By Dr. Biplab Pal and Som Pal Choudhury
The Internet of Things (IoT) has been a hotbed of activity for the last several years. Until about 2015, IOT was at its peak of the ‘Gartner Hype Cycle’ but the euphoria has died down considerably. Other technologies, like AI and blockchain, have moved to the top of the hype cycle. Though IoT is on a downward path of the hype cycle now as the authors have articulated in previous posts, articles, and interviews, IoT, especially Industrial IoT should see steady but slow progress over the next decade. IT deployment and automation took decades as well, and the adoption did not happen overnight.
While the IoT story in consumer, homes, logistics and automotive have taken off in a reasonably big way with several tens of millions of devices deployed, the Industrial IoT deployments have been relatively slow. There have been several small to mid-scale pilots but the infrastructure in industrial settings has several complications and lack the simplicity and standardization of the consumer side.
Industrial IoT, especially in factories, is also mired with business Rol, cultural and organizational issues that slow down its deployment, primarily because of misalignment between workers, managers, and owners. This has been talked about in several settings and we had a classic example on how Bharat Forge overcame this and executed on its Digital Transformation comprehensively at IoTNext 2018.
Focusing more on the technical aspects, factory automation has been non-standard, using age-old protocols and different brands and makes of machines that run for decades, essentially a patchwork of legacy and new. Hence, IoT systems in an industrial scenario have to be extremely versatile. Just basic connectivity of sensors and sending every bit and byte of data to the cloud severely restricts Industrial IoT deployments and the cost goes up significantly. It should also be noted that bringing Internet connectivity to the shop floor is relatively new. Also, factories will not replace their machines overnight and invest in new smart, IoT enabled, machines from OEMs. Industrial systems are a hodge-podge of different systems, some have a robust implementation of PLC systems and SCADA networks, others are fully manual without any PLC running for ages. Typical comments we hear are, ‘Don’t touch it, if it is not broken yet’, ‘Let it fail, before we replace it’, ‘AMC contractors come periodically to test and maintain’, ‘misuse of machinery is common’, ‘we are cluelessness about power quality impact on machines’. These are the usual patterns we see, not just in the developing countries but in more developed markets as well.
The following series of articles is based on real practitioners’ viewpoints over 5 years of learning across industries, seeing a plethora of IoT deployments and startups fail, discussions with several startups and real-life experience running a full-fledged IoT system with multiple customers with several thousands of devices in the field. The following are the topics that we intend to cover in this series.
- A versatile and flexible architecture that works at the edge, in-premise and cloud
- Overcoming the issues of connectivity and the importance of robust build management
- ‘IoT Data Simulator’ to simulate a multitude of data, simulate corner cases, simulate erratic and erroneous data flow and how to deal with them
- Formal API Management for data analytics and for the full systems
- The learning and signal processing system for 2V ( volume and velocity) data in edge computing
Interestingly most of these above aspects are either not required or are side-stepped during Pilot and Proof of Concepts, but they come back to cause complications as you scale-up.
While we will discuss each of the above aspects in subsequent articles, let us focus on the overall IoT and Analytics architecture here for Industrial use cases.
A versatile and flexible architecture that works both at Edge, In-premise and Cloud
IoT is all about sensor data. These data are recorded in cloud or in edge with a time stamp. The timestamp indicates the time of data origin and hence sensor data is a classical time series data. The challenge is how to extract useful analytics or actionable intelligence out of these sensor signals. Some of the issues like elimination of noise and detection of artifacts from the signal trend can be solved using traditional signal analysis. Additional areas to consider are infrastructural issues, end-user requirements and restrictions, running the signal analysis on a platform that can easily be ported into cloud, edge or on-premise. There are difficulties associated with extracting the signal artifacts too quickly or over a larger period of time using same computational system.
We believe that the entire signal analysis ideally has to be done by an edge platform for sensor signal analysis and transforming that analysis into actionable intelligence accommodating a large diversity of architecture, such as working with sensor edge, edge cloud or a public cloud (like Azure PaaS/IaaS). Sometimes even a cloud is not required, you can connect the data to a platform straight to HMI or mobile app or to a Desktop app. Industrial 4.0 is not yet standard and therefore, it makes sense to offer an architecture which is highly flexible and offers all the cross- platform importing capability.
So what does the Flexible Edge Architecture really mean?
Edge analytic or edge computation is set on a massive growth path. However, in the context of a factory or commercial building, edge architecture can pan into diversified system architecture. Edge computation can happen with sensor electronics as close to the sensor as possible, it can also happen on a gateway device, a PC or server connected to the Gateway or Router, a factory cloud which may be a server inside the factory or an external cloud framework. The whole system may or may not be connected to a public cloud (like AWS or Azure) depending on end customer requirements and restrictions. On top of that, all the analytics results may have to be made available to an HMI or display panel of a PLC.
Due to lack of a unified or standardized architecture of Industrial 4.0, it is extremely important for OEM (who wants to make a smart machine) to be cognizant of the reality that their customers (manufacturing plants) may or may not allow an external cloud. Many may not have a factory cloud. They may or may not want the data to be displayed in a PLC. They may want the system to be incubated into any of the major Industrial IoT PaaS platforms, like Azure IoT PaaS or Siemens Mindsphere.
MachineSense.com also went through its own set of learning, spending enormous resources with each customer with its ‘public cloud only’ approach and had to re-engineer and re-architect its entire platform to be completely flexible, with the ability to feed data to any form of repository, be it a resident computer, a handheld device, an in-premise cloud or an external cloud. The architecture had to adapt to the requirements and had to be scaled up or down based on the restrictions.
Subsequent articles will focus on the challenges in larger scale deployments and typical ways to mitigate and address these issues. Many platforms do not provide these bells and whistles to manage and hence a lot of watchdog, calibration routines, and automation tools had to be built to ensure smooth operation for a large IoT deployment and to manage the device efficiently.
About the Authors
Dr. Biplab Pal is the CTO and Founder of MachineSense. He has been a pioneer and practitioner in the IoT domain with years of experience in sensors, sensor networks and started his IoT company back in 2012. He has been instrumental in developing and deploying IOT solutions across factories, structural health monitoring for bridges and high value assets, water, energy management, as well as predictive maintenance of machines and power quality for the last two decades.
Som Pal Choudhury is a Partner at Bharat Innovation Fund, a $100M venture Fund focused on core technology startups from India. He is also an advisor to several IoT companies, part of the core group of IoT Forum (IoTforIndia.org), Co-Founder/Co-Chair of India’s premium conference IoTNext and is a frequent speaker and thought leader on IoT. He was involved in the IoT/M2M space from early last decade as the first employee of a Smart Grid company American Grid and has traversed the entire ‘IoT stack’ from ‘Sensor to the Cloud’ while working for companies like Analog Devices and NETGEAR.