Intelligence
Ambient intelligence and autonomous
control are not part of the original concept of the Internet of things.
Ambient intelligence and autonomous control do not necessarily require
Internet structures, either. However, there is a shift in research (by
companies such as Intel) to integrate the concepts of IoT and autonomous
control, with initial outcomes towards this direction considering
objects as the driving force for autonomous IoT.
In the future, the
Internet of Things may be a non-deterministic and open network in which
auto-organized or intelligent entities (web services, SOA components)
and virtual objects (avatars) will be interoperable and able to act
independently (pursuing their own objectives or shared ones) depending
on the context, circumstances or environments. Autonomous behavior
through the collection and reasoning of context information as well as
the object’s ability to detect changes in the environment (faults
affecting sensors) and introduce suitable mitigation measures
constitutes a major research trend, clearly needed to provide
credibility to the IoT technology. Modern IoT products and solutions in
the marketplace use a variety of different technologies to support such
context-aware automation, but more sophisticated forms of intelligence
are requested to permit sensor units and intelligent cyber-physical
systems to be deployed in real environments.
Architecture
Tier 1 of the
IIoT architecture consists of networked things, typically sensors and
actuators, from the IIoT equipment, which use protocols such as Modbus,
Zigbee, or proprietary protocols, to connect to an Edge Gateway. Tier 2
includes sensor data aggregation systems called Edge Gateways that
provide functionality, such as pre-processing of the data, securing
connectivity to cloud, using systems such as WebSockets, the event hub,
and, even in some cases, edge analytics or fog computing. Tier 3
includes the cloud application built for IIoT using the microservices
architecture, which are usually polyglot and inherently secure in nature
using HTTPS/OAuth. Tier 3 also includes storage of sensor data using
various database systems, such as time series databases or asset stores
using backend data storage systems such as Cassandra or Postgres. In
addition to the data storage, we analyze the data using various
analytics, predictive or threshold-based or regression-based, to get
more insights on the IIoT equipment.
Building on the Internet of things,
the web of things is an architecture for the application layer of the
Internet of things looking at the convergence of data from IoT devices
into Web applications to create innovative
use-cases. In order to
program and control the flow of information in the Internet of things, a
predicted architectural direction is being called BPM Everywhere which
is a blending of traditional process management with process mining and
special capabilities to automate the control of large numbers of
coordinated devices.
The Internet of things requires huge scalability in
the network space to handle the surge of devices. IETF 6LoWPAN would be
used to connect devices to IP networks. With billions of devices being
added to the Internet space, IPv6 will play a major role in handling the
network layer scalability. IETF’s Constrained Application Protocol,
ZeroMQ, and MQTT would provide lightweight data transport.
Complexity
In
semi-open or closed loops (i.e. value chains, whenever a global finality
can be settled) IoT will often be considered and studied as a complex
system due to the huge number of different links, interactions between
autonomous actors, and its capacity to integrate new actors. At the
overall stage (full open loop) it will likely be seen as a chaotic
environment (since systems always have finality). As a practical
approach, not all elements in the Internet of things run in a global,
public space. Subsystems are often implemented to mitigate the risks of
privacy, control and reliability. For example, domestic robotics
(domotics) running inside a smart home might only share data within and
be available via a local network. Managing and controlling high dynamic
ad hoc IoT things/devices network is a tough task with the traditional
networks architecture, Software Defined Networking (SDN) provides the
agile dynamic solution that can cope with the special requirements of
the diversity of innovative IoT applications.
Size considerations
The
Internet of things would encode 50 to 100 trillion objects, and be able
to follow the movement of those objects. Human beings in surveyed urban
environments are each surrounded by 1000 to 5000 trackable objects. In
2015 there were already 83 million smart devices in people‘s homes. This
number is about to grow up to 193 million devices in 2020 and will for
sure go on growing in the near future. The figure of online capable
devices grew 31% from 2016 to 8.4 billion in 2017. Space considerations
In the Internet of things, the precise geographic location of a
thing—and also the precise geographic dimensions of a thing—will be
critical. Therefore, facts about a thing, such as its location in time
and space, have been less critical to track because the person
processing the information can decide whether or not that information
was important to the action being taken, and if so, add the missing
information (or decide to not take the action). (Note that some things
in the Internet of things will be sensors, and sensor location is
usually important.) The GeoWeb and Digital Earth are promising
applications that become possible when things can become organized and
connected by location. However, the challenges that remain include the
constraints of variable spatial scales, the need to handle massive
amounts of data, and an indexing for fast search and neighbor
operations. In the Internet of things, if things are able to take
actions on their own initiative, this human-centric mediation role is
eliminated. Thus, the time-space context that we as humans take for
granted must be given a central role in this information ecosystem. Just
as standards play a key role in the Internet and the Web, geospatial
standards will play a key role in the Internet of things.
A solution to
“basket of remotes”
Many IoT devices have a potential to take a piece
of this market. Jean-Louis Gassée (Apple initial alumni team, and BeOS
co-founder) has addressed this topic in an article on Monday Note, where
he predicts that the most likely problem will be what he calls the
“basket of remotes” problem, where we’ll have hundreds of applications
to interface with hundreds of devices that don’t share protocols for
speaking with one another. For improved user interaction, some
technology leaders are joining forces to create standards for
communication between devices to solve this problem. Others are turning
to the concept of predictive interaction of devices, “where collected
data is used to predict and trigger actions on the specific devices”
while making them work together.
Frameworks
IoT frameworks might help
support the interaction between “things” and allow for more complex
structures like distributed computing and the development of distributed
applications. Currently, some IoT frameworks seem to focus on real-time
data logging solutions, offering some basis to work with many “things”
and have them interact. Future developments might lead to specific
software-development environments to create the software to work with
the hardware used in the Internet of things. Companies are developing
technology platforms to provide this type of functionality for the
Internet of things. Newer platforms are being developed, which add more
intelligence.
REST is a scalable architecture that allows things to
communicate over Hypertext Transfer Protocol and is easily adopted for
IoT applications to provide communication from a thing to a central web
server.
3D printing
Outline
3D printing is any of various processes in which
material is joined or solidified under computer control to create a
three-dimensional object, with material being added together (such as
liquid molecules or powder grains being fused together). 3D printing is
used in both rapid prototyping and additive manufacturing. Objects can
be of almost any shape or geometry and typically are produced using
digital model data from a 3D model or another electronic data source
such as an Additive Manufacturing File (AMF) file (usually in sequential
layers). There are many different technologies, like stereolithography
(SLA) or fused deposit modeling (FDM). Thus, unlike material removed
from a stock in the conventional machining process, 3D printing or
Additive Manufacturing builds a three-dimensional object from a
computer-aided design (CAD) model or AMF file, usually by successively
adding material layer by layer.
The term “3D printing” originally referred to a process that deposits
a binder material onto a powder bed with inkjet printer heads layer by
layer. More recently, the term is being used in popular vernacular to
encompass a wider variety of additive manufacturing techniques. United
States and global technical standards use the official term additive
manufacturing for this broader sense (Fig. \ref{749370}).
Terminology
The umbrella term
additive manufacturing (AM) gained wide currency in the 2000s, inspired
by the theme of material being added together (in any of various ways).
In contrast, the term subtractive manufacturing appeared as a retronym
for the large family of machining processes with material removal as
their common theme. The term 3D printing still referred only to the
polymer technologies in most minds, and the term AM was likelier to be
used in metalworking and end use part production contexts than among
polymer, inkjet, or stereolithography enthusiasts.
By the early 2010s,
the terms 3D printing and additive manufacturing evolved senses in which
they were alternate umbrella terms for additive technologies, one being
used in popular vernacular by consumer-maker communities and the media,
and the other used more formally by industrial end-use part producers,
machine manufacturers, and global technical standards organizations.
Until recently, the term 3D printing has been associated with machines
low-end in price or in capability. Both terms reflect that the
technologies share the theme of material addition or joining throughout
a 3D work envelope under automated control. Peter Zelinski, the
editor-in-chief of Additive Manufacturing magazine, pointed out in 2017
that the terms are still often synonymous in casual usage but that some
manufacturing industry experts are increasingly making a sense
distinction whereby Additive Manufacturing comprises 3D printing plus
other technologies or other aspects of a manufacturing process.