General Sensors Network application approach


ACTA IMEKO 
ISSN: 2221-870X 
March 2023, Volume 12, Number 1, 1 - 5 

 

ACTA IMEKO | www.imeko.org March 2023 | Volume 12 | Number 1 | 1 

General Sensors Network application approach 

Martin Koval1, Marek Havlíček1, Jiří Tesař1  

1 Czech Metrology Institute, Okružní 31, 63800 Brno, Czech Republic  

 

 

Section: RESEARCH PAPER  

Keywords: Sensor Network; uncertainty measurement; artificial intelligence 

Citation: Martin Koval, Marek Havlíček, Jiří Tesař, General Sensors Network application approach, Acta IMEKO, vol. 12, no. 1, article 13, March 2023, 
identifier: IMEKO-ACTA-12 (2023)-01-13 

Section Editor: Daniel Hutzschenreuter, PTB, Germany  

Received November 18, 2022; In final form March 2, 2023; Published March 2023 

Copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, 
distribution, and reproduction in any medium, provided the original author and source are credited. 

Corresponding author: Martin Koval, e-mail: mkoval@cmi.cz  

 

1. WHAT IS THE SENSOR NETWORK? 

We can imagine a Sensor Network (SN) as a group of sensors 
that are interconnected in different ways and can create a system 
that can be understood as a backbone of the processes which 
need to be monitored and/or optimized. In the system where 
many different sensors provide a complex overview of the 
ongoing processes, a model representing such an ensemble can 
be created. We can refer to this model as to a Digital Twin [1] of 
the system. The Digital Twin enables a real-time system 
monitoring, and processes history analysis and effective 
prediction of future events which can be forecasted with the use 
of advanced algorithms and AI. The result of such an interplay 
between the sensor network and the effective feedback control 
is the minimization of negative events within the system. 

2. SENSOR NETWORK STRUCTURE 

The basis of the Sensor Network comprises of the Input, 
Signal Processing and Output. These three areas can be further 
extended according to their intended use in particular processes. 
The basic type of the SN consists of sensors of the same type 
with fixed network topology. These sensors usually send data to 
the Data Processing Unit in fixed time intervals. The output may 
consist of the processed measured data with metadata which 
store additional information about the process history. An 
example of such process could be a system for temperature 
monitoring of sensitive goods according to BS EN 12830:2018.  

Many more factors have to be taken into account in complex 
systems, e.g., dynamic topology of the network, Big Data, 

different sensor types, location of data processing, complex 
mathematical algorithms, prediction, security, etc. These factors 
will be discussed in more detail in the following sections. 

2.1. Sensor Network Inputs 

The Sensor Network may consist of many devices/sensors 
which can widely differ in numbers, complexity, signal types and 
data formats. Units of simple sensors as well as thousands of 
sophisticated devices can both form a structure which can be 
considered as the Sensor Network. Considering the amount of 
data collected and processed in such network, we may face 
challenges with their processing as the amount of data increases. 

In such case we talk about Big Data. The Big Data can be 
well described as the 5 V model: Value, Volume, Veracity, 
Variety and Velocity. Each "V" has its specific influence on the 
SN architecture [2].  

The Value represents the usefulness of the data. In the SN 
data hierarchy, the data priority is set from the most to the least 
important. There is an evident difference in the value of the real 
measured data, which are used for calculations, and the metadata 
of measurement data. The Value provides very useful 
information which can help to interpret data more accurately. 
One important information category comes from, e.g., alarms. 
The alarms also have their own hierarchy which defines their 
roles in informing about the limits of sensors and correct 
functioning. 

The Volume of the data generated in the SN depends on the 
recording frequency and the number of variables which are 
recorded. If just the measured data with the corresponding 
metadata are stored, the amount of such data can reach typically 

ABSTRACT 
The paper describes the general approach for Sensor Networks and deals with principled components of Sensor Networks, architecture 
as well and opportunities for the implementation of current and new technologies. The paper also illustrates an example of the 
application of the EN 12830:2018 standard. 

mailto:mkoval@cmi.cz


 

ACTA IMEKO | www.imeko.org March 2023 | Volume 12 | Number 1 | 2 

TB or PB levels. In the case that further data such as text and 
graphics are transferred, the volume of the data can exceed EB 
levels and can go even beyond that. 

The Veracity represents the quality of information, their 
uncertainty or accuracy. The information can be inherently 
inconsistent, non-complete, ambiguous or its reliability can be 
reduced. These facts form a set of requirements which have to 
be applied so that it can be decided which data can be used for 
further analysis.  

The Variety in the SN describes different forms of 
information. The data coming from various types of sensors and 
appliances can be transferred in either structured or unstructured 
form. In the first case, the subsequent separation and analysis is 
relatively easy. The situation is diametrically different for 
unstructured data. In such case, the data mining, sorting and 
analysis is more complex which may result in errors in extracted 
data sets.  

The Velocity in the SN is the key parameter which describes 
the speed of the data transfer and processing. Combinations of 
various sensors and data structures influence the final data 
transfer and processing velocity which correlate with the 
computational power needed. In some applications, a real-time 
process monitoring is necessary such as in the medical 
applications or nuclear power plants, where any delay may have 
fatal consequences.  

One of the important aspects which play a crucial role in 
Inputs is the configuration of the Sensor Network topology [3]. 
Different time of data delivery from distributed sensors has to 
be also considered. Another important factor for Inputs, which 
play an important role, are the dynamic changes of participating 
sensors. The sensors may be disabled, replaced, maintained, 
damaged or exposed to disturbances which can possibly have a 
significant effect on the whole Sensor Network. 

2.2. Data Processing 

Data Processing can be considered as the core of the Sensor 
Network. This task can be divided into separate fields which 
need an individual approach. The Data Processing can include 
the real measured data as well as the predicted data with their 
corresponding uncertainty. The data prediction has already 
become an integral part of the state-of-the-art Sensor Networks. 

Data Prediction 

The effectivity and reliability of the data prediction are crucial 
for the modelling of specific missing or corrupted data. Reliable 
data prediction on different timescales (minutes, hours, days, 
etc.) and information about their uncertainties are necessary. 
Another aspect that plays an important role is missing data due 
to various reasons such as service, calibration, or the failure of 
sensors. Historical data of Sensor Networks can also be used for 
the prediction of the network topology in near future. A careful 
data analysis may help to predict the situations which will occur 
during the expected events and prepare adequate measures to 
cope with them, such as maintenance, overloads etc. These data 
can be modelled with the use of various algorithms based on the 
Artificial Intelligence (AI). Nowadays, the progress in the AI 
development is accelerating. It mainly focuses on three areas 
which can be characterized as learning, reasoning and self-
corrections. All these aspects can be directly applied in the SNs. 
The machine learning focusses on the data mining and creating 
rules for their conversion into useful information. The machine 
reasoning aims at searching for the most convenient algorithm 
from the family of available solutions and its implementation in 

the particular process. Automated self-correction mechanisms 
are employed in many processes in order to reach the best results 
in particular processes. The AI can be divided into different 
categories based on its capabilities: Artificial Narrow Intelligence 
(ANI), Artificial General Intelligence (AGI) an Artificial Super 
Intelligence (ASI). The ANI is frequently used in different 
applications, the AGI and the ASI are still subjects of research. 
Another categorization of the AI into four classes is based on its 
functionality (see Table 1) [4], [5].  

Environment 

Another important factor for the Data Processing represents 
the environment where the data are physically processed. Current 
technology enables the use of a variety of different virtual 
environments such as Cloud Computing, remote servers or 
special-purpose built-in computers. The real location of the data 
processing influences also the quality of the Sensor Networks. 

In the case of virtual environments, the problem with 
insufficient power is not necessarily the limiting factor. One of 
the most important parts of the SN is the cyber security of the 
environment, communication channels and sensors themselves. 
From the security point of view, the SN begins at sensors. If data 
reliability shall be guaranteed then all sensors have to be secured 
from the HW and SW point of view [6]. The HW security is 
essential in order to prevent any unauthorized change of parts 
containing the SW, which could possibly compromise the 
measured data. The HW security can be realized in a non-
destructive or destructive way. Any unauthorized access to the 
sensor/device results in its destruction in the case of a 
destructive solution [7]. Any fraudulent data manipulation using 
such damaged sensor is either physically impossible or technically 
challenging. The non-destructive solutions typically involve 
different ways of sealing, which indicate an unauthorized access 
into HW parts. It is worth noting that the availability of 
technologies which are capable to substitute HW parts is higher 
than in the past.  

Table 1. AI basic categories overview [4], [5]. 

Type of AI Use Example 

Reactive AI  
(type ANI) 

effective for simple classification and 
pattern recognition tasks; 
incapable of analysing scenarios that 
include imperfect information or require 
historical understanding; 
 

Sorting 
machines 

Limited memory  
(type ANI) 

can handle complex classification tasks 
and use historical data to make 
predictions; 
capable of completing complex tasks 
(e.g., autonomous driving); 
needs big amounts of training data to 
learn tasks; 
vulnerable to outliers or adversarial 
examples; 
 

Self-driving 
cars 

Theory of mind 
(type AGI) 

should be able to provide results based 
on an individual's motives and needs; 
training process would have a lower 
number of examples than type ANI; 
 

Under 
research 

Self-aware AI  
(type ASI) 

should be aware of the mental state of 
others entities and itself; 
it is expected to outperform human 
intelligence; 

Under 
research 



 

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In the case that sensors contain SW it is necessary to deal with 
the security from the SW point of view which shall include a 
basic minimum of the integrity check, authenticity and alarms. In 
the case of more advanced sensors with bi-directional 
communication where the remote control of sensors is possible, 
calibration parameters are available, etc., it is essential to secure 
access rights. If the system parameters can be changed, it is a 
good practice to use an event logger in order to guarantee the 
traceability of changes. One of the important factors which help 
with the data analysis is the presence of the metadata related to 
the particular data file. This metadata contains additional 
information which may be essential for a subsequent analysis. 
One of the most effective ways for the metadata protection 
represents a blockchain list of records [8]. For the network itself, 
communication is an essential prerequisite. Hundreds of 
different communication solutions including protocols and 
interfaces are now available on the market. Criteria which shall 
be considered during the SN communication design include 
energy demand, network type, compatibility, security, open 
source, etc. The SN design shall also include a risk analysis [9]. 

Uncertainty Evaluation Methods 

Uncertainties in the field of Sensor Networks represent a 
crucial aspect which should be always taken into account. Each 
sensor should be considered as an independent device placed in 
a certain environment and it should be treated as such. 

The uncertainty evaluation in the field of metrology is the 
integral part of all processes where the measurement is realized. 
Depending on the processes and the field of measurement, the 
used models can vary substantially. In the case of SNs, the 
uncertainty evaluation can be challenging. Relatively simple SNs 

working with basic measurement models and consisting of a few 
types of sensors represent the case in which the standard 
procedures can be applied. In the case that the data are collected 
in regular intervals and only one process is monitored, then it is 
possible to use The Law of Propagation of Uncertainties (LPU) 
[10], Monte Carlo [11], etc. In the case of more sophisticated 
SNs, it is necessary to use mathematical models which are 
suitable for the particular situation. In the case that some data are 
not available at the moment or were removed from the data set 
models like Bayesian statistical models [12], Fuzzy theory, etc., 
should be used. An overview of commonly used methods for the 
uncertainty evaluation is shown in Table 2. 

2.3. Sensor Network Output 

The output of the SN depends on the particular application. 
Examples shown in Figure 1 to Figure 5 imply that the output 
can consist not only of the measured data but also contains the 
metadata which can enable more efficient data processing. The 
metadata can contain the data directly recorded by the sensors or 
they can be generated during the data processing (e.g., actual 
topology of the SN), alarm analysis, event logger messages, 
sensor IDs, etc. Further utilization of the data depends on the 
particular application. The outputs can be used in different ways 
such as process indicators, triggers (process breaks, notifications) 
or for analyses. Alternatively, the analysis can be directly in a 
machine-readable format which can be directly used by another 
SN with minimal changes in the configuration. 

3. EXAMPLE OF SENSOR NETWORK 

One of the practical examples of the SN realization can be 
done according to EN 12830:2018 Temperature recorders for 
the transport, storage, and distribution of temperature-sensitive 

 

Figure 1. An example of a Simple Sensor Network according to EN 
12830:2018. 

 

Figure 2. An example of Complex Sensor Network. 

Table 2. Example of using Uncertainty evaluation methods [2], [9]-[12]. 

Uncertainty method Use 

LPU general uncertainty evaluation for complete 
datasets; 
 

Monte Carlo uncertainty evaluation for asymmetric and 
inadequate datasets; 
 

Shannon’s Entropy determining the amount of missing information 
on average in a random source; 
 

Fuzziness/Fuzzy Theory processing of vague or ambiguous datasets for 
complex models; 
 

Bayesian Statistical 
Models 

they are particularly useful when there exists 
information about the true value of the 
measurand prior to obtaining the results of a 
new measurement 

 

Figure 3. An Example of a Complex Sensor Network – Inputs. 



 

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goods [13]. As the name suggests the goal of this norm is 
temperature monitoring of sensitive goods such as food, 
pharmaceuticals, blood, organs, biological material, etc. The 
standard contains practical examples and guidelines for the 
correct implementations. A simple example is a monolithic 
temperature recorder which is physically wired to the recorder 
and is used for monitoring outer and inner space. The data 
collected is sent to the Signal conditioner and synchronized. The 
data is subsequently stored in the Base station where it is given 
the status of relevant data (see Figure 1). An advanced example 
is a recorder that utilizes cloud services (Figure 6). 

It has similar architecture as in the previous case but in 
addition to it, sensors can communicate with the Base station 
also wirelessly. It implies that the sensors have to have SW 
communication modules and space for temporary data storage. 
Such a system can contain elements that are used for data transfer 
(e.g. gateway). As a consequence, security cryptographical tools 
have to be implemented in order to secure transferred data from 
malicious technical compromise or unauthorized disclosure. In 
the following step, the relevant data can be transferred into the 
cloud which usually enables more effective data processing and 
data management. 

The requirements for specific device types are listed in EN 
12830:2018. The SW for devices are divided into three classes 
according to their complexity:  

P1- SW is embedded in a closed HW, 
P2- SW runs on a general-purpose computer, 
P3- SW runs on an external provider of cloud (e.g. SaaS). 

Specific requirements focused on functions, data protection, 
and safety measures are listed for specific types and 
arrangements. The requirements are based on WELEMC Guide 
7.2 [14] and divided into the following blocks:  

G - Basic requirements 
L - Specific SW requirements for long-term storage, 
T - Transmission of relevant information via communication 

networks, 
S - SW separation, 
D - Download of relevant SW. 
Basic requirements have to be met for all types of P1 and P2.  

Requirements relevant to block T have to be met for type P3. It 
is strongly recommended to fulfil the requirements of ISO/IEC 
27001 and take into account the requirements for control of the 
user and communication interface. Complete overview of 
requirements according to EN 12830:2018 is shown in Table 3. 

A complex SN design with requirements applications may 
look like the one shown in Figure 7. 

 

Figure 4. An example of the Complex Sensor Network – Data Processing. 

 

Figure 5. An example of the Complex Sensor Network – Final Data. 

 

Figure 6. An example of the complex Sensor Network according to according 
to EN 12830:2018. 

Table 3. List of requirements according to EN 12830:2018. 

Blocks Requirements 

Basic requirements SW Identification, Influence via user 
interface, Influence via communication 
interface, Protection against accidental, 
unintentional and intentional changes, 
Parameter protection, SW authenticity and 
presentation of results. 
 

Specific SW requirements 
for long-term storage 

Completeness of measurement data stored, 
Protection against accidental of 
unintentional changes, Integrity of data, 
Authenticity of measurement data stored, 
Confidentiality of keys, Retrieval of stored 
data, Automatic storing, Storage capacity 
and continuity. 
 

Transmission of relevant 
information via 
communication networks 

Completeness of transmitted data, 
Protection against accidental or 
unintentional changes, Integrity of data, 
Authenticity of transmitted data, 
Confidentiality of keys, Handling of 
corrupted data, Transmission delay, 
Availability of transmission services. 
 

SW separation Realization of SW separation, Mixed 
indication, Protective SW interface;  
 

Download of relevant SW Download mechanism, Authentication of 
downloaded SW, Integrity of downloaded 
SW, Traceability of relevant SW download 



 

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The P1 requirements are applied to sensors, whether they are 
connected to the base station by hardwired or wireless 
communication technologies and can also be, for example, on a 
Gateway. Devices, where P1 requirements are applied, are mostly 
devices where complex computational power is not required 
because the tasks of the device are mostly single-purposed such 
as measurement, data transfer, or storage. The application of P2 
requirements can be seen at the Base Station, where there may 
be a need for final data processing using database systems, or use 
of static processing where operating systems are used. The P3 
application is only for the use of Clouds, where partial 
responsibility for data security is assumed by the cloud operator, 
but the manufacturer must be careful how access rights are 
implemented in combination with the applied user and 
communication interfaces. 

The requirements of block L, are applied in cases where is a 
need to deal with data storage. It can be sensors or gateways, 
where it is mostly temporary storage. Or it may be longer-term 
storage in the Base Station where data needs to be stored for the 
intended use. 

The requirements of block T are applied in cases where data 
transmission is involved. Figure 7 is shown data transmission 
between digital probes, base station, gateway, and information 
cloud. 

The requirements of block S (SW Separation) are applied in 
cases where it is necessary to distinguish between relevant and 
non-relevant SW, often it is OTS (off-the-shelf) type SW, which 
was created for general purposes (e.g. various libraries, drivers, 
etc.). This type of SW can be in base station.  

The requirements of block D can be applied to almost any 
SW where an update is required, it can be sensors or even SW in 
the Base Station, but it should be ensured that the SW cannot be 
repaired or updated by an unauthorized person. 

4. CONCLUSION 

The Sensor Networks are becoming an inherent part of many 
upcoming technologies, including smart cities, smart grids, 
complex processes monitoring in industry, autonomous driving, 
medicine and many other applications.  

One of the many examples of SN is the application of EN 
12830:2018, for the purpose of process monitoring. When we 
compare the general approach with what the standard addresses, 
we can see shortcomings that do not address state of the art 
options, such as the use of AI, or issues related to network 
topology. While the standard provides a relatively appropriate 

approach, it should be pointed out that as new technologies 
evolve, a wider range of possible applications of technology in 
SN need to be addressed. 

Together with other technologies such as the AI and with the 
utilization of the Big Data, the SN is becoming an important tool 
for effectivity optimisation of many processes. The SN has 
helped to push the limits in metrology towards new effective 
algorithms in the AI or in challenges in the uncertainty evaluation 
related to the utilization of the AI as well as in the 
implementation of solutions for digital transformation. 

ACKNOWLEDGEMENT 

This work was funded by the Institutional Subsidy for Long-
Term Conceptual Development of a Research Organization 
granted to the Czech Metrology Institute by the Ministry of 
Industry and Trade. Project number: UTR 22E601104. 

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Figure 7. An example of the complex Sensor Network according to according 
to EN 12830:2018 with application of requirements. 

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