Preface
With an exponentially increasing number of devices connected to the Internet, the
Internet of Things (IoT) is encompassing and connecting multiple domains. As a
matter of fact, it becomes difficult to point to domains where IoT-based ideas are not
explored. This brings about interesting developments related to, what could be
considered as, the “second wave of IoT.” Here, local and/or domain-specific IoT
deployments have to be joined to deliver more complex services to the users.
A typical example could be a seaport IoT ecosystem, being combined with a smart
city IoT deployment, and with a truck company IoT platform, to optimize logistics
and reduce generation of CO 2 (and other forms of pollution). Observe that we
assume, here, that the initial deployments were created independently of each other,
and they were dedicated to solve specific problems/deliver unique solutions.
Moreover, each of them “belongs to” a different stakeholder/group of stakeholders.
In addition, it is possible that the newly instantiated ecosystem connects “competitors.” For instance, in the above example, multiple (competing) trucking companies are expected to seamlessly work within the developed logistics ecosystem. It
should thus be relatively simple to realize that this process brings about (among
others): (a) need for interoperability between joining IoT ecosystems and (b) need
for provenance and trust management. The first is relatively obvious; systems that
were not meant to work together have to communicate and understand each other to
deliver required services. Let us now assume that the provenance can be understood
as an ability to record the origin and history of pertinent data (elements). In the case
of creating multi-stakeholder IoT ecosystems, being able to automatically manage
data sharing/exchange/access is crucial, also as one of the key aspects of “building
trust.”
Let us keep this in mind and reflect on how IoT deployments are realized. The
simplest conceptualization involves hardware devices, such as sensors and actuators, connected to gateways, intermediate computing nodes, and the cloud.
Depending on the approach, an IoT ecosystem is realized using concepts anchored,
among others, in edge, fog, cloud, mist, dew computing, as well as software-defined
networking, network virtualization, and stream processing. Obviously, components
realizing machine learning/data analytics are also present. All these elements are
v
With an exponentially increasing number of devices connected to the Internet, the
Internet of Things (IoT) is encompassing and connecting multiple domains. As a
matter of fact, it becomes difficult to point to domains where IoT-based ideas are not
explored. This brings about interesting developments related to, what could be
considered as, the “second wave of IoT.” Here, local and/or domain-specific IoT
deployments have to be joined to deliver more complex services to the users.
A typical example could be a seaport IoT ecosystem, being combined with a smart
city IoT deployment, and with a truck company IoT platform, to optimize logistics
and reduce generation of CO 2 (and other forms of pollution). Observe that we
assume, here, that the initial deployments were created independently of each other,
and they were dedicated to solve specific problems/deliver unique solutions.
Moreover, each of them “belongs to” a different stakeholder/group of stakeholders.
In addition, it is possible that the newly instantiated ecosystem connects “competitors.” For instance, in the above example, multiple (competing) trucking companies are expected to seamlessly work within the developed logistics ecosystem. It
should thus be relatively simple to realize that this process brings about (among
others): (a) need for interoperability between joining IoT ecosystems and (b) need
for provenance and trust management. The first is relatively obvious; systems that
were not meant to work together have to communicate and understand each other to
deliver required services. Let us now assume that the provenance can be understood
as an ability to record the origin and history of pertinent data (elements). In the case
of creating multi-stakeholder IoT ecosystems, being able to automatically manage
data sharing/exchange/access is crucial, also as one of the key aspects of “building
trust.”
Let us keep this in mind and reflect on how IoT deployments are realized. The
simplest conceptualization involves hardware devices, such as sensors and actuators, connected to gateways, intermediate computing nodes, and the cloud.
Depending on the approach, an IoT ecosystem is realized using concepts anchored,
among others, in edge, fog, cloud, mist, dew computing, as well as software-defined
networking, network virtualization, and stream processing. Obviously, components
realizing machine learning/data analytics are also present. All these elements are
v