Conference PaperPDF Available

The Interoperability of Things: Interoperable solutions as an enabler for IoT and Web 3.0


Abstract and Figures

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.
Content may be subject to copyright.
The Interoperability of Things:
Interoperable solutions as an enabler for IoT and Web 3.0
George Hatzivasilis, Ioannis Askoxylakis,
George Alexandris
Institute of Computer Science
Foundation for Research and Technology–Hellas
Heraklion, Crete, Greece,,
Darko Anicic, Arne Bröring, Vivek Kulkarni
Siemens AG, Corporate Technology
Munich, Germany,,
Konstantinos Fysarakis, George Spanoudakis
Sphynx Technology Solutions AG
Zug, Switzerland,
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 [1]. 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 [2]-[7]. 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
exchanged data.
Organizational: cross-domain service integration
and orchestration through common semantics and
programming interfaces.
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
forthcoming sections.
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 [8]. 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. [8], [9], [10]).
IoT is a main enabler of Web 3.0 [11]. The resolution
of the abovementioned interoperability issues emerges as
one of the most significant obstacles for materializing the
concept of the Wisdom-of-Things [12]. The SEMIoTICS
project [13] 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 [14] and [15], 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 [16]. 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) [16], [17]. Middleware solutions are
reviewed in [18]. 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) [18], [16]. The key aspect of
Discovery on the IoT is surveyed in [19]. 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 [20] and [21], 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 [21],
facilitating the machine-to-machine (M2M) interaction.
Integration of IoT with cloud computing is then deployed
[22], 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 [23].
The basic intra-domain interoperable approaches are
presented in [23], 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
uniform manner.
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 [24]. 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. [25], [26], [27], [28]).
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 [17].
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 [29]. 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 [17].
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
system components.
Each platform utilizes specific message protocols.
Through a multiprotocol proxy they can also expand their
functionality and interact with devices that support
different protocols.
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.
V. S
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
[30]. 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
Things [11].
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) [31]
and INTER-IoT [21], 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 [32], 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) [33] and the MyOntoSens [34].
More recently, W3C has launched a working group
called Web of Things (WoT) [35] 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) [36], 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 [37], JSON-LD [38], and Linked
Data [39]. 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
[40]. It is an extension of well-known for IoT. 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, 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 [41], 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 [42] 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 [42]).
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 [44], 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
[42]. 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
different vendors.
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 [42] and
[45]. 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
detailed below.
With the cross platform pattern (as described in [42]),
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 [42]
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
fog/device level).
Platform independence pattern [42] 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 [42] 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 [42] 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-
term assets.
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 [9].
Big IoT
Yes No No No
Yes No No No
Yes Yes Yes Yes
Yes Yes Yes No
Yes Yes No No
Pattern-based semi-
automatic management
Yes No No No
Table I summarizes the main interoperability features
for four EU funded IoT projects (SEMIoTICS, BIG IoT
[42], OpenIoT [31], INTER-IoT [21]). 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 [46]). 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 [42]. 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).
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] SEMIoTICS, 2018-2020:
[14] Li, S., Xu, L. D. and Zhao, S., 2015. The internet of things: a
survey. Information Systems Frontiers, Springer, vol. 17, issue 2,
pp. 243-259.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] 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,
pp. 684-700.
[23] 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.
[24] Manyika, J., et al., 2015. Unlocking the potential of the Internet of
Things. McKinsey Global Institute Report, McKinsey&Company,
June 2015, pp. 1-4.
[25] 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.
[26] 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.
[27] 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.
[28] 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-
[29] 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.
[30] 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.
[31] 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.
[32] 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.
[33] 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.
[34] Bajaj, G., et al., 2017. A study of existing ontologies in the IoT-
domain, HAL Archives, hal-01556256, pp. 1-24.
[35] Web of Things (WoT):
[36] Thing Description (TD):
[37] RDF:
[38] JSON-LD:
[39] Linked Data:
[40] &
[41] Kiljander, J. et al., 2014. Semantic interoperability architecture for
pervasive computing and Internet of Things. IEEE Access, IEEE,
vol. 2, pp. 856-873.
[42] 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.
[43] 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.
[44] 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.
[45] 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-
[46] 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.
... Developing the BiG-IoT API [35,43] relies on semantic addressing and IoT interoperability issues to realize real Internet of Things (IoT) ecosystems. The project is a Web platform that connects various platforms and middleware systems. ...
... • The application layer uses an application (which runs on smart devices) to parse and display the results to end users. [35,43,74] Expands the standards of the Web of things. Vocabulary management for handling the semantics. ...
Full-text available
The apparent growth of the internet of things (IoT) has allowed its deployment in many domains. The IoT devices sense their surroundings and transmit the data via the Web. According to statistics, due to the proliferation of smart devices, the number of active IoT devices is expected to exceed 25.4 billion by 2030.1 A large number of IoT objects gather an enormous amount of raw data. The data generated by various IoT objects and sensors are heterogeneous, with varying types and formats. Therefore, it is difficult for IoT systems to share and reuse raw IoT data, which causes the problem of lack of interoperability. The lack of interoperability in IoT systems creates a problematic issue that prevents IoT systems from performing well. To address this issue, data modeling and knowledge representation using semantic web technologies may be an appropriate solution to give meaning to raw IoT data and convert it to an enriched data format. The primary goal of this research section is to highlight the best outcomes for semantic interoperability among IoT systems, which can serve as a guideline for future studies via the presentation of a literature review on semantic interoperability for Internet of Things systems, including challenges, prospects, and recent work. The paper also provides an overview of the application of semantic web technologies in IoT systems, such as specific ontologies, frameworks, and application domains that use semantic technologies in the IoT areas to solve interoperability and heterogeneity problems.
... 2-Secure Hardware Components: In Web 3.0, the security of hardware components becomes paramount. This includes ensuring the authenticity and integrity of hardware devices, such as IoT sensors and gateways, by implementing secure boot processes, tamper-resistant designs, and secure element integration [44,45]. Hardware components should be resistant to physical attacks, such as tampering, reverse engineering, and side-channel attacks, to safeguard the overall security of the Web 3.0 ecosystem. ...
Full-text available
The advent of Web 3.0, underpinned by blockchain technologies, promises to transform the internet's landscape by empowering individuals with decentralized control over their data. However, this evolution brings unique security challenges that need to be addressed. This paper explores these complexities, focusing on enhancing privacy and anonymous auditing within blockchain structures. We present the architecture of Web 3.0 based on the blockchain, providing a clear perspective on its workflow and security mechanisms. A security protocol for Web 3.0 systems, employing privacy-preserving techniques and anonymous auditing during runtime, is proposed. Key components of our solution include the integration of privacy-enhancing techniques and the utilization of Tor for anonymous auditing. We discuss related work and propose a framework that meets these new security requirements. Lastly, we offer an evaluation and comparison of our model to existing methods. This research contributes towards the foundational understanding of Web 3.0's secure structure and offers a pathway towards secure and privacy-preserving digital interactions in this novel internet landscape.
... Among the IoT projects, SEMIoTICS not only offers interoperability at four levels but goes 2 steps ahead of its competitors. It utilises semi-automatic pattern-driven techniques for the crossdomain operation and interaction of applications [11]. ...
... According to Hatzivasilis et al. (2018), there are different types of interoperability named technical, syntactic, semantic, and organizational interoperability. The former makes the cooperation of heterogeneous devices, which use different communication protocols, possible. ...
Full-text available
The adoption of Internet of Things (IoT) devices, applications and services gradually transform our everyday lives. In parallel, the transition from linear to circular economic (CE) models provide an even more fertile ground for novel types of services, and the update and enrichment of legacy ones. To fully realize the potential of the interplay between IoT and CE, the design-time definition of IoT orchestrations with proven circularity properties, and the run-time management of these orchestrations based on said properties, is of paramount importance. Nevertheless, the circularity requirements and associated properties are not only difficult to achieve at the IoT orchestration design and deployment initialization phases, but also hard to prove and maintain at run-time. Motivated by this, this paper presents the CIRCE framework for circular and trustworthy by-design IoT orchestrations. The CIRCE approach leverages concepts from pattern-driven engineering, whereby patterns are used to encode proven dependencies between the Location, Condition, and Availability (LCA) properties of individual smart objects and corresponding properties of orchestrations (compositions) involving them. These are augmented by patterns encoding trustworthiness-related properties, namely Connectivity, Security, Privacy, Dependability, and Interoperability (CSPDI). Thereby, these patterns are used to generate IoT orchestrations with proven LCA and CSPDI properties, as needed, at design time. At runtime, these properties are monitored in real-time, leveraging reasoning engines deployed across system layers, triggering adaptations to return the deployed orchestration to the desired LCA and CSPDI states, when required. Details are provided on the above novel combination of IoT, CE and pattern-based engineering, along with a proposed architecture and implementation approach. Furthermore, an assessment of a proof-of-concept implementation is provided, validating the feasibility of the proposed approach.
... In this section, we systematically present some existing IoT relevant frameworks supporting semantic interoperability in IoT systems. [23,29] relies on semantic addressing and IoT interoperability problems by developing the BiG-IoT API to realize the real Internet of Things (IoT) ecosystems. It is a web platform which unifies several platforms and numerous middleware systems. ...
Full-text available
The rapid development of the Internet of Things has transformed our daily life by reaching all areas that we can imagine and does not stop evolving. It exchanges any information at anytime from anywhere. The Internet of Things is a set of physical objects carrying embedded technologies to gather data from their environment. The huge amount of raw data produced by different IoT devices is heterogeneous with different types and formats. As a result, sharing and reusing raw IoT data is a challenging task for IoT applications. The lack of interoperability in IoT systems creates a difficult issue that prevents them from functioning properly. To overcome this problem, data modelling and knowledge representation with semantic web technologies can be an adequate solution. In this paper we present a survey on the state of the art of current solutions to the problem of semantic interoperability in IoT systems and how semantic web technologies can be used to achieve the conceptual Internet of Things.
Due to decentralized infrastructure in modern Internet-of-Things (IoT), the tasks should be shared around the edge devices via network resources and traffic prioritizations, which weaken the information interoperability. To solve this issue, a Minimized upgrading batch Virtual Machine (VM) Scheduling and Bandwidth Planning (MSBP) was adopted to reduce the amount of batches needed to complete the system-scale upgrade and allocate the bandwidth for VM migration matrices. But, the suboptimal use of VM and possible loss of tasks may provide inadequate Resource Allocation (RA). Hence, this article proposes an MSBP with the Priority-based Task Scheduling (MSBP-PTS) algorithm to allocate the tasks in a prioritized way and maximize the profit by deciding which request must handle by the edge itself or send to the cloud server. At first, every incoming request in its nearest fog server is allocated and processed by the priority scheduling unit. Few requests are reallocated to other fog servers when there is an inadequate resource accessible for providing the request within its time limit. Then, the request is sent to the cloud if the fog node doesn’t have adequate resources, which reduces the response time. However, the MSBP is the heuristics solution which is complex and does not ensure the global best solutions. Therefore, the MSBP-PTS is improved by adopting an Optimization of RA (MSBP-PTS-ORA) algorithm, which utilizes the Krill Herd (KH) optimization instead of heuristic solutions for RA. The simulation outcomes also demonstrate that the MSBP-PTS-ORA achieve a sustainable network more effectively than other traditional algorithms.
Full-text available
The Internet of Things has provided people with a seamless, automated home and industrial experience. The concept is now integrated into more domains like Internet of robotic things (IoRT), Internet of medicine Things (IoMT), etc., to improve domain-specific outcomes. For IoRT, which is the robotics implementation of Internet of Things (IoT), poor network security could cause economic and physical damage to both the networked devices and human users of the network. Also, the tendency for data and privacy breaches becomes more prevalent with an increase in the number of devices in the network. Hence, these identified vulnerabilities are the limiting elements for proper IoRT implementation. Various works have proposed security schemes for ensuring the realization of a secure and efficient IoRT network, but with computational time and complexity limitations. However, machine learning methodologies have shown the best promise for identifying malicious traffic in an IoRT network. This work proposes a security architecture using a Deep Neural Network and an ensemble of Decision Trees. This architecture can be implemented online or offline with minimal trade-offs between resources and efficiency. Also, the proposed machine learning models are compared with other commonly implemented schemes using the IoT-23 Dataset. Experimentation and comparison show that the proposed model and architecture are optimal for the malware detection task and security of a typical IoRT network. These contributions are significant for realizing secure and efficient IoRT networks for the future of industrial automation in this post-COVID era.
Internet-connected consumer devices have rapidly increased in popularity; however, relatively little is known about how these technologies are affecting interpersonal relationships in multi-occupant households. In this study, we conduct 13 semi-structured interviews and survey 508 individuals from a variety of backgrounds to discover and categorize how consumer IoT devices are affecting interpersonal relationships in the United States. We highlight several themes, providing exploratory data about the pervasiveness of interpersonal costs and benefits of consumer IoT devices. These results inform follow-up studies and design priorities for future IoT technologies to amplify positive and reduce negative interpersonal effects.
The new Industry 4.0 approach contributes to addressing evolving industrial requirements, which are continuously fueled by changing market demands. This situation leads to growing complexity and considerable increases in development and maintenance costs. A significant portion of engineering time is dedicated to the integration and interconnection of heterogeneous components. The solution for interoperability issues and the reduction in the associated engineering time are thus key tasks for increasing productivity and efficiency. Therefore, this paper provides an engineering approach to create interoperability among heterogeneous systems in Service Oriented Architecture (SOA) based environments by means of generating an autonomous consumer interface code at runtime. This paper aims to present a novel interoperability solution. The proposed approach makes use of service interface descriptions to dynamically instantiate a new autonomously generated interface that solves service mismatches between a provider and a consumer. This paper includes the definition of the consumer interface generator system, as well as the benefits and challenges associated with the autonomous generation and deployment of a consumer interface code at runtime. To illustrate the potential of this approach, a prototype of the system, which shows positive results, is implemented and tested.
Full-text available
The joint W3C (World Wide Web Consortium) and OGC (Open Geospatial Consortium) Spatial Data on the Web (SDW) Working Group developed a set of ontologies to describe sensors, actuators, samplers as well as their observations, actuation, and sampling activities. The ontologies have been published both as a W3C recommendation and as an OGC implementation standard. The set includes a lightweight core module called SOSA (Sensor, Observation, Sampler, and Actuator) available at:, and a more expressive extension module called SSN (Semantic Sensor Network) available at: Together they describe systems of sensors and actuators, observations, the used procedures, the subjects and their properties being observed or acted upon, samples and the process of sampling, and so forth. The set of ontologies adopts a modular architecture with SOSA as a self-contained core that is extended by SSN and other modules to add expressivity and breadth. The SOSA/SSN ontologies are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Internet of Things. In this paper we give an overview of the ontologies and discuss the rationale behind key design decisions, reporting on the differences between the new SSN ontology presented here and its predecessor [9] developed by the W3C Semantic Sensor Network Incubator group (the SSN-XG). We present usage examples and describe alignment modules that foster interoperability with other ontologies. A. Haller et al. / The Modular SSN Ontology: A Joint W3C and OGC Standard 1
Full-text available
Smart interconnected devices, including Cyber-Physical Systems (CPS), permeate our lives and are now an integral part of our daily activities, paving the way towards the Internet of Things (IoT). In the industrial domain, these devices interact with their surroundings and system operators, while often also integrating industrial cloud applications. This 4th Industrial Revolution guides new initiatives, like the introduction of 5th Generation Mobile Networks (5G), to implement exible, effcient, QoS- and energy- aware solutions that are capable of serving numerous heterogeneous devices, bringing closer the vision of a sustainable, Circular Economy. However, the lack of interoperable solutions that will accommodate the integration, use and management of the plethora of devices and the associated services, hinders the establishment of smart industrial environments across the various vertical domains. Motivated by the above, this paper proposes the Hy-LP - a novel hybrid protocol and development framework for Industrial IoT (IIoT) systems. Hy-LP enables the seamless communication of IIoT sensors and actuators, within and across domains, also facilitating the integration of the Industrial Cloud. The proposed solution is compared with existing standardised solutions on a common application, working around the protocols' intrinsic characteristics and features to produce each variant. The developed systems are evaluated on a common testbed, demonstrating that the proposed solution is around 10 times faster for the same CPU usage level, while consuming 7 times less memory. Moreover, the applicability of the proposed solutions is validated in the context of a real industrial setting, analyzing the network characteristics and performance requirements of an actual, operating wind park, as a representative use case of industrial networks.
Full-text available
Wireless ad-hoc networks are becoming popular due to the emergence of the Internet of Things (IoT) and cyber-physical systems (CPS). Due to the open wireless medium, secure routing functionality becomes important. However, the current solutions focus on a constrain set of network vulnerabilities and do not provide protection against newer attacks. In this paper, we propose SCOTRES – a trust-based system for secure routing in ad-hoc networks which advances the intelligence of network entities by applying five novel metrics. The energy metric considers the resource consumption of each node, imposing similar amount of collaboration and increasing the lifetime of the network. The topology metric is aware of the nodes’ positions and enhances load-balancing. The channel-health metric provides tolerance in periodic malfunctioning due to bad channel conditions and protects the network against jamming attacks. The reputation metric evaluates the cooperation of each participant for a specific network operation, detecting specialized attacks, while the trust metric estimates the overall compliance, safeguarding against combinatorial attacks. Theoretic analysis validates the security properties of the system. Performance and effectiveness are evaluated in the NS2 simulator, integrating SCOTRES with the DSR routing protocol. Similar schemes are implemented using the same platform in order to provide a fair comparison. Moreover, SCOTRES is deployed on two typical embedded system platforms and applied on real cyber-physical systems for monitoring environmental parameters of a rural application on olive groves. As is evident from the above evaluations, the system provides the highest level of protection while retaining efficiency for real application deployments.
Full-text available
The need to manage embedded systems, brought forward by the wider adoption of pervasive computing, is particularly vital in the context of secure and safety-critical applications. Technology infiltrates in ordinary things, hitching intelligence and materializing smart systems. Each of these individual entities monitors a specific set of parameters and deduces a constrained local view of the surrounding environment. Many distributed devices exchange information in order to infer the real system state and achieve a consistent global view. However, conflicts may arise due to the integration of deficit pieces of local knowledge. Robust and efficient conflict resolution is essential, especial in cases of emergency where the system must contribute with timely and accurate data to the overall crisis management operation. In this paper, we present AmbISPDM - a formal framework for the management of embedded systems with a coherent conflict resolution mechanism. The process is implemented as a software agent's reasoning behaviour and applied in the multi-agent domain. As a proof of concept, a smart university campus setting is deployed, with agents controlling embedded devices to assist living conditions in normal operation and the evacuation planning in case of fire.
Full-text available
Several domains have adopted the increasing use of IoT-based devices to collect sensor data for generating abstractions and perceptions of the real world. This sensor data is multi-modal and heterogeneous in nature. This heterogeneity induces interoperability issues while developing cross-domain applications, thereby restricting the possibility of reusing sensor data to develop new applications. As a solution to this, semantic approaches have been proposed in the literature to tackle problems related to interoperability of sensor data. Several ontologies have been proposed to handle different aspects of IoT-based sensor data collection, ranging from discovering the IoT sensors for data collection to applying reasoning on the collected sensor data for drawing inferences. In this paper, we survey these existing semantic ontologies to provide an overview of the recent developments in this field. We highlight the fundamental ontological concepts (e.g., sensor-capabilities and context-awareness) required for an IoT-based application, and survey the existing ontologies which include these concepts. Based on our study, we also identify the shortcomings of currently available ontologies, which serves as a stepping stone to state the need for a common unified ontology for the IoT domain.
Conference Paper
Full-text available
Railways constitute a main mean of mass transportation. Public, private, and military settings are traversing long distances everyday. Railway controlling software must collect spatial information and effectively manage these systems. The need to manage the underlying embedded systems, brought forward by the wider adoption of pervasive computing, is vital in the context of safety-and security-critical applications. This work presents RailwaySPD, a management framework of embedded systems for railway settings. It implements an artificially intelligent reasoning process for controlling and configuring the system at runtime. Metrics for security, privacy, and dependability (SPD) are embodied along with semantic knowledge regarding the safety aspects of the setting. RailwaySPD is applied on a railway application and safeguards two distinct WSNs. The first outdoor network monitors the railway's routes. The second network is located inside a train's carriage that transports dangerous cargo. Two scenarios are modelled where the system is configured at runtime to counter cyber-attacks and manage safety-related events respectively.
Conference Paper
Full-text available
The Internet of Things (IoT) is growing and more and more devices, so-called " things " , are being connected every day. IoT platforms provide access to those " things " and make them available for services and applications. Today, a broad range of such IoT platforms exist with differing functional foci, target domains, and interfaces. However, to fully exploit the economic impact of the IoT, it is essential to enable applications to interoperate with the various IoT platforms. The BIG IoT project aims at enabling this interoperability and supporting the creation of vibrant IoT ecosystems, which facilitate the development of cross-platform and cross-domain applications. While the value of interoperability for the overall economy is well understood and cannot be underestimated, some stakeholders may still need to find their business value in interoperable IoT ecosystems. Thus, this paper identifies the different stakeholders of such ecosystems, and analyzes how these stakeholders can enhance their existing business models when taking part in an interoperable IoT ecosystem.
Conference Paper
Full-text available
The Internet of Things (IoT) is maturing and more and more IoT platforms that give access to things are emerging. However, the real potential of the IoT lies in growing IoT cross-domain ecosystems on top of these platforms that will deliver new, unanticipated value added applications and services. We identified two crucial aspects that are important to grow an IoT ecosystem: (i) interoperability to enable cross-platform and even cross-domain application developments on top of IoT platforms as well as (ii) marketplaces to share and monetize IoT resources. Having these two crucial pillars of an IoT ecosystem in mind, we present in this article the BIG IoT architecture as the foundation to establish IoT ecosystems. The architecture fulfills essential requirements that have been assessed among industry and research organizations as part of the BIG IoT project. We demonstrate a first proof-of-concept implementation in the context of an exemplary smart cities scenario.
The proliferation of advertising applications facilitated by Web 2.0 technology has changed the way organizations interact with customers. Sites like YouTube and Facebook have caused firms to modify advertising models to take into consideration the value of social media and user-generated content. However, with such content come critical issues about information accuracy, source credibility, and amateurish presentation. Web 3.0 combines human and artificial intelligence to make information richer, more relevant, accessible and timely. This paper describes applications made possible because of the generational changes of the Web and offers educators course content suggestions and experiential learning opportunities for their curriculum.
Conference Paper
The Internet of Things (IoT) is on the rise. Today, various IoT platforms are already available, giving access to myriads of things. Initiatives such as BIG IoT are bringing those IoT platforms together in order to form ecosystems. Such IoT ecosystems facilitate cross-platform and cross-domain application developments and establish centralized marketplaces to allow resource monetization. This combination of multi-platform applications, heterogeneity of the IoT, as well as enabling marketing and accounting of resources results in crucial challenges for security and privacy. Hence, this article analyses the requirements for security in IoT ecosystems and outlines solutions followed in the BIG IoT project to tackle those challenges. Concrete analysis of an IoT use case covering aspects such as public, private transportation, and smart parking is also presented.