [21], focused on these
algorithms, would communicate advice and recommendations persuading customers
of energy from their smartphones [22]. Once excessive energy consumption has
been detected, suggestions are sent to users to advise them on how to reduce
it. The simulation of energy consumption data, the use of existing datasets
and the setting up of the actual environment are all used to check the
validity of the recommendations provided.

In addition, since the smart plug can be connected to a socket extension,
multiple appliances can be connected and identified. It can inhibit the
accuracy of understanding what device absorbs how much electricity. For this
purpose, we are focusing on the implementation of a range of appliance
recognition algorithms to recognize the appliances used with high precision
[15]. It is noteworthy to mention that the internal power consumption of the
plug is designed to minimally affect the overall consumption and multi-
appliance identification is under-development.

Table 1: Smart plug sensors Name |  Description |  Components Used
---|---|---
Energy monitoring |  Measures appliances power consumption in Watts. |  ACS712 invasive Hall effect current sensor
Occupancy |  Detects whether room is occupied. Selected for accuracy. |  AM312 motion sensor
Temperature and humidity |  Measures indoor ambient temperature. Selected for cost-efficiency and adequate accuracy. and relative humidity |  DHT22
Luminosity |  Measures room’s luminosity in Lux. Selected for accuracy and wide range. |  Adafruit TSL2591

##  5 Consumer Data Visualization

Figure 2: The $(EM)^{3}$ mobile application: (a) the electric power
consumption, (b) air-related consumption, and (c) appliance control screens.

After data collection and preliminary data analysis, data visualization is
provided through a mobile application where users can actually see their
consumption. In addition, meaningful information and data are provided to
moderate users’ behavior towards energy efficiency. In this way, the smart
plug data is easily displayed and the signals behind it are shown.

The $(EM)^{3}$ smartphone device seeks to encourage energy conservation by
visualizing smart plug data in real-time. Fig. 2 shows the main screens of the
first version of the $(EM)^{3}$ mobile app. Line plots are commonly used to
provide data along with a tiny overview that offers supplementary detail on
the chosen device (as seen in Fig. 2a and 2b). Additional energy efficiency
standards are shown in 2a and ambient environmental information (i.e. indoor
and outdoor temperature and humidity, indoor illumination and room occupancy).

A data visualization analysis has been undertaken to evaluate the right
visualization for energy end-users in order to improve the field of data
visualization. Best visualizations for the current usage of energy data
visualization are suggested111http://em3.qu.edu.qa/index.php/data-
visualization-app.

##  6 Results

This segment outlines the latest implementation and performance of the new
smart micro-moment connection. In terms of configuration, the smart plug
comprises a printed circuit board (PCB), a 3D-printed plastic casing, a
connector and an extension of the outlet. The results are described for both
system’s sub-units.

###  6.1 Power Consumption Unit

As the current smart micro-moment plug requires several devices to be linked
at the same time, it is of vital importance to incorporate an appliance
identification program that can recognize each unit utilizing its power
consumption signature. In this regard, we are presenting a simple but
successful method focused on first detecting appliance events using a
cepstrum-dependent detector defined in [15]. Following, a combination of two
time-domain attribute extraction algorithms, including root mean square (RMS)
and mean absolute deviation (MAD) is used to identify each linked system
utilizing observed events.

In this way, various machine learning algorithms are deployed to identify five
types of appliances utilizing specific parameter settings, namely support
vector machine (SVM), K-nearest networks (KNN), decision tree (DT), deep
neural networks (DNN) and decision bagging tree (DBT). In fact, a 10 USD
cross-validation contract has been used to test the existing device
recognition method. Table 2 shows the accuracy and the results of the F1 score
obtained using the proposed summation based fusion technique compared to the
use of the RMS and MAD descriptors, separately. It is clear that this fusion
strategy can improve identification accuracy by 2.46% and 2.38% compared to
MAD and RMS, respectively. In comparison, the F1 value was improved by 3.34%
and 2.59%, respectively, relative to MAD and RMS. It is worth mentioning that
smart plug device recognition capabilities are in the early stages of growth.

Figure 3: Power consumption unit PCB. Table 2: Performance of the proposed descriptor fusion used to recognize electrical appliances. ML | Classifier |  RMS | MAD | Fusion
---|---|---|---|---
algo |  parameters |  Acc |  F1 |  Acc |  F1 |  Acc |  F1
SVM | Linear Kernel | 89.05 | 88.74 | 88.22 | 88.74 | 91.76 | 91.59
SVM | Quadratic kernel | 90.22 | 89.57 | 89.22 | 88.57 | 92.83 | 91.57
SVM | Gaussian kernel | 91.63 | 90.6 | 91.27 | 89.71 | 92.7 | 92.23
KNN | K=1/Euclidean distance | 92.24 | 91.19 | 93.24 | 90.19 | 93.95 | 93.66
KNN | K=10/Weighted Euclidean dist | 93.65 | 92.33 | 93.11 | 91.96 | 94.92 | 94.85
KNN | K=10/Cosine dist | 90.75 | 89.43 | 89.75 | 89.43 | 92.87 | 92.7
DT | Fine, 100 splits | 93.94 | 93.69 | 93.59 | 93.22 | 95.63 | 95.51
DT | Medium, 20 splits | 90.79 | 90.17 | 90.44 | 90.32 | 93.44 | 93.11
DT | Coarse, 4 splits | 87.92 | 86.83 | 87.77 | 87.4 | 90.57 | 90.41
DNN | 50 hidden layers | 93.25 | 92.68 | 92.22 | 91.55 | 95.11 | 94.78
DBT |  30 learners, 42 k splits | 96.41 | 95.93 | 96.33 | 95.18 | 98.49 | 98.32

In addition, the impact of the fusion strategy on the performance of the
proposed recognition of appliances has been assessed. The findings indicate
strong performance rates obtained by combining TD descriptors in respect to
the DBT classifier (30 learners, 42 k splits) and considering three fusion
strategies. It is obvious that the summation-based approach produces the best
precision and F1 ratings. Specifically, 98.79% accuracy and 98.52% F-score
were achieved by combining the number, while 96.98% accuracy and 96.49% F1
score and 97.43% accuracy and 97.11% F1 score were achieved by combining and
multiplication approaches, respectively.

In terms of hardware, the PCB is the heart of the power plug. The board, seen
in the Fig. 3, features a self-powered system, eliminating the need for a
specific power source to run it. In addition, a relay is installed to allow
remote control of the device, and intrusive energy monitoring is used due to
direct contact to the device.

The PCB is designed to handle two types of micro-controllers, the most fitting
of which are assisted by two micro-controllers. Both the Arduino MKR-1010 and
the ESP32 can be assisted. In fact, the (EM)3 smart plug is projected to
greatly accelerate the implementation of domestic energy use control systems
worldwide. This can be manufactured at a cost of between 20 USD and 40 USD.
Performance-wise, the micro-controllers used will do fairly sophisticated
computation in addition to real-time wireless connectivity.

###  6.2 Environmental Monitoring Unit

Similarly to the power consumption unit, the environmental monitoring unit is
housed in a PCB that contains the micro-controller and the sensors needed to
monitor temperature, humidity, luminosity, and presence. The micro-controller
is an ESP32, which supports wireless transmission to the CouchDB backend after
preliminary post-processing. In effect, on-board processing reduces noise in
data as well as the packet size. Performance, communication latency, and costs
have been computed and compared in Table 3. The smart plug micro-controller
has been programmed with a platform-agnostic Arduino program. The PCB is shown
in Fig. 4.

Table 3: Smart plug performance per micro-controller Used Board Name |  Processing Speed (s) |  Communications Latency (s) |  Cost (USD)
---|---|---|---
ESP-WROOM-32 |  0.16 |  3.19 |  10
Arduino MKR 1010 |  1.05 |  2.25 |  33.90
Figure 4: Environmental sensing unit PCB.

The concept of a smart plug that processes data before pushing it to the cloud
is linked to the trend of edge computing, where some processing takes place
where data is produced, saving communication resources and increasing
efficiency.

Compared to the literature reviewed, the proposed approach provides the
advantage of micro-moment extraction, which allows for a more accurate
analysis of daily consumption. It also enables several devices to be attached
at the same time.

However, there is indeed a range of drawbacks of the new application. The
thickness of the device is a downside that is deemed cumbersome relative to
many current solutions. In addition, in terms of cyber security, the current
implementation lacks the various cyber-attack defense mechanisms that will be
considered in future publications. Eventually, a more computationally
efficient board such as the ESP32-S2 may be used to help operate more
complicated in-chip classification algorithms.

##  7 Conclusions

In this paper, a micro-moment smart plug is proposed as part of the $(EM)^{3}$
framework. The smart plug, which includes two sub-units: the power consumption
unit and environmental monitoring unit collects energy consumption of
appliances along with contextual information such as temperature, humidity,
luminosity, and room occupancy respectively. The plug also allows home
automation capabilities. With the $(EM)^{3}$ mobile app, end-users can see
visualized power consumption data along with ambient environmental
information. In addition, the foundations of the appliance recognition method
was successfully verified by combining two time-domain descriptors, resulting
in high accuracy and F1 score benchmarks.

## Acknowledgments

This paper is made possible by National Priorities Research Program (NPRP)
grant No. 10-0130-170288 from the Qatar National Research Fund (a member of
Qatar Foundation). The statements made herein are solely the responsibility of
the authors.

## References

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