Top 5 Industrial IoT Trends to Look Out for in 2020
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Top 5 Industrial IoT Trends to Look Out for in 2020

By IndraStra Global Editorial Team


Top 5 Industrial IoT Trends to Look Out for in 2020

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. But, make no mistake, the machine whispers (a.k.a., plant operators) are not being replaced, but just getting augmented. 



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 Magazine54(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 Journal9(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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