The Interoperability of Things:
Interoperable solutions as an enabler for IoT and Web 3.0
George Hatzivasilis, Ioannis Askoxylakis,
Institute of Computer Science
Foundation for Research and Technology–Hellas
Heraklion, Crete, Greece
Darko Anicic, Arne Bröring, Vivek Kulkarni
Siemens AG, Corporate Technology
Konstantinos Fysarakis, George Spanoudakis
Sphynx Technology Solutions AG
Abstract—This paper presents an overview of the
interoperability concepts along with the challenges for the
IoT domain and the upcoming Web 3.0. We identify four
levels of interoperability and the relevant solutions for
accomplishing vertical and horizontal compatibility between
the various layers of a modern IoT ecosystem, referred to as:
technological, syntactic, semantic, and organizational
interoperability. The goal is to achieve cross-domain
interaction and facilitate the proper usage and management
of the provided IoT services and applications. An
interoperability framework is also proposed where the
involved system components can cooperate and offer the
seamless operation from the device to the backend
framework. This by-design end-to-end interoperation
enables the interplay of several complex service composition
settings and the management of the system via patterns. The
overall proposal is adopted by the EU funded project
SEMIoTICS as an enabler towards the IoT and Web 3.0,
even when products from different vendors are utilized.
Keywords—IoT; Web 3.0; interoperability; by-design; end-
to-end; semantic; syntactic; multimode radio; multiprotocol
proxy; semantic broker;
Interoperability is the ability of a system to work with
or use the components of another system. It is easy enough
to achieve integration of different systems within the same
domain or between different implementations within the
stack of a specific software vendor . In the current
Internet-of-Things (IoT) ecosystems, the various devices
and applications are installed and operate in their own
platforms and clouds, but without adequate compatibility
with products from different brands -. For example, a
smart watch developed in Android cannot interact with a
smart bulb without the relevant proprietary gated
application provided by the same vendor. Thus, islands of
IoT functionality are established that lead towards a
vertically-oriented ‘Intranet-of-Things’ rather than the
‘Internet-of-Things’. To take advantage of the full
potential of the IoT vision, we need standards to enable the
horizontal and vertical communication, operation, and
programming across devices and platforms, regardless
their model or manufacturer. Thus, from bottom-up, four
levels of interoperability emerge:
• Technological: includes the seamless operation
and cooperation of heterogeneous devices that
utilize different communication protocols on the
transmission layer (e.g. WiFi, ZigBee, 802.15.4).
• Syntactic: establishes clearly defined and agreed
formats for data, interfaces, and encodings
• Semantic: settles commonly agreed information
models and ontologies for the used terms that are
processed by the interfaces or are included in the
• Organizational: cross-domain service integration
and orchestration through common semantics and
Although the boundaries of each level are not strict, we
consider in our methodology that technological, syntactic,
and semantic interoperability enable horizontal
compatibility between the involved technologies and
platforms, while vertical operation is accomplished
through organizational interoperability. Details regarding
these four levels and the relevant state-of-the-art
interoperability mechanisms are provided in the
In general, it is considered that Web 1.0 is a static
‘read-only’ setting, Web 2.0 or Social Web is a ‘read-
write’ environment, and Web 3.0 or Semantic Web will
Fig. 1. Multimode Radio Hub.
enable the ‘read-write-execute’ perspective . At first,
the users were able only to passively access web pages.
Now, they can also create content and interact with sites
and other users through forums, social media, etc. Next,
the Web will become even more intelligent compared with
the current solutions and will additionally permit the
composition of more complex functionality and services
by the user (e.g. , , ).
IoT is a main enabler of Web 3.0 . The resolution
of the abovementioned interoperability issues emerges as
one of the most significant obstacles for materializing the
concept of the Wisdom-of-Things . The SEMIoTICS
project  considers all interoperability scopes, but it will
focus on the high-level administration of services and the
appliance of pattern-based management strategies.
The remaining paper is organized as: Section II
presents the state-of-the-art and related work. Section III
details the technological interoperability. Section IV
describes the syntactic interoperability. Section V refers to
semantic interoperability. Section VI analyzes the
organizational interoperability solutions. Section VII
sketches the offered functionality of the proposed
framework that will be utilized by the SEMIoTICS project.
Finally, Section VIII concludes.
Surveys for IoT and Industrial IoT (IIoT) are presented
in  and , respectively, highlighting the state-of-the-
art techniques and the main design challenges. Inter-
domain interoperability is effectively supported by various
solutions while intra-domain cooperation remains
Several researchers have studied and resolved
interoperability issues for specific system components. IoT
protocols are detailed in . They offer the main
messaging functionality between the various IoT devices
and simplify the programming effort for an application.
The common choices are the Constrained Application
Protocol (CoAP), eXtensible Messaging and Presence
Protocol (XMPP), Advanced Message Queuing Protocol
(AMQP), MQ Telemetry Transport (MQTT), Data
Distribution Service (DDS), and Hybrid Lightweight
Protocol (Hy-LP) , . Middleware solutions are
reviewed in . They provide information discovery and
orchestration of the underlying system, while promoting
the Service oriented Architecture (SoA) of the modern IoT
settings with enhanced scalability. Popular solutions
include the Devices Profile for Web Services (DPWS),
Universal Plug and Play (UPnP), and Open Services
Gateway initiative (OSGi) , . The key aspect of
Discovery on the IoT is surveyed in . Four different
patterns of discovery have been identified that relate to the
search for things around the client, on the network, on a
directory, as well as the query for metadata. Technologies
in these four respective discovery patterns are for example
Bluetooth Low Energy, SSDP or mDNS, the CoRE
Resource Directory, and the CoRE Link Format. Context-
aware and semantic approaches for interoperability are
mentioned in  and , respectively. XML
technologies for the Semantic Web are utilized, enabling
knowledge extraction, discovery, and use of resources.
Thus, general or domain specific ontologies are modelled
for different sectors, like e-health and transportation ,
facilitating the machine-to-machine (M2M) interaction.
Integration of IoT with cloud computing is then deployed
, easing the overall management of the system and
performing Big Data analysis. All these application
settings utilize the abovementioned technologies in order
to enable device and service discovery and administration,
accomplishing the inter-domain interaction .
The basic intra-domain interoperable approaches are
presented in , resembling the case of smart homes.
Such techniques tackle the cooperation only at the higher
system layers. Semantic Information Brokers (SIB)
correlate the different semantics and ontologies of the
equipped products and provide a common interpretation of
the various system aspects. Then, high-level common
programing interfaces provide a uniform manner in
developing and maintaining new applications.
Fig. 2. Multiprotocol Proxy.
This paper reviews solely the interoperability issues in
the modern IoT ecosystem. The involved technologies in
the different system layers are detailed along with their
interconnection setting. The goal is to implement end-to-
end and seamless operation from the field to the backend
infrastructure while interplaying with products and
services from various vendors. According to our
knowledge, the proposed framework is the only solution
that tackles the interoperation for the four layers in a
Technological interoperability still remains a
significant barrier in IoT settings as up to 60% of the
overall potential value is currently locked due to lack of
compatible solutions . Multimode radio equipment
constitutes the main technical solution towards the
integration of the various heterogeneous devices that
utilize different networking and communication means.
Smart phones are a representative example. They
deploy a cellular modem, which supports 7 radio interfaces
and enable the connection to Global System for Mobile
Communications (GSM), Code Division Multiple Access
(CDMA), or Long Term Evolution (LTE) networks. Thus,
a smart phone can operate with any cellular network and
communicate with any other phone.
A similar approach can be followed in various IoT
ecosystems, like a smart house. Home hubs, like routers
and gateways, implement multimode radios and support
various communication technologies (e.g. WiFi, Bluetooth,
ZigBee, 802.15.4). These hubs act as bridges and provide
the desired interoperable functionality. Thus, modern TVs
and thermostats that use WiFi, speakers that communicate
with Bluetooth, as well as switches and light bulbs that
connect with ZigBee, can interact with each other,
providing the user with flexible and convenient ways to
interoperate with different smart home ecosystems. For
example, a WiFi TV can communicate with ZigBee light
bulbs through the home’s multimode radio router. This
setting can facilitate the installation and synchronization of
new devices, and ease the connection to the network. Fig.
1 presents a typical multimode radio hub for IoT that
supports 6 different communication interfaces (TCP/IP,
WiFi, 6LowPan, 802.15.4, ZigBee, and NFC).
Once the devices are connected, most of the required
interoperability functionality can be implemented in
software. For instance, ZigBee can be developed in a
networking stack if the devices support the 802.15.4
technology. Software solutions ease manufacturers to
update their products, fix bugs, and add new features
without requiring to redesign the underlying hardware.
This capability addresses diversity and fragmentation, and
can also reduce replacement and management costs.
However, security issues may raise. Deploying
multiple wireless technologies in a device can potentially
expose more attack points where malicious entities could
inject unauthorized code and sniff network traffic.
Hardware security protection mechanisms, such as
cryptographic protocols or secure boot and trusted
environment execution, can safeguard the system and
counter such attacks (e.g. , , , ).
IoT vendors utilize standardized and widely used
technologies and platforms in order to increase the
acceptance of their products. Common solutions include
the messaging protocols CoAP, XMPP, AMQP, MQTT,
DDS, and Hy-LP, as well as the platforms DPWS, UPnP,
and OSGi .
However, these solutions offer only inter-domain
compatibility and they usually act as closed silos with
narrow application focus, imposing specific data formats
and interfaces. Mechanisms for resolving these issues and
achieving horizontal interoperability include gateway
proxies for the messaging protocols.
The main setting is suggested in . The proposal
automatically converts messages from one messaging
protocol to the compatible format of another protocol. The
functionality is offered among RESTful HTTP, CoAP,
XMPP, MQTT, and DDS, and can be easily extended in
order to support the rest of the popular protocols .
Fig. 2 depicts the deployment setting for the main
CoAP, XMPP, and MQTT, along with the related data
formats of each protocol. CoAP operates similarly with
HTTP. XMPP requires a resource server, while MQTT
imposes a central broker that administrates the
communication. The messages from the three distinct
settings are parsed through a message broker that
implements the core functionality of the multiprotocol
proxy, translating the messages from one protocol to the
other and managing the communication flow. The
messages can be also maintained locally with a topic
router, easing the discovery process of the communicating
Each platform utilizes specific message protocols.
Through a multiprotocol proxy they can also expand their
functionality and interact with devices that support
All these methods provide the main inter-domain
interoperability features at the syntactic level. The devices
can communicate seamlessly, but until this point, they
cannot understand each other. Thus, additional
mechanisms are required to represent and explicate the
information semantics in a machine-interpretable format,
as described in the following section.
Today, the semantic technologies that enable and
facilitate the interoperability in web services are
commonly adapted in the IoT domain. This includes
widely-used and well-studied XML schemes, like the
Resource Description Framework (RDF), RDF Schema
(RDFS), and Web Ontology Language (OWL) for
ontologies, and the Web Services Description Language
(WSDL) for services. Such technologies offer common
description and representation of data and services,
characterize things and their capabilities, and deal with the
semantic annotation, resource discovery, access
management, and knowledge extraction in a machine-
readable and interoperable manner.
Towards these goals, the most notable effort in the IoT
field is the Semantic Sensor Network (SSN) ontology and
Sensor Observation Sampling Actuator (SOSA) ontology
by the World Wide Web Consortium (W3C) community
. The SOSA/SSN ontologies model sensors, actuator,
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 towards the Web of
More specifically, the SSN ontology is a suite of
general purpose ontologies. It embodies the following 10
conceptual modules: 1) Device, 2) Process, 3) Data, 4)
System, 5) Deployment, 6) PlatformSite, 7) SSOPlatform,
8) OperatingRestriction, 9) ContraintBlock, and 10)
MeasuringCapability. The modules consist of 41 concepts
and 39 object properties.
The general approach regarding the semantic
interoperability that is followed by several IoT initiatives,
like the European Union (EU) funded projects Open
source solution for the Internet of Things (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). Other similar and popular
IoT ontologies include the Smart Appliance REFerence
(SAREF)  and the MyOntoSens .
More recently, W3C has launched a working group
called Web of Things (WoT)  with the goal to counter
the fragmentation of the IoT and enable interoperable IoT
devices and services, thereby reducing the costs of their
development. A notable feature of W3C WoT approach is
Thing Description (TD) , used to describe the metadata
and interfaces of (physical) Things in a machine
interpretable format. TD has been built upon W3C's
extensive work on RDF , JSON-LD , and Linked
Data . TD defines a domain agnostic vocabulary to
describe any Thing in terms of its properties, events and
actions. In order to give a semantic meaning to a set of
properties, events and actions for a particular Thing
various semantic models can be used, e.g., SOSA/SSN,
SAREF etc. One notable community effort to create a
semantic schema for IoT applications is iot.schema.org
. It is an extension of well-known schema.org for IoT.
iot.schema.org introduces a semantic model to describe a
capability of a Thing. A capability is the set of affordances
needed to interact with a single function of a connected
Thing, e.g. an on/off switch capability. Together, W3C
WoT and iot.schema.org, provide a semantic
interoperability layer that enables software to interact with
the physical world. The interaction is abstracted in such a
way that it simplifies the development of applications
across diverse domains and IoT ecosystems.
The common interpretation of semantic information in
a globally shared ontology could be quite useful. However,
this is not always the case. Although several local systems
may utilize popular or standardized ontologies, eventually
they extend them and establish their own semantics and
interfaces. The direct interaction between these systems is
not feasible. The use of Semantic Information Brokers
(SIBs) is proposed in , which correlate the required
information and enable the interoperability of systems with
different semantics or cross-domain interaction.
Moreover, a common and generic Application
Programming Interface (API) is established by the EU
funded project BIG IoT  between the different IoT
middleware platforms. The API and the related
information models are determined in cooperation with the
Web of Things Interest Group at the W3C, enhancing the
supported standards of this community. The API eases the
development of software services and applications for
different platforms according to a well-defined architecture
Fig. 3. The SEMIoTICS interoperability framework (adjusted and extended from ).
Thus, the cooperation of an SIB with the
abovementioned common API permits complex service
composition and added value applications. Such APIs
provide well-defined functionalities that can also
implement interoperability on device-, fog-, and cloud-
level. The main functionalities include: i) identity
management and registration to resources, ii) resource
discovery based on user-defined criteria, iii) access to data
and meta-data (e.g. publish/subscribe of data streams), iv)
command forwarding to things, v) vocabulary
management of semantic information, vi) security
management (key management, authentication,
authorization, etc.), and vii) charging and billing
management for using the provided assets. Being able to
monetize IoT resources is also key to establishing business
models for a flourishing ecosystem , which is crucial
for organizational interoperability.
The manufacturer’s resources are advertised on the
marketplace. Clients can discover the offered applications
and gain access to them. In the near future, it is expected
that there will be multiple marketplaces for IoT products
. The marketplaces could be set for each application
domain (e-health, smart home, etc.) or there could be
multiple marketplaces for a single domain but set by
As the developers comply with the defined interfaces,
the marketplaces enhance the organizational
interoperability. In cooperation with SIBs, the cross-
domain IoT vision is further fostered. Thus, a modern IoT
application can utilize services from different
manufacturers and implement horizontal interoperable
solutions that also utilize the three vertical interoperability
layers, accomplishing seamless operation from the device
end to the backend infrastructure.
The SEMIoTICS proposal utilizes the aforementioned
state-of-the-art mechanisms for the four interoperability
levels. It implements by-design cross-domain operation
and interaction, and enables the interplay with all layers.
The overall framework is depicted in Fig. 3 (similarly with
Once these mechanisms have been placed, we need a
systematic way to model the ecosystem’s features and
administrate the provided functionality. Pattern-driven
techniques are utilized for these purposes, such as  and
. The pattern-driven framework is built upon existing
IoT platforms and guarantees the secure and dependable
actuation. A semi-automatic behavior is supported that
evaluates the integration of the various system components
and orchestrates the interoperable operations. Thus, five
core access settings are enabled under this framework, as
With the cross platform pattern (as described in ),
applications or services access resources from multiple
platforms though the common interfaces. Thus, an
application, like an air quality monitor, can discover
platforms (of the same or different vendor) that process
related data and support the same interfaces and data
formats. The requested information is then collected by the
various compatible platforms enabling the required
The Platform-scale independence pattern 
integrates the resources from platforms at different scale.
Cloud/server level platforms can host high volumes of data
from a vast amount of devices. Fog level platforms interact
with nearby devices in the field and maintain information
in a constraint spatial scope. The device level platforms
have direct communication with the things, managing
small amounts of data. Through SEMIOTIC, an
application can uniformly aggregate information for the
different scale platforms (e.g. collect air quality values for
a specific area via cloud or raw data via a platform at the
Platform independence pattern  refers to distinct
platforms that implement the same functionality, like an
IoT parking service in different cities. The platforms may
utilize different equipment and techniques in order to
discover a free parking spot (e.g. via radar-based sensors
or smart cameras on the street lamps). Hench, a single
driver application can interoperate with both platforms in a
uniform manner without requiring any changes.
The cross application domain pattern  setting
extends the previous ones, with applications or services
accessing now information not only from several
platforms, but also from platforms that process data from
different application domains or verticals. Therefore, the
application can gather data for the air quality and traffic, in
order to propose the least polluted routes to bicyclists.
Higher-level service facades pattern  expand the
abovementioned platform functionality to high-level
services. Henceforth, services can also interact through the
common API, acting as facades for IoT platforms and
implementing value-added operations. For instance, the air
quality monitoring application can interact with a platform
that maintains related information with aggregated data.
The application can also interact with a service that
aggregates data from a second platform, which on the
other hand does not possess the computational capabilities
to aggregate these pieces of knowledge or maintain long-
Once the aforementioned settings are deployed,
services can be composed and reused while data can be
integrated by various platforms. The goal is to achieve
dynamic information discovery and orchestration of the
underlying system components. An application can
interoperate across different domains and platforms. Thus,
the user can integrate services dynamically and adapt the
available services according to his/hers needs, even during
travelling in different cities or countries. All these features
contribute towards the fruitful interplay of IoT and Web
3.0, supporting the vision of user-composed intelligent
services with complex functionality .
TABLE I. S
Yes No No No
Yes No No No
Yes Yes Yes Yes
Yes Yes Yes No
Yes Yes No No
Yes No No No
Table I summarizes the main interoperability features
for four EU funded IoT projects (SEMIoTICS, BIG IoT
, OpenIoT , INTER-IoT ). The main efforts for
cross-domain operation are focused on the semantics and
the high-level programming interfaces. SEMIoTICS
advances the current solutions by also resolving the
compatibility issues at the lower layers. Moreover, the
pattern-driven modelling and management guarantees the
correct operation of the system and simplifies the
integration process of new components and service
Nevertheless, there remain several unsolved open
issues. Automatic charging becomes a fundamental
element once the seamless operation is achieved. Security
is always a main concern (see ). Enforcing
authorization and access control of the available features is
still a challenging task.
The SEMIoTICS project concerns all four levels of
interoperability, but the research efforts focus on the
systematic modelling and administration of the cross-
domain interaction. The main goal is to establish
interoperability patterns that will facilitate the modelling
and real-time management of the underlying IoT
ecosystem. SEMIoTICS will formally analyze the five
core interoperability patterns that are suggested by the
related BIG IoT project . These patterns cover the
main compatibility issues for composing services from
inter- to cross-domain topologies.
This work has received funding from the European
Union Horizon’s 2020 research and innovation programme
under grant agreement No. 780315 (SEMIoTICS), as well
as the Marie Skodowska-Curie Actions (MSCA) Research
and Innovation Staff Exchange (RISE), H2020-MSCA-
RISE-2017, under grant agreements No. 777855 (CE-IoT)
and No. 778229 (Ideal Cities).
 Ganzha, M., Paprzycki, M., Pawlowsji, W., Szmeja, P. and
Wasielewska, K., 2016. Semantic technologies for the IoT – an
Inter-IoT perspective. 1
International Conference on the Internet-
of-Things Design and Implementation (IoTDI), IEEE, Berlin,
Germany, pp. 271-276.
 Fysarakis, K., Hatzivasilis, G., Papaefstathiou, I. and Manifavas,
C., 2016. RtVMF – A secure Real-time Vehicle Management
Framework with critical incident response. IEEE Pervasive
Computing Magazine (PVC) – Special Issue on Smart Vehicle
Spaces, IEEE, vol. 15, issue 1, pp. 22-30.
 Hatzivasilis, G., Papaefstathiou, I. and Manifavas, C., 2017. Real-
time management of railway CPS. 5
Workshop on Embedded and Cyber-Physical Systems (ECYPS
2017), IEEE, Bar, Montenegro, 11-15 June.
 Hatzivasilis, G., Papaefstathiou, I., Plexousakis, D., Manifavas, C.
and Papadakis, N., 2017. AmbISPDM: Managing Embedded
Systems in Ambient Environment and Disaster Mitigation
Planning, Applied Intelligence, Springer, pp. 1-21.
 Hatzivasilis, G., Papaefstathiou, I. and Manifavas, C., 2017.
SCOTRES: Secure Routing for IoT and CPS, IEEE Internet of
Things Journal (IoT-J), IEEE, vol. 4, issue 6, pp. 2129-2141.
 Vilalta, R., Mayoral, A., Pubill, D., Casellas, R., Martinez, R.,
Serra, J., Verikoukis, C. and Munoz, R., 2016. End-to-end SDN
orchestration of IoT services using an SDN/NFV-enabled edge
node. Optical Fiber Communications Conference and Exhibition
(OFC), Anaheim, CA, USA, pp. 1-3.
 Serra, J., Pubill, D., Antonopoulos, A. and Verikoukis, C., 2014.
Smart HVAC control in IoT: energy consumption minimization
with user comfort constraints. The Scientific World Journal,
Hindawi, vol. 2014, article ID 161874, pp. 1-11.
 Kadyan, S. and Singroha, R., 2014. Web 3.0 in library services: an
utilitarian effect. Journal of Informaiton Management, SPLP, vol.
1, no. 2, pp. 159-166.
 Murugesan, S., Rossi, G., Wilbanks, L. and Djavanshir, R., 2011.
The future of Web apps. IEEE IT Professional, IEEE, vol. 13, issue
5, pp. 12-14.
 Scovotti, C. and Jones, S. K., 2011. From Web 2.0 to Web 3.0:
implcations for advertizing courses. Journal of Advertising
Education, vol. 15, issue 1, pp. 6-15.
 Zeng, D., Guo, S. and Cheng, Z., 2011. The Web of Things: a
survey. Journal of Communicaitons, Academy Publisher, vol. 6,
no. 6, pp. 424-438.
 Zhong, N., Ma, J., Huang, R., Liu, J., Yao, Y., Zhang, Y. and Chen
J., 2013. Research challenges and perspectives on Wisdom Web of
Thigns (W2T). The Journal of Supercomputing, Springer, vol. 64,
issue 3, pp. 862-882.
 SEMIoTICS, 2018-2020: https://www.semiotics-project.eu
 Li, S., Xu, L. D. and Zhao, S., 2015. The internet of things: a
survey. Information Systems Frontiers, Springer, vol. 17, issue 2,
 Xu, L. D., He, W. and Li. S., 2014. Intenet of Things in industries:
a survey. IEEE Transactions on Industrial Informatics, IEEE, vol.
10, issue 4, pp. 2233-2243.
 Al-Fuqaha, A. et al., 2015. Internet of Things: a survey on enabling
technologies, protocols, and applications, IEEE Communication
Surveys & Tutorials, IEEE, vol. 17, issue 4, pp. 2347-2376.
 Hatzivasilis, G., et al., 2018. The Industrial Internet of Things as an
enabler for a Circular Economy Hy-LP: A novel IIoT Protocol,
evaluated on a Wind Park’s SDN/NFV-enabled 5G Industrial
Network. Computer Communications – Special Issue on Energy-
aware Design for Sustainable 5G Networks, Elsevier, vol. 119, pp.
 Razzaque, M. A., Milojevic-Jevric, M., Palade, A. and Clarke, S.,
2016. Middleware for Internet of Things: a survey. IEEE Internet
of Things Journal (IoT-J), IEEE, vol. 3, issue 1, pp. 70-95.
 Bröring, A., Datta, S.K. and Bonnet, C., 2016. A Categorization of
Discovery Technologies for the Internet of Things. 6
Conference on the Internet of Things (IoT 2016), ACM, 7-9
November, Stuttgart, Germany, pp. 131-139.
 Perera, C., Zaslavsky, A., Christem, P. and Georgakopoulos, D.,
2013. Context aware computing for the Internet of Things: a
survey. IEEE Communcations Surveys & Tutorials, IEEE, vol. 16,
issue 1, pp. 414-454.
 Ganzha, M. et al., 2017. Semantic interoperability in the Internet of
Things, as overview from the INTER-IoT perspective. Journal of
Network and Computer Applications, Elsevier, vol. 81, issue 1, pp.
 Botta, A., Donata, W., Persico, V. and Pescape, A., 2016.
Integration of Cloud computing and Internet of Things: a survey.
Future Generation Computer Systems, Elsevier, vol. 56, issue 1,
 Korzum, D. G., Balandin,S. I. and Gurtov, A. V., 2013. Deploment
of smart spaces in the Internet of Things: overview of design
challenges. Internet of Things, Smart Spaces, and Next Generation
Networking, Springer, LNCS, vol. 8121, pp. 48-59.
 Manyika, J., et al., 2015. Unlocking the potential of the Internet of
Things. McKinsey Global Institute Report, McKinsey&Company,
June 2015, pp. 1-4.
 Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H. and Zhao, W.,
2017. A survey of Internet of Things: architecture, enabling
technologies, security and privacy, and applications, IEEE Internet
of Things Journal, IEEE, vol. 4, no. 5, pp. 1125-1142.
 Hatzivasilis, G., Floros, G., Papaefstathiou, I. and Manifavas, C.,
2016. Lightweight Authenticated Encryption for Embedded On-
Chip Systems. Information Security Journal: A Global Perspective,
Taylor & Francis, vol. 25, issue 4-6, pp. 151-161.
 Chen, C., Raj, H., Saroiu, S. and Wolman, A., 2014. cTPM: a
cloud TPM for cross-device trusted applications, 11th USENIX
Symposium on Networked Systems Design and Implementation
(NSDI), 2-4 April, Seattle, WA, USA, pp. 187-201.
 Paverd, A. J. and Martin, A. P., 2012. Hardware security for device
authentication in the smart grid, International Workshop on Smart
Grid Security (SmartGridSec), Springer, LNCS, col. 7823, pp. 72-
 Al-Fuqaha, A., Khreishah, A., Guizani, M., Rayes, A. and
Mohammadi, M., 2015. Toward better horizontal integration
among IoT services. IEEE Communications Magazine, IEEE, vol.
53, issue 9, pp. 72-79.
 Haller, A., et al., 2018. The SOSA/SSN ontology: a joint WeC and
OGC standard specifying the semantics of sensors, observations,
actuation, and sampling. Semantic Web, IOS Press, vol. 1-0X, pp.
 Soldatos, J. et al., 2015. OpenIoT: Open source Internet-of-Things
in the Cloud. Interoperability and Open-Source Solutions for the
Internet of Things, Springer, LNCS, vol. 9001, pp. 13-25.
 Cameron, J. D., Ramaprasad, A. and Syn T., 2015. An ontology of
mHealth. 21st Americas Conference on Information Systems
(AMCIS), Puerto Rico, pp. 1-11.
 Daniele, L., den Hartog, F. and Roes, J., 2015. Created in close
interaction with the industry: the smart appliances reference
(SAREF) ontology. International Workshop Formal Ontologies
Meet Industries (FOMI), Springer, LNBIP, vol. 225, pp. 100-112.
 Bajaj, G., et al., 2017. A study of existing ontologies in the IoT-
domain, HAL Archives, hal-01556256, pp. 1-24.
 Web of Things (WoT): https://www.w3.org/WoT/
 Thing Description (TD): https://w3c.github.io/wot-thing-
 RDF: https://www.w3.org/TR/rdf11-concepts/
 JSON-LD: https://www.w3.org/TR/2014/REC-json-ld-20140116/
 Linked Data: https://www.w3.org/standards/semanticweb/data
 iot.schema.org: https://github.com/iot-schema-collab &
 Kiljander, J. et al., 2014. Semantic interoperability architecture for
pervasive computing and Internet of Things. IEEE Access, IEEE,
vol. 2, pp. 856-873.
 Bröring, A., Schmid, S., Schindhelm, C.-K., Kheli, A., Kabisch, S.,
Kramer, D., Phuoc, D., Mitic, J., Anicic, D. and Teniente, E., 2017.
Enabling IoT ecosystems through platform interoperability. IEEE
Software, IEEE, vol. 34, issue 1, pp. 54-61.
 Schmid, S., Bröring, A., Kramer, D., Kaebisch, S., Zappa, A.,
Lorenz, M., Wang, Y. and Gioppo, L., 2017. An Architecture for
Interoperable IoT Ecosystems. 2
International Workshop on
Interoperability & Open Source Solutions for the Internet of Things
(InterOSS-IoT 2016) at the 6th International Conference on the
Internet of Things (IoT 2016), 7. November 2016, Stuttgart,
Germany, Springer, LNCS., vol. 10218, pp. 39-55.
 Schladofsky, W., Mitic, J., Megner, A.P., Simonato, C., Gioppo,
L., Leonardos, D. and Bröring, A., 2017. Business Models for
Interoperable IoT Ecosystems. 2
International Workshop on
Interoperability & Open Source Solutions for the Internet of Things
(InterOSS-IoT 2016) at the 6th International Conference on the
Internet of Things (IoT 2016), 7. November 2016, Stuttgart,
Germany. Springer, LNCS. Volume 10218, pp. 91-106.
 Petroulakis, N., Spanoudakis, G. and Askoxylakis, I., 2016.
Patterns for the design of secure and dependable software defined
networks, Computer Networks, Elsevier, vol 109, issue 1, pp. 39-
 Hernandez-Serrano, J., Munoz, J.L., Bröring, A., Esparza, O.,
Mikkelsen, L., Schwarzott, W., Leon, O. and Zibuschka, J., 2017.
On the Road to Secure and Privacy-preserving IoT Ecosystems. 2
International Workshop on Interoperability & Open Source
Solutions for the Internet of Things (InterOSS-IoT 2016) at the 6th
International Conference on the Internet of Things (IoT 2016), 7.
November 2016, Stuttgart, Germany. Springer, LNCS. Volume
10218, pp. 107-122.