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The semantics stack in IoT
Secure Semantic Interoperability for IoT
Applications with Linked Data
George Hatzivasilis, Othonas Soultatos,
Eftychia Lakka, Sotiris Ioannidis
Institute of Computer Science
Foundation for Research and
Technology – Hellas (FORTH)
Heraklion, Crete, Greece
Darko Anicic, Arne Bröring
Corporate Technology Siemens
Agrate Brianza, Italy
Konstantinos Fysarakis, George
Sphynx Technology Solutions AG
BlueSoft Sp. z.o.o.
Abstract—Interoperability stands for the capacity of a
system to interact with the units of another entity. Although it
is quite easy to accomplish this within the products of the same
brand, it is not facile to provide compatibility for the whole
spectrum of the Internet-of-Things (IoT) and the Linked Data
(LD) world. Currently, the different applications and devices
operate in their own cloud/platform, without supporting
sufficient interaction with different vendor-products. As it
concerns the meaning of data, which is the main focus of this
paper, semantics can settle commonly agreed information
models and ontologies for the used terms. However, as there
are several ontologies for describing each distinct ‘Thing’, we
need Semantic Mediators (SMs) in order to perform common
data mapping across the various utilized formats (i.e. XML or
JSON) and ontology alignment (e.g. resolve conflicts). Our goal
is to enable end-to-end vertical compatibility and horizontal
cooperation at all levels (field/network/backend). Moreover,
the implication of security must be taken into consideration as
the unsafe adoption of semantic technologies exposes the
linking data and the user’s privacy, issues that are neglected by
the majority of the semantic-web studies. A motivating
example of smart sensing is described along with a preliminary
implementation on real heterogeneous devices. Two different
IoT platforms are integrating in the case study, detailing the
main SM features. The proposed setting is secure, scalable, and
the overall overhead is sufficient for runtime operation, while
providing significant advances over state-of-the-art solutions.
Keywords—semantics, linked data, data mapping, ontology
alignment, interoperability, IoT, JSON-LD, SPARQL-LD
This paper tackles the semantic interoperability issues
that arise in the Internet of Things (IoT) domain .
Semantic interoperability is the designed property where
various systems can interact with each other and exchange
data with unambiguous, shared meaning. This enables
knowledge discovery, machine computable reasoning, and
federation of different information systems.
Interoperability is materialized by including information
regarding the data (metadata) and linking each element to a
commonly shared vocabulary (e.g. , ). Thus, the
meaning of the data is exchanged along the data itself in a
self-describing information package. The shared vocabulary
and the associations to an ontology enable machine
interoperation, logic, and inference. Ontology is the explicit
specification of a conceptualization and includes a formal
representation of the properties and relations between the
entities, concepts and data of a specific application domain.
In general, technologies from the Semantic Web are adapted
in order to capture the inherited properties of an IoT
ecosystem , . They are mainly eXtensible Markup
Language (XML) schemes, such as the Resource Description
Framework (RDF), RDF Scheme (RDFS), and Web
Ontology Language (OWL) for ontologies, and for services
the Web Services Description Language (WSDL). These
primitives provide common definitions of data or services,
describe things with the underlying properties, and
accommodate the semantic annotations, discovery of
resources, inference of knowledge, and access control, in an
interoperable and machine-readable fashion.
The common format and meaning of semantics in a
universally accepted ontology, as suggested above, would be
fruitful. Yet, this is not the current status . While various
systems could employ standardized or popular ontologies,
eventually they extend them and settle own interfaces and
semantics (e.g. , ). Thereby, the direct interaction of
such systems is infeasible. A smart watch for example,
which is developed in IOS could not interwork with smart
bulbs without a relevant proprietary gated application from
the same brand. Therefore, islands of IoT functionality are
established, leading towards a vertical ‘Intranet-of-Things’
instead of the actual vision of an ‘Internet-of-Things’. To
presume upon the full potential of the IoT setting, we require
standards for accomplishing the desired horizontal and
vertical operation, communication, and programming across
platforms/devices, independent of their vendor and/or model.
Nevertheless, the cyber-security concerns must be also
taken into account throughout the whole semantic resolution
process. The mainstream network defences alone (i.e. TLS)
are not adequate in protecting the communication against an
emerging type of malicious entity, the semantic attacker ,
. While the ordinary attacks could exploit the lack or bad
configuration of cryptography, adversaries in the Semantic
Web try to manipulate the semantic relations and the RDF
rules. The goal is to control the inference operation of the
reasoning components that collect, correlate, and process
data. The semantic attacks exploit network or Web level
vulnerabilities. They do not attack the reasoning system itself
but they try to compromise the input data to influence the
deduced conclusions. The distributed nature of the IoT and
the linking data of the social Web exaggerates the problem,
especially in the case of the follow your nose algorithm 
that is performed by many Linked Data (LD) applications.
Thus, the deployment of Semantic Mediators (SMs) is
recommended in this article in order to correlate the required
information and materialize cross-domain interaction with
interoperability between systems of different semantics. The
SMs transform data in the same format and resolve potential
conflicts between the different thing descriptions. Security
countermeasures are also deployed, protecting the data both
in transit and at rest. The main contributions of the proposed
SMs include: i) cooperation with legacy, XML, and
World Wide Web Consortium (W3C) initiatives for IoT and
LD (i.e. standardized ontologies, iot.schema.org, JSON for
Linking Data (JSON-LD)), iii) direct processing of JSON-
LD data by the inference and reasoning modules (with
SPARQL-LD ), iv) secure transmission of data (Transport
Layer Security (TLS) at all communications) defending the
system against data in transit attacks, v) validation of the data
legitimacy prior their usage (i.e. JSON Web Signature (JWS)
framework ), protecting against the data in rest semantic
attacks, vi) distributed functionality across the edge,
network, and backend systems, vii) efficient and scalable
operation, viii) integration of two EU funded IoT initiatives:
the SEMIOTICS project and the FIWARE cluster, and ix)
advancements over state-of-the-art solutions.
The rest article is structured as: Section II refers the
background theory regarding the semantic interoperability in
the IoT domain and related works for semantic
mediators/brokers. Section III evinces the security
perspective. Section IV describes the proposed SM
component. Section V details the implementation of a
preliminary version along with the performance evaluation.
Section VI discusses the evaluation results and presents a
comparative study with alternative candidates, while Section
VII concludes and mentions future extensions.
There are several Sematic Web initiatives that try to
describe and model specific domain ontologies. The most
notable effort for semantics formation in the IoT field are the
Semantic Sensor Network (SSN) and Sensor Observation
Sampling Actuator (SOSA) ontologies by the W3C
community . Combined together, SOSA/SSN model
sensors, actuators, samplers as well as their observation,
actuation, and sampling activities. The ontologies capture the
sensor and actuator capabilities, usage environment,
performance, and enabling contextual data discovery. This
also constitutes the standardized ontologies for the semantic
sensor networks. The cooperation of SSN and SOSA offers
different scope and degrees of axiomatization that enable a
wide range of application scenarios of Web 3.0 .
The general approach regarding the semantic
interoperability that is followed by several IoT initiatives,
like the EU funded projects OpenIoT  and INTER-IoT ,
is the usage of the SSN/SOSA ontologies as the semantic
base. The ontologies are then extended with the additional
required concepts to model the targeted application
scenarios. Such concepts usually include relevant standards
and ontologies for specific application areas, like e-health
, and less often extensions at the sensor level (as the
relevant SSN/SOSA information is quite complete).
In the ongoing shift towards the Web 3.0, we move from
a Web of linked documents into a Web of linked data ,
. Except from modelling ontology schemas as mentioned
before, this also includes methods for publishing structured
data in a manner that it can be interlinking and accessed by
semantic queries, like in the LD approach. Just recently, the
working group Web of Things (WoT) was initiated by the
W3C in an attempt to circumscribe the fragmentation issues
in IoT and enable interoperable services and devices,
therefore decreasing the overall development costs. The
Thing Description (TD) constitutes a considerable aspect of
this W3C WoT interplay. TDs describe the interfaces of
(physical) Things and their metadata in a machine
interpretable manner. They are built upon the W3C's
extensive efforts on RDF and JSON- LD, and determine a
domain agnostic vocabulary for defining any Thing in terms
of its properties, actions, and events. Here, several semantic
models can express the semantic meaning of these attributes
for each particular Thing. The iot.schema.org is such a
meritorious communal effort to establish a semantic schema
for the IoT ecosystems. Jointly, W3C WoT and
iot.schema.org, instate a layer for semantic interoperability
which renders the software capable in interacting with the
physical world. This interplay is abstracted in such a manner
where the development of applications across various IoT
settings and domains is ease and simplified.
Then, data can be transmitted in an RDF format and
stored in triple stores (e.g. Sesame and Virtuoso) .
Thereafter, tools are used which process semantic queries
. The standardized SPARQL is the query language for
the Semantic Web . It acts as a semantic database and
constitutes the main option for semantic reasoning. Methods
are also supported to interrogate multiple triple-stores over
Hypertext Transfer Protocol (HTTP). SPARQL can process
data with XML format and exploit the RDF rules in order to
answer queries for the stored information. An interesting
variant is the SPARQL-LD . This version parses JSON-
LD data and can also gather linking information directly
from the Internet. The implementation extends a popular
SPARQL processor for the Jena Apache server  and is
quite efficient (an ask query on DPedia requires around
300ms on average).
Fig. 1 illustrates the overall semantics stack of a modern
IoT setting. Thereafter, these semantic layers are adopted in
the SEMIOTICS project and we utilize the SMs in order to
align the semantics of other cooperative platforms, like the
FIWARE cluster. Also, the SMs embodies the SPARQL-LD
for the implementation of the semantic reasoning and the
direct processing of JSON-LD data across the Internet.
B. Ontology Alignment
Depending on the completeness and expressiveness of
the processed ontologies, the aforementioned reasoning tools
can infer associations among the different semantic domains.
OWL rules are exploited for this purpose, like the
owl:sameAs, owl:equivalentClass, and owl:subClassOf.
They provide the basic ontology alignment functionality
between the underlying linking information. Thus, data are
adapted and transformed across signified ontologies.
However, as Halpin et al. state , expressing
relationships on LD is a much more complex problem than
just applying the owl:sameAs rule. Currently, there is a
plethora of distinct datasets that have been developed
independently. The problem arises when someone tries to
integrate/correlate these pieces of knowledge together, as
many of the applied owl:sameAs rules tend to be mutually
incompatible. Indicative cases of erroneous usage include
definitions for: i) the same thing but in different context, ii)
the same thing but in referentially opaque, iii) different
representations of the same thing, and iv) very similar things
. Moreover, the expressive capabilities of OWL are
constrained and are not always sufficient for modeling
complex interrelations or resolving semantic conflicts ,
. Doulaverakis et al. also mention  that the
knowledge base have to be extended with rules in order to
capture situations that cannot be defined by OWL alone.
Therefore, semantic brokers are deployed to perform
ontology alignment and resolve semantic conflicts , ,
retaining the reliability and Quality of Service (QoS) in the
IoT setting. The Semantic Information Brokers (SIBs)  is
a typical option for semantic interoperability in pervasive
computing. The semantics are described by mainstream
solutions, like RDF/OWL, and distributed SIBs resolve
semantics in the local smart space scale. Then, Resolve
Servers interlink the knowledge originated by SIBs and
permit cross-domain interaction, exploiting the modelling
capabilities of OWL. SPARQL is utilized for reasoning.
The Intelligent Information Fusion (IIF) 
recommends a low level mechanism for consolidating
information from heterogeneous sensory equipment. The
case study considers a city-wide public surveillance system
that has to process information from multiple resources (e.g.
visible spectrum or IR cameras, and acoustic sensors) in real-
time. The semantics of each domain are modelled by the
mainstream XML/RDF technologies and the reasoning is
implemented as an SQL-like procedural language in
SPARQL. Then, the user defines fusion functions describing
which pieces of information are to be retrieved (e.g. number
of persons, detection of smoke, etc.) by each platform and
how to use them. IIF successfully correlates semantics of the
same format for different domains and overcomes some
restrictions of the OWL expressiveness. On the other hand, it
does not resolve semantic conflicts.
Similarly, the semantically-enabled Plug & Play
approach for the Sensor Web (, ) facilitates the
automatic association of sensors to data hosting Web
services. A mediation approach is implemented based on an
ontology that extends SSN and a set of SWRL rules. First,
the sensor metadata (expressed in standard SensorML) is
auto-translated into the ontology. Then, the matchmaking is
performed through subsumption reasoning between those
advertised sensor metadata and the requirements specified by
Web services. Finally, for spatial, temporal and unit
matchmaking, SWRL rules are executed and also employed
for mediation between convertible mismatches.
The Semantic Web utilizes the widely-known Uniform
Resource Identifiers (URIs) as a mean to address and link
data and their sources. The consumer (user or service)
discovers the required information in the Web, retrieves the
data from the related URI, and processes it. However, several
security aspects have been neglected throughout the overall
operation . W3C standardized the core semantic
technologies before the formalization of the Same Origin
Policy and the TLS, and thus, the Semantic Web had been
designed without taking into account the security
implications . Until even today, there are almost no
academic works on the Semantic Web security , .
Furthermore, there is also considerable confusion regarding
the underlying security aspects, like the usage of HTTP URIs
or the misuse of cryptographic solutions (i.e. TLS,
WebID+TLS.2, etc.). These facts also raise significant
privacy risks for the personal data that can be exposed and
linked across the Internet.
If TLS has not been properly set in the origin, a network
attacker acting as a man-in-the-middle can manipulate the
transmitted traffic. The malicious entity can gain the control
of the exchanged information and perform a series of
specialized attacks (e.g. Coercive parsing, SOAPAction
spoofing, Metadata spoofing, attack obfuscation, WS-
Addressing spoofing, attacks on Web Service Compositions
through Business Process Execution Language (BPEL) state
deviation, signature wrapping with namespace injection, etc.)
, . These exploits could potentially change endpoint
URLs, message schemas, cryptographic parameters, or
remove security assertions, and even add/delete/change/fake
operations. Over and above, the attacks can be done quite
easily with open-source tools (e.g. sslstrip or wireshark),
while it is impossible for the consumer to discriminate the
Nevertheless, except from securing the information in
transit with TLS, we must also protect the data in rest at the
backend side. This is an even more neglected research issue
for the Semantic Web specialists and practitioners. As
Thuraisingham states , securing RDF is a much more
challenging task than in the ordinary HTML/XML settings as
we also need to retain the security of the semantic level. So
far the highest majority of researchers and Semantic Web
users simply consider the protection mechanisms for access
control and secure transmission .
Thus, consider the case where the hacker infiltrates a
vulnerable server that hosts ontologies or schemas, and
replaces them with malicious ones (in a similar fashion as
they can change the HTML code of popular sites). The same
effect could be accomplished through Domain Name System
(DNS) poisoning, where the attacker makes the traffic to be
routed in a compromised server instead to the legitimate one.
The result will be successful attacks, as in the
aforementioned cases of data in transit assaults (, ).
Note that some versions of these attacks can be performed
even if the TLS communication has been set correctly. Then,
take as an indicative example, an application that utilizes
linking data form the Web in order to determine if a citizen is
categorized in a particular class, like the terrorist group. A
semantic attacker who manipulates one of the resources that
is parsed by the inference engine (either in transit or at rest)
could alter the terrorist definition. The OWL/RDF triples
which denote that the crime must be political by virtue of a
certain government-approved definition, are erased. Then,
any person who exhibits a less important deviating behaviour
(e.g. violation of the road traffic code, robbery, etc.) would
be erroneously categorized as a terrorist. If more triples are
deleted, any citizen could be denoted as a terrorist by this
correctly functioning inference procedure, due to the
utilization of data with poor quality.
Therefore, as the Semantic Web reasoning is based on
collecting and integrating trusted data across the Internet, the
whole information retrieval infrastructure must deploy TLS
for every involved URI. If triples are originated from Web-
level protocols, the protocols must also utilize TLS and
retain their security properties. Moreover, in order to protect
the inference engine that gathers linked data from the Web
for the data in rest attacks, we need to authenticate the
legitimacy of the information before proceeding to further
processing. For these purposes, the IETF standard JWS has
been proposed . The information that is contained in
JSON files is signed and the consumer can verify the
source’s authenticity along with the integrity of the received
data. The full framework, called JOSE , can also support
encryption for confidentiality.
For the proposed SMs, except from deploying TLS and
signing data, we mainly utilize JWS to sign the TDs that are
processed and validate the trustworthiness of the reported
transformation rules. As these rules are pieces of code that
will be executed by the legitimate system for accomplishing
ontology alignment, they have to be also inspected by the
system operator prior their integration to the knowledge base.
Otherwise, the SM could be vulnerable to code injection
attacks. Nevertheless, this is not considered a significant
burden for the operator as it is done only once, when a new
or updated TD is parsed. Then, the runtime interoperation of
the various components is done automatically.
This section details the operation of the semantic
mediators. The SMs utilize the Yet Another Next Generation
(YANG) data model in order to map the data in a common
format (i.e. JSON). For ontology alignment, they retrieve
transformation rules from the related signed JSON-LD files
and then perform the rules as regular expressions in Perl.
A. Data Mapping
The YANG data language is defined in the RFC 7950. It
is a current programming trend and facilitates the
deployment of new applications in various platforms. For
this purpose, YANG supports the NETCONF and
RESTCONF interfaces for the deployment of network and
RESTful services, respectively. The service operations are
modelled in YANG. Then, the YANG processor parses the
model and exports the abstract development project in a
denoted programming language (e.g. JAVA, C/C++, etc.).
For our motivating example below, we utilize these features
in order to deploy the smart functionality that collects,
processes, and transmits the sensed information. More
specifically, we exploit the RESTCONF and implement
RESTful web services that run in the field and backend
systems. RESTCONF is defined in the RFC 8040.
Thereafter, we additionally exploit YANG to establish a
common data mapping between the involved operations. The
interfaces can process messages with semantic information.
At the design phase, we have described the structure of these
messages in YANG (e.g. get current temperature value from
a sensor). Then, at runtime, we can transform XML
messages into JSON ones and vice versa, according to the
specific format which is supported by each interface. The
mapping is accomplished via the IEFT Internet Draft draft-
ietf-netmod-yang-json, which establishes a one-to-one
mapping between JSON and the subset of XML that can be
modelled by YANG. The overall functionality is also
tailored in order to cooperate with legacy formats, as in the
IoT domain there could be several constrained devices, like
motes/sensors, that do not process structured data (e.g. ,
, , and ).
B. Ontology Alignment & Semantic Reasoning
After we have achieved the common format, the next step
is to resolve semantic conflicts and perform ontology
alignment between the interacting domains. Thus, we need
transformation rules that describe how we can transform
data that are processed by one application into a compatible
form which is understandable by another machine.
For the proposed SM components, the rules are modelled
as specific JSON tags that are included in the related
TD/JSON-LD files. Each rule tag contains the identification
of the two domains (from-to) and a Regular Expression
(RE). The RE is a valid PERL program that models the
search pattern (for matching the data to be altered) and the
transformation formula itself (how the data will be changed).
For example, the next TD sample transforms the temperature
value from the Celsius to the Fahrenheit scale. Once parsed
to the inference engine, the rule takes as input the JSON-LD
file from a FIWARE’s set_temperature service, searches for
the temperature value, and changes it to the other scale. The
expressiveness of this RE type is even more advance than
just performing a single mathematic formula. REs can
perform complex transformations and successfully resolve
conflicts that occur by the incorrect OWL correlations .
A. Motivating Example – Smart Sensing
As a motivating example, we consider a smart sensing
scenario, where a smart building deploys several sensing
equipment in order to support pervasive and ubiquitous
Fig. 2. The smart sensing interoperability scenario.
functionality. Energy management is such a popular service.
Horizontal operation in the field system is mandatory as well
as vertical cooperation with the backend. The Customer
Energy Manager (CEM) is a logical function for optimizing
energy consumption and can be deployed either in the home
gateway and/or in the cloud. The interoperability of the
underlying IoT devices and the CEM service must be
guaranteed regardless their brand or manufacturer. The user
should be able to buy and install any smart device while
retaining the full functionality of the integrated system.
As an indicative scenario, we consider the case where the
user installs temperature sensors in the rooms. Three types of
sensory devices are modelled: the first one is bought from a
European manufacturer – measures the temperature in the
Celsius scale (
C) and transmits data in an XML format; the
second one is bought from USA – measures the temperature
in the Fahrenheit scale (
F) and transmits JSON messages;
and the third sensor is compatible with the FIWARE’s
semantics – measures the temperature in
C and transmits
JSON messages. Then, we model two reasoning processes
where the system collects data and takes runtime decisions.
Edge reasoning: The CEM functionality that runs in the
local gateway must retain a specified temperature value in
the building. At first, the SM component in the gateway
maps all gathered data in a common format and aligns all
semantics in the SEMIOTICS schema (iot.schema.org).
Thus, the temperature information is stored in JSON and in
the Celsius scale. If the temperature in a room goes beyond a
threshold, the relevant fan is adjusted accordingly.
Backend reasoning: If one device is damaged or
malfunctioning, the deductive capabilities of JSON-LD are
utilized to search for a technician who can fix it. Thus, the
equipment descriptions and the technicians’ expertise are
collected via Internet. The information is stored in the CEM
cloud service (i.e. SPARQL-LD) and the SM’s ontology
alignment can be performed if it is required.
The SMs are deployed in the gateway and the cloud to
ensure common data mappings and ontology alignment.
They also include a local repository for maintaining TDs and
sensed data. Then, semantic reasoning can be performed, i.e.
with SPARQL/SPARQL-LD. Fig. 2 depicts this scenario.
From bottom-up, we consider 3 main data flows that
implement the abovementioned functionality. The first data
flow includes the local communication of the interconnected
devices at the edge system. The devices can interact directly
(if they are compatible) or indirectly through a gateway. If it
is required, the gateway also performs the SM services,
applying common data mappings and semantics. In the
second setting, the devices or a gateway application interplay
with the backend. Here again, the gateway can execute the
SM services for semantic interoperability. In cases where the
communication between the field and the backend (flow 2)
must be encrypted, the SM functionality is performed in the
cloud by the end-point that decrypts and processes the data.
B. Performance Evaluation & Comparison
A preliminary version of the proposed setting is
implemented. We deploy two different embedded platforms
that emulate the smart sensing equipment, consisting of
Zolertia Z1 motes and BeagleBone nodes. Two Z1 transmit
C messages with 6LoWPAN, a BeagleBone sends
F data over Ethernet, while another BeagleBone
C information via USB-WiFi. A laptop
acts as the local gateway that gathers data from the edge
system. It also runs the SM service and emulates a fan device
that exchanges legacy-formatted/
C messages. A similar
virtual machine runs the backend SM in the cloud platform
Proxmox along with end-user services.
We measure the performance of the SM component in
the laptop (the cloud version performed similarly). For the
initialization process, 100 TDs are parsed by the mediator
(for the sake of this testbed, the operators consent is gained
automatically, without the manual inspection of the
transformation rules). Then, the devices sent totally 100
sensed messages, requiring common data mapping and
ontology alignment. Table I details the average evaluation
results. As is evidence, the overall overhead of the SM is
adequate for real-time applications which will have to
process many messages simultaneously.
TABLE I. P
Operation CPU (ms) RAM (KB)
Sign JSON-LD (RSA-2048) 0.3 7.0
TLS 10.7 11.5
Verify signature (offline) 0.2 0.5
Signature verification of TD 0.2 0.5
Processing of TD and extraction of
transformation rules 0.1 0,2
Intermediate data mapping models (Yang) 800 32,658
Total resource consumption 800.3 33.358
Operation CPU (ms) RAM (KB)
TLS+Signature verification 10.9 12
Data mapping (Yang) 800 32.658
Ontology alignment (Perl execution) 4.7 452
Maximum resource consumption 815.6 33.122
Table II summarizes the main features that are provided
by the proposed SM and the related SIB  and IIF . In
general, SIB/IIF are suitable for RDF/OWL ecosystems
while SM also exploits the capabilities of the modern LD
approach. The expressive power of the SM/REs is far more
advance than a specific notification schema that must be
supported by all entities, as proposed by the related works.
Thus, SM resolves a high variety of semantic conflicts,
including the four main cases of erroneous OWL usage ,
and offers a general ontology alignment approach. Moreover,
security and trust are considered, with the validity of TDs
and semantic data being verified before further processing.
TABLE II. C
Feature SM SIB  IIF 
Architecture Distributed Distributed Distributed
Data formats Legacy, XML,
JSON XML XML
Data mapping JSON-LD RDF/OWL RDF/OWL
Reasoning engine SPARQL-LD SPARQL SPARQL
Semantic Security TLS and
JOSE/JWS No No
This article presents the landscape for semantic
interoperability in the IoT. To do so, the state-of-the-art
approaches are reviewed, including technologies for
semantics, data mappings, ontologies alignment, semantic
reasoning, etc. The main outcome is the proposal of the
Semantic Mediator (SM) component which can be deployed
across the various IoT layers (field, network, backend) and
provide the required common representation and meaning of
data. The platform integration of 2 EU funded IoT initiatives
(SEMIOTICS and FIWARE) is described in a smart
temperature sensing scenario. This includes the appliance of
the various interoperability methods from the field to the
backend. The overall deployment is scalable and sufficient
for real-time operation. In comparison with related settings,
the SM retains security and exhibits more advanced data
mapping and ontology alignment capabilities. As future
extension, the SMs’ can be applied as privacy mediators,
where the transformation rules anonymize or generalize the
exchanged data and enhance the user’s privateness.
This work has received funding from the European Union
Horizon’s 2020 research and innovation programme under
the grant agreements No. 780315 (SEMIOTICS), No.
786890 (THREAT-ARREST), and No. 830927
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