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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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Proximity labeling: an enzymatic tool for spatial biology

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In this Forum, we highlight how cutting-edge, proximity-dependent, enzymatic labeling tools, aided by sequencing technology developments, have enabled the extraction of spatial information of proteomes, transcriptomes, genome organization, and cellular networks. We also discuss the potential applications of proximity labeling in the unexplored field of spatial biology in live systems.

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Source: https://www.cell.com/trends/biotechnology/fulltext/S0167-7799(21)00211-0?rss=yes

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Synthetic biology applications of the yeast mating signal pathway

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Glossary

Central carbon metabolism (CCM)

as the main source of energy, CCM oxidizes carbon through glycolysis, the pentose phosphate pathway, and the tricarboxylic acid cycle.

Chassis

a cell host or an organism for the production of biochemicals such as enzymes by introducing synthetic modules or devices into the cell.

Circuit

an assembly of biological parts that enables cells to perform logical functions, such as genetic switches, oscillators, and logic gates.

Convolutional neural network

a class of artificial neural networks with multiple building blocks that automatically and adaptively learn spatial hierarchies of features through back-propagation.

Clustered regularly interspaced short palindromic repeats (CRISPR)

a genome-editing tool in which CRISPR-associated nuclease 9 (Cas9)–guide RNA (gRNA) complexes recognize a protospacer adjacent motif through base-pairing and then cleave the target DNA,

CRISPR activation or interference (CRISPRa/i)

a tool that uses dead Cas protein and gRNA to activate or repress genes, resulting in gene upregulation or downregulation, respectively.

Cubic ternary complex model

an equilibrium model that describes the interactions between receptor and ligand. This model simulates the interactions of G proteins and receptors in both their active and inactive conformations.

G proteins

heterotrimeric G protein complexes are composed of α, β and γ subunits. Replacement of GDP by GTP in Gα causes a conformational change that dissociates the Gβγ subunits, leading to the activation of downstream signaling.

G protein-coupled receptor (GPCR)

a generic class of versatile, seven transmembrane-domain proteins that regulate a diverse array of intracellular signaling cascades in response to hormones, neurotransmitters, and other stimuli.

Karyogamy

a cascade of molecular events that finally lead to fusion of the nuclei and the formation of diploid cells.

Metabolic engineering

a new scientific field that combines multi-gene recombination technology with metabolic regulation and biochemical engineering to overproduce desired products.

Mitogen-activated protein kinases (MAPKs)

a family of serine/threonine kinases that convert extracellular signals into a diverse range of cellular responses.

Omics

studies include genomics, transcriptomics, proteomics, and metabolomics that characterize and quantify pools of biological molecules, and together give rise to the field of integrative genetics.

Oscillator

a genetic circuit where oscillation is generated by the inhibition and activation of transcriptional/translational feedback loops.

Pheromone-response element (PRE)

a cis element that is present in multiple copies in the promoters of a variety of pheromone-responsive genes; PREs interact with Ste12 to initiate the transcription of pheromone-induced genes.

Quorum sensing

a cell density-dependent phenomenon in which cells adapt their behavior by synthesizing, secreting, perceiving, and reacting to small diffusible signaling molecules termed autoinducers.

Scaffold protein

proteins that recruit other proteins to form a functional unit, thus enhancing signaling efficiency and fidelity.

Ste5ΔN-CTM

a Ste5 mutant that lacks the Gβγ-binding site because its N-terminus has been truncated; Ste5ΔN-CTM is no longer recruited to the plasma membrane following pheromone treatment.

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Source: https://www.cell.com/trends/biotechnology/fulltext/S0167-7799(21)00210-9?rss=yes

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Biotechnology of functional proteins and peptides for hair cosmetic formulations

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  • PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
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    Source: https://www.cell.com/trends/biotechnology/fulltext/S0167-7799(21)00213-4?rss=yes

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    VW’s 9-month electric vehicle deliveries to China more than triple

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    FRANKFURT (Reuters) – Volkswagen’s deliveries of battery-powered electric vehicles to China more than tripled in the first nine months of the year, the carmaker said on Friday, less than two months after it flagged the need to change its e-car strategy there.

    Deliveries of battery electric vehicles (BEV) to the world’s largest car market stood at 47,200 in the January-September period, up from 15,700 in the same period last year.

    “As planned, we significantly accelerated the BEV market ramp-up in China in the third quarter, and we are on track to meet our target for the year of delivering 80,000 to 100,000 vehicles of the ID. model family,” Christian Dahlheim, head of group sales, said.

    Volkswagen Chief Executive Herbert Diess in July said the carmaker had to change its approach to how it markets its BEVs in China after first-half deliveries stood at just 18,285.

    (Reporting by Christoph Steitz; Editing by Maria Sheahan)

    Image Credit: Reuters

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    Source: https://datafloq.com/read/vws-9-month-electric-vehicle-deliveries-china-triple/18644

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