- What is Internet of things?
- IOT Architecture
- Benefits of IOT
- Features of IOT
- Advantages and Disadvantages of IOT
- IOT Applications
- IOT Consumer Applications
- IOT Government Applications
- IOT Industrial Applications
- IOT Energy Applications
- IOT Agriculture Application
- IOT Devices
- IOT Protocols
- Communication Protocol
- IOT Testing
- What is M2M in IOT
- Salesforce IOT
- IOT Security Challenges
- Future Challenges for IOT
- IOT Raspberry Pi
- IOE (Internet of Everything)
- AI vs IOT
IOT Industrial Applications
The industrial internet1 is an approach to bringing software and machines together, not a particular group of technologies.
These are the principles driving its development.
Internet architecture and practice applied to industrial.
The industrial Internet isn’t necessarily about connecting big machines to the public Internet; instead, it refers to devices becoming nodes on pervasive networks that use open protocols. Internet-like behaviour follows: devices publish data to authorized recipients and receive operational commands from trusted senders.
Think of the difference between an aeroplane built 40 years ago and a modern design like the Boeing 787. Older planes have direct linkages between systems — from the landing-gear switch to the landing gear, for instance. Newer aeroplanes use standard networks, in which the landing gear is a node that’s accessible to any other authorized part of the system — not only the landing-gear switch, but also safety, autopilot, and data-logging systems. The software can understand the status of the aeroplane in its entirety and optimize it in real-time (and, with a data connection to dispatchers and the air-traffic control system, the software can also understand the aeroplane’s relationship to other planes and the airspace around it).
The infrastructure of the Internet is highly flexible and scalable. Once a system of machines is brought together on a network, it’s easy to add new types of software intelligence to the design and to encompass more devices as the scope of optimization expands.
Software abstraction makes the physical world.
Web services mask their underlying complexity through software interfaces.
Need to convert an address to latitude and longitude? Google’s geocoder API2 will make the conversion almost instantaneously, masking the complexity of the underlying process (text parsing, looking up possible matches in a database, choosing the best one). Geolocation thus becomes accessible to anyone building a Web site — no expertise in cartography needed. These services become modules in Web applications, which are designed with minimal assumptions about the services they use so that a change or failure in one module won’t break the entire application.
In the same way, the industrial Internet presents machines as services, accessible to any authorized application that’s on the network. The scope of knowledge needed to contribute to a physical-world solution becomes smaller in the process.
Making a furnace more efficient, for instance, might involve some combination of refining its mechanical and thermal elements (machine design) and making it run in better relation to the building it’s in and the occupants of that building (controls). The industrial Internet makes it possible to approach these challenges separately: connect the furnace to a network and give it an API that guards against damaging commands, and the control problem becomes accessible to someone who knows something about software-driven optimization, but not much about furnaces.
In other words, the industrial Internet makes the physical world accessible to anyone who can recast its problems in terms that software can handle: learning, analysis, system-wide optimization.
At the same time, this transfer of control to software can free machines to operate in the most efficient ways possible. Giving a furnace an advanced control system doesn’t obviate the need for improvements to the furnace’s mechanical design; a machine that anticipates being controlled effectively can itself be designed more efficiently.
Optimization above the level of a single machine
With machines connected in Internet-like ways, intelligence can live anywhere between an individual machine’s controller and the universal network level, where data from thousands of machines converges.
In a wind turbine, for instance, a local microcontroller adjusts each blade on every revolution. Networked together, a hundred turbines can be controlled by software that understands the context of each machine, adjusting every turbine individually to minimize its impact on nearby turbines.
Optimization becomes more significant as the size of the system being optimized grows, and the industrial Internet can create designs that are limitless in scope. Upgrades to the American air-traffic control system, for example, will tie every aeroplane together into a single system that can be optimized at a national level, anticipating a flight’s arrival over a congested city long before it approaches. (The current system is essentially a patchwork of space controlled at the local and regional level.)
Software intelligence, which relies on collecting lots of data to build models, will become smarter and more granular as the scope of data collection increases. We see this already in the availability of traffic congestion data gathered by networked navigation systems and smartphone apps. The next step might be cloud-level software that collects, analyzes, and re-broadcasts other machine data from networked cars — the state of headlights and windshield wipers to detect rain, for instance.
Optimization can go beyond a single kind of machine to take into account external market conditions. “Each silo has achieved its highest possible level of efficiency,” says Alok Batra, the CTO and chief architect for GE Global Research.3 “If we don’t break down silos, we can’t generate more efficiency. Nothing operates in isolation anymore. If you operate a manufacturing plant, you need to know about wind and power supplies.”
Everything becomes a sensor.
Any machine that registers state data can become a valuable sensor when it’s connected to a network, regardless of whether it’s built for the express purpose of logging data. A car’s windshield wiper switch, for example, can be a valuable human-actuated rain sensor if it’s connected to the vehicle’s internal network.
Software operating across several machines can draw from aggregate data conclusions that can’t be drawn from local data. One car running its windshield wipers doesn’t necessarily indicate rain, but a dozen cars running their windshield wipers in close proximity strongly suggests that it’s raining.
Software operating across several types of machine data can also draw out useful systemic insights. Combined with steering-wheel, speed, GPS, and accelerator-pedal readings, a sensor-driven rain indication could warn a driver that it is moving too fast for road conditions, or help him improve his fuel economy by moderating his acceleration habits.
Machines built nightly
The Web brought about the end of the annual software release cycle provided as a loosely-coupled service on the Internet, and the software can be improved and updated frequently. The industrial Internet will bring about a similar change in the physical world.
Some of the value of any machine is in its controls. By replacing controls regularly, or running them remotely and upgrading them every night like a Web service, machines can be constantly improved without any mechanical modifications. The industrial Internet means that machines will no longer be constrained by the quality of their onboard intelligence. Development timelines for certain types of devices will become shorter as software development, and hardware development can be separated to some degree.
Automakers, for instance, build cars with mechanical services that are designed to last more than ten years in regular use. Entertainment and navigation systems are outdated within two years, though, and the software running on those systems might be obsolete in a few months. Automakers are experimenting with ways to decouple these systems from the cars they’re installed in, perhaps by running entertainment and navigation software on the driver’s phone. This scheme effectively gives the car’s processor an upgrade every couple of years when the driver buys a new phone, and it provides the car new software every time the driver upgrades his apps.
It’s easy to imagine something similar coming to the mechanical aspects of cars. A software update might include a better algorithm for setting fuel-air mixtures that would improve fuel economy. Initiatives like OpenXC8, a Ford program that gives Android developers access to drivetrain data, portend the coming of “plug and play intelligence,” in which a driver not only stocks his car with music and maps through his phone but also provides his software and computational power for the car’s drivetrain, updated as often as his phone. One driver might run software that adjusts the car’s driving characteristics for better fuel economy, another for sportier performance. That sort of customization might bring about a broad consumer market in machine controls.
This could lead to the separation of markets in machines and in controls: buy a car from General Motors and buy the intelligent software to optimize it from Google.
Manufacturers and software developers will need to think in terms of broad platforms to maximize the value of both their offerings.
Security problems arise from systems that were built without connectivity in mind.
Security vulnerabilities in the industrial Internet often arise from the assumption that some system is isolated. Contraband connectivity invariably makes its way into any system, though. The best way to approach security is to assume connectivity and plan for it, not to avoid it entirely. Counterintuitively, Internet Protocol and other open, widespread internet technologies, under their having been under attack for decades, can be more secure than specialized, proprietary technologies.