Sometimes it is good to look back and remind ourselves what some contemporary tech terms really mean and what new technologies bring us in reality. When talking about topics related to the Internet of Things, different business domains display various perspectives, needs, and contexts. Sometimes there are construction machines somewhere on a field, a production line, an energy plant, a business building, a sports car, even livestock, or a cornfield. Each of them deals with various implementation specifics and challenges.
The object of business interest can be any place or object where it is possible to read information over sensors or remotely start an individual device or technical system. It seems that we understand what the term Things stands for. For each kind of these Things there is a diverse data set to be collected, but also specific technical equipment like sensors, controllers, and network connecting devices.
Nowadays everyone knows what the Internet is. In the context of our IoT story maybe they still do not.
By the Internet, we generally mean the global communication network, mainly consumed over Internet browsers or mobile apps. But here we have to set our mindset in a whole different way. What we need is a heterogeneous data transfer channel which consists of just one simple segment or complex mutually interconnected local and wide area networks based on standard or proprietary protocols and hardware. Simply put, sometimes for an IoT connection to be established, Internet as we usually see it is not needed.
Generally speaking, machines are the already-mentioned Things. On the other side, there are other kinds of artificial entities – mostly IT systems – composed of hardware and software, intended for ingesting, processing, and storing data sent by machines located on the business operation site. Once processed, this data can be used for generating a wide range of standard reports and graphic visualizations, and most importantly for statistics and predictive analysis purposes.
Yeah sure, as usual, here is where a new abbreviation has emerged – M2M, which stands for Machine to Machine communication. It’s important to note that there is no human intermediation nor direct intervention, except monitoring and technical maintenance of the set course.
This level of autonomy and automatization brings to each IoT project a few specific challenges which do not exist to such an extent in other types of IT projects.
Expect the Best, Be Aware of and Ready for the Worst
Edge devices, actuators, and sensor installation and maintenance, especially in manufacturing and construction domains, means collecting data from systems exposed to all kinds of weather conditions, mud, dust, heavy dirt, and other various hazards. Device mounting, configuring, and maintenance in such environments could be complicated, sometimes even dangerous.
Besides physical devices that have to be installed and maintained, handling control over a huge data stream, without careful planning and skilled engineers might cause a blockage of one or more layers or even the whole system.
This is where a few questions emerge from the very start:
Which device features and events should be picked and processed?
How often should each feature be read and sent?
Higher data resolution causes more records per time unit, meaning higher pressure on the data ingestion layer, data processing, and aggregation systems.
The next point of potential struggle is the fact that equipment vendors often use proprietary data formats and protocols, hence the IoT platform needs to support transformation to some of the standard formats. Too many and/or too complex incoming data formats lead to a situation where data transformation before processing could be unacceptably costly and erroneous.
When the raw data has once been transformed into logs and records and persisted on the platform, it’s crucial to transform them into objects, documents, and/or time series ready to be used for tabular reporting, generating charts, displaying routes and positions on the map, or a combination of all of these actions. To prepare such datasets, the platform should be capable of processing complex calculations and aggregations in an acceptable timeframe in order to serve business purposes. One possible problem that could appear, is the need for retention and recurrent recalculation of the same data. Since we are dealing with a huge amount of data, we can think of it under the popular industry term Big Data. Again, we need the right technologies and the right field experts to get what we expect from the IoT platform.
IoT represents a market and industry term that stands for an extremely wide range of concepts, technologies, and technical components that are defined and described as frameworks, standards, protocols, and technical solutions to enable interconnectedness between artificial entities.
Numerous edge devices, different and complex data formats, the huge data stream exchanged between IoT endpoints which needs to be transformed and processed –where any human impact in real-time is not possible – means that IoT equipment, tools, and software implemented in industrial plants and construction machines should be carefully planned to serve its purpose.
This is especially important for safety reasons, but also in general for any serious business applications. It should be well-designed by trained, experienced, and skilled consultants and technical specialists to ensure it is secure, fault-tolerant, and finally serves its purpose in the business process.
What should you be looking at in an outsourcing partner? How do you know you are getting what you need? Here’s a checklist to guide you:
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