Tools for the Conﬂuence of Semantic Web and IoT: A Survey

Jos

e Miguel Blanco

, Bruno Rossi

and Tom

s Pitner

Faculty of Informatics, Masaryk University, Brno, Czech Republic

Keywords:

Semantic Web, IoT, Tools, Survey.

Abstract:

The last decade has meant a whole revolution in the context of Internet of Things (IoT), becoming one of the

most important areas of research nowadays. The Semantic Web emerged as a way to add semantics to data to

enable advanced reasoning. Due to this, the integration of both areas represents a beneﬁcial convergence for

more ”semantic-aware” devices and services. In this paper we present a review to analyze the state of the art

of the conﬂuence of the Semantic Web and IoT: extracting an overview of the ad-hoc implemented tools, their

categorization, and the supported sub-domains on which they exert their inﬂuence.

1 INTRODUCTION

Internet of Things (IoT) represent the convergence

of devices with sensors, remote sensing and anal-

ysis capabilities supported by heterogeneous net-

works (Dorsemaine et al., 2015; Atzori et al., 2017).

However, the deﬁnition and view of IoT has not been

static, rather it has changed over time: from pure

device-to-device communication to a more ”social

network-oriented” view enabled by technologies such

as cloud computing, big data and the emergence of

social networks (Atzori et al., 2017). In this context,

the importance of semantic reasoning and the Seman-

tic Web has run in parallel with the goal of supple-

menting the IoT domain with more knowledge about

semantic-enabled operations to support the provision

of smart services to ﬁnal users (Jara et al., 2014).

The goal of this paper is to review the proposed

ad-hoc tools based on the Semantic Web in the con-

text of the IoT domain. The ﬁnal aim is to gather an

overview of the domains of application, the type of

tools implemented and the main research results.

To run the research review, we followed the Sys-

tematic Literature Review (SLR) modality (Kitchen-

ham and Charters, 2007), in which the review follows

a formalized approach from the identiﬁcation of the

research goals, to the deﬁnition of the search queries,

to the ﬁltering by researchers of the gathered articles

based of pre-set criteria, followed by data synthesis

and knowledge representation. Such method allows

https://orcid.org/0000-0001-9460-8540

https://orcid.org/0000-0002-8659-1520

https://orcid.org/0000-0002-2933-2290

to summarize all the reviewed research with more for-

malized ﬁnal results.

The article is structured as follows. In Section

2 we provide the background about IoT and the Se-

mantic Web, together with the summary of previous

reviews that were conducted in the area. In Section

3, we summarize the method adopted to conduct the

review, together with setting the research questions

and the main annotations when performing the review

process. In Section 4 we summarize the main results

from the review, by answering each research question.

In Section 5 we close the article with the main conclu-

sions.

2 BACKGROUND

Created for speciﬁc use cases, each IoT device is

meant to solve a speciﬁc task with the protocols pro-

vided by the manufacturer to allow the connection of

sensors and different networks (Jara et al., 2014). As

such, their connectivity is limited to a single domain –

the concept of Web of Things (WoT)

was introduced

for the integration of IoT with the Web architecture,

making IoT devices connectable in general to the Web

to allow some form of data reasoning. Such integra-

tion makes IoT inter-connectable to other instances

that have their own schema of Web connection, ab-

stracting to a higher layer. The biggest advantage that

WoT offers is not just the connection to the Web archi-

tecture, but the fact that all the capabilities of Seman-

https://www.w3.org/WoT/

150

Blanco, J., Rossi, B. and Pitner, T.

Tools for the Conﬂuence of Semantic Web and IoT: A Survey.

DOI: 10.5220/0011064100003176

In Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2022), pages 150-161

ISBN: 978-989-758-568-5; ISSN: 2184-4895

Table 1: Previous Surveys about Ontologies & IoT.

Year Survey Domain

2021 (Krishnamoorthy et al.,

2021)

Healthcare

2020 (Rhayem et al., 2020) General

2020 (Sobin, 2020) Agriculture

2019 (Balaji et al., 2019) WoT

2019 (Balakrishna et al., 2019) Smart Cities

2019 (Li et al., 2019) WoT

2019 (Zgheib et al., 2019) Healthcare

2018 (Gyrard et al., 2018) Smart Cities

2018 (Zaidan et al., 2018) Smart Home

2017 (Bajaj et al., 2017) Sensors

2017 (Goudos et al., 2017) Smart Cities

2017 (Marques et al., 2017) Smart Homes

2016 (Jahan et al., 2016) General

2016 (Szilagyi and Wira,

2016)

General

tic Web are set to be used, supporting the concept of

Semantic Web of Things, allowing to share ”things”

and compose services built on top of IoT devices (Jara

et al., 2014).

Furthermore, the integration of IoT with Seman-

tic Web enables the use of ontologies, which allow

the data to be processed by machines, while making

it more understandable for humans. This further ex-

pands the capabilities of IoT giving access to semantic

reasoners and certain databases built around Semantic

Web notions such as RDF

or OWL2

, enhancing the

possibilities of the provision of composed services.

It is easy to see how the conﬂuence of Semantic

Web and IoT can be beneﬁcial for the provision of

smart services to users, building on top of low-level

layers. Therefore, a new breed of tools and instru-

ments have been born from the conﬂuence of IoT and

Semantic Web to bring a higher interoperability for all

of IoT devices as well as new and enhanced features

for end-users.

2.1 Previous Surveys

There are several existing studies that discussed IoT,

ontologies, Semantic Web and their interactions in

different domains (Table 1). We summarize in this

section the main reviews identiﬁed.

There are some reviews that focus generally on the

aspects of integration of ontologies and IoT. For ex-

https://www.w3.org/RDF/

https://www.w3.org/TR/owl2-overview/

ample, (Bajaj et al., 2017) review focuses on sensor

and general ontologies and the technologies and chal-

lenges that come along with them, such as interoper-

ability. (Balaji et al., 2019) focuses on an overview

of IoT technology in general terms focusing more on

the communication aspects (such as RFID and Wire-

less Sensor Network). Also (Zaidan et al., 2018) cen-

ters more on aspects related to communication in the

context of IoT and integration of semantics. (Balakr-

ishna et al., 2019) reports about the semantic inter-

operability in Smart Cities, covering aspects such as

RDF, RDF schema, OWL, SPARQL, semantic anno-

tations, and semantic reasoning. (Goudos et al., 2017)

focuses on Smart Cities integration of IoT devices

covering aspects such as transportation and logistics,

discussing the implication of usage of Semantic Web

for IoT. (Rhayem et al., 2020) provides a Systematic

Literature Review focusing on Semantic Web Tech-

nologies applied in IoT covering many aspects such as

semantic technologies, reuse of ontologies, modular-

ity, context, methodology, and evaluation techniques.

(Sobin, 2020) covers challenges such as security or

scalability, classifying the ﬁndings according to the

domain, like healthcare or smart agriculture, provid-

ing a new taxonomy in terms of communication pro-

tocols and architectures.

Some reviews focus speciﬁcally on tools support,

like (Gyrard et al., 2018) that centers the interest on

analyzing ontology-software tools for interoperabil-

ity. Some catalogs of ontologies are reviewed by

domain, and offer a tool for evaluating the different

ontologies and approaches investigated. Also (Jahan

et al., 2016) focuses on different frameworks for the

Web of Things. (Krishnamoorthy et al., 2021) puts

the emphasis on different architectures for Healthcare

IoT differentiating up to 19 different critical appli-

cations. Also (Marques et al., 2017) has the central

point on different IoT projects in the context of IoT

architectures with applications such as smart homes,

or healthcare. Overall, 10 different IoT platforms are

included. (Szilagyi and Wira, 2016) focuses more on

technologies and ontologies that have a commercial

use, organized according to their position in the IoT

Stack. (Zgheib et al., 2019) focuses more on middle-

ware architectures for healthcare IoT, determining up

to 7 different middleware approaches going towards

semantic middleware approaches. (Li et al., 2019)

centers on standardized IoT ontologies and how they

cover one of the different WoT layers – looking at six

different ontologies and their integration within IoT

and WoT.

This review is different than the aforementioned

ones as we speciﬁcally focus on the classiﬁcation of

implementations that were considered for the conver-

Tools for the Conﬂuence of Semantic Web and IoT: A Survey

151

Table 2: Queries run on the digital repositories.

Repository Query # Articles Found

IEEEXplore (”Abstract”:”IoT” OR ”Abstract”:”Internet of Things”) AND (”Abstract”:”ontology” OR ”Abstract”:”semantic web” OR

”Abstract”:”WoT”) AND (”Abstract”:”tool” OR ”Abstract”:”technology”)

ACM DL (Abstract:”IoT” OR Abstract:”Internet of Things”) AND (Abstract:”Semantic Web” OR Abstract:”ontology” OR Ab-

stract:”WoT”) AND (Abstract:”technology” OR Abstract:”tool”)

Elsevier (”Abstract”:”IoT” OR ”Abstract”:”Internet of Things”) AND (”Abstract”:”ontology” OR ”Abstract”:”semantic web” OR

”Abstract”:”WoT”) AND (”Abstract”:”tool” OR ”Abstract”:”technology”)

114

SpringerLink ((Abstract:”IoT”) OR (Abstract:”Internet of Things”)) AND ((Abstract:”Technology”) OR (Abstract:”Tools”)) AND ((Ab-

stract:”Semantic Web”) OR (Abstract:”WoT”) OR (Abstract:”Ontology”)) AND (Language:”English”)

368

gence of Semantic Web and IoT (framework, environ-

ment, platform, ontology, architecture, middleware)

linking to the different domains (e.g., Smart Cities,

Healthcare). Overall, we provide the distinguishing

characteristics of the tools that were implemented as

enablers for the conﬂuence of Semantic Web and IoT.

3 METHOD

To look into ad-hoc tools implemented for the conﬂu-

ence of Semantic Web and IoT, we adopted the Sys-

tematic Literature Review (SLR) research approach.

A SLR is a formalized review process to collect,

synthesize, and report previous research in a do-

main (Kitchenham and Charters, 2007). The process

starts with the deﬁnition of the survey needs, the def-

inition of the research questions and derived queries.

Afterwards, researchers go trough a series of steps to

review the collected articles taking into account inclu-

sion / exclusion criteria. After a collaborative process

about the ﬁnal decision to include the papers, all the

research is synthesized in the ﬁnal presentation of the

results, answering the research questions.

The goal of the current review was to investigate

the tools for the conﬂuence of Semantic Web and IoT.

Since a large and recent number of papers have fo-

cused on the IoT domain, we wanted to look into the

availability of tools to support the IoT context with ca-

pabilities related to reasoning and the Semantic Web.

To run the review, we selected four main digital repos-

itories:

DR1. IEEEXplore (ieeexplore.ieee.org)

DR2. ACM Digital library (dl.acm.org)

DR3. Elsevier ScienceDirect (sciencedirect.com)

DR4. SpringerLink (link.springer.com)

For each repository we run the queries on the ab-

stracts as it can be seen in Table 2. Inclusion and ex-

clusion criteria were the following:

Inclusion Criteria: i) there is a tool for Semantic

Web - IoT conﬂuence described in the paper; ii) the

tool is applied to a concrete case in a speciﬁc domain;

iii) only papers written in English; iv) only articles

from the range of years 2011-2021;

Exclusion Criteria: i) other reviews (secondary

/ tertiary studies); ii) vision / position / challenges /

editorials / posters;

To reach the main goal of the review, we set-up

the following Research Questions (RQs):

RQ1. What are the domains on which the differ-

ent tools are applied/developed for?

RQ2. What are the different designs of tools that

are being developed for the conﬂuence of the Se-

mantic Web and IoT?

RQ3. Is there a more prevalent type of tool that is

used to cover the topic?

RQ4. What is the main focus of this kind of tools

and how do they relate to the different types that

can be found in the literature?

RQ5. Is there any speciﬁc secondary tool (e.g.,

software library / framework) that was mentioned

more in the proposals of the reviewed tools?

Overall, 572 papers were found from the digital

repositories divided into 334 journal articles and 238

found in proceedings. These were ﬁltered based on

inclusion and exclusion criteria by two authors of the

paper. After agreement on the inclusion consulting

the abstracts (and in case of indecision the full paper),

60 papers were used to answer the research questions.

These papers, as well as the characteristics of the tools

that appear in them, have been collected and assigned

a key for easier referencing in Table 3.

4 REVIEW RESULTS

We present the results from the review divided by

RQs, with a classiﬁcation of the tools by domain,

type, category and focus, as well as a domain tree.

Furthermore, let it be noted that all the different divi-

sions that we have created to classify the papers are

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Table 3: References Index.

Reference Key Design Type Domain Focus

(Kim et al., 2018) K01 Modelled Tool Architecture Smart Grids Security

(Muppavarapu et al.,

2021)

K02 Semantic System Framework Smart Homes Interoperability

Smart Buildings User-friendliness

(Ibaseta et al., 2021) K03 Semantic System Architecture Smart Buildings Interoperability

(Turchet et al., 2020) K04 N\A Ontology Music N\A

(Awad et al., 2019) K05 Modelled Tool Environment WoT Interoperability

(Provoost et al., 2020) K06 Modelled Tool Platform Smart Cities User-friendliness

(Janowicz et al., 2019) K07 N\A Ontology Sensors N\A

(Baldassarre et al.,

2019)

K08 Semantic System Environment Social IoT Interoperability

(Loseto et al., 2016) K09 Semantic System Framework WoT Interoperability

(Silva et al., 2020) K10 Semantic System Architecture Smart Cities Interoperability

Platform Smart Buildings

(Platenius-Mohr et al.,

2020)

K11 Modelled Tool Environment Industry Interoperability

(Charpenay et al., 2015) K12 Semantic System Framework Building Au-

tomation

Interoperability

(Xu et al., 2017) K13 Modelled Tool Environment Security Interoperability

(Yu et al., 2018) K14 Semantic System Framework IoEverything Interoperability

(Koorapati et al., 2018) K15 Semantic System Framework General Security

(Lian et al., 2020a) K16 Modelled Tool Middleware General Interoperability

Ontology

(Sciullo et al., 2019) K17 End-user Tool Platform Industry User-friendliness

Smart Home

(Zeng et al., 2019) K18 Semantic System Framework Cloud\Edge

Computing

Interoperability

Ontology

(Hwang et al., 2016) K19 Modelled Tool Environment Virtual Agent Interoperability

Ontology

(Ming and Yan, 2013) K20 Semantic System Middleware General Interoperability

Ontology

(Al Sunny et al., 2017) K21 Semantic System Architecture Industry User-friendliness

Ontology

(Tomi

c and Grd,

2020)

K22 N\A Ontology Security N\A

(Burns et al., 2018) K23 Semantic System Framework Smart Cities Security

Reasoning

(Zhou et al., 2018) K24 Modelled Tool Platform Social IoT Security

(Willner et al., 2017) K25 Semantic System Platform Smart Factories Interoperability

(Liang et al., 2019) K26 Semantic System Framework General Security

(Shimoda et al., 2020) K27 Semantic System Environment Sensors Interoperability

(Reda et al., 2021) K28 Semantic System Environment Healthcare Interoperability

Reasoning

(Fensel et al., 2013) K29 Semantic System Environment Energy Con-

sumption

Interoperability

Smart Grids

(Su et al., 2017) K30 Semantic System Framework Cloud\Edge

Computing

Interoperability

Ontology Reasoning

(Kotis et al., 2012) K31 Semantic System Framework Sensors Interoperability

Tools for the Conﬂuence of Semantic Web and IoT: A Survey

153

Table 3: References Index. (cont.).

(Dolan et al., 2020) K32 Semantic System Framework Smart Homes User-friendliness

(Sanctorum et al., 2021) K33 End-user Tool Platform Toxicology User-friendliness

(Kyriazakos et al.,

2015)

K34 Modelled Tool Platform General Interoperability

Architecture

(Govoni et al., 2017) K35 Modelled Tool Middleware Smart Cities User-friendliness

(Hashemian et al.,

2019)

K36 End-user Tool Framework WoT Interoperability

(Durand et al., 2017) K37 Modelled Tool Framework WoT Security

(Seok et al., 2019) K38 Semantic System Environment Sensors Interoperability

(Sciullo et al., 2020) K39 End-user Tool Platform WoT User-friendliness

Sensors

(Negash et al., 2019) K40 Modelled Tool Architecture WoT Interoperability

(Khodadadi and Sin-

nott, 2017)

K41 Semantic System Framework Energy Con-

sumption

Interoperability

(Teixeira et al., 2020) K42 Semantic System Architecture General User-friendliness

Framework

(Garc

ıa Mangas and

arez Alonso, 2019)

K43 Modelled Tool Framework WoT Interoperability

(De et al., 2014) K44 Semantic System Architecture General Interoperability

(Thramboulidis et al.,

2019)

K45 Modelled Tool Framework Industry Interoperability

Automation

(Khan et al., 2016) K46 End-user Tool Architecture Smart Home Interoperability

Environment Healthcare

(Elsayed and Elgamel,

2020)

K47 Semantic System Platform Smart Cars Interoperability

User-friendliness

(Duy et al., 2019) K48 Semantic System Framework Sensors Interoperability

(Lian et al., 2020b) K49 Modelled Tool Middleware General Interoperability

Reasoning

(Hu et al., 2020) K50 Modelled Tool Framework General Interoperability

(Luecking et al., 2020) K51 Semantic System Framework General Security

(Sun et al., 2018) K52 Semantic System Environment General Interoperability

Modelled Tool Reasoning

(Steinmetz et al., 2017) K53 Modelled Tool Environment Industry Interoperability

End-user Tool

(Xu et al., 2018) K54 Semantic System Middleware Smart Home User-friendliness

Framework Reasoning

(Berat Sezer et al.,

2016)

K55 Semantic System Framework General Interoperability

(Khattab et al., 2018) K56 End-user Tool Environment General User-friendliness

(Miori and Russo, 2012) K57 Semantic System Environment Smart Home Reasoning

Healthcare

(Mavrogiorgou et al.,

2020)

K58 Semantic System Platform Healthcare User-friendliness

End-user Tool

(Machorro-Cano et al.,

2019)

K59 End-user Tool Platform Smart Home User-friendliness

(Nakatani et al., 2019) K60 Modelled Tool Environment Virtual Agent User-friendliness

Ontology

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non-exclusive, meaning that the same paper could fall

under two different classes.

4.1 RQ1 — Domain of the Tools

We classiﬁed the provided tools by the domain of ap-

plication (Table 4). There are 21 domains described

with most of them having an occurrence of about a 3%

of the total. The General domain comprises a 21.6%

of the articles and describes those articles that focus

in IoT as a whole, without describing or introducing

any further speciﬁc topics. This is due to the high

heterogeneity of the research. To answer the ques-

tion about the possible relation between different do-

mains, we have included a representation of a domain

tree (Fig. 1) where we classiﬁed the domains from the

more general to the more speciﬁc concepts.

The conﬂuence of Semantic Web with IoT has

covered many diverse ﬁelds each supported by differ-

ent tools and implementations. This is to be expected

as IoT represents a central concept of many emerging

smart domains, and at the same time it also comes to

show how cross-cutting the IoT concept is — being

applied to many different domains: from industry and

automation, smart cities, smart grids to the healthcare

domain.

Figure 1: Domain Tree.

Table 4: Domain of Tools.

Domain Count Percentage Items

Smart Grids 2 3.3% K01, K29

Smart Home 7 11.6% K02, K17, K32

K46, K54, K57

K59

Smart Buildings 3 5% K02, K03, K10

Music 1 1.6% K04

WoT 5 8.3% K05, K09, K36

K37, K43

Smart Cities 5 8.3% K06, K10, K23

K35, K39

Sensors 7 11.6% K07, K27, K31

K38, K39, K40

K48

Industry 5 8.3% K11, K17, K21

K45, K53

Building Automation 1 1.6% K12

Cybersecurity 2 3.3% K13, K22

IoEverything 1 1.6% K14

General 13 21.6% K15, K16, K20

K26, K34, K42

K44, K49, K50

K51, K52, K55

K56

Cloud/Edge Computing 2 3.3% K18, K30

Virtual Agent 2 3.3% K19, K60

Social IoT 2 3.3% K08, K24

Smart Factories 1 1.6% K25

Healthcare 4 6.6% K28, K46, K57

K58

Energy Consumption 2 3.3% K29, K41

Toxicology 1 1.6% K33

Automation 1 1.6% K45

Smart Cars 1 1.6% K47

4.2 RQ2 — Design of Tools

As shown in Table 5 we have divided the tools found

in three non-exclusive categories as high-level goals

of the tools: i) End-user Tools, ii) Semantic Systems

and iii) Modelled Tools. This categorization helps to

provide a perspective on the type of each tool. The

ﬁrst category, End-user Tool, refers to any tool that

has been developed with an end-user in mind; i. e.:

a tool that has been designed to be used by a non-

specialist in the IoT/Semantic Web topic. These tools

only represent about a 15% of the total, showing the

relative low number of tools speciﬁcally designed for

end-user integrating IoT and the Semantic Web. Some

of the End-user Tools are considerably different from

each other. As an example we have K17 where WoT

Store is enabled in such a way that a non-expert could

include new functions in the pre-existing devices. On

the other hand, K33, presents DIY-KR-KIT, that while

still being an End-user Tool, it is intended to help

developers work faster and with greater ease in the

domain of ontologies, providing a GUI in which the

Tools for the Conﬂuence of Semantic Web and IoT: A Survey

155

components of the ontology behave as pieces from a

jigsaw thanks to the Blockly technology

Table 5: Categorization of the Tools.

Category Count Percentage References

Semantic System 32 53.3% K02, K03, K08, K09, K10

K12, K14, K15, K18, K20

K21, K23, K25, K26, K27

K28, K29, K30, K31, K32

K38, K41, K42, K44, K47

K48, K51, K52, K54, K55

K57, K58

Modelled Tool 19 31.6% K01, K05, K06, K11, K13

K16, K19, K24, K34, K35

K37, K40, K43, K45, K49

K50, K52, K53, K60

End-user Tool 9 15% K17, K33, K36, K39, K46

K53, K56, K58, K59

With the term Semantic System we are referring

to any tool that is built upon semantic technologies, i.

e.: that uses the support provided by the main seman-

tic standards to create its functionalities. These tools

represent the 53.3% of the total. These tools rely on

the core semantic technologies (e.g., OWL, SPARQL,

RDF) and are building full support based on the most

common standards. It is interesting to see how these

technologies have been applied in research related to

IoT. For example K14 develops an unnamed frame-

work that uses standards such as OWL or RDF to re-

trieve sensors information, while K23 uses the NIST

CPS Framework and the same standards to propose a

better understanding of the trustworthiness of the dif-

ferent devices thanks to reasoning.

Finally, by Modelled Tools we are referring to

tools that are based and implemented from a previ-

ously introduced model that provides theoretical sup-

port. These tools, a 31.6% of the total, show how the

conﬂuence can be based on the support of previous

models that need emerging tools for the application in

the speciﬁc context. Between these tools we can ﬁnd

an important heterogeneity — K34 provides BETaaS

Platform for integrating IoT devices from a context-

aware perspective, but on the other end K49 solves the

same problem with the use of a middleware approach,

such as I2oTegrator, through the use of multiple op-

erations geared towards the use of reasoning.

4.3 RQ3 — Types of Tools

When pondering the question about the type of tool

used in the conﬂuence, we categorized the tools in 6

different types, as it can be seen in Table 6. These

6 types are: platform, environment, framework, mid-

https://developers.google.com/blockly

dleware, architecture, and ontology. Even though on-

tologies can be considered a more cross-cutting con-

cept than the other categories, we preferred to include

it in the classiﬁcation, as it can give a more detailed

view of the representation of ontologies among the

reviewed software implementations. Furthermore, it

could be argued that ontologies are not tools, but we

are considering them as so as they supply a critical

role when addressing IoT data.

• Platform. We refer by the deﬁnition given by

IGI

: ”speciﬁc platforms on which technical ar-

chitecture is laid out and is made to run. This type

of platform mostly consists of mixture of hardware

and software services”.

• Environment. The term environment denotes a

higher level than platform: ecosystems that allow

the implementation of new capabilities of IoT tak-

ing into account the integration of platforms, ser-

vices, and business functionality (Messerschmitt

and Szyperski, 2003).

• Framework. Represents a layered structure on

which functions are developed and interrelate

with some developed software platform – in other

terms, ”a software structure with facilities for

software development, such as language transla-

tors, debugging facilities, libraries”

• Middleware. Formed by software components

that enable connection between different lay-

ers often through network connectivity (Etzkorn,

2017).

• Architecture. The building blocks of software

systems and the design of the different function-

alities and capabilities (Bass et al., 2003).

Middleware

Ontology

Architecture

Platform

Environment

Framework

Number of Tools

Figure 2: Different Types of Tools.

https://www.igi-global.com/dictionary/

technology-platforms/29539

https://www.igi-global.com/dictionary/

software-framework/27680

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156

Table 6: Types of Tools.

Platform (15.28%) Environment (19.44%) Framework (31.94%) Middleware (6.94%) Architecture (12.50%) Ontology (13.89%)

K10, K17, K24, K25, K33 K05, K08, K11, K13, K19 K02, K09, K12, K14, K15 K16, K20, K35, K49, K54 K01, K03, K10, K21, K34 K04, K07, K16, K18, K19

K34, K39, K47, K58, K59 K27, K28, K29, K46, K52 K18, K23, K26, K30, K31 K40, K42, K44, K46, K60 K20, K21, K22, K30

K06 K53, K56, K57, K60 K31, K32, K36, K37, K41

K42, K43, K45, K48, K50

K51, K54, K55

• Ontology. We are referring to the vocabularies

that ”deﬁne the concepts and relationships used

to describe and represent an area of concern”

There are many ontologies available in different

domains — e.g., in the energy domain (Blanco.

et al., 2021).

The classiﬁcation (Fig. 2 and Table 6) shows the

diversity of the IoT implementations that were cate-

gorized, where multiple types of tools can be used to

implement solutions in the conﬂuence with the Se-

mantic Web. We discuss next the main ﬁndings.

The less represented type of tools are those that

fall under the label of middleware. While there have

been some interesting tools such as SPF of K35,

which supports IoT deployment and distributed com-

puting for Smart Cities, the representation of this type

of tools is relatively low (6.94%).

Architecture (architectural descriptions), Ontol-

ogy and Platform are distributed similarly, in the

range 12-16%. In particular, the case of ontology

type tools is interesting as they are usually provided

alongside other type of tools, such as frameworks as

OntModel in K30 for the transference of ontologies,

or such as architectural descriptions as MTComm in

K21 for Cyber-Physical Manufacturing Cloud. This

is mostly due to the fact that ontologies are introduced

not as a standalone tool, but rather as a complement

to empower other parts of the infrastructure. For ex-

ample, the case of K19, in which unnamed ontology

models are deﬁned to facilitate the work of an envi-

ronment focused on providing rules for installing new

functionalities based on virtual agents.

The two most relevant categories were Environ-

ments and Frameworks, with a 19.44% and 31.94% of

the cases respectively. This gives us two different per-

spectives, as on the one hand, environments are more

catered towards the integration and usage of IoT tech-

nologies, while frameworks focus on the development

of the IoT technology itself. This can be easily appre-

ciated in K57 where an environment developed with

DomoNet and DomoML is described to prevent health

hazards in a Smart Home thanks to the use of IoT de-

vices. On the other hand, in K36 a framework named

WoTbench for measuring the performance of differ-

https://www.w3.org/standards/semanticweb/ontology.

html

ent IoT devices is introduced, and thus offering a new

tool for the development of new IoT technologies.

All in all we can take as a conclusion that the work

currently done in the conﬂuence of IoT and Semantic

Web is focused on a variety of software platforms, en-

vironments, frameworks with ontologies playing an

important role to support the different implementa-

tions. We also found relatively low number of arti-

cles that were applying machine learning, despite the

current interest for it. One example of this is K06

in which sensors and WoT were used to predict the

parking occupancy comparing the results obtained by

Neural Networks and Random Forests methods.

4.4 RQ4 — Focus of Tools

As for the different focus of the tools we extracted

four main categories that were represented in the ar-

ticles: i) Security, ii) User-friendliness, iii) Reason-

ing, and iv) Interoperability (Fig. 3 and Table 7). By

”focus on security” we are referring to tools that try

to improve the integrity and resistance to attacks of

IoT devices. By ”user-friendliness” we refer to those

tools whose focus is offering a better experience of

the IoT environment for the user, trying to relieve the

need for technical expertise. By ”reasoning” we are

strictly referring to those tools whose main objective

is to enhance the semantic reasoning offered by the

so-called Semantic Web reasoners with IoT integra-

tion. And, ﬁnally, by ”interoperability” we are includ-

ing the work done to overcome the heterogeneity that

deﬁnes IoT devices enhancing the integration process.

Both reasoning and security cover each a 11.6%.

An example of security can be found in K01 where

we can see the intention of modelling ontologies to

prevent Advanced Persistent Threat (APT) attacks –

one of the most common kind of attacks in IoT. In the

same sense, reasoning is a really advanced tool for

processing data that requires a well-established do-

main. For now, reasoning is used as a complement as

in K52, where it is used to enable the interoperability

of IoT devices connected to a data stream.

User-friendliness represents a 25% and the sec-

ond one focus in terms of size. This focus represents

the intention of making the domain more amicable for

users that adopt new services based on semantic IoT.

Tools for the Conﬂuence of Semantic Web and IoT: A Survey

157

0 20 40

Reasoning

Security

User-friendliness

Interoperability

Number of Tools

Figure 3: Focus of Tools.

A substantial part of the community cares for grow-

ing further away from its frontier and proposes tools

that are able to bring the IoT/Semantic Web to most

non-expert users. Probably the best example of this is

K17, where the WoT Store, akin to most common app

stores, is set up so the end-user is able to integrate new

functionalities in pre-existing devices.

Table 7: Focus of Tools.

Focus Count Percentage References

Security 7 11.6% K01, K15, K23, K24, K26

K37, K51

Interoperability 36 60.0% K02, K03, K05, K08, K09

K10, K11, K12, K13, K14

K16, K18, K19, K20, K25

K27, K28, K29, K30, K31

K34, K36, K38, K40, K41

K43, K44, K45, K46, K47

K48, K49, K50, K52, K53

K55

User-friendliness 15 25.0% K02, K06, K17, K21, K32

K33, K35, K39, K42, K47

K54, K56, K58, K59, K60

Reasoning 7 11.6% K23, K28, K30, K49, K52

K54, K57

Finally, interoperability is by far the biggest focus

of all the reviewed papers with a count of 60%. This

is due to the nature of IoT and how heterogeneous

the ﬁeld can be. It also points out how it is an open

problem that is yet to be solved by an all-integrating

solution. One of the papers that is best at showing

how the work is done in interoperability is K50, where

the framework Things2Vec is implemented for using

generated graphs to model the function sequence re-

lationships between IoT devices.

4.5 RQ5 — Secondary Tools

Answering this RQ has been more challenging, as

third party tools used in the research might not have

been mentioned in the articles. We have noted up

to 54 different secondary tools without a speciﬁc one

being used generally enough that it could be consid-

ered as a key one. On this regard, the most used

secondary tool is Prot

e with a total of 7 instances,

which is far from having signiﬁcance given the num-

ber of reviewed articles. Other articles cite common

secondary tools such as Pellet, Apache Jena, HermiT.

As highlighted when answering other RQs, the ma-

jority of the papers mention OWL and SPARQL —

which are more than expected standards for the con-

vergence of IoT and the Semantic Web.

5 CONCLUSION

We have proposed a review about tools that were built

to support the conﬂuence of Semantic Web and IoT,

offering a comprehensive perspective on the current

state of the art. We have deﬁned ﬁve different re-

search questions that range from the domains the tools

are developed for, to the focus of the tools – includ-

ing questions about the design, the type of tools and

about any other secondary tools that might have been

used. We have been able to identify 6 different types

of tools, 3 different types of categories for the tools,

and 4 different types of focus. The classiﬁcation has

been analyzed from the perspective of the 21 differ-

ent domains that we have been able to identify (e.g.,

Industry 4.0, Smart Cities). When answering the re-

search questions, we have provided some interpreta-

tions about the current state of the art and the research

directions. Overall, we found out that the tools for

the convergence have focused more on interoperabil-

ity aspects, and generally more on the application of

modelling and building semantic systems, rather than

on the provision of end-user tools. As well, there is a

variety of heterogeneous domains that are covered by

the tools: from the energy domain, to the healthcare.

ACKNOWLEDGEMENTS

The research was supported from ERDF/ESF ”Cy-

berSecurity, CyberCrime and Critical Informa-

tion Infrastructures Center of Excellence” (No.

CZ.02.1.01/0.0/0.0/16 019/0000822).

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