Does your management team indicate your company needs to have an IoT or IIoT strategy and you don’t know where to begin? Are you overwhelmed and looking for a “place to start” with IoT and/or IIoT in your organization? If so, hopefully this blog will help to provide some perspective and clarity.
For this first post (of a multi-part series) we will begin to break down IIoT to the fundamentals, starting with an overview and some key definitions.
IoT vs IIoT
IoT and IIoT are just acronyms for:
“Internet of Things”
“Industrial Internet of Things”
In both cases, we are referring to how things (objects, devices, machines, equipment, resources, etc.) can be connected in various ways – wired or wirelessly – often to/through the internet. Generally, it is implied that these things are “smart” (intelligent) and that these devices are providing a bridge from the physical world to a digital one. For example, this might mean measuring the temperature and humidity of an area in a warehouse then recording that data in a digital format for archiving purposes – perhaps alerting someone (in real-time, via email or text message) if either measurement is outside a pre-determined range.
Ironically, the main difference between IoT and IIoT is primarily just the venue in which the device(s) are found. IoT generally refers to devices that appear in the consumer space versus IIoT devices, which are usually found in more industrial, commercial, or manufacturing environments. An IIoT object may vary in function from a simple sensor (temperature, humidity, vibration, etc.) to a complex piece of capital equipment that can report large amounts of data on numerous processes.
As our audience is generally from the Manufacturing, Supply Chain, and/or Transportation industries, we will pivot toward a more industrial viewpoint for this and future posts. Because of this, we will tend to use IIoT instead of IoT in this series, but the reader should be able to interchange IoT and IIoT in the context of our posts without issue.
One major goal of IIoT is to improve Business Intelligence and help entities make informed decisions based on insights and data that previously were not available. Taking it a step further, might be adding some Artificial Intelligence (AI) and Machine Learning that could potentially automate the decisions (within a set of constraints) or at least propose clever, actionable suggestions.
The Things (nodes, sensors, devices):
It is hard to find a word more generic than “thing”, which is good because anything more specific could be limiting or misleading in this context.
Typical IIoT “things” are:
- Dedicated sensors
- Power consumption
- Computers (Desktop, Tablets, Smartphones)
- Security devices
- Door Locks
- Locating Devices
- Information Sources
- RFID readers
- Barcode scanners
- Many others
Some more complex/innovative IIoT “things”:
- Intelligent lighting that senses motion or light from other sources
- Drones that monitor inventory conditions
- Sensors that alert personnel of potential collisions with hazards
- Devices in packaging that gather data on how product is handled in transit
- Safety vests that can alert the wearer to hazardous conditions
- Smart Safety Shoes that provide feedback about poor posture
The Internet (connectivity)
Other concepts, like how IIoT devices “communicate” can be a source of confusion for those first approaching the subject. The actual medium used in the communication process can be wired or wireless.
Power Line Carrier
Cellular (LTE, LTE-M, LTE Cat 1, NB-IoT, EC-GSM-IoT…)
In many cases, we could be trying to connect old, legacy devices that don’t have standard IT/OT options, and this often requires another type of device to “translate” between the old and new protocols and mediums. For example, there are devices that can convert data received from an RS-232 port into Ethernet packets. These can be labeled as “protocol converters” or “Intelligent Gateways”. The subject of protocols (agreed upon standards for how the data is logically arranged on the medium) will be addressed in a future post.
The Analysis (Analytics)
Once the devices are connected and gathering data, the next step is to store and forward the relevant data. In some cases, organizations are just collecting the data and keeping it for future analysis. Some entities refer to these repositories as “Data Lakes” since they are often flat in structure and contain raw data waiting for analysis as opposed to “Data Warehouses”, which instead have a more hierarchical architecture akin to files and folders.
After the collection of data, the inspection and investigation can occur. Data Analytics is the name given to the process of examining data sets with the intent of drawing conclusions about the information they contain – typically with the aid of specialized systems and software. This may involve specialists known as Data Scientists and Data Engineers, who have the objective of generating conclusions supported by relevant facts and data.
In the end, when broken down into the basic components this new technological terrain isn’t as difficult to navigate as one might expect. In simple terms, devices are interconnected and provide data that can be analyzed to provide actionable insights.
Our best advice is to embrace the momentum propelling us toward this new landscape.
Just like when visiting a new place or country it is often advisable to find a local guide, and this analogy can be extended to IIoT as well. There are many knowledgeable companies, teams, and individuals who have gone before you – why not seek guidance from them?
As we continue our series about IIoT, we will investigate more topics such as:
1) Nodes, Gateways, and The Cloud
3) Protocols (JSON, MQTT, RESTful APIs)
6) Build vs. Buy