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 thingand also the precise geographic dimensions
of a thingwill 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 Gass´ee (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. 3).