By IndraStra Global Editorial Team
By IndraStra Global Editorial Team
Sooner or later, Private 5G adoption will accelerate and take over public 5G deployment. Industrial and urban infrastructure will accelerate the rollout of the 5G network and influence use cases like the rollout of Automated Guided Vehicles, Real-time edge analytics for more secure operations, video surveillance, etc.
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Industrial IoT
(IIoT) trends are going to own 2020 and years to come. As a rapidly evolving
technology, it covers a wide range of use cases — from extended enterprises to
vertical markets and services — each of them having different requirements that
are generally well specified by IIC’s Industrial Internet Reference
Architecture.[1]
But, before we kick start let's get our basics cleared by asking the most important question of this hour - "What are the Industrial Internet
of Things (IIoT)?". It can have an "N number" of definitions, but we will go
with an academic one — According to an MDPI technical paper, "IIoT is envisioned by the fusion of
the physical world of industrial production with the digital world of
information technology" [2] — creating a 360-degree digitized
ecosystem, making the "industrial"
as a concept more flexible and transparent.
At the basic level, it starts with machines and processes; getting them
interconnected through a vast array of "data
points". These data points can be defined in a programmable logic controller (PLC)
variable and stored in the Software-as-a-Service
(SaaS) platform by way of edge computing servers. But harvesting the data
is only one part of the job. The primary challenge is to derive
"actionable intelligence" from that data and create insights that can
be further used to make machines or processes "smart".
As we move into 2020, there have been a lot of IIoT predictions and
trends grabbing headlines. Here are five that caught our attention:
1) Sustainability
In the year 2016, Chinese researchers proposed a framework based on the IoT Architectural Reference Model (IoT ARM)[3],
in which they particularly focused on the "sense entities domain"
where huge amounts of energy are consumed by a tremendous number of nodes. The
proposed framework included three layers: the sensor-based layer, the gateway
layer, and the control layer. This hierarchical framework balances the traffic
load and enables a longer lifetime of the whole system. Based on this deployment,
sleep scheduling and wake-up protocol are designed, supporting the prediction
of sleep intervals. The shifts of states support the use of the entire system
resources in an energy-efficient way.[4] Simulation results were positive
which demonstrated the significant advantages of such architecture in resource
utilization and energy consumption.
In alignment with such research outcomes, companies like CuBE Packaging are expanding its sustainable packaging business by deploying an IIoT platform in partnership with HPE Technology and ecosystem partners PTC, PCM and Callisto. CuBE is gathering data from the injection molding plant’s edge servers and gaining real-time insights to streamline its operations. The entire premise of IIoT is all about creating such a sustainable use-case that is better for the environment — which, in turn, will improve
productivity, reduce uncertainty and provide greater visibility to all
stakeholders.
Shortly, like the CuBE Packaging example, we expect the corporations, governments, and technology companies will be partnering like never before to play their part and make a difference. Solutions that address sustainable electrification in mobility, adaptive traffic control to reduce emissions in megacities and smart waste management will gain unprecedented attention in 2020.
2) Adding
"Predictive Factor" in the Maintenance
As per the existing scenarios, there is one or the other form of Supervisory Control And Data Acquisition
(SCADA) system on which the whole industrial process(s) is dependent. And,
when we are trying the add the "Predictive
Factor", then the SCADA has its limitations — one cannot unify all the
devices, storage and computing cost, shortcomings in analyzing and interpreting
historical data, and scalability issues. Running effective predictive
maintenance operations require the ability to process large amounts of data
and run sophisticated algorithms, which cannot be achieved with localized
implementation within SCADA.[5] On the other hand, an IIoT-based "Predictive Maintenance Solution"
allows storing terabytes of data and running machine learning algorithms on
several computers in parallel to forecast potential hazards and pinpoint when
industrial equipment is likely to fail.
In the longer run, "Predictive Maintenance Solutions" will
continue to mature and will become part of the overall industrial maintenance
workflow. With better data quality, advanced Artificial Intelligence (AI) and Machine Language (ML) models that will be more
and more semi-supervised or unsupervised. Putting it in simple terms – software
will not need us to tough the data as much as we have in the past. It can
figure things out for itself. The predictions will become far more accurate,
insightful and fast than what we have experienced before.
3) Getting
Accelerated with Private 5G
A new generation of private 5G networks (Private LTE Industrial Networks) is emerging to address critical
wireless communication requirements in industrial operations (which are having
critical infrastructure). These private networks are physical or virtual
cellular systems - such as Enhanced
Mobile Broadband (eMBB), Ultra-Reliable
Low-Latency Communication (uRLLC), Massive
Internet of Things (mIoT) or a mixture of all three — that have been
deployed for private use by a government, company or group of companies.
Private 5G network can be desirable for several reasons [6]:
- High quality-of-service requirements
- High-security requirements, met by dedicated
security credentials
- Isolation from other networks, as a form of
protection against malfunctions in the public mobile network. Also,
isolation may be desirable for reasons of performance, security, privacy,
and safety
- Accountability; A non-public network makes it easier to identify responsibility for the availability, maintenance, and operation
Sooner or later, Private 5G adoption will accelerate and take over public 5G deployment. Industrial and urban infrastructure will accelerate the rollout of the 5G network and influence use cases like the rollout of Automated Guided Vehicles, Real-time edge analytics for more secure operations, video surveillance, etc.
4) The Rise of
Cobots
Cobot, also known as Collaborative Robots is a new trend in the field of
industrial and service robotics as a part of the strategy Industry 4.0.
Technically, "a Cobot intended for
cooperation with humans does not have to have a strictly different design from
standard industrial robots that conform with safety standard ISO EN
10218." [7]. In February 2016, recommendations for Cobots are
summarized in a new technical specification ISO/TS 15066 (Robots and robotic devices – Collaborative robots).[8]
Currently, traditional industrial robots dominate the market. They’re
built to carry out specific actions repeatedly, without variation and to a high
degree of accuracy, determined by programmatic routines that specify the
direction, velocity, and distance of coordinated movements. In short, they are
not smart enough. To overcome this, cobots are beginning to take center stage
due to advancements in computer-based optical readers, AI, and motion-sensing
capabilities.
In the near future, cobots will go beyond the world of
manufacturing and supply chain/logistics because they have proven their worth
in helping to address labor shortages and take on dangerous tasks. In other
emerging markets where robots have not yet made a disruptive impact, robotic
companies can still get by with a single point solution that solves a problem
or automates a task.
All this has important repercussions for the IIoT because cobots are
typically equipped with far more sensors and produce more data to be processed
and analyzed than their SCADA-based counterparts. Their mass deployment in
factories is likely to force a new focus among factory owners on edge computing
to support them all.
5) The Evergrowing
World of Edge Computing
Multiple sources define edge computing as “cloud computing systems that perform data processing at the edge of
the network, near the source of the data”. [9] This will actually provide
ultra-low latency ideal for "time-critical
situations including any use-case where the operation is either mission-critical
or where things are in motion." [10]
In the current scenario, edge computing is gaining momentum
rapidly. As we process and compute more data in real-time, edge computing will
be more and more pervasive in the industrial setup and it will enable carrying
out most mission-critical processes and functions including all-sorts of
analytics.
References:
[1] Monteiro, P., Carvalho, M., Morais, F., Melo, M., Machado, R. J.,
& Pereira, F. (2018, September). Adoption of architecture reference models
for industrial information management systems. In 2018 International
Conference on Intelligent Systems (IS) (pp. 763-770). IEEE.
[2] Beier G, Niehoff S, Xue B. More Sustainability in Industry through
Industrial Internet of Things? Applied Sciences. 2018; 8(2):219.
[3] Bauer M. et al. (2013) IoT Reference Model. In: Bassi A. et al.
(eds) Enabling Things to Talk. Springer, Berlin, Heidelberg
[4] Wang, K., Wang, Y., Sun, Y., Guo, S., & Wu, J. (2016). Green
industrial Internet of Things architecture: An energy-efficient
perspective. IEEE Communications Magazine, 54(12),
48-54.
[5] Chhabra, A. (2018). 4 Key Differences Between SCADA and Industrial
IoT, Schneider Electric Blog
[6] Flynn, K. (2019) 5G-ACIA, Non-Public Networks for Industrial Scenarios.
3gpp.org
[7] Vysocky, A. L. E. S., & Novak, P. E. T. R. (2016). Human-Robot
collaboration in the industry. MM Science Journal, 9(2),
903-906.
[8] ISO/TS 15066:2016 Robots and robotic devices — Collaborative robots
[9] Edge Computing Task Group (2018), Introduction to Edge Computing in
IIoT. iiconsortium.org/
[10] Appelquist, G. (2017), 4.0 Reasons Why Edge Computing is Relevant
for Industry 4.0, IIoT World
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