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Summary 2IMN15 Internet of Things
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Summary on all lectures and background literature for the Computer Science & Engineering-master course 2IMN15 Internet of Things at the TU/e.
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2IMN15 - Internet of Things University of TechnologyEindhoven
Lecture 1: Introduction
Early ideas of the Internet of
Things comprised the idea of
connecting the internet to the
real world through sensing
where databases are filled by
things rather than by documents
created by people. So to say, to
let the computers smell, see and
hear while operating on ground
truth about the world. Now, the
Internet of Things is regarded as the network of physical devices, vehicles,
and other items embedded with electronics, software, sensors, actuators,
and network connectivity which enable these objects to collect and
exchange data. Each thing is uniquely identifiable (by unique identifiers;
UIDs) through its embedded computing system but is able to interoperate
within the existing Internet infrastructure. Experts estimate that the IoT
will consist of about 30 billion objects by 2020. By pure definition, the IoT
is similar to the internet in that it involves identifiable devices (attached to
objects) and interconnected networks. However, these devices (the
‘things’) have a number of properties:
They have limited functions (not useful by themselves; e.g.
sensors and actuators).
They are connected to low capacity networks (limited data per
device).
They have no proper user-interface.
They work in large numbers to interact with the real world.
The devices are constrained, where constraints pertain to:
o Memory: static background (flash) and dynamic (RAM).
o Processing power: #instructions / second.
o Available energy.
o Accessibility, uptime (duty cycling).
They are connected to constrained networks that are typically
constrained due to node constraints. The local constraints pertain
to:
o Low bitrate.
o Duty cycle limits (may not use network more than x%).
o High packet loss and variability.
o Asymmetric links.
o Small packet size.
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,2IMN15 - Internet of Things University of Technology
Eindhoven
o Limited group communication primitives.
The constrained networks with their
constrained nodes are eventually
united with the ‘regular’ Internet
services and fast networks. These
networks deal with large amounts of
data which require storage clouds,
transport towards storage and delivery
(e.g. media, data-applications) while
employing content delivery networks,
software defined networking and
network function virtualization
(process of running a virtual instance of a computer system in a layer
abstracted from the actual hardware. Most commonly, it refers to running
multiple operating systems on a computer system simultaneously). All the
while, these networks support processing such as data analysis. For large
corporations, such data analyses can provide a resourceful ground since
models and knowledge can be sold to other platforms for a considerable
amount of money. Also, deep insight and market dominance can be
obtained through finding such data patterns. To understand IoT systems,
different layers need to be considered:
Domains (e.g. home, mobile, office, industry, public, city).
Architecture: layered and deployment view (e.g. devices, things,
functionality placement alternatives, data and control flow; use to
get an overall understanding of structures, dependencies,
data/control flows).
Communication stack (e.g. set of all required protocols; use to
have a context for explaining details and to understand behaviors,
and how these fit together).
Lifecycles (e.g. of devices, services and application; use to learn
the common issues and primary use cases of IoT systems).
To understand all of these layers, the researchers should be familiar with a
wide variety of concepts such as how the internet works (e.g. IP protocols
connectivity), wireless sensor networks (low
resource communication standards) and
semantics of data (smartness, reasoning,
learning). In addition, important concepts
are cyber physical systems (tight
integration of communication, computation
and the physical world) and cloud
computing (building powerful services and
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,2IMN15 - Internet of Things University of Technology
Eindhoven
applications on top of massive amounts of data collected through the
embedded devices). A very typical example of a simple IoT system is the
monitoring of energy (e.g. in a household monitoring the use of energy
during periods of the day). The architecture used in such a local system is
displayed below, where the transformer regularly reports transformer data
to an E-webserver, the raspberry Pi creates and shows an overview report
and stores data from the smart meter and the thermometer and the smart
meter regularly reports metering data. In this architecture, where
components are deployed to devices, we can categorize all elements into
devices (transformer, raspberry PI, smart meter), tasks (report data,
create overview, etc.) and components (E-webserver, database,
recorder, etc.). Using this architecture, we can define two protocols with
which the full functionality of the IoT system can be described:
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, 2IMN15 - Internet of Things University of Technology
Eindhoven
When considering such IoT systems applied to a larger scale, where
multiple stakeholders factor in, a large variety of questions arise. For
example: Which management concerns play a role? E.g. updating
application logic, monitoring and resetting systems. Access control,
security keys and secure connections. Where do you find management
functionality? E.g. within transformer and user devices and within each
device to manage itself, including managing external access. Which
quality concerns are there? E.g. robustness, safety, data integrity,
interoperability, privacy and programmability. Who owns the data? E.g.
the houseowner, house renter or who the data is about. What interests are
there in the data? E.g. monitoring energy use by the owner, monitoring
system quality and behavior of the panel owner and typical energy use
and monitoring what happens at home. Who controls the data? E.g.
depends on how much seeps through the HTTP server and whether data is
encrypted. Also storage wise: the controllers of the stores, the policies of
the store-writers and the readers of the pages.
The system is composed of subsystems (devices and components) that
were not designed to work together and that have their own evolution
(a.k.a. life cycle). This is evident because there are software updates for
each single components carried out by possible a large number of
different manufacturers or third parties. Also, this possibly requires
different data definitions and protocols. Also, their own private function is
independent of this collective application (e.g. a smart meter must do the
metering, transformers must convert energy). The integration is at the
network level and relies on standards, this resolves most functional
interoperability issues. Subsystems can constitute a short cycle (e.g.
where multiple fuse for computation and later give feedback or actuate an
event), a short-cycle with memory (e.g. same as previous but with a
local storage unit) and a long cycle (e.g. where multiple sensors
communicate via a user device to a global storage unit which will then
fuse and compute the data to provide feedback and actuate an event; in
the meanwhile it can provide knowledge through service providers for
monetary purposes). Extra-functional aspects are emergent from
subsystem properties, but also need to be addressed separately (e.g. the
security and robustness of the monitoring and reporting application relies
on security and robustness of the subsystems, but must build on top). We
also call this a system of systems-integration. Furthermore, one can
distinguish between vertical analytics and horizontal analytics. Vertical
analytics involve data from a single unit (e.g. person, item, household,
office) and analysis results in knowledge about that single unit. On the
other hand, horizontal analytics involve data from many units and
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