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Deep Learning Image Captioning Technology for Business Applications

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Deep Learning Image Captioning Technology For Business Applications
Illustration: © IoT For All

Technologies applied to turning the sequence of pixels depicted on the image into words with Artificial Intelligence aren’t as raw as five or more years ago. Better performance, accuracy, and reliability make smooth and efficient image captioning possible in different areas – from social media to e-commerce. The automatic creation of tags corresponds with a downloaded photo. This technology could help blind people to discover the world around them.

This article covers use cases of image captioning technology, its basic structure, advantages, and disadvantages. Also, we deploy a model capable of creating a meaningful description of what is displayed on the input image.

As a vision-language objective, image captioning could be solved with the help of computer vision and NLP. The AI part onboards CNNs (convolutional neural networks) and RNNs (recurrent neural networks) or any applicable model to reach the target.

Before moving forward to the technical details, let’s find out where image captioning stands.

AI-Driven Image Tagging and Description Use Cases

“Image captioning is one of the core computer vision capabilities that can enable a broad range of services,” said Xuedong Huang, a Microsoft technical fellow and the CTO of Azure AI Cognitive Services in Redmond, Washington.

He has a point as there is already the vast scope of areas for image captioning technology, namely: Image tagging for e-commerce, photo sharing services, and online catalogs.

In this case, the automatic creation of tags by photo is being carried out. For instance, it can simplify users’ life when they upload an image to an online catalog. In this case, AI recognizes the image and generates attributes – these can be signatures, categories, or descriptions. The technology could also determine the type of item, material, color, pattern, and fit of clothing for online stores.

At the same time, image captioning can be implemented by a photo-sharing service or any online catalog to create an automatic meaningful description of the picture for SEO or categorizing purposes. Moreover, captions allow checking whether the image suits the platform’s rules where it is going to be published. Here it serves as an alternative to CNN categorization and helps to increase traffic and revenue.

Note: Creating descriptions for videos is a much more complicated task. Still, the current state of technology already makes it possible.

Automatic Image Annotations for Blind People

To develop such a solution, we need to convert the picture to text and then to voice. These are two well-known applications of Deep Learning technology.

An app called Seeing AI developed by Microsoft allows people with eye problems to see the world around them using smartphones. The program can read text when the camera is pointed at it and gives sound prompts. It can recognize both printed and handwritten text, as well as identify objects and people.

Google also introduced a tool that can create a text description for the image, allowing blind people or those who have eyesight problems to understand the context of the image or graphic. This machine learning tool consists of several layers. The first model recognizes text and hand-written digits in the picture. Then another model recognizes simple objects of the surrounding world–like cars, trees, animals, etc. And a third layer is an advanced model capable of finding out the main idea in the full-fledged textual description.

AI Image Captioning for Social Media

Image caption generated with the help of an AI-based tool is already available for Facebook and Instagram. In addition, the model becomes smarter all the time, learning to recognize new objects, actions, and patterns.

Facebook created a system capable of creating Alt text descriptions nearly five years ago. Nowadays, it has become more accurate. Previously, it described an image using general words, but now this system can generate a detailed description.

Logo Identification with AI

Image captioning technology is being deployed with other AI technologies as well. For instance, DeepLogo is a neural network based on TensorFlow Object Detection API. And it can recognize logotypes. The name of the identified logotype appears as a caption on the image. The research on the GAN-based logotype synthesis model could bring light to how GANs work.

Researching Deep Learning Models for Image Captioning

We applied a model that creates a meaningful text description for pictures, keeping in mind possible use cases. For example, the caption can describe an action and objects that are the main objects on each image. For training, we used Microsoft COCO 2014 dataset.

COCO dataset is large-scale object detection, segmentation, and captioning dataset. It contains about 1.5 million different objects divided into 80 categories. Each image is annotated with five human-generated captions.

We applied Andrej Karpathy’s training, validation, and test splits for dividing datasets to train, validate, and test parts. Also, we needed Metrics like BLEU, ROUGE, METEOR, CIDEr, SPICE, to evaluate results.

Comparing ML Models for Image Captioning

Typically, baseline architecture for image captioning encodes the input into a fixed form and decodes it, word by word, into a sequence.

The Encoder encodes the input image with three color channels into a smaller print with “learned” channels. This smaller encoded image is a summary representation of all that’s useful in the original image. For encoding, any CNN architecture can be applied. Also, we can use transfer learning for the encoder part.

The Decoder looks at the encoded image and generates a caption word by word. Then, each predicted word is used to create the next term.

Before moving forward, take a look at what we have received as a result of the model creation and testing with the Meshed-Memory transformer model.

Example of using a deep learning model for image captioning
Example of using a deep learning model for image captioning
Example of using a deep learning model for image captioning

AI-Based Image Captioning

We also studied examples that led to errors. There are several reasons why errors appear. The most common mistakes are poor image quality and the absence of certain elements in the initial dataset. The model was trained on a dataset with general pictures, so it makes mistakes when it does not know the content or cannot identify it correctly. This is the same way the human brain works.

Errors made by AI image captioning
An error made by AI image captioning model

Here is another case to illustrate how Neural Networks operate. There were no tigers in the dataset model. Instead, AI picked the closest object it knows – it is quite the same, as our brain deals with the unknown.

Neural Networks operate captioning unknown objects

Up-Down Attention Model for Image Captioning

This is the first model to compare. The Up-Down mechanism combines Bottom-Up and the Top-Down attention mechanism.

Faster R-CNN is used to establish the connection between object detection and image captioning tasks. The Region proposal model is pre-trained on object detection datasets due to leveraging cross-domain knowledge. Moreover, unlike some other attention mechanisms, both models use one-pass attention with the Up-Down mechanism.

Faster R-CNN (fig 5a) is used for image feature extraction. Faster R-CNN is an object detection model designed to identify objects belonging to certain classes and localize them with bounding boxes. Faster R-CNN detects objects in two stages.

The first stage, described as a Region Proposal Network (RPN), predicts object proposals. Using greedy non-maximum suppression with an intersection-over-union (IoU) threshold, the top box proposals are selected as input to the second stage.

At the second stage, region of interest (RoI) pooling is used to extract a small feature map (e.g. 14×14) for each box proposal. These feature maps are then batched together as input to the final layers of the CNN. Thus, the final model output consists of a softmax distribution over class labels and class-specific bounding box refinements for each box proposal. The scheme is taken from the official poster.

Faster R-CNN model for image annotation

Given image features V, the proposed captioning model uses a ‘soft’ top-down attention mechanism to weigh each feature during caption generation. This is LSTM with an added up-down attention mechanism. On. This is LSTM with an added up-down attention mechanism. At a high level, the captioning model is composed of two LSTM layers.

Meshed-Memory Transformer Model for Image Captioning

Another model that we took to solve the image captioning task is Meshed-Memory Transformer. It consists of encoder and decoder parts. Both of them are made of stacks of attentive layers. The encoder also includes feed-forward layers, and the decoder has a learnable mechanism with weighting.

Regions of the image are encoded in a multi-level fashion. The model takes into account both low-level and high-level relations. Learned knowledge is encoded as memory vectors. Layers of encoder and decoder parts are connected in a mesh-like structure. The decoder reads from the output of each encoding layer and performs self-attention on words and cross attention overall encoding layers after that results being modulated and summed.

So, the model can use not only the visual content of the image but also a prior knowledge of the encoder. The schemes are taken from the official paper.

Schema for AI image captioning Schema for AI image captioning with Meshed-Memory Transformer model
Schema for AI image captioning with Meshed-Memory Transformer model

Comparison of Two Models for Image Captioning

Based on our research, we’re able to compare the Up-down model and the M2transform model, as they were trained on the same data. The table below provides a summary of both models.

Table – Evaluation metrics

BLEU1 BLEU2 CIDEr ROUGE METEOR
UpDown model 0.8 0.358 1.16 0.573 0.275
M2Transformer 0.8078 0.3834 1.278 0.58 0.2876

Table – Inference time and memory

Time Memory
CPU GPU CPU GPU
Updown model 104.47s 17s 1479mb 1181mb
M2Transformer 23 m 32 s 3m 16s 1423mb 1310mb

Image Captioning: Results Analysis and Future Prospects

Both used models showed fairly good results. With their help, we can generate meaningful captions for most of the images from our dataset. Moreover, thanks to the feature pre-extracting with Faster-RCNN, pre-trained on the huge Visual Genome dataset, the model can recognize many objects and actions from people’s everyday life and therefore describe them correctly.

What Is the Difference?

The Updown model is faster and more lightweight than the M2Transformer. The reason is that the M2Transformer uses more techniques, like additional (“meshed”) connections between encoder and decoder, and memory vectors for remembering the past experience. Also, these models use different mechanisms of attention.

Updown attention can be performed in a single pass, while multi-headed attention that is used in M2Transformer should be running in parallel several times. However, according to the obtained metrics, M2Transormer achieved better results. With its help, we can generate more correct and varied captions. M2Transformer predictions contain fewer inaccuracies in description both for pictures from the dataset and for some other related images. Therefore, it performs the main task better.

We compared two models, but there are also other approaches to the task of image captioning. It’s possible to change decoder and encoder, use various word vectors, combine datasets, and apply transfer learning.

The model could be improved to achieve better results suitable for the particular business, either as an application for people with vision problems or as additional tools embedded in e-commerce platforms. To achieve this goal, the model should be trained on relevant datasets. For example, for a system to correctly describe cloth, it is better to run training on datasets with clothes.

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Source: https://www.iotforall.com/deep-learning-image-captioning-technology-for-business-applications

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Viezo Wins 2nd place at Future of Emerging Europe Awards 2021

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Viezo wins 2nd place at Future of Emerging Europe Awards 2021

Sep 22, 2021

  • Almost 50 organisations and individuals from across Central, Eastern, South-Eastern Europe and the Caucasus have been nominated for the fourth edition of the Emerging Europe Awards Programme. 
  • The winners of the awards were chosen by the public – For the first time since the programme was launched in 2018
  • Viezo, a Lithuanian startup, won the 2nd place for their focus on developing vibration energy harvesting solution, capable of converting vibrations into useful electricity

Vilnius, Sep. 22, 2021 – Leading developers of the most powerful PVDF vibration energy harvester Viezo, has celebrated winning the Future of Emerging Europe Awards 2021 distinguishing themselves among other many Green Energy nominees.

Winning of the Future of Emerging Europe Awards 2021

The main theme of the Future of Emerging Europe Summit and Awards is towards a resilient and sustainable emerging Europe. The event this year, that was hosted at the European Parliament in Brussels, took place on September 15th and it was geographically diverse.

Almost 50 organisations and individuals from across Central, Eastern, South-Eastern Europe and the Caucasus have been nominated for the fourth edition of the Emerging Europe Awards Programme.

The Summit focused on five areas: providing fair, equal, quality health care across emerging Europe; lifestyles for a greener and more sustainable emerging Europe redefining and strengthening a post-growth emerging Europe economy; inspiring unity and new transformational leadership in emerging Europe, and building a forward-looking, secure and democratic emerging Europe.

Viezo participated as part of the Green Energy section of the competition among other distinguished nominees as Fuergy from Slovakia & Respect Energy from Poland. For the first time since the programme was launched in 2018, the winners of the awards were chosen by the public. Donat Ponamariov, the founder and CEO of Viezo, presented the company’s overview talking about the importance of vibration energy harvesting and its crucial role in boosting the development of IOT in railways. Upon public votes, Viezo was announced to win the second place.

The rejoice of getting acknowledged

Viezo is a startup, established in early 2018 with the idea and goal to help fourth Industrial revolution expand faster and cheaper without needing to think about powering the sensing equipment. The technology can convert vibrations into usable electricity, therefore powering sensors indefinitely which are deployed on vibrating, dynamic machinery.

Viezo is the first company in the world, commercializing the vibration energy harvesting technology within the piezoelectric PVDF material, which is environmentally friendly and low-cost. Viezo team has expressed their sincere happiness to be recognized in this competition. Their collective commitment of time and effort has contributed to the development process that led to this success.

Public events participation

Viezo has been actively participating in many exhibitions recently as 7th Railway forum in Berlin, Infrarail in Birmingham, and SITL in Paris. Many important meetings took place with current and potential customers planning to many fruitful cooperations to come. Viezo is also participating in the 14th International Railway Fair TRAKO which is taking place in Gdansk, Poland from 21st to 24th of September 2021. Everyone is invited to visit their booth at the exhibition.

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Source: https://www.iotforall.com/press-releases/viezo-wins-2nd-place-at-future-of-emerging

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How LoRa Enables Life Post-Pandemic | Semtech’s Alistair Fulton

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In this episode of the IoT For All Podcast, Semtech Vice President and General Manager of Wireless and Sensing Products Group Alistair Fulton joins us to talk about LoRa. Alistair shares the history of LoRa, how it was developed to support automation in the utilities industry, and how both the technology and needs have evolved over the years, with LoRa becoming a key connectivity technology for a myriad of use cases and applications across a number of industries. He also speaks to the history of the LoRa Alliance, how the group was created and how it benefits the IoT space at large. 

Alistair shares some insight as to where he sees IoT going, speaking to some of the use cases that have become more prominent during the pandemic, including hospital and disaster relief initiatives, and what obstacles still exist in those spaces. He also shares how LoRa might play a role in future sustainability initiatives and what might cause companies to turn to IoT to support greener initiatives. 

Alistair Fulton is the Vice President and General Manager of Semtech’s Wireless and Sensing Products Group. He joined Semtech in 2018 with over 25 years of experience in the Internet of Things (IoT), connected devices, machine to machine (M2M)/embedded, and analytics spaces. Before joining Semtech, Mr. Fulton led the development of Hitachi’s Lumada Industrial IoT Platform, the leading “visionary” IIoT platform in Gartner’s 2018 magic quadrant. Prior to Hitachi, he led Microsoft’s early IoT initiatives, including the development and incubation of Microsoft’s v1.0 IoT platform (the precursor to the v3.0 Azure IoT platform).

Interested in connecting with Alistair? Reach out to him on Linkedin!

About Semtech: Semtech’s LoRa devices and the open LoRaWAN® standard offers an efficient, flexible and economical solution to real-world problems in rural and indoor use cases, where cellular and Wi-Fi/BLE based networks are ineffective. Learn why LoRaWAN is becoming a leading standard of low power wide area networks (LPWAN)

Key Questions and Topics from this Episode:

(00:54) Intro to Alistair Fulton

(01:39) Intro to Semtech

(03:29) Use Cases for LoRa and Semtech’s Offerings

(11:45) How has the pandemic changed the smart hospital landscape? Where is it going?

(14:55) When referring to LoRa versus LoRaWAN, what’s the difference there?

(20:15) How can companies use IoT to promote sustainability?

(23:29) How can IoT technologies help us prepare and recover from natural disasters?

(26:33) How can LoRa work alongside satellites to provide connectivity?

(29:07) What does the future of LoRa look like?


Transcript:

– [Narrator] You are listening to the IoT For All Media Network.

– [Ryan] Hello everyone and welcome to another episode of the IoT For All Podcast on the IoT For All Media Network. I’m your host Ryan Chacon one of the Co-Creators of IoT For All. Now, before we jump into this episode, please don’t forget to subscribe on your favorite podcast platform or join our newsletter at iotforall.com/newsletter, to catch all the newest episodes as soon as they come out. Before we get started, if any of you out there are looking to enter the fast growing and profitable IoT market, but don’t know where to start, check out our sponsor, Leverege’s IoT solutions development platform, which provides everything you need to create turnkey IoT products that you can white label and resell under your own brand. To learn more, go to iotchangeseverything.com. That’s iotchangeseverything.com. So, without further ado, please enjoy this episode of the IoT For All Podcast. Welcome Alistair to the IoT For All show, thanks for being here this week.

– [Alistair] Hi Ryan, you’re welcome, good to be here.

– [Ryan] Can you start off just by quickly introducing yourself to our audience? Maybe a little background information, anything you think would be relevant to give our audience some context, who they’re listening to?

– [Alistair] Yeah, sure. I’m the general manager of the Semtech wireless and sensing business, which is responsible for a technology called LoRa and beginning of LoRaWAN. My background; I’ve been in IoT for probably the last 30 years or so. Way back before it was called the IoT, when it was still called M2M telematics. Like most, I started in cellular, but spent a number of years working with Microsoft on the Azure IoT platform, and then spent a few years building Hitachi’s Lumada industrial IoT platform before joining Semtech three years ago.

– [Ryan] Fantastic. Talk a little bit more about Semtech and kind of what you all do. LoRa’s obviously very well known in the industry from a connectivity option. And you know, when people hear LoRa, LoRaWAN, what does that mean? Kind of, what does it do? How does it operate? You know, and what of the benefits of it?

– [Alistair] Well, LoRa is really quite a unique technology that provides for low bandwidth, long range, and low power communication using the industrial and medical band. So free spectrum. It specifically was originally developed for utilities, for things like automated, remote meter reading, but over the course of the last few years, we’ve seen that use expand exponentially across pretty much every single use case you could think of in the IoT. Semtech’s role is we provide semiconductors, which might lead you to think, well, what’s a platform or a software person like me doing in a semiconductor company? And the answer is quite simple. IoT has long lacked, easy means of connecting everything. A lot of the use cases I’ve certainly worked on over the years have really required a very ubiquitous dataset that is either extraordinarily expensive to generate using more professional methods or just very difficult to implement. You know, wired solutions, et cetera. So LoRa really fills a gap that’s been around for a good long while for applications that require the connection of, you know, tens of thousands, hundreds of thousands of sensors, all feeding data into a centralized system. And as such, it lends itself particularly to use cases that involve large, complex distribution systems or production systems like agriculture and so on.

– [Ryan] Fantastic. Let’s expand a little bit on the use case side there and kind of bring this full circle for our audience and talk about how LoRa and other offerings you have in the market are kind of being used in everyday life. So, if there are any kind of just prominent or more applicable use cases, you’re comfortable sharing to our audience that’d be fantastic.

– [Alistair] Oh gosh. I mean, I always say to my team that barely, there goes a day goes by where I don’t find a new– Something else it’s being used for.

– [Ryan] Right, right.

– [Alistair] So, that’s really part of you know, our approach is we provide the underlying radio. We have a league–

– [Ryan] Mm hmm.

– [Alistair] That we’re members of called the LoRa Alliance, which is 400 plus members, doing everything you can imagine. I mean, as I said, originally, LoRa was really developed to support some quite challenging use cases and utilities. Being able to penetrate the ground to, you know, a meter that was 10 meters below concrete. And obviously utilities still remains a really quite significant use case. Both metering, but also increasingly grid management. Electricity grids, certainly in the U S, where both of us reside are a source of very significant challenges, both in terms of power cuts, but also, at least in the US West, where I live, causing wildfires, et cetera. So, LoRa, quite extensively used to monitor equipment throughout a grid. But beyond that, we see LoRa, particularly at the moment, a very significant uptick in use of LoRa in the logistics space. Again, as I was saying earlier, you know, really complex systems where you have 200,000 specialized pallets circulating through an entire European wide distribution system. LoRa’s used to track each and every one of those pallets so that the customers in that multi-party distribution chain know where everything is, specifically to prevent loss and theft, et cetera. On the agricultural side, as briefly mentioned, we see LoRa used to perform soil monitoring, monitoring the usage of agrochemicals, monitoring the quality of food products as they come from farm to table, monitoring origin, et cetera. But we also see LoRa used very extensively in smart cities, smart building type environments, where, either for monitoring energy consumption or actively managing energy consumption. You can optimize how you are consuming electricity, gas, et cetera, or even water, based upon the actual usage of buildings and how people are interacting in those built spaces. So, a lot of LoRa is really about deriving, as much of the IoT is actually, it’s about deriving more from less, you know, getting more products from less input, increasing efficiency, and of course the natural consequence of that is reducing the environmental impact of many of the activities that we as humans are responsible for.

– [Ryan] Right. Now, would you, are there any use cases or applications that potential audience members may be kind of thinking about that LoRa wouldn’t be a good fit for? I know it’s kind of a loaded question, but, kind of anything you would advise, like, if this is kind of the parameters or the characteristics of your use case, LoRa’s probably not the best fit, but just to, kind of, get a sense of where the lines are?

– [Alistair] Yeah. It’s primarily high bandwidth, and with high bandwidth comes high power demand. So typically, security video cameras, et cetera. But, that said, we do have customers who use LoRa to send heavily condensed video signal, but for things that really require either real time, near real time, or higher bandwidth connections, LoRa’s not a great fit, I think in most IoT solutions though, there are rarely, rarely solutions which use one technology. And so one of the things that follows a design principle really is LoRa is part of a toolkit and our job is to deliver tools to our customers that are interoperable and easy to use. So we customers use LoRa in combination with 5G, with 4G, with wifi, with Bluetooth, pretty much every other connectivity technology, you could imagine. And that’s the way that things should be. I think, you know, the IoT has long suffered from people taking a bit more of a proprietary kind of closed walled garden approach in the past. You know, at least in my experiences, both customer and solution provider is rarely the right answer from the customer’s perspective. So, we aim to be compatible, but we know what we’re good at, and that’s low bandwidth, long range, ultra low power, low cost connectivity, and we know what we’re not good at, and that’s high bandwidth. And we aim to be compatible with all the other solutions in this space that support some of those other requirements.

– [Ryan] Fantastic. And throughout the pandemic, I’m curious, have you all discovered any new use cases that, you know, LoRa has been used for or could be applicable for just based on this kind of, you know, abnormal year, year and a half that we’ve had to go through?

– Yeah, it’s quite an abnormal year and a half isn’t it? It’s gone in phases actually, to be honest, Ryan. And at the beginning, when we were, I think collectively, all in a bit of a blind panic about what was happening, we saw a lot of customers taking pre-existing LoRa solutions and applying them to this new problem. So, things like emergency panic buttons, originally developed for workers in the hotel and hospitality industries were taken and used to provide emergency call buttons for patients in field hospitals in Europe. Totally orthogonal use case, but absolutely the same problem; how do I quickly deploy a super low cost network solution that doesn’t require that I plan a network. I can just install it and connect everything. We saw an increase in existing use cases like tracking assets. So tracking hospital equipment, crash cars, venting in particular. And an adaptation of asset monitoring solutions rather than monitoring, you know, slurry pumps, then monitor the performance of said respirators and other pieces of equipment. So, I think collectively, you know, the IoT ecosystem, just like everybody else, we looked at what we had in the cupboard that would help solve a–

– [Ryan] Right.

– [Alistair] And we applied it as quickly as possible. What we’ve seen now, as we kind of move, hopefully, I was hesitate when I say this, but hopefully, towards a slightly more normal situation. We’re seeing quite extensive use of LoRa and LoRaWAN based solutions to connect buildings. We use this in our own building. So, monitoring presence either from a static presence sensor, is there someone in the room or actually in some of our facilities with tags that can monitor proximity. So, if Alistair’s stood next to Bryan for too long, we have a record of that. But CO2 monitors, monitoring the performance of our air blowers, as well as moving toward touch-less interaction with, you know, bathroom–

– [Ryan] Right.

– [Alistair] Et cetera. So, and that I think is more of a, an evolution of the sorts of solutions we saw in smart building that is, and that evolution is specific to the challenge created by COVID. It’s not so much a reuse of what’s in the cupboard. It’s actually the ecosystem having a bit of time to really try and figure out, okay, well, how do we solve this problem? Because if people are gonna go back into the built environment, then we’re gonna need to have some more confidence about whether that environments clean and is the air circulating effectively, et cetera. And I think that’s probably a much more sustained set of use cases that are honestly, I think, it just gonna be part of the way that we live our lives, now.

– [Ryan] Yeah, I totally agree with you. We’ve heard very similar stories from a lot of other companies we’ve spoken to on the podcast, just about how pandemic has, has influenced their business, how, you know, they’ve gotten more involved in the healthcare space and they thought they were before, because exactly what you’re saying, is people are looking for things that were already created, how can we adapt them to these new challenges that we’re seeing? I’m curious to hear your take on how you see the smart hospital landscape kind of shifting or how it has shift, I guess, shifted over the last year or so and where you kind of see it heading? You know, we’re seeing more people starting the transition to be leaving hospitals, you know, tend to be able to be monitored remotely. So, I’m just curious your take on how you view the smart hospital landscape in general?

– [Alistair] I think that’s a super interesting space. And as I said, you know, I’ve been in this space for a good 30 odd years at this point. And I think throughout that 30 years, not a year has gone by where I’ve not looked at some kind of smart health application. I think the appetite has changed. I think earlier, you know, a couple of decades ago, there was a lot of trepidation around data privacy, et cetera. And that still remains, but I would separate the two though. I would separate the hospital as a building and a healthcare system, because I think the two are quite distinct. We see use cases in the hospital as a building, which are just the same as you would see in any other smart building. Am I utilizing my energy resources in a responsible way? Do I know where my assets are? Certain instances, do I know where my workers are and are they safe? And we see all of those use cases within the hospital environment. You touch on this, though. The health care system is something different. And I think what I’m seeing in these last few years, is as populations age, the health outcomes of patients who stay in their own homes for longer period of time are significantly better than the health patients who are in institutional settings, particularly for memory care patients. Alzheimer’s and so on. And then what we’re seeing and LoRa is being used in these types of applications, as well as other technologies. What we’re seeing is the development of solutions, which enable both the remote monitoring of patients. So, is grandma moving around in her apartment?

– [Ryan] Right, right, right.

– [Alistair] But they’re also self use products, which I think is particularly exciting. So, the ability to wear a sensor that can provide real time feedback on the health condition that you may suffer from, whether it’s arrhythmia or respiration issues, or indeed some of the follow on issues that the patients with so-called with long COVID or long lasting COVID symptoms, are experiencing. And I think that the most interesting thing, as I said, is it’s the use of technologies like LoRa, IoT technologies in that health care system that I think has the greatest promise in terms of improving the quality of all of our lives, honestly.

– [Ryan] Yeah, I totally agree with you. That’s kind of fantastic insights and a very, you know, the smart hospital, the smart healthcare space is a very unique one, a very kind of exciting one just to see what transformations are gonna be happening, because I think there’s a renewed focus on it ever since the pandemic and IoT is such an exciting industry already, let alone kind of adding this layer on top of it. So, I totally agree with you. I wanted to ask you just kind of a unrelated question, real quick, is when people say LoRa versus LoRaWAN, what is the difference? And kind of just, because I know it’s sometimes used interchangeably, but I think our audience would benefit from just understanding why there is a difference at times?

– [Alistair] Yes. LoRa is the radio. So, its the physical bit. And LoRaWAN is the protocol. And the reason that the distinction, well, obviously the distinction is important because one is one thing and one is the other. The way that Semtech has approached this space really, is, so on the radio side, having this essentially the same radio in every single LoRaWAN based device means that you have marginality at the hardware layer, which makes the job much, much easier. LoRaWAN, the protocol, we recognized at the very outset of this journey that, a semiconductor company was not going to come up with all of the different capabilities and requirements for an effective, low power protocol. So we, along with several others very quickly formed the LoRa Alliance to drive the definition of that protocol based on the experience and understanding of, at this point, 400 plus companies. And so the protocol has evolved really rapidly, driven by market need. And we’ve kind of focused this provider on providing the radio. Now, downside of that model would be single source provision. And so, we’ve also acted in parallel to license our IP, to partners like SD micro, for example, because, you know, we recognize the diversity of supply is key, diversity of choice. And by the same measure, you know, there are many other folks in our space who have ideas that we won’t have about how to evolve the solutions. And if we act to prevent customers having access to that, then we’re not doing right by the customer at the end of the day. So, and I do think that that’s somewhat of a differentiated approach. Again, I’m somewhat looking into the semiconductor industry from the outside. I don’t know that people have always started with interoperability and openness as a design principle in the way that we have. I think it’s very important.

– [Ryan] Oh, I totally agree with you. I mean, just seeing over the last, I haven’t been in the space nearly as long as you have, but I’ve been in it, whoo, almost five years now. And, you know, when I first got into this space, when you hear, LoRa was already becoming a popular name thrown out and connected to obviously, Semtech, but just seeing what LoRa’s enabled in the space with that openness and the LoRa Alliance’s growth and all those things kind of attached to it has been fantastic for the industry. Something that I think a lot of other companies and connectivity options could learn a lot from. So, it’s been fantastic to see.

– [Alistair] But it’s striking the right balance between, maintaining homogeneity so that the developer’s life is easier with insuring choice, and honestly, avoiding, helping customers avoid lock-in because, you know, lock-in is not good for customers. There’re several companies, a couple of whom I’ve worked for, who’ve shown customers why not a good thing. I think for IoT to flourish, there’s got to be an openness. There’s got to be flexibility. There’s got to be ease of use. And, honestly, one of the opportunities I think that Semtech has is, we’re able to support that, we’re able to provide on this product that it can be open at the same time.

– [Ryan] Yeah. it’s always interesting, kinda, when I have this conversation with people, just to think about how we view adoption and what we feel like is required for adoption to increase across any number of industries. And, you know, there’s tons of connectivity options out there that oftentimes confuses people and sometimes deters people from adopting IoT. But in reality, all these connectivity options are more opportunities to find the right package of components to build a solution that fits their use case perfectly, gives them the ROI they’re looking for and so forth. So, the more that something like LoRa can work and be interoperable with other technologies and provide the opportunity for more, for IoT to be more easily adopted, more easily understood, and to see success, only helps anyone that’s involved, right? It’s not pushing against anyone else. It’s only contributing to the good of the industry and, you know, helping us try to reach those projections that these analysts have promised us for so many years, now.

– [Alistair] I agree with you. It turns out shockingly, that customers, like simplicity. They like a quick return.

– [Ryan] Who woulda known? Who woulda known?

– [Alistair] And I do think in this space, that’s kind of quite, you know, we have all engineers to one degree or another. It’s very easy to get lost in the, you know, the fantastic technology that we–

– [Ryan] Of course.

– [Alistair] And forget that, that simple and quick is, and cost-effective, always wins. And, you know, that’s what we really focus on from a design point of view. We want to make the products simple.

– [Ryan] Of course.

– [Alistair] Deep embedded development skills to be able to pick up a LoRaWAN based device and do something with it. You should be able to work with that device as a cloud developer, or a mobile application developer.

– [Ryan] Yeah. It increases the value of that technology, the more flexible it could be. So, I totally agree with you. I wanted to shift conversation quickly, for a second, and talk about sustainability because it’s a conversation that comes up from time to time. But from your perspective, how do you kind of view companies or how can companies kind of turn to the Internet of Things to promote sustainability and kind of, the role that it can play in success, there?

– [Alistair] Well, it’s funny, and I’ve said this before, but now, I genuinely think that the IoT is the technology, world’s way of putting its best foot forward to solve some of the major issues that we face as humanity. We, through our genius have come up with myriad ways to produce food and energy, and the consequences of that genius are visible around us. We’re in a position, now, where we’re seeing unprecedented levels of warming, unless you go back to the PSTN era, we’ve got a serious problem to solve. And I think IoT plays a critical part in that. I think, one of the ways it does that is that it helps align economic, the economics of the world with the environmental challenges that we face. And I say that because of the following, if I have perfect information on a distribution system, and I am economically motivated to optimize that system and to reduce waste. The direct by-product of reducing wastage is lessen environmental impact. And that’s true, whether it’s in the food production system, which is probably the most complex global supply chain model that you see. Whether it’s car parts or pretty much anything else. More from less. And IoT plays a critical part in delivering on that. But to do so, it has to be accessible. It has to be easy. It has to be cheap. It has to be. And, you know, we talked about some of the other technologies that higher bandwidth technologies earlier. They’re great. And again, I, like many folks in this space, come from a cellular background, you can’t beat 5G for a high bandwidth connection that’s gonna get you vast amounts of data, but you can’t deploy a 5G radio on a smoke sensor that you drop every 50 meters throughout a fire zone. It just doesn’t work. So, but I think IoT is a critical part of the jigsaw in solving some of our sustainability issues, but it’s because it ties into the economic drivers or the other incentives, the commercial enterprises. I was gonna say earlier, actually, in the context of smart hospitals, the same is true. We can build all the technologies that you need to actually deliver on that vision of patients living in home or for more of their lives, but until align the economic incentives to that. And it varies globally, but in the US, the economic incentive for hospital is to see inpatients. People walk in or they get paid.

– [Ryan] Right.

– [Alistair] I think in the broader environmental context, those economic incentives already exist and they’re already aligned. What’s been missing is the technology to give companies the data to act on those economic incentives.

– [Ryan] That makes total sense. I completely agree with you. So, I wanted to ask, kind of, it’s not necessarily, it didn’t directly connect to sustainability, but it talks, it’s more about the time of year right now. Obviously, we’re getting into kind of hurricane season. My sister actually lives down in Key West. So, she’s been involved in some pretty big hurricanes over the years, and we were, I visited her last week and she was asking more about kind of what I do, what is IoT, et cetera. And I was kind of giving her some high level thoughts about how IoT kind of plays into natural disasters, how it could potentially play into, you know, hurricane season down here. And I wanted to get your thoughts on how you all, from your perspective, or just from your experience, either one, how IoT technology can help with disaster preparedness, how it can, kind of just help when these situations unfortunately occur, how we can better, not just prepare ourselves, but also better recover from them?

– [Alistair] Yeah. And I think that’s a very sensible distinction actually, between those two, how can we better anticipate, you know?

– [Ryan] Right.

– [Alistair] And how can we recover more effective? ‘Cause they are quite different scenarios. Now, the application of IoT technologies in anticipating disaster, is probably a little bit clearer. So, the use of sensors to determine changing weather conditions. Several years ago, I worked on very similar use case in Indonesia, looking at the how to anticipate the onset of tropical storms. And so sensor networks, which allow you to monitor sea level change, to see the waves coming in, in the front of the storm, weather sensors, et cetera. That gives you more data to anticipate what’s gonna happen. However, the problem is that period of anticipation is very short. You’ve only got in some cases, minutes before the thing you’re trying to forecast, actually hits you. Very true in the case of the . The second part, which is, I think is, maybe where that data can be used to greater effect, is in planning response, whether it’s the deployment of emergency equipment based upon historical pattern and the application of current data to that pattern, to identify, this is where the hurricane is likely gonna hit, whether it’s anticipating impact on electricity grids. As we talked about earlier, electricity grids are a prime source of wildfire. And one of the key dangers after wind events is wildfire exacerbated by breakage of gas lines, et cetera. And so being able to module based on real time data, the impact of the storm event on a grid allows the grid operator to shut down parts of the grid that the most vulnerable. And so mitigating the impact and enabling a faster spring back, I think is a very, very valuable area of IoT or IoT solutions as well. But the short answer is I think, again, IoT is a critical part of the willingness to better anticipate and better respond to all sorts of natural disasters, both slow and fast impacting.

– [Ryan] Yeah, absolutely. I totally agree. The last question I want to ask you before we wrap up here is kind of just, it goes back to the LoRa conversation and I’ve had a couple of guests on recently talking about satellite connectivity and satellite communications. How does LoRa kind of work with satellite or how could it potentially work alongside satellite comms to provide connectivity?

– [Alistair] Well, we have a, that some of them have, we have several customers who are working to deploy LoRa based, LoRaWAN based satellite solutions. Both, low earth orbit and geostationary orbit. And, you know, I guess in some respects, that’s testament to range that you can achieve with LoRa. From ground to low earth orbit is, it’s a decent distance. But these solutions are able to pick up ground generated signals from LoRa by census communicates. Though, it’s an additional, it’s an add on the LoRaWAN protocol called LR-FHSS, which is very much the technical name, but it enables LoRa to reach further and to support denser deployments. But those networks, and we have a couple like Swarm and Fleet Space, who’ve already deployed recently. You probably saw announcements from EchoStar and others on future plans. Those networks are both targeted at use cases like logistics and vehicle tracking, the remote monitoring of pipeline assets, et cetera, all the way through to use cases as novel as monitoring the movements of endangered species like black rhino in Tanzania, where a small LoRa sensor is actually physically drilled into the rhino’s horn. And the data generated from that sensor on the rhinos movement, is captured over satellite. That data is then used to model the behavior of the rhino and determine whether or not it’s under stress. If it’s being chased by poachers, for example, which can initiate a response from the game wardens to actually go check the animal out. So say, ubiquitous global coverage with the low-power sensors I think is kind of being one of those holy grails in IoT. And we’re starting to see the emergence of that, with our partner policy we’re using LoRa now for these novel applications. I don’t think when we first came up with LoRa, several years ago, that we would have anticipated that we would see multiple satellite networks deployed using the tower. It’s very exciting.

– [Ryan] That absolutely is. So, last question I have is what’s the future of LoRa look like? Where do you kind of see it going? What are some things that, maybe, our audience should be on the lookout for, be excited about, coming out of the Semtech side of things?

– [Alistair] Quite recently, actually, we’ve released what is in fact the third generation of LoRa. And even that we’ve only been in the market for a few years, It’s kind of, it’s fairly swift progress, but the third generation hardware platform called LoRa Edge, adds low-power location to low-power, long-range communication. And it does that by adding the ability to sniff GPS satellites and sniff Wi-Fi. What does that mean? That means the ability to connect essentially the ID, the address of satellites and or Wi-Fi base stations. And pretty much, use that data to look up where the asset is. Now, that’s an area where we’ve seen LoRa used in conjunction with traditional GPS chips, which are fairly power hungry. And so we went down this path to enable customers to use LoRa, both for indoor location, using wifi and outdoor location, using GPS, which is a unique combination in a single chip design. The purpose is cheaper, lower cost, easier ability to track it. We’ve also as part of our drive to simplify the use of LoRa and LoRaWAN over the last couple of years, introduced some cloud services, which take complex areas development like calculating location and turn it into an API call. And that really is part of our, I would say our overarching strategy, which is to drive simplicity into the development process. It’s too hard, today, to build IoT solutions, period. I think on the cloud side, Microsoft, Amazon, many others, the cloud platforms that we have at our disposal now to analyze the data that we can generate from IoT solutions are incredibly powerful. I mean, you can achieve in a few hours, what would take months, years ago, to achieve. But the problem of getting the data into those platforms, remains. And I think all too often, working with sensors is involved, as I said earlier, quite deep embedded development skills. And this also, don’t hate me but college graduates don’t come out with, vested in C and C++. They’re coming out and having learnt much more modern programming languages that are much more suited to cloud applications. And so, one of our central design philosophies is, DORA is a fantastic solution for a range of very difficult to solve problems, if we can make it easy enough for an average cloud development to be able to work with. And that’s not belittling cloud developers in any way, but if we can make it easy and accessible, then we’ve actually done something that has meaning. You know, we’ve really untangled the growth of IoT. And you mentioned it earlier. We’ve been looking at these forecasts for years, the billions and billions and billions of devices are gonna be here. The technologies have existed. The business case, very often, has existed. Why are these things not here? And the simple answer is, it’s too dang hard. And so, our focus going forward and you see it pretty much in everything that we do is delivering tools, making the core product simpler, applying very simple techniques, like wrapping hardware and software with modem type products that really drove the adoption of cellular based on IoT solutions. Applying that same approach that the developer can focus on the application layer and not worry about how the radio works.

– [Ryan] Right.

– [Alistair] That all of that stuff is black boxed and just taken care of. We’re also seeing that kind of black box thing in, you know, that simplification, feed into some of the integrations with cloud platforms. So, Amazon recently announced a LoRaWAN for AWS IoT Core, and that takes the network side of LoRaWAN and black boxes it. So, literally you can go buy a sensor and a gateway, plug it in, provision it to LoRaWAN for AWS IoT Core, write an application, and away you go. And that’s a distinctly different model from what we’ve seen in the past. And the one that we think is essential. Not only LoRa, I think, you know, I look at some of the other technologies that we work with and kind of think, geez, you know? If only they could do the same. If only they could make it simpler to use Wi-Fi or to use Bluetooth, et cetera, then you know, then we’d be really, you know, making a contribution.

– [Ryan] Yeah, I think people will need to understand that the end user and the person who’s making the purchasing decisions to adopt IoT, they don’t really care what goes into it. They just want it to be simple for them. And we, as you know, the individuals who are helping the industry grow by building and specializing in one of the many components that go into building IoT solutions, whether it’s hardware, connectivity, the cloud side, the application layer, you know, et cetera, we are responsible for packaging that up and presenting it to the users in an easy way. So, and you know, something that is easily understood, something that the ROI is clearly realized, so that adoption can increase, the more complicated it seems, which, before we started IoT for all that, that’s kind of how the industry was. All the content we were seeing to help educate the market was very dense, very engineered, engineer focused, but the decision makers in these variety of industries, that’s, you know, too long to list, we’re not technical individuals. So they’re reading this technical content, having no idea what IoT really could do for their business. And until people started writing it, you know, with those individuals in mind and building solutions to those individuals and those users in mind, it’s hard to see how IoT was ever gonna be adopted at the scale that we wanted it to be. So, we tried very hard to educate, you know, right layman term content and make, to speak to those who are getting confused by IoT, because we understood what IoT could do for their business, but they had no idea about the technologies that were involved, nor do they really care. Honestly, they just want to make sure that it works. It provides the value that we say it’s gonna provide, and if they can afford it and get the ROI out of it. And a lot of that burden falls on the companies within the ecosystem of IoT. And that’s where I think, what you guys are doing is fantastic. The approach you’re taking to simplify is something that a lot of companies could learn a lot from.

– [Alistair] Yeah, I’ve had the privilege. I was invited by years in the industry to work with some of the most brilliant engineers you can come across. There’s a point at which a brilliant engineer reaches the conclusion that, yeah, the customer actually doesn’t care about the technology. And that’s quite a bitter pill to swallow sometimes. More over, delivering a solution that is simple requires that you eat the complexity, you know? That you consume that complexity on the part of the customer. Spare them from having to deal with the internet of some things something simple and easy. I do think that there is this competitive dynamic that has existed to the detriment of the industry, which is, “Well, if I designed it in such a way” “that you can only use this component with that component.” And you see this all too often in smart home solutions, for example–

– [Ryan] Sure. Sure.

– [Alistair] Your home, that I have at least seven networks running in my house, running the doorbell on it. And I work in this industry and I can’t figure out how to integrate that.

– [Ryan] Exactly.

– [Alistair] So, seeing that crossover of, but I do think, and again, I might be being overly optimistic, but I do think that that is changing. I do think that people in the industry are really recognizing that easy is the way forward and listening to customers. I mean, this is the main thing.

– [Ryan] A hundred percent.

– [Alistair] That agility. Build an agile product that you can change. Go listen to customers and understand what they’re trying to feed, change your product, to do what they want. Very simple. That’s a problem that software companies, in particular, have struggled with for a number of days at this point. So, there’s a sign that we are changing, which is, I think very positive.

– [Ryan] Totally agree. I mean, they tried it one way. They didn’t see the adoption. They realized, you know, what they needed to do to start getting people, to adopt the technology, which is what we’re talking about here. And I think a lot of technology, over just the history, has gone through those same processes, right? Like the people who build it, the very, you know, the smart individuals who are brilliant enough to come up with some of these technologies are so close to it that when somebody doesn’t, to their expectation, appreciate it in the way they want it to be. It’s hard to stomach, you know? So, getting them to turn that connection off and actually realizing that there’s, they do appreciate it. It’s just in a different way, once it’s able to be applied to their use case, applied to their company, to see that benefit. They’re just looking at it from a different lens. And I think that’s often something that gets lost in a lot of these conversations. So, I appreciate you, kind of, all your insights and everything you’ve shared today. It’s been fantastic. The last thing I wanted to ask you was, if anybody out there wants to learn a little bit more about LoRa, wants to learn about Semtech, you know? Kind of, just get in touch in any capacity. What’s the best way to do that?

– [Alistair] I would start with loradeveloper.com, which is our developer portal. And again, in mind with what I’ve been saying around simplicity, there, you can find a wealth of information on how to get started. Semtech.com, but also the LoRa Alliance. The LoRa Alliance is a great place to start. As I said, we’re proud to be members of that ecosystem. The things that, I see them at daily basis, from across the ecosystem, and it’s simply outstanding. And the LoRa Alliance website is a great place to start to learn about some of the applications that LoRaWAN is being used to address.

– [Ryan] Fantastic. Well, Alistair, it’s been great conversation. Thank you so much for making the time. And we look forward getting it out to our audience. We’d love to have you back at any point to talk more about what’s going on over at Semtech.

– [Alistair] Yeah. I’d love to be able to come back. Cheers. Thanks, Ryan.

– [Ryan] Thank you. All right, everyone. Thanks again for joining us this week on the IoT For All Podcast. I hope you enjoyed this episode. And if you did, please leave us a rating or review and be sure to subscribe to our podcast on whichever platform you’re listening to us on. Also, if you have a guest you’d like to see on the show, please drop us a note at [email protected] and we’ll do everything we can to get them as a featured guest. Other than that, thanks again for listening and we’ll see you next time.

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Source: https://www.iotforall.com/podcasts/e134-lora-post-pandemic-use-cases

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IoT And SCADA Systems, Forced To Coexist And Understand Each Other

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SCADA Systems
Illustration: © IoT For All

Until the first half of the 20th century, industrial organizations relied primarily on the human factor to control and monitor their processes. However, with increasingly complex operations and ever-larger factories, in the 1970s, digital PLCs (programmable logic controllers) and computers became famous as an interface for data transmission to remote control centers. Soon later, the «telemetry» was born, from the Greek «metria» (measurement) and «tele» (remote), and with it, a control system the SCADA: Supervisory Control and Data Acquisition Systems. It is called the third industrial revolution, and today, there is no industrial company that does not have PLCs or SCADAs in its operation.

Internet of Things (IoT) and Artificial Intelligence (AI) allow today to witness another great technological leap that many dare to call the fourth industrial revolution.

The scope goes far beyond «data acquisition and monitoring.» It focuses on the advanced processing of large volumes of data that allows faster and more efficient decision-making processes and less risk and margin for error. However, we are still in the process of consolidating this new revolution, as the limits between the investment made in the third industrial revolution and the one needed for the fourth one are not yet clear.

In this article, we give three keys to the roadmap that must be followed by any company that doesn’t want to be left out of the fourth industrial revolution.

The IoT Platform As A Complement to SCADA

First, the elephant in the room: SCADAs are not ready for advanced processing of large amounts of data. In the same way that IoT platforms are not prepared for centralized real-time process monitoring and automation. Therefore, there are two types of technologies that are forced to coexist.

The centralized control process of a SCADA can only be realized using databases that ensure reliability and fast response to queries. Generally, centralized databases, structured query languages (SQL), and the financial cost are linked to the «number of variables.»  However, these architectures are too rigid for the processing of large volumes of distributed and changing data.

In this sense, IoT Platforms rely on distributed databases, with unstructured languages (NoSQL) and cost per «used resources» (CPU, Memory). IoT platforms are the best suited for creating mathematical models that require advanced AI queries, but there are not optimal for highly reliable real-time processing.

When we look at visualization and user interface functionalities, the goal of a SCADA platform is to model complete processes in a way that makes it simple and easy for an operator to control the process without errors. So, HMI (Human Machine Interface) graphics generation frameworks are optimal.

A dashboard web-like visualization framework is more suitable in the case of an IoT Platform, whose objective is to illustrate loads of historical data, cross-references, or future trends. It seems highly unlikely that shortly there will be one platform that can combine the reliability and speed of a traditional SCADA with the flexibility and scalability of an IoT Platform. Both systems will have to coexist and integrate, for which the correct budget allocation and the coordination of OT and IT departments are critical.

IoT Edge Nodes As A Complement To PLCs

Similarly to what happens in the «control rooms,» near the assets «in the field,» there are also systems that must complement the existing ones. Automated controllers or PLCs are devices whose primary function is to digitize and automate the production process, and their real-time requirements are even more restrictive than in a SCADA. A millisecond error can mean that a robotic arm can fail or that an electrical substation does not coordinate the relays properly, and there is a major global system failure. A PLC aims to focus on its function, and it would not be good to be programmed to perform actions other than those related to the production process.

And so, returning to the previous examples, it does not make sense that the PLC that controls the robotic arm or the relays of the substation is checking other variables that are needed, for instance, making more global decisions such as the environmental conditions of the plant or the presence or not of operators in it. Moreover, to obtain these additional data for AI, it does not make sense to use PLCs since they usually require particular expertise for programming.

Where real-time is not a requirement, but the flexibility to acquire data and treat it in an efficient and scalable way is, the IoT Edge Nodes is the best alternative. These Edge Nodes are mini-computers with high-level languages programming (i.e., Python, C/C++, or able to store Docker containers), and several inputs and outputs as well ass combined connection interfaces (e.g., industrial buses with cellular connectivity).

Cybersecurity As A Complement To Safety

Safety refers to the condition of being protected against events that can cause injury. Safety standards, risk management, or disaster response plans are on the daily schedule of any industrial organization, in many cases forced by regulations.

With IoT and AI, we move into the cyber-physical world, where industrial networks (or OT networks) are becoming less isolated and more interconnected. Therefore, they are more vulnerable to both external and internal cyber-attacks that can affect not only the safety of workers but also the continuity of the company’s operations.

In this sense, the traditional risk management and incident response plans and certifications of «Safety» must be complemented by their counterparts in the world of cybersecurity.

The standards that seem likely to become de facto standard in this regard are ISO 27001 for information security management and IEC 62443 for IT security of networks and systems in industrial communications. The introduction and management of elements such as IoT Platforms and Edge Nodes must be done under the umbrella of good practices and standards such as those mentioned above, ensuring the future of this new technology roadmap.

New technologies, such as IoT, AI, or Edge Computing, have not come to replace SCADAs or PLCs but complement them. A correct coexistence and integration of product, human, and processes, between IT and OT, and a vast technological openness, is the response for those industrial organizations that want to jump onto the fourth industrial revolution.

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Source: https://www.iotforall.com/iot-and-scada-systems-forced-to-coexist-and-understand-each-other

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