GoAT: A Sensor Ranking Approach for IoT Environments

Felipe S. Costa

, Silvia M. Nassar

and Mario A. R. Dantas

2,3 c

Department of Informatics and Statistic, Federal University of Santa Catarina (UFSC), Florianopolis, Brazil

Department of Computer Science, Federal University of Juiz de Fora (UFJF), Juiz de Fora, Brazil

INESC P&D, Santos, Brazil

Keywords: Sensor, Ranking, Active Perception, Middleware, IoT, Industry 4.0, Latency, Reliability.

Abstract: The data collected and transmitted by the sensors, in the Internet of Things environment, must be stored and

processed in order to enable Smart Cities and Industry 4.0. However, due to the growth of number of devices,

it becomes necessary to implement techniques to select most suitable sensors for each task. This approach is

important to make possible to execute applications, where low latency requirements are present. Thus, several

works were dedicated to the study on how to search, index, and rank sensors to overcome these challenges. A

method, called GoAT, is presented in this paper to rank sensors based on the theory of active perception. The

solution was evaluated using four real datasets. Our results successfully demonstrate that the proposal solution

can provide an interesting level of reliability of the utilization of sensor data. Furthermore, GoAT requires a

low computational resource, and at the same time, reduces latency in the sensor selection process.

1 INTRODUCTION

The growth of number of objects in the Internet of

Things (IoT) environment makes the existing

resources of IT architectures and infrastructures

insufficient to process all these data, especially when

real-time requirements are considered. Taking into

account the scalability and the real-time of big data,

they must be “extracted” in different levels during

analysis, modeling, visualization, and prevision to

reveal their intrinsic property; thus, improving

decision- making (M. Chen et al., 2014). Therefore,

IoT middleware solutions have been adopted to allow

data sharing (Pattar et al., 2018). An IoT middleware

will allow users to collect data from a large number

of sensors so that they can be used through different

applications, acting as an interface between the

user/application and the IoT network (Kertiou et al.,

2018). The main activities of an IoT middleware are

acquisition, research or discovery, indexing, ranking,

and query.

The activity of indexing, in the middleware,

involves storing and indexing the collected data in the

search space in the IoT network to allow a quick and

https://orcid.org/0000-0002-6647-7624

https://orcid.org/0000-0001-7130-0420

https://orcid.org/0000-0002-2312-7042

efficient search. Ranking of IoT resources,

considering that resources include sensors, devices

and services, focus on prioritization of criteria such as

data quality, device availability, efficient energy,

network bandwidth and latency. This task can be done

based on generated observation (content) and

measurement data (context). Ranking is a decision-

making process in which different criteria should be

considered depending on the requirements of the

domain. Typical applications, for example industry

and healthcare, require confidence and high-quality

data associate with low latency data processing and

delivery (Fathy et al., 2018).

The sensors incorporated into the IoT objects

collect data in real-time about the surrounding

environment. In the real world, they detect events and

then generate data about these events. The data come

from devices connected to the IoT network which

have several characteristics, such as dynamics, huge

size, dynamic data generation rate and volatility

(Pattar et al., 2018). All these characteristics favor the

appearance of noise in the data. Besides, once the

sensors aim to capture the state of the surrounding

environment, and considering that, eventually,

Costa, F., Nassar, S. and Dantas, M.

GoAT: A Sensor Ranking Approach for IoT Environments.

DOI: 10.5220/0010403801690177

In Proceedings of the 11th International Conference on Cloud Computing and Services Science (CLOSER 2021), pages 169-177

ISBN: 978-989-758-510-4

169

anomalies may occur in that environment, arise the

challenge of differentiating a real warning

(abnormality) from a failure (noise). Such failures can

be caused through problems in data transfer or by

interference in the sensor readings.

To overcome these problems, this work focuses

on the ranking of sensors based on the data generated

by them. The main contribution of this work is to

provide a fast (reduction of latency) and safe method

to indicate which sensors are providing correct

information. Another contribution consists of

offering resources to identify possible anomalies in

environments or failures in IoT devices acting as a

support in the control of monitored environments. To

do this, the techniques used are based on content

(generated data by the sensor) but also, using a

parameter related to the state of the sensor at the time

of data capture, it means, a context parameter. This

paper presents an enhanced and differential level of

development compared with the previous one

presented in (Costa et al., 2019).

The remainder of the paper is organized as

follows: Section II presents the details of this

proposal. In Section III, the related works are

presented. The results obtained are analyzed in

section IV, and section V brings the conclusions of

the research work and future perspectives.

2 PROPOSED MODEL AND

BACKGROUND

In this section, we present the proposed method,

called GoAT (Greatest of Actual Time), and the

architecture of the experimental environment. This

scenario was developed to allow the evaluation of the

method which is carefully discussed.

2.1 Sensor Ranking

Figure 1 shows an overview of the proposed method.

In the pre-processing step of the method, the datasets

are labeled, and the data of the variables are

discretized. The training of the algorithms and

techniques used is also performed. In the processing

step, the boxes with a blue bottom and vertically

arranged to identify the layer (Edge, Fog, and Cloud)

in which each middleware activity (acquisition,

modeling, reasoning, and distribution) occurs. In the

following boxes, the most common activities found in

IoT middleware are presented. Below the

identification of each activity (blue text), the

functions performed by the application in the

experimental environment of this proposal are

presented (orange text). Boxes with a green bottom

and black text show activities used in this proposal.

In these boxes, the orange text represents the action

performed or the input data sent to the tools used,

identified by the gray text.

Figure 1: Proposed method: layers of processing, activities,

and tools for each layer.

In this research work, the normal, anomaly, and

failure tags are used to identify the sensor status. Data

marked as normal represent that the sensor and the

environment in which the measurement is being made

are stable (without anomalies) and the reading

process was not affected by interference during data

capture and transmission. The data marked as an

anomaly are those that indicate the occurrence of

abnormalities in the environment. Finally, the data

considered as a failure are marked as a result of

failures in devices or data transmission.

This paper proposes a method based on the theory

of active perception (Biel & Wide, 2000; Schiffman,

2001) as a way to identify correctly each of the three

types of data mentioned above. Considering that

perception is always dependent on context, other

senses and time, the active perception approach is

divided into four levels: sensation, perception, time

perception and active perception. Sensation is the

process in which captured data enter in the system.

After the sensation, the perception interprets data and

gives its meaning. When time is added to the process,

a dynamic perception process is obtained where

several "fingerprints" (sequence of captured data

chronologically) provide meaning and, therefore,

knowledge. This process is called time perception

(Biel & Wide, 2000; Schiffman, 2001).

Figure 2 shows the flow of information according

to the concepts presented. The image shows the flow

(left side) of the data collection information

(sensation) in the environment, followed by the

improvement of perception through the addition of

meaning (sense). The next level uses the time

property, considering the dynamic aspect of the

environment. The process ends with active perception

in which from the information generated by the

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previous steps, decisions are made, and actions can be

performed (alerts and feedback). Thereby, the flow in

Figure 2 returns to the environment indicating

possible actions to be taken.

Figure 2: The flow of information in active perception.

The first stage of the data flow is carried out by

the sensors. Consequently, out of the scope of this

paper. Thus, the first-three stage is related to

perception. This step was implemented using an

algorithm belonging to the Gradient Tree Boosting

class, as shown in Figure 3.

Figure 3: Classification of a single sensor data.

The algorithms from Gradient Tree Boosting class

are algorithms that start by creating only one decision

tree. After training, the values forecasted incorrectly

by the tree are recorded. Then, a new tree is created

to see the other one's mistakes. So, the cycle is

repeated up to a certain limit, always trying to reduce

the error rate (T. Chen & Guestrin, 2016).

The XGBoost implementation improves the

performance of the conventional gradient

augmentation tree by introducing two techniques:

weighted quantile sketching (a data structure that

supports merging and removing operations) and

dispersion recognition split location (default direction

at each node in the tree). XGBoost has been applied

to several machine learning problems and has

obtained better results than the other algorithms (Shi

et al., 2019). The training of the algorithm was carried

out with a sample of 15,000 cases from each dataset.

This step is the first contact of the method with the

information provided by the sensor. The algorithm

tries to assign a meaning to the data using only the

current information and the previous knowledge of

the algorithm acquired from training phase. This

algorithm has linear time complexity.

Figure 4: Use of a set of sensor data in the evaluation of

sensor status.

Following the flow, the next step uses the time

aspect to evaluate the data provided by the sensor.

The adoption of this approach (time) occurs because

the sensor's failures can be persistent or transient.

Then, if there is an interference in a specific moment

of data capture for a given sensor, leading to the

generation of an incorrect value, and considering that

the concept of time is not used, the probability of a

wrong interpretation of the sensor data increases.

When the time window is used it can identify whether

the fault is temporary or permanent. The flow of

information passes through the second level just if the

output of the first level indicates an abnormality in the

data. Otherwise, the second level is skipped. To

implement this concept, the outputs (labels) of the

first level are saved in a vector.

In the implementation of the second level, tests

were performed with different sizes of time windows

(5, 10, 15 and 20). These sizes represent the number

of observations, in other words, sensor readings to be

considered when calculating the probability of

evidence is correct. At the end of these tests, a time

window of size 10 showed promising results in the

datasets used, considering an intermediate value

between performance and computational cost. In the

case of the time window with five cases, there is an

increase of approximately 30% in the error rate

compared to the error rate of the time window with

ten cases. On the other hand, in tests with the time

windows of 15 and 20 cases, the error rate remains

practically stable, but there is an increase of

approximately 20% in latency. These values are

similar between data sets.

Figure 4 shows an example of this type of subset.

In the image, a subset of data cases generated by the

sensor is presented. The qt_n, qt_a, and qt_f

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indicators represent the quantities of each type of data

(normal, anomaly, and failure, respectively). The

values of qtl_n, qtl_a, and qtl_f represent the count of

the last data type that appears in the subset. In the case

of Figure 4, the latter type is normal and has only one

contiguous occurrence of this type, since the

immediately preceding item is of type failure, that is,

a different type. With this approach, only one of these

indicators will be greater than zero.

The vector is then submitted to a Fuzzy Inference

System (FIS). Fuzzy logic (Zadeh, 1965), is inspired

by real-world phenomena in which events can hardly

be considered entirely false or completely true. Fuzzy

logic offers a method based on rules and

mathematical sets for the treatment of inaccuracy.

Fuzzy logic can deal with imprecise or qualitative

terms, such as "Low", "Medium" and "High" which

can not be expressed using binary logic. Similarly,

Fuzzy logic, the data history of each sensor will be

analyzed using the qualitative values "Low",

"Medium" and "High". Applying Fuzzy rules, the

system output to the time perception level will be

defined to identify the probable state of the sensor.

Figure 5: Use of fuzzy rules for decision making.

In Figure 5, it is possible to view the pertinence

functions of six indicators used in the assessment, as

well as three of the fuzzy rules used in the IntelLab

dataset. These indicators represent the number of

states for each category (qt_a, qt_n, qt_f) and the

identification of how long the sensor is already in that

state (qtl_a, qtl_n, and qtl_f). These indicators are

also represented in the Figure 4.

In the final stage of active perception, the outputs

of two previous stages are evaluated together. If the

produced output at the first level is normal, no further

treatment will be necessary. If the second level is

triggered, the system's response will use that output

as the final response. After that, the sensor index is

updated with this new information.

To update the index, the data are classified using

the concepts Quality of Context theory (QoC). The

trustworthiness parameter (Equation 1) is used to

check the quality of each sensor, where 0 (zero)

means that this context source is not reliable, and 1

(one) represents total confidence in the context source

(Bringel Filho & Agoulmine, 2011).

T(ctxi) = NumberOfReliableSamples(ctxi) / W, W > 0

(1)

in which

T is trustworthiness

cxti is the set of trusted context elements for a

sensor

W is the total number of context elements and

must be greater than zero.

The value of the trustworthiness context

parameter is then evaluated using a FIS. The modeled

FIS has an input variable (trustworthiness) and an

output variable (sensor state). The input variable is

modeled using the categories very low, low, medium,

high and very high. The output variable can assume

the values normal, anomaly and failure. As a final

step the sensor ranking is updated with the new values

of the trustworthiness parameter.

2.2 IoT Platform

The proposal of this work use an architecture adapted

from the FASTEN project (Costa et al., 2020).

Flexible and Autonomous Manufacturing Systems

for Custom-Designed Products (FASTEN) is a

project funded by the EU Horizon 2020 program.

FASTEN's Industrial IoT Platform aims to

manipulate data from devices (robots, sensors and

actuators) in industrial environments and work as an

intelligent data repository for the optimization and

forecasting layer, allowing to improve the quality of

the services offered and at the same time it is within

the Industry 4.0 requirements.

The bottom box in Figure 6 represents the sensors

(devices). The datasets were sent to the platform

through simulators (scripts). These scripts sent the

data to the platform through the Message Queuing

Telemetry Transport (MQTT) protocol using the

VerneMQ tool. A connector continuously monitors

topics (such as a message queue) in MQTT, and, as

soon as a message arrives, it is automatically

transferred to the Apache Kafka streaming platform.

Following the flow, the next box, GoAT, represents

the proposal of this research work for ranking sensors.

After processing by the GoAT broker, the

indexing activity is represented by storing the

messages as context entities in the Orion Context

Broker tool, one of the components of the FIWARE

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framework used in this architecture. Context entities

reflect the current state of each sensor and can be

persisted in a database for IoT, as is the case with the

CrateDB database. The connection between the Orion

Context Manager and the database is made by the

QuantumLeap application, also belongs to the

FIWARE framework. These data, when persisted, can

be used for monitoring the system. This activity is

performed here by the Grafana monitoring tool. In

this way, this architecture allows parallel processing,

also reinforcing the scalability of the proposal.

Figure 6: Proposed experimental environment - IoT

Platform.

2.3 Datasets Used for Evaluation

To use the same datasets of the related works, thus

facilitating the comparison between the methods, four

real datasets were selected to evaluate the proposed

method.

The IntelLab dataset (Intel Lab Data, [s.d.])

contains information about data collected from 54

sensors with weather boards that collected

timestamped topology information, along with

humidity, temperature, light, and voltage values once

every 31 seconds. The sensors, deployed in the Intel

Berkeley Research lab, were arranged in the lab

according to the diagram shown in Figure 7.

Figure 7: IntelLab Dataset (Intel Lab Data, [s.d.]).

The other datasets refer to environmental data and

were collected from stations of the Phenonet project

(Phenonet, [s.d.]) from stations of the Bureau of

Meteorology (BOM) (Bureau of Meteorology, [s.d.])

and from stations of the National Oceanic and

Atmospheric Administration (NOAA) (NOAA, [s.d.])

as presented in Table 1.

Table 1: Datasets (Costa et al., 2019).

Dataset Cases (millions) Stations

BOM 4.1 111

IntelLab 2.3 54

NOAA 127 14181

PhenoNet 1.9 7790

The combination of these datasets, in which the

data were generated under different conditions,

contributed to the evaluation of our proposal. A

computer with an Intel Core i7 processor with 32GB of

RAM and a 250GB SSD was used to perform the tests.

3 RELATED WORK

Applications such as industry, agriculture, and

healthcare require reliability associated with

computing in services that require low latency

requirements (Fathy et al., 2018). In this section, the

most relevant works related to the ranking of sensors

are presented.

In Costa et al. (Costa et al., 2019) sensor data is

evaluated at three levels. In the first level, the

XGBoost (XGB) algorithm is used. In the second

step, the method uses the Viterbi algorithm to

evaluate a subset of the data (time slice). In the final

step, the algorithm assigns weights to each answer

obtained at the previous levels to make the final

decision. Despite presenting an interesting proposal,

the performance of the method fails with regard to

reducing latency since all levels of data evaluation are

always performed. In addition, the use of the Viterbi

algorithm also contributes to increased latency. Also,

the use of weights at the last level is configured as a

mechanism subject to failure and generate the

premise of creating a lot of rules for the correct

assignment of these weights.

In the work of Ruta et al. (Ruta et al., 2019), the

ranking of the devices is based on a metric that

calculates the semantic distance between the user's

requirements and the semantic description of each

device. In Kertiou et al. (Kertiou et al., 2018), the

authors use context information from sensors with a

dynamic skyline operator to reduce the search space

and select the best sensors according to user

requirements. In Dilli et al. (Dilli et al., 2018), the

ranking is calculated by the Simple Additive

Weighting (SAW) algorithm. Nunes et al. (Nunes et

al., 2018) propose a Selection-Elimination (ES)

algorithm to filter and classify the response data.

GoAT: A Sensor Ranking Approach for IoT Environments

173

Neha and Saxena (Neha & Saxena, 2016) work

with weights for the importance of context properties

and calculate the ranking of each sensor using a metric

called Weighted Index Based on Preference (PBWI).

In Wang et al. (W. Wang et al., 2015), a sensor ranking

mechanism based on the cost of accessing the service

(device) is proposed. The cost is calculated using

sensor properties and context information.

In the work of Perera et al. (Perera et al., 2014),

the ranking of sensors is done using a weighted

Euclidean distance metric, called Comparative

Priority Based Weighted Index (CPWI). In the

Snoogle (H. Wang et al., 2010) framework, the sensor

ranking is based on the relevance of the object

description, informed in the parameters query.

Ostermaier et al. (Ostermaier et al., 2010) were the

first to use the term ranking of sensors. The work

focuses on the use of data produced by sensors

centered on people, considering that habits can

indicate future behavior, and thus use the data

generated by these sensors to create forecasting

models. This process calculates an estimate of the

probability, in decreasing order, that each sensor

corresponds to the query parameters.

According to the bibliographic research performed,

only one of the articles (Skarmeta et al., 2018)

considers aspects related to the identification of

failures or anomalies. Despite this, the work does not

describe how this is done and does not show any

results. None of the related works considers the fact

that sensor failures can be transient or persistent, given

the dynamic nature of the IoT environment, more

specifically the generation of data by sensors. Thus,

considering only the state of the sensor at a specific

time to generate the ranking can compromise the

quality of the responses provided by the algorithms.

The researched works also do not have a previously

created list (ranking), which makes it impossible to

respond immediately to a request for a list of trusted

sensors. The active perception theory for ranking the

sensors was not used in any of the related works. This

approach has as main objective to create a method that

adds knowledge to the reasoning, improving the data

analysis process. Only three studies use more than one

technique for data evaluation.

The literature review presents some approaches

that demonstrate the importance of this activity for

the development of the IoT. However, there are still

challenges and opportunities to be overcome:

- Differentiate data that represent real

measurements from data generated by failures or

interferences, reducing uncertainty in decision

making.

- Reduce the amount of data to be analyzed (reduce

the time) to meet services with low latency

requirements.

4 EXPERIMENTAL RESULTS

Since the cases of datasets do not have labels, and to

provide some metrics capable of demonstrating the

quality of our method, the clustering techniques are

used to generate labels for the data.

This approach provides a way to evaluate the

proposed method, allowing the comparison of the

outputs generated by the proposal with the

information obtained in the cluster methods.

Before creating clusters, metrics were used to

assess the tendency to clustering and the number of

clusters and can be seen in Table 2.

Table 2: Tendency to clustering and quality of clusters.

Dataset Tendency

[%]

[-1, +1]

[+]

[-]

BOM 0.99 0.23 6458.31 3.44

IntelLab 0.84 -0.29 128.61 157.38

NOAA 0.86 0.07 254.57 4.90

PhenoNet 0.93 0.23 754.55 1.14

Figure 8: Number of clusters for the IntelLab dataset.

The clustering tendency and the quality of each

generated cluster were evaluated using the Hopkins

test (Hopkins & Skellam, 1954). The quality of the

clusters was evaluated using three different metrics:

Silhouette Coefficient (SC) score (Aranganayagi &

Thangavel, 2007), Calinski-Harabasz (CH) index

(Caliński & Harabasz, 1974), and Davies-Bouldin

(DB) index (Davies & Bouldin, 1979). For the SC

index, the values vary from -1 to +1, indicating better

and worse clusters, respectively. In the case of the

metric CH, the higher the value, the better the quality

of the generated cluster. In the DB metric, the lower

the value, the better the quality of the cluster. Figure

8 shows the indication of the best number of clusters

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for the IntelLab dataset generated using the Elbow

metric (Pascual et al., 2010).

A graphical representation of the division of the

data from the dataset IntelLab into the normal,

anomaly, and failure groups are shown in Figure 9.

The graph was generated using the Gaussian Mixture

Model (GMM). In addition to the GMM method, the

methods Spectral clustering, Agglomerative

clustering, DBSCAN, and k-means were applied.

Despite this, in all datasets, the best result was

obtained with the GMM method. Figure 9 shows, in

green color (representing normal values), the natural

variations of the temperature during the day (between

C and 30

C), in yellow the intermediate or

anomaly values (indicating real changes in the

environment) and finally the values in red

representing the values considered failures (capture

or transmission failures).

Figure 9: Clusters of dataset IntelLab generated using

GMM (Costa et al., 2019).

It should be noted that the values considered as

anomalies are in a range of possible values for the

environment, which is not the case for values

considered as failures. The data shown in the image

refer to a random sample containing data from all 54

sensors. This graph gives an idea of the approach of

this work to classify the data generated by the sensors

in three categories.

The Grafana tool is used to display the state of the

system (Figure 10). The image shows the monitoring

of the IntelLab dataset and is divided into four regions.

In the upper left area is presented the monitoring, over

time, of the values of the column’s temperature,

humidity, and voltage of this dataset. In this region, it

is possible to observe that the temperature rises

considerably over time, while the humidity tends to

decrease, and the voltage remains stable.

Figure 10: A tool for monitoring tests develop in Grafana

(Costa et al., 2019).

In the upper right region, a box is displayed for each

sensor in the dataset with the colors green, yellow or

red, indicating the current state of the sensor, that is,

normal, anomaly or failure. In the lower-left region, the

time of the last sensor data capture, the sensor

identification and the value of the capture sequence are

shown, and three columns in the colors green, yellow

and red, indicating the total data considered in each

category (normal, anomaly and failure). Finally, in the

lower right region, three columns are presented with

the overall values considered as normal, anomaly, or

failure for this dataset.

The idea of the tool showed in Figure 10

demonstrates a possible practical application of this

research work. The tool reflects the possibility of

monitoring an environment and its sensors,

identifying sensors in a failure state, and

environments in which an anomaly may be occurring.

Table 3: Error rates and Normalized Confusion Matrix for

BOM and IntelLab datasets.

Actual

BOM (2.96) IntelLab (0,07)

Predicted

N A F N A F

N 0.840 0.000 0.000 N 0.801 0.000 0.00

A 0.000 0.101 0.000 A 0.000 0.036 0.00

F 0.000 0.029 0.030 F 0.000 0.000 0.163

Table 4: Error rates and Normalized Confusion Matrix for

NOAA and PhenoNet datasets.

Actual

NOAA (6.58) PhenoNet (7.55)

Predicted

N A F N A F

N 0.371 0.041 0.001 N 0.839 0.001 0.000

A 0.000 0.376 0.000 A 0.074 0.005 0.00

F 0.024 0.000 0.187 F 0.000 0.000 0.081

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Comparing the results generated by the algorithm

executions with the label values in each sample, Table

3 and Table 4 presents the general error rates (next to

the dataset name, in parentheses) and the confusion

matrices for all datasets. Since the datasets cases had

no labels, the ranking algorithm responses were

compared with the clustering algorithm results

applied to each dataset.

The error rates obtained in the datasets BOM and

IntelLab are shown in Table 3, and the error rates for

the datasets NOAA and PhenoNet are displayed in

Table 4. These values demonstrated the excellent

performance of our algorithm. In the PhenoNet

dataset, the error rate remains at a higher value and

will be investigated in conjunction with the values

obtained from cluster quality metrics for this dataset.

Table 3 and Table 4 show the error rates in each class,

i.e., normal (N), anomaly (A), and failure (F).

Figure 11: Response time and latency costs.

Figure 11 shows the times spent, per dataset, for

processing a request. The item "Response Time"

refers to the total time taken from the submission of

the request to the receipt of the response. The item

"Latency" refers to the processing time of the ranking

algorithm.

5 CONCLUSIONS AND

RESEARCH DIRECTIONS

In this paper was presented the GoAT method, which

is characterized by successfully reducing the amount

of data to be analyzed in decision making (reduction

in latency), selecting the most reliable sensors, and

providing the identification of anomalies in the

environments.

The error rates showed interesting levels

according to the results obtained in all datasets.

Although it presents a higher error rate in one of the

four datasets, the results presented in this work

demonstrate the feasibility of our proposal. As this

work is under development, and these are the initial

results obtained, it will still be possible to reduce the

error rate in the PhenoNet and NOAA datasets.

The solution allows to reduce drastically the

computational effort involved in the data processing

once the data is analyzed only at the first level of the

proposal. This happens because only data considered

non-normal are handled at the second and third level

of proposal processing.

In addition, considering that only a small part of

the data needs to be analyzed in three evaluation

steps, a reduction in latency is also possible because

the solution allows only the most reliable sensors are

selected. Thus, the model is able to provide reliability

and lower latency, at the same time, in the use of

sensor data.

Anomaly and failure identification, on the other

hand, allows quick answers to correct problems in

environments, such as feedback from cyber-physical

systems.

Finally, considering that the main objective of this

proposal is precisely to make the selection of the most

reliable sensors, the results can be regarded as

promising. Future works intend to use the data

collection of online sensors as well as tests with

configurations for distributed environments.

ACKNOWLEDGEMENTS

This research is funded by the Federal Institute of

Santa Catarina, FASTEN Project, INESC P&D

Brazil, Ministry of Education, and by the CNPQ

(National Center for Scientific and Technological

Development) of the Brazil Government.

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